Jesse Nieminen is VP Product at HYPE Innovation, the global leader in innovation management software. Prior to being acquired by HYPE, Jesse was co-founder and Chairman at Viima, the leading agile innovation platform. He’s passionate about helping leaders drive innovation in their organizations.
What role can AI play in innovation management, and how can we unlock its true potential?
Unless you’ve been living under a rock, you’ve probably heard a thing or two about AI in the last year. The launch of ChatGPT has supercharged the hype around AI, and now we’re seeing dramatic progress at a pace unlike anything that’s come before.
For those of us into innovation, it’s an exciting time.
Much has been said about the topic at large so I won’t go over the details here. At HYPE, what we’re most excited about is what AI can do for innovation management specifically. We’ve had AI capabilities for years, and have been looking into the topic at large for quite some time.
Here, I share HYPE’s current thinking and answer some key questions:
What can AI do for innovation management?
What are some common use cases?
How can you operationalize AI’s use in innovation management?
The Current State of Innovation Management
Before we answer those questions, let’s review how most organizations carry out innovation management.
We’re all familiar with the innovation funnel.
To oversimplify, you gather ideas, review them, and then select the best ones to move forward to the pilot stage and eventual implementation. After each phase, poor ideas get weeded out.
It’s systematic, it’s conceptually simple, and investment is tiered so that you don’t spend too much time or money before an idea has shown its potential. What’s not to love?
Well, there are a few key challenges: the process is slow, linear, and is usually biased due to the evaluation criteria selected for the gates or decision points (if you use a Phase-Gate model).
Each of these challenges can be mitigated with smart adaptations of the process, but the funnel has another fundamental limitation: It’s generally built for a world where innovation requires significant capital expenditures and vast amounts of proprietary information.
But, regardless of your industry, that just isn’t the case anymore. Now most information is freely available, and technology has come a long way, in many cases because of AI. For example, pharmaceutical companies use AI to accelerate drug discovery while infrastructure and manufacturing companies use advanced simulation techniques, digital twins (virtual replicas of physical objects or systems), and rapid prototyping.
It’s now possible to innovate, test, and validate ideas faster than ever with minimal investment. With the right guidance, these tasks don’t have to be limited to innovation experts like you anymore. That can be an intimidating thought, but it’s also an empowering one. Soon, thanks to AI, you’ll be able to scale your expertise and make an impact significantly bigger than before.
For more than 20 years, we’ve been helping our customers succeed in this era of systematic innovation management. Today, countless organizations manage trends at scale, collect insights and ideas from a wide and diverse audience, and then manage that funnel highly effectively.
Yet, despite, or maybe because of this, more and more seemingly well-run organizations are struggling to keep up and adapt to the future.
What gives?
Some say that innovation is decelerating. Research reveals that as technology gets more complex, coming up with the next big scientific breakthrough is likely to require more and more investment, which makes intuitive sense. This type of research is actually about invention, not innovation per se.
Innovation is using those inventions to drive measurable value. The economic impact of these inventions has always come and gone in waves, as highlighted in ARK Investment’s research, illustrated below.
Throughout history, significant inventions have created platforms that enable dramatic progress through their practical application or, in other words, through innovation. ARK firmly believes that we’re on the precipice of another such wave and one that is likely to be bigger than any that has come before. AI is probably the most important of these platforms, but it’s not the only one.
Whether that will be the case remains to be seen, but regardless, the economic impact of innovation typically derives from the creative combination of existing “building blocks,” be they technologies, processes, or experiences.
Famously, the more such building blocks, or types of innovation, you combine to solve a specific pain point or challenge holistically, the more successful you’re likely to be. Thanks to more and more information and technology becoming free or highly affordable worldwide, change has accelerated rapidly in most industries.
That’s why, despite the evident deceleration of scientific progress in many industries, companies have to fight harder to stay relevant and change dramatically more quickly, as evidenced by the average tenure of S&P500 companies dropping like a stone.
In most industries, sustainable competitive advantages are a thing of the past. Now, it’s all about strategically planning for, as well as adapting to, change. This is what’s known as transient advantage, and it’s already a reality for most organizations.
How Innovation Management Needs to Change
In this landscape, the traditional innovation funnel isn’t cutting it anymore. Organizations can’t just focus on research and then turn that into new products and expect to do well.
To be clear, that doesn’t mean that the funnel no longer works, just that managing it well is no longer enough. It’s now table stakes. With that approach, innovating better than the next company is getting harder and more expensive.
When we look at our most successful customers and the most successful companies in the world in general, they have several things in common:
They have significantly faster cycle times than the competition at every step of the innovation process, i.e., they simply move faster.
For them, innovation is not a team, department, or process. It’s an activity the entire organization undertakes.
As such, they innovate everything, not just their products but also processes, experiences, business models, and more.
When you put these together, the pace of innovation leaves the competition in the dust.
How can you then maximize the pace of innovation at your organization? In a nutshell, it comes down to having:
A well-structured and streamlined set of processes for different kinds of innovation;
Appropriate tools, techniques, capabilities, and structures to support each of these processes;
A strategy and culture that values innovation;
A network of partners to accelerate learning and progress.
With these components in place, you’ll empower most people in the organization to deliver innovation, not just come up with ideas, and that makes all the difference in the world.
What Role Does AI Play in Innovation Management?
In the last couple of years, we’ve seen massive advancements not just in the quality of AI models and tools, but especially in the affordability and ease of their application. What used to be feasible for just a handful of the biggest and wealthiest companies out there is now quickly commoditizing. Generative AI, which has attracted most of the buzz, is merely the tip of the iceberg.
For innovation managers too, AI will have dramatic and widely applicable benefits by speeding up and improving the way you work and innovate.
Let’s dive a bit deeper.
AI as an Accelerator
At HYPE, because we believe that using AI as a tool is something every organization that wants to innovate needs to do, we’ve been focusing on applying it to innovation management for some time. For example, we’ve identified and built a plethora of use cases where AI can be helpful, and it’s not just about generative AI. Other types of models and approaches still have their place as well.
There are too many use cases to cover here in detail, but we generally view AI’s use as falling into three buckets:
Augmenting: AI can augment human creativity, uncover new perspectives, kickstart work, help alleviate some of the inevitable biases, and make top-notch coaching available for everyone.
Assisting: AI-powered tools can assist innovators in research and ideation, summarize large amounts of information quickly, provide feedback, and help find, analyze, and make the most of vast quantities of structured or unstructured information.
Automating: AI can automate both routine and challenging work, to improve the speed and efficiency at which you can operate and save time so that you can focus on the value-added tasks at the heart of innovation.
In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.
While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.
In a nutshell, with the right AI tools, you can move faster, make smarter decisions, and operate more efficiently across virtually every part of the innovation management process.
While effective on their own, it’s only by putting the “three As” together and operationalizing them across the organization that you can unlock the full power of AI and take your innovation work to the next level.
Putting AI Into Practice
So, what’s the key to success with AI?
At HYPE, we think the key is understanding that AI is not just one “big thing.” It’s a versatile and powerful enabling technology that has become considerably cheaper and will likely continue on the same trajectory.
There are significant opportunities for using AI to deliver more value for customers, but organizations need the right data and talent to maximize the opportunities and to enable AI to support how their business operates, not least in the field of innovation management. It’s essential to find the right ways to apply AI to specific business needs; just asking everybody to use ChatGPT won’t cut it.
The anecdotal evidence we’re hearing highlights that learning to use a plethora of different AI tools and operationalizing these across an organization can often become challenging, time-consuming, and expensive.
To overcome these issues, there’s a real benefit in finding ways to operationalize AI as a part of the tools and processes you already use. And that’s where we believe The HYPE Suite with its built-in AI capabilities can make a big difference for our customers.
Final Thoughts
At the start of this article, we asked “Is AI the future of innovation management?”
In short, we think the answer is yes. But the question misses the real point.
Almost everyone is already using AI in at least some way, and over time, it will be everywhere. As an enabling technology, it’s a bit like computers or the Internet: Sure, you can innovate without them, but if everyone else uses them and you don’t, you’ll be slower and end up with a worse outcome.
The real question is how well you use and operationalize AI to support your innovation ambitions, whatever they may be. Using AI in combination with the right tools and processes, you can innovate better and faster than the competition.
At HYPE, we have many AI features in our development roadmap that will complement the software solutions we already have in place. Please reach out to us if you’d like to get an early sneak peek into what’s coming up!
Our industry of innovation management software is quite an interesting one. It’s been around for a while, but it’s still not a mainstay that every organization would use, at least not in the same way as CRM and team communication software are.
Hence, there’s quite little independent research available out there to prove its efficacy, or even for determining which parts of it are the most valuable.
So, when I saw a new study, conducted jointly by a few German universities, come out on the topic, I was naturally curious to learn more.
In this article, I’ll share the key findings of the study with you, as well as some personal thoughts on the how and why behind these findings. We’ll also wrap up the discussion by considering how these findings relate to the wider trends within innovation management.
About the Study
Before we get to the results, let’s first briefly cover what the study was actually about and how it was conducted.
First, the focus of the study was to analyze the role of Innovation Management Software (IMS) adoption for New Product Development (NPD) effectiveness and efficiency, as well as the factors (software functionality and offered services) that actually led to successful adoption of said innovation management software.
The data was collected with an online questionnaire that was answered by innovation managers from 199 German firms of varying sizes, 45% of which used an Innovation Management Software, and 55% of which didn’t.
While this is the largest independent piece of research I’ve yet seen on innovation management software, we should remember that all research comes with certain limitations and caveats, and it’s important to understand and keep these in mind.
You can read the paper for a more detailed list, but in my opinion, this boils down to a few key things:
First, the study uses NPD performance as a proxy for innovation outcomes. This is an understandable choice to make the research practical, but in reality, innovation is much more than just NPD.
Second, while the sample size of companies is respectable, the demographic is quite homogenous as they are all German companies that employ an innovation manager, which obviously isn’t representative of every organization out there.
Third, the results are analyzed with regression analyses, which always brings up the age-old dilemma: correlation doesn’t imply causation. In other words, the study can tell us the “what”, not the “why” or “how”.
And finally, while the chosen variables are based on validated prior research, the questions still require subjective analysis from the respondent, which can introduce some bias to the results.
So, let’s keep these in mind and move on to the actual findings.
The Main Findings of the Study
The authors have done a great job in summarizing the hypothesis and respective results in a table, which you’ll also find reproduced below.
Let’s break the results down by hypothesis and cover the main takeaways for each.
Innovation Management Software Adoption Leads to Better NPD Performance
The first hypothesis was that using an Innovation Management Software would lead to better New Product Development performance. This can further be broken down into two parts: efficiency and effectiveness.
The results show that IMS adoption does indeed improve NPD efficiency, but the impact on NPD effectiveness wasn’t significant.
Innovation Management Software improves New Product Development efficiency, but the impact on effectiveness isn’t significant.
Intuitively, this makes sense and is also well in line with our experience. Innovation, especially in terms of NPD, is hard and requires a lot of work and difficult decisions, usually in the face of significant uncertainty. No software can magically do that job for you, but a good tool can help keep track of the process and do some of the heavy lifting for you.
This naturally helps with efficiency which allows innovators to focus more of their efforts on things that will lead to better results, but those results still aren’t a given.
Functionality That Leads to Higher IMS Adoption
The second hypothesis is focused on the functionality provided by the innovation management software, and the impact of said functionality on overall IMS adoption.
To be more specific, the respondents were asked how important they considered each functionality to be for their firm.
Here, Idea Management was the only functionality that had an impact for these firms.
Idea Management was the only functionality that had a significant positive impact for the surveyed firms.
Again, that intuitively makes sense and is well in line with our experience. Idea management is the part that you embed in the organization’s daily processes and use across the organization to make ideation and innovation systematic. And as mentioned, it’s the part that does a lot of the heavy lifting, such as increasing transparency, communication and collecting and analyzing data, that would otherwise take up a lot of time from people running innovation, which naturally helps with efficiency.
So, while Strategy and Product Management capabilities do have their uses, they are not nearly as essential to IMS adoption, or innovation success for that matter.
In our experience, this primarily comes down to the fact that most companies can manage those capabilities just fine even without an IMS. The value-add provided by the software just isn’t nearly as high for most organizations there.
Services That Lead to Higher IMS Adoption
The third and final hypothesis focused on the importance of the services offered by IMS vendors for the respective firms.
Here the spectrum covered consulting, training, customer support, customizations, as well as software updates and upgrades.
Here, the only factor that made a positive difference for the respondents was software updated and upgrades. This category includes both minor improvements as well as new functionality for the software.
Interestingly enough, for consulting that relationship was negative. Or as the authors put it, adopters more alienate than appreciate such services.
Software updates and upgrades were the only service with a positive impact, whereas consulting actually had a negative one.
Let’s first cover the updates and upgrades as that is probably something everyone agrees on.
Good software obviously evolved quickly and as most companies have embraced the Software as a Service (SaaS) model, they’ve come to expect frequent bug fixes, usability and performance improvements, and even new features for free. Over the lifetime of the product, these make a huge difference.
Thus, most understand that you should choose a vendor that is committed and capable of delivering a frequent stream of updates and new capabilities.
Let’s then move on to consulting and discuss why it is detrimental to adoption.
While we’ve always kept professional services to a minimum at Viima, this still came as a bit of a surprise for me. As I’ve raised this point up in discussions with a couple of people in the industry, that do offer such services, they seem to respond with varying degrees of denial, dismissal, and perhaps even a hint of outrage. When such emotions are at play, it’s always a good time for an innovator to lean in and dig a bit deeper, so let’s do that!
Looking at this from the point of view of the customer, there are a few obvious problems:
Misaligned incentives
… which leads to focusing on the wrong issues
Lack of ownership
Each of these could be discussed in length, but let’s focus on covering the keys here.
First, it’s important to understand that every software company makes most of their profits from software licenses. Thus, while generally speaking modern SaaS models do incentivize the vendor to make you successful, that isn’t the whole picture. The focus is actually on keeping the customer using the software. With the right product, that will lead to good outcomes, but that isn’t necessarily always the case.
However, when you add consulting to the mix, it’s only natural that it focuses primarily on the usage of the software because that’s what they know best, and what’s also in their best interest.
And, while making the most out of the software is important, it’s usually not the biggest challenge organizations have with their innovation efforts. In our experience, these are usually in topics such as organizational structure, resource allocation, talent, culture, as well as leadership buy-in and understanding.
And, even if the vendor would focus more on some of these real challenges the customer has, they rarely are the best experts in these matters due to their experience coming from matters related to the product.
Now, once you have a consultant come in, you of course want to listen to them. However, a consultant’s job is to give advice, it isn’t to get to the outcomes you want or need, and there’s a big difference there. That is one of the fundamental challenges in using consultants in general, and a big reason for why many don’t like to use them for long-term issues that are core to your future success, such as innovation.
Having said that, if you do use consultants, you can’t lose track of the fact you still need to take ownership for delivering those results. The consultant might be able to help you with that, or they might not. It’s still your job to make the decisions and execute on the chosen plan.
Put together, these reasons are also why we have been reluctant to do much consulting for our customers. We simply think the customer is best served by taking ownership of these matters themselves. We do, on the other hand, seek to provide them with the information, materials and advice they might need in navigating some of these decisions – with no additional cost through channels such as this blog and our online coaching program.
How do these findings relate to wider IMS trends?
Now that we’ve covered the key findings, let’s discuss how these are present in the wider trends within the Innovation Management Software industry.
In addition to what we hear in our discussions with customers and prospects, we’ve also discussed the topic quite extensively with industry analysts and would break these down into a few main trends.
Focus on enterprise-wide innovation
One of the big trends we see is that more and more companies are following in the footsteps of the giants like Tesla, Amazon, Apple and Google, and are moving innovation from separate silos to become more of a decentralized organization-wide effort.
This isn’t always necessary for pure NPD performance, which is what the study was focused on, but it is certainly key for scaling innovation in general, and one where efficient idea management can play a key role.
Once you embark on that journey, you’ll realize that your innovation team will initially be spread very thin. In that situation, it’s especially important to have easy-to-use tools that can empower people across the organization and improve efficiency.
Simultaneous need for ease of use and flexibility
That enterprise-wide innovation trend is also a big driver for the importance of intuitiveness, ease of use, and flexibility becoming more important.
In the past, you could have an innovation management software that is configured to match your stage-gate process for NPD. You might still need that, but it’s no longer enough. You probably want more agile processes for some of your innovation efforts, and more lightweight ones for some of the more incremental innovation many business units need to focus on.
If people across the organization don’t know how to use the software, or require extensive training to do so, you’ll face an uphill battle. What’s more, if you need to call the vendor whenever you need to make a change to the system, you’re in trouble. Top innovators often run dozens or even hundreds of different simultaneous innovation processes in different parts of the organization, so that quickly becomes very tedious and expensive.
Reducing operational complexity and costs
A big consideration for many is the operational complexity and running costs associated in running and managing their infrastructure and operations.
Extensive configuration work and on-premises installations significantly add to both of these, so even though they can be tempting for some organizations, the costs do pile up a lot over time, especially since it requires a lot more attention from your support functions like IT to manage.
What’s more, if you want to make changes or integrate these systems with new ones you may introduce, typically you only have one option: you need to turn to your IMS vendor.
As IMS tools have matured and off-the-shelf SaaS services have become much more capable, the compromises in increased rigidity, complexity and running costs, as well as less frequent updates are no longer worth it and off-the-shelf SaaS is now the way to go for almost everyone. With SaaS, you benefit immensely from economies of scale, and you are no longer held captive by the sunk cost fallacy of up-front license payments and extensive configuration and training work.
Commoditization in Idea Management
As the study pointed out, idea management is at the core of most innovation management software. However, in the last decade, the competition in the space has increased a lot.
There are now native SaaS platforms, like Viima, that are able to offer extremely competitive pricing due to efficient operations and a lean organizational structure. This has put a lot of pressure on many vendors to try to differentiate themselves and justify their higher price tags with additional professional services, as well as adjacent products and capabilities.
In our experience, while these might sound good on paper, they aren’t often leading to more value in real life, and the respondents of this study would seem to concur.
Conclusion
So, to conclude, what did we learn from the research?
In a nutshell, no innovation management software or vendor will miraculously turn you into a successful innovator. A good software, however, will help you become more efficient with your innovation efforts, as well as lead to softer benefits such as improvements in communication, knowledge transfer and culture. Put together, these can make your life a lot easier so that you can focus on actually driving results with innovation.
What then should you consider when choosing your innovation management vendor?
Well, the evidence shows that you should focus on idea management, as that’s where the biggest impact on the factors mentioned above come from. And therein, you should focus on vendors that continuously update and evolve their software with the help of modern technology and that has made all the above so easy and intuitive that they don’t need to sell you consulting.
And of course, ask them the tough questions. Ask to test the software in real life. If you can’t, that is a red flag in and of itself. See how flexible and easy-to-use their software really is. Does it require consulting or configuration by the vendor?
This article was originally published in Viima’s blog.
Image credits: Unsplash, Viima
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Many would-be innovators obsess over ideas, wait for inspiration to strike, and believe that with the right idea, success can miraculously come overnight.
However, as we’ve written before, that’s just not going to happen. In fact, usually the only thing separating the winning innovators from the rest is execution. It makes all the difference in the world, and yet, it’s still a vastly underrated capability.
As part of our coaching program, we’ve asked hundreds of corporate innovators and innovation leaders to reflect on their strengths and weaknesses. And, by far, the most common answer is that they’re great at coming up with ideas and thinking about the big picture but lack the patience and discipline to see things through to results.
As such, it’s safe to say that as a community, we innovators need to take a hard look in the mirror and admit that this an area where most of us have a lot of room for improvement.
So, in today’s article, we’ll explore the topic of executing innovation in more detail to try to understand what the problems associated with it are, and what successful execution of an innovation really takes. This is designed to be a guide to help leaders get it right, but I think there’s a lot that every innovator regardless of job title can learn from.
What does executing innovation mean?
Before we dive deeper, it’s probably a good idea to clarify what we mean with the term “executing innovation”, and how it relates to “implementing innovation”.
These are often used interchangeably, but I think it’s useful to distinguish them from one another. The way we like to put this is as follows:
Implementing innovation is the process of taking an idea and then turning that into reality.
Executing innovation, on the other hand, is the entire process of creating value with innovation.
In other words, implementation is what you do for an individual idea to make that happen. Execution covers the implementation, but also the process of turning that (along with many other ideas and innovations) into something that actually creates value and can be scaled up.
Implementation isn’t always easy, but it’s still typically a linear project that you can usually plan out in advance. Execution, on the other hand, is a much more complex and multidisciplinary effort.
To succeed at delivering value, you need to get a lot of things right. And with innovation, there are many assumptions in that plan. Some of those assumptions will always prove to be false, and you’ll need to deviate from the plan.
That combination of multidisciplinary collaboration and the need to deviate from original plans often leads to a myriad of practical challenges in many large organizations.
However, before we dive deeper into those challenges, let’s first take a step back to realize why execution is so critical.
Why execution is critical for innovation success
There’s a reason for innovation being defined as the act of introducing something new.
Everyone has ideas. Many can even implement some form of them, typically a prototype, but few successfully realize the full potential of the idea by truly executing on it successfully.
To clarify, ideas are an important starting point, but with every great idea, there are hundreds or even thousands of people across the world who’ve had the same exact idea.
Most never start working on it. Many give up in the process. Some make it to market, and a few might even make that into a feasible business. There are usually only a couple of winners. Those are the ones that succeeded in executing that idea.
Everyone has ideas, but few successfully realize the full potential of their ideas. The ones that do are the ones that know how to execute well.
This is of course a bit of an oversimplification but should help explain the fundamental importance of proper execution.
And that is not just true for individual ideas and innovations, but it’s also the case for corporate strategies at large. Look at any given industry, and it’s quite likely that you’ll see many companies with a nearly identical strategy. Again, the difference comes down to how well the company succeeded in executing that strategy.
In other words, your idea or strategy sets the ceiling for your impact if successful, but execution determines how close to that ceiling you’ll get. Even the best idea or strategy is worth nothing unless it’s executed well.
On the other hand, even with a mediocre strategy or idea, you can achieve remarkable success if you just execute it well enough. There are dozens of well-known companies like McDonald’s and FedEx that are obvious examples of this. There’s nothing particularly remarkable or distinctive about their ideas or strategies. They weren’t the first in their respective fields, they just executed on their ideas brilliantly.
What’s more, if you’re a strong executor, you’ll soon find out the limits of the original strategy or idea, at which point you can adapt and change course accordingly. But, it doesn’t work the other way around.
Thus, no matter the situation, execution will always be more important than your idea or strategy.
Misconceptions about executing innovation
As you might have realized by now, execution is of course a massive, nuanced, context-specific and very complex endeavor. In practice, it’s an endless jungle of interlinked choices and actions affecting one another that you need to navigate with limited information to get to the other side.
Thus, the space of possible challenges and problems you might encounter is pretty extensive. So, instead of looking at the individual problems themselves, it’s more helpful for us to try to understand the common misconceptions that ultimately lead to teams underappreciating execution and thus subsequently failing at it.
A big factor behind most of these is the fundamental uncertainty that innovation is always associated with. Because you can’t know everything in advance, it’s not going to be a nice and linear process of doing simple steps one after another. Instead, it’s a messy and iterative process of creative problem-solving.
Anyway, with that, here are the top four that I most commonly see innovation leaders and their teams have.
1. The leader’s job is just to get the big picture right
This is probably the most common problem I’ve come across, and it’s especially common among inexperienced executives, or ones that otherwise lack execution experience, such as some management consultants and academics.
There are many shapes this one might take, and we’ll return to it later, but what it ultimately comes down to is the glorification of strategy work and/or surface-level creativity.
In business school, and in consulting, we’re taught to think about the big picture as the job of top management. We’re led to believe that a leader or innovator takes in a market analysis, compares a few scenarios, chooses a positioning, and then paints an inspiring vision to show direction for the company. Then the pieces will simply fall in place and success happens.
While the above mentioned are of course still useful activities, if you’ve ever actually turned an innovative idea into a successful business, you know that in practice, there’s a lot more to it than that, and experienced executives are of course well aware of that
Strategic choices can be made across the organization, but the responsibility for execution always lies at the top.
As Professor Martin has well put it, CEOs should stop thinking that execution is somebody else’s job, and the same applies for every innovation leader. Strategic choices can be, and frequently are, made where the action is. Yet, the responsibility for execution always lies at the top. After all, there’s a reason for the CEO being the Chief Executive Officer.
2. I don’t need to understand the details
The second is closely related to our first one. It’s easy to think that as a leader or visionary innovator, you’re the person responsible for the vision, ideas, and big picture decisions, and then the experts will then figure things out in practice. After all, that’s why you hired them, right?
Well, that might work if you’re operating in a static industry where all the variables are known and static, but with innovation that really isn’t the case.
You need to get the big picture right, but it isn’t enough to succeed. You need to also have the right product, business model, technology, customer experience, customer acquisition channels and tactics, operating models, etc. All of these have a wide variety of choices that depend on one another and changes in any of the areas will force you to change many of the other pieces in the puzzle too.
With innovation, the devil is in the details!
As an innovation leader, connecting the dots is ultimately your job, and you can’t do that without understanding the details.
That’s why you’ll find an obsession for the details in pretty much every successful innovator, both past and present. They have the same in-depth understanding and attention to detail as the best artists, athletes and top representatives of other fields do too.
So, while you absolutely need to engage with and empower the experts, they are experts in their own field and likely don’t know how to consider all the other moving pieces in the puzzle. As an innovation leader, connecting the dots is ultimately your job, and you can’t do that without understanding the details.
It’s the one responsibility you simply can’t delegate away.
3. Execution requires a clear and unambiguous plan
Even if you are an experienced executive and value the importance of execution highly, it doesn’t mean you couldn’t fail when executing innovation. Here the most common problems occur if the leader’s experience comes primarily from operations within the known and well understood confines of “business as usual”.
When the environment is well understood, and the scale large from the get-go, it’s of course valuable to try to plan carefully, analyze business cases and craft detailed project plans prior to execution.
Also, since everyone knows that innovation is a risky endeavor, it of course makes sense to try to reduce those risks before your start a big innovation project to try to avoid major mistakes and generally just ensure that you’ve done a good job in planning and preparation before committing to the project.
This often leads to large companies commissioning all kinds of market studies and strategy projects. Some of those can certainly be useful in increasing your understanding of the landscape, but most invest way too much time, energy, and money into these. Also, every now and then these projects seem to be ordered only to have a scapegoat in case something goes wrong.
Regardless, there’s a fundamental problem: with innovation, you can’t have all the answers in advance. You’ll always need to make a number of assumptions upon which your plan relies on, some of which will inevitably prove to be wrong.
With innovation, you won’t have all the answers in advance.
Thus, if you require innovators to propose clear, detailed and unambiguous plans for you, or conversely create such plans and then hold innovators accountable for successfully executing them, it just won’t work out. And, whenever it then comes to surface that everything hasn’t gone according to the plan, innovation projects are frequently shut down, even if they’d still hold a lot of potential.
You obviously still need to align with the strategy, plan ahead, and have a disciplined approach to execution, but it’s not so much about creating a detailed roadmap, as it is about choosing direction and figuring out which questions or problems you’ll need to address first.
In other words, you need to embrace the uncertainty and the fact that you can’t have a perfectly unambiguous and detailed plan before starting to execute it. Instead, figure out what the assumptions and uncertainties in your plan are and commit to a disciplined learning effort to figure out the right path forward.
4. Innovation is fun
There’s a stereotype around people working in innovation being these visionaries that are bursting with great ideas and seem to come up with great new concepts all the time. And as mentioned in the intro to this article, that is often true.
That skillset is of course very useful for innovation, but there’s also a downside. There are naturally exceptions, but many of us working on innovation can find execution too boring and repetitive, and/or lack the perseverance, discipline, and patience needed to succeed at it.
Innovators often spend too much on the creative and “fun parts” of innovation, as opposed to what’s really needed to turn an idea into a successful innovation
As a group, we generally love creative work, and are always looking for fresh, new stimuli to feed that inspiration. That often leads us to spend too much time and effort on the “fun parts” of innovation, and too little on the not so fun, more repetitive, and laborious parts of the process that execution essentially is comprised of. The reality is that for every minute you spend coming up with ideas, you’ll probably need to spend a day, a week, or even more implementing those ideas.
So, if your innovation team is primarily filled with, or led by, such “idea people”, which is quite common, then there’s a big risk of a systematic lack of respect for and capabilities in execution. This will lead to a very suboptimal culture for innovation, and ultimately disappointing business outcomes.
Getting Execution Right
As already mentioned, there are a lot of similarities between successful execution in “business as usual”, and in innovation. However, there are also clear differences between the two.
So, to help you navigate the differences, and to succeed at executing on whatever innovation you’re working on, here are the five most important factors to keep in mind whenever you’re trying to execute on an innovation and build something truly novel.
1. Take the path most likely to succeed, but keep your options open
As mentioned, with innovation planning and strategy work need to be done a bit differently than you would with an existing business.
Good decisions here make it much easier for your team to figure out how to move forward and can save a lot of time money going down the wrong path. Regardless, you’ll soon end up at another crossroads and need to make another decision. Heck, sometimes you might even come across a dead-end and need to backtrack to an earlier crossroads. Sometimes Plan C or D is the way to go.
The point is that no matter which path you choose, you won’t see what’s ahead all the way to the end.
Thus, good strategy work requires you to embrace uncertainty, test assumptions critically, and think deeply about the real-life feasibility of each path ahead.
And it’s certainly not a one-time project you do at the beginning, but more of a continuous learning process as you unravel the puzzle piece by piece.
If you keep an open mind and build your teams and products to embrace that uncertainty, you can quickly recover and learn from setbacks, as well as embrace new opportunities you couldn’t even think of before you set out. This is what’s known as cognitive and organizational flexibility.
2. Solve the biggest problems first
As humans, most of us have a bit of a tendency to go for the comfortable low-hanging fruits and procrastinate on the hard but important problems, as well as uncomfortable truths.
I’ve certainly been guilty of this on many occasions, even while writing of this article. Getting a number of small things done makes us feel like we’re making good progress, but unfortunately that’s often a bit of a false sensation as we might not really be any better off than when we began.
With the inherit uncertainty in innovation, that is naturally a bit of a problem. When you’re executing any given innovation, there’s countless things that need to be done so it’s easy to just start checking off boxes like building more features, creating marketing materials, getting compliance approvals, or whatever you may have on your agenda.
But, it’s the big things that make or break your innovation early on. For example: will a customer benefit from my product, how much are they willing to pay, can I even build the product I’ve envisioned, etc.
While you need to care about the details, it’s the big things that make or break your innovation early on. So, start from the big problems, even if it hurts!
The key is finding a way to figure out what these big problems or critical assumptions are, and then find ways to quickly test and address them. This allows you to quickly figure out if you’re on to something, which of course saves a lot of time and money for you in the inevitable case that you weren’t quite there from the get-go.
Also, if you get the big things right, you can already deliver most of the value, and that means you can more quickly start capturing some of that value to get a return for your investments.
Plus, if you tackle these early on when you still have a small team, changing course will be much quicker and easier, and you’ll have spent much less money solving the same important problems than you would with a larger team later on.
In most businesses, these critical assumptions revolve around how much value you can deliver to customers, and how valuable they see that to be. However, in certain circumstances, those can be related to something entirely different, such as the feasibility of implementation when developing a new breakthrough drug.
Solving for the hardest problems first does generally require a bit more of a leadership commitment as you won’t always be able to show quick wins as early on, but at least it can save you from an embarrassing and costly failure like CNN+.
3. Build the right team
It might be a bit of an obvious statement, but it’s still probably worth pointing out: innovation is a bit of a team sport. So, to do well at it, you need the right team.
However, what might not be as obvious is that ‘the right team’ means in practice. In our experience, there are two key parts to this:
Multidisciplinary team with talented individuals in each area
Leadership and individuals that share the right mindset for innovation
The prior is pretty self-explanatory. Innovation is almost always a cross-disciplinary effort. The specifics depend on what kind of an innovation you’re working on, but usually you need expertise in at least design, engineering, commercial and operational matters.
The most impactful innovations are actually comprised of a stack of innovations in many of these areas, each designed to work together to address a specific problem or ‘job’ for the customer. Thus, if you have talent at every position, the outcome will be much more than the sum of its parts.
The latter, however, is the part that many teams fail to appreciate. Innovation is, by definition, doing something that others haven’t succeeded at before, so the journey won’t be easy.
Your team will face a lot of uncertainty and struggles, and will still need to perform at their best, often under a lot of pressure. That requires a very specific type of culture within the team, but also the right mindset for each individual. You want people that can cope with uncertainty and are able to remain optimistic and overcome difficult situations while still being realistic and ruthlessly critical of their own capabilities. They need to have an innate passion to strive for excellence, and a lot of discipline, grit, and perseverance.
And, of course, because it’s a team sport, people need to be able to work well together and perform as a team. This, however, isn’t usually much of an issue as long as people can leave their egos at the door. The struggles you will face together as a team will build bonds and gel you into a team.
4. Make sure every decision and detail are aligned
As we already discussed, you don’t need (and usually can’t have) a clear and unambiguous plan for an innovation project where every role and task would be charted out in advance. However, as we also discussed, the devil is often in the details and seemingly small things can derail the project from its goals?
So, what gives?
Well, the point is that with innovation, you need to keep an eye on everything. As an innovation leader, you need to maintain excellent awareness of both the big picture and the details throughout the project. But, because the environment changes dynamically and you need to move fast, you can’t really do that work upfront.
Nor can you just look at some KPIs and financial reports to figure out if things are moving in the right direction because the important things won’t show up in these for quite a while, and at that point, it’s often too already too late to react.
As a leader, your primary job is to keep up with what’s going on both with the ever-changing big picture, and the details on the ground so that you can spot problems early and intervene before it’s too late, no matter where the issues might arise from. If you don’t understand how everything works in practice and know what problems everyone is working on and why, it will be pretty much impossible to do that.
Some might see the latter as micro-management, but it doesn’t mean you have to dictate what everyone does. It just means that as a leader, you need to be the person that connects the dots and then empowers the team to succeed. There’s a clear difference.
Which brings us nicely to our last point.
5. Take full ownership for the execution
As we’ve covered, execution is the make-or-break part in the lifecycle for every innovation.
It’s always a bit of an exploratory process where you need to remain flexible, while still moving forward quickly and executing at a high level.
And, at the same time, seemingly inconsequential low-level choices related to implementation turn out to become existential issues for any innovation project.
Again, you don’t need to decide everything on behalf of your team. In fact, often it’s best to let the experts solve problems and do their job, as long as you can give them the right guidance and constraints to work with. Instead, you need to think of every potential problem as your fault and then figure out a way to get past them together with your team.
The bottom line is that being an innovation leader isn’t easy. It takes a lot of time and work to understand and stay on top of things, but as already mentioned, that’s the one thing you can’t really skip, automate, or delegate. Essentially everything else you can.
The only way to succeed at that is to take full ownership and commit to the process.
Conclusion
We’ve covered a lot of ground, so let’s do a bit of a recap.
Innovation isn’t a linear project that you can plan out in advance and monitor progress with a Gantt chart. There will always be plenty of surprises. Many unpleasant, but usually some positive ones too. You’ll need to be flexible enough to react to these and alter course accordingly.
It’s an inherently messy and iterative process of figuring out a way to build new things and align all the pieces so that everything works out.
Fundamentally, an innovation leader’s job is to show direction and try to keep track of everything that’s happening, align those puzzle pieces together with the big picture while always being on the lookout for potential problems and then eliminate those before they derail the project, as there will be many.
It’s not an easy or comfortable job, but if you can get it right, it’s an incredibly rewarding one.
Ironically, despite all the talk about practical issues and attention to detail being vital, this has been a bit of a high-level overview on the topic. So, if you’re interested in learning more about the details related to what we’ve discussed today, I have a couple of practical recommendations for you:
First, the best way to learn to innovate is by doing. So, get your hands dirty, keep these tips in mind, do your best, and I’ll guarantee you’ll learn a lot.
But, if you currently don’t quite have the time to commit to an innovation project, a good alternative way to learn more about innovation management is with our Innovation System online coaching program. We’ve now made the program completely free of charge for the first 1000 readers to sign up for it.
This article was originally published in Viima’s blog.
Image credits: Unsplash, Viima
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The effects of cultural differences for innovation are an interesting and extremely multifaceted topic.
For most of us, it probably goes without saying that cross-cultural and multicultural capabilities are crucial in today’s globalized and hyperconnected world, and innovation is no exception. These capabilities are especially important if you’re working on it in a large international organization, as many of our customers are.
Such an organization must obviously think about how to adapt new innovative products and services to the cultures and unique characteristics of different markets and regions. But, in addition to that, they also need to manage the cultural differences within their organization while trying to innovate. Given that we have customers all over the world, it’s a theme we often get asked about.
And, of course, there’s also the age-old debate about the cultures of certain regions or countries being better suited to innovation to begin with.
So, in this today’s article, we’ll dive deeper on this nuanced topic and each of those three themes around cultural differences in innovation. We’ll also end by providing you with practical advice on how to look at and take these into account in your innovation work.
How can cultural differences be observed?
However, before we dive deeper, let’s first take a step back and consider the question of how to observe cultural differences in the first place.
I’m sure we all agree that there are significant cultural and behavioral differences between people coming from different backgrounds, be it based on geographical, ethnic, religious, or just the past corporate cultures people have been a part of.
As these differences are often hard to pin down, people usually have an innate urge to try to group people into specific buckets to make sense of those differences. There are significant challenges in doing that as it can lead to putting people into predefined boxes and reinforcing stereotypes, and then treating people based on those stereotypes instead of the individuals they really are. That is why these kinds of approaches shouldn’t be considered universal truths or used as recipes for making decisions even from a purely pragmatic point-of-view, let alone from an ethical one.
Still, with that major caveat, there are also benefits in using such frameworks since they can help us make sense of the world in a more structured way. They can help everyone get a better understanding of the big picture and can serve as a starting point for creating a shared understanding, as well as debating the practical implications of cultural differences.
There are many such methods available, but the general approach is always the same: to break a culture down into several behavioral and/or value-based dimensions ranging from one extreme to another, and then rating each culture on each of these dimensions to form an overview of their respective cultures.
The most popular and widely researched of these are probably the GLOBE project, and the Hofstede cultural dimensions model, but there are also other popular ones like the Culture Map. Each of these frameworks uses the above described approach, and most of the research on them is primarily focused on the differences between individual nations. Having said that, the same approaches have also been applied to other levels, such as gender, organizational, etc. often just with slightly different dimensions.
Next, we’ll briefly explain the Hofstede cultural dimensions model because it’s one of the earliest, and by far the most popular model in the field. If you’re already familiar with the model, you can skip the next paragraph and jump right into the takeaways.
Hofstede’s Cultural Dimensions
Geert Hofstede worked at IBM back in the 60’s when it was one of the first true global, multinational corporations. As part of his work on improving cross-cultural communication, he ran the same survey on values for more than 100,000 employees from different countries and analyzed the differences, which then led to the creation of his model some years later.
Initially the model consisted of four dimensions, but upon additional research, has since been expanded to six. I’ll briefly explain each of these next, and then share a few examples to illustrate how that works.
Power Distance Index (PDI) determines how equally power is distributed and how hierarchical a society is. High scores indicate a structured and hierarchical society, whereas low values indicate a more distributed power structure and willingness to question authority.
Individualism vs. Collectivism (IDV) looks at how heavily individuals are integrated into groups. This is mostly self-explanatory, but it’s worthy pointing out that collectivist cultures are highly loyal to the close-knit groups they belong to.
Uncertainty Avoidance (UAI) determines how much ambiguity and uncertainty a society is comfortable with. High scores indicate that a society values clear, often strict, rules and guidelines and believes in there being a “singular truth”. Low scores mean that a society is more willing to explore new ideas and divergent thoughts and is less structured overall.
Masculinity vs. Femininity (MAS) is of a dimension that’s subject to some controversy, but here refers to values associated with traditional gender roles. A masculine society values achievement, assertiveness, and material rewards for success, whereas a more feminine one values cooperation, modesty, care, and quality of life.
Long-term orientation vs. Short-term orientation (LTO) is pretty self-evident. Long-term oriented societies tend to think more about the future and view adaptation and pragmatic problem-solving as important, whereas more short-term oriented one tends to value traditions and the current state and be less willing to change.
Indulgence vs. Restraint (IND) in turn refers to how much a society indulges and encourages freedom for individuals to “just have fun and enjoy life”. More restrained societies tend to have stricter social norms regarding such behavior as they see these indulgences as counter-beneficial for bigger, longer-term ambitions.
There’s been some research on how these tendencies affect innovation, and as you can probably guess, some tend to be more favorable for high innovation performance than others. Which brings us to the big question: are some cultures intrinsically better at innovation than others?
Are some cultures better than others at innovation?
Well, in short, the answer is yes. At least to some extent. As mentioned, there’s research that shows a relatively strong correlation between certain cultural characteristics and innovation performance.
However, here it’s worth pointing out that almost all of the research done on the topic would seem to focus on country level data as that is widely and freely available thanks to studies like the Global Innovation Index (GII).
While certainly useful, we should take these findings with a grain of salt due to a number of factors, such as the studies again being high-level generalizations based on correlations, and the indices like GII being predominantly focused on inputs for innovation such as education and R&D spending. Even the output focused parts tend to be a bit biased towards activity metrics, such as number research papers and patents, instead of the real value and economic impact of innovation.
What’s more, I think it’s important to point out that most natural cultures evolve much slower than the GII rankings change, so it should be quite evident that there are also many other factors than culture that affect these scores.
But with that out of the way, let’s now look at the actual findings.
Characteristics of top innovation cultures
Based on the available studies, there would seem to be a pretty good consensus on the ideal innovation cultures having the following characteristics on the Hofstede model, in rough order of importance:
Low power-distance
High levels of long-term orientation and pragmatism
High levels of individualism
High levels of indulgence
Low levels of uncertainty avoidance
Lower levels of masculinity
These findings are obviously mostly in line with what most of us think of as a pro-innovation culture, so there aren’t really that many surprises here.
If people can question authority, are comfortable with ambiguous and uncertain environments, and can think about the long-term instead of just the next quarterly results, innovation is a lot more likely to happen.
While there’s more to innovation performance than culture, certain characteristics are likely to lead to a culture being better at innovation.
In most studies, the level of masculinity seemed to make the least amount of difference of any of the variables for innovation performance. Some studies found no correlation, but some did find a preference for a feminine, more collaborative culture instead of the more competitive and assertive, masculine one.
However, in my opinion, the most interesting findings are that high levels of individualism and indulgence are favorable for innovation, when intuitively we might think that a culture that is more collaborative and favors restraint and delayed gratification would be preferable.
This can be explained with the way that the Hofstede dimensions are constructed.
A more collaborative culture is one where certain in-groups, typically your own family, come first, and where loyalty and obedience are absolute values. So, collaboration according to the Hofstede model isn’t so much for the “greater good”, but more about the benefit of that specific “inner circle” ahead of your own interests. More individualist societies, on the other hand, tend to be more comfortable disagreeing, exploring, and “letting the best ideas win”, which is what likely led to these cultures over-performing.
A similar explanation also applies for the preference for indulgence. According to the authors of the study linked above, people in indulgent cultures have a greater drive for improving things and making life more enjoyable, and are generally more optimistic, which they viewed as the primary factors driving innovation here, perhaps alongside a general willingness to just try new things.
So, in that context, I do think the findings make sense, but I think it’s also a good example of some of the challenges associated with more nuanced sides of these cultural frameworks.
Takeaways from country level innovation performance
Looking at the GII study, and the mapping of the top countries from that to the Hofstede model, there are a couple of points worth noting out.
First, the top countries in the GII are pretty much what most people would probably expect. The top 15 consists primarily of the US, the Nordics, as well as some Western European and East Asian countries.
However, the interesting part is that when we map these out to the Hofstede model, it’s immediately obvious that even the top performing countries are essentially all over the spectrum. Once we look a bit closer, it’s also evident that no individual country has the perfect innovation culture, as defined above.
To elaborate further, I think there are a few key takeaways from all of this:
There’s more than just one way to be a great innovator
While there are a few distinct types of cultures that generally do better, every culture has itsown strengths and weaknesses when it comes to innovation
You can improve your odds of succeeding at innovation by quite a bit if you recognize the biases of your culture that are likely holding you back
Top performing organizations should thus take these biases and cultural differences into account, and purposefully shape an organizational culture that is distinct from the average of any individual country and instead designed to drive more innovation. Here, diversity can be a real asset, but that’s another massive topic on its own.
Every culture has its own strengths and weaknesses when it comes to innovation. You can improve your odds of succeeding at it by recognising the biases that are holding yours back.
Having said that, there’s quite a bit more to creating this kind of an innovation culture than just what the Hofstede model captures, and we’ve written about that in detail in this earlier article.
However, one aspect that I’d like to highlight here is that innovation is requires a strong combination of both exploration and execution, so your culture should have a good mix of capabilities in both extremes.
With that said, let’s now move on to the more practical implications of cultural differences for innovation work.
Multi and cross-cultural innovation capabilities
Let’s start from the first and most obvious challenge innovators in a globalized world face: how can their products and services, as well as sales and marketing efforts be relevant when doing international business, especially in different, highly culturally diverse regions?
In certain situations, and for certain products, it can be completely fine to just do minor localizations like translations, and primarily use the same channels, models, and messaging across the world. This will keep things much simpler and there are situations where these benefits can outweigh the costs for both your customers and your business. For example, this is the route we’ve so far decided to take with Viima.
Having said that, if you don’t adapt your offering and operations to different cultural and market preferences, you often can’t reach your full potential. In some situations, it might even take a completely different approach to reach the same goal in different cultures.
P&G is these days often cited as an example of a multinational company that has been able to successfully grow in emerging markets, but one of the lessons they learned the hard way was that just operating with the same products and models as they did back home wouldn’t work.
For example, according to ex-CEO Lafley, when P&G decided to focus on the baby-care market in Asia, the initial approach was to just cut away material from the diapers sold in Western markets. The problem was that to get to a cost-level that was acceptable, they had to cut out so much that the products no longer worked as intended. Once they went back to the drawing board and created an entirely new product with a completely different design focused primarily on costs instead of the latest technology, they succeeded in creating an attractive product and eventually became the market leader in China.
However, in most cases, either extreme isn’t the way to go. You need to look for a solution that allows you to build on your strengths, but still cater to the different cultural preferences of those whom you choose to serve – and usually that isn’t everyone.
Of course, for most of us who are innovators, that isn’t really that different from what we do anyway: we know that whatever great ideas we have, many will never survive first contact with the real world.
Cultural differences and local preferences of different markets are just another variable that we’ll need to take into account in our innovation work. Still, if you’re aiming for international business, it is a topic that you’d be wise to consider during your development process as it can save you a lot of trouble down the road.
Now, if you already have team members that are intimately familiar with these different cultures, it’s just common sense that the whole process is likely to be quite a bit smoother. And the evidence backs it up: this is one of the reasons for diversity being an asset for innovation.
But with that, let’s finally cover the practical considerations of what all of the above means for our organization before we wrap up.
Managing cultural differences within the organization
This is of course another massive topic, so we’ll keep things focused and will seek to provide you with the three key principles we’ve generally found to work well for getting great innovation outcomes in an international, multicultural organization in our work with such organizations.
While many of these are quite practical, depending on your role, you might not be able to put all of them into practice right away. Still, I’d recommend thinking about ways you can apply the same core ideas within the scope of your innovation work.
Communicate about cultural biases and expectations openly
To illustrate this, I’ll share a story from No Rules Rules, which is a great book that I’d warmly recommend if you’ve made it this far into the post.
Before Netflix expanded internationally, it had a somewhat stereotypical US style task-oriented culture. It was quite common for employees to have lunch while working on their computers. However, as they expanded to Brazil, it quickly became obvious that this was a bit of a problem as, in general, Brazilians really value the relationships built over shared meals. As a result, early employees didn’t exactly feel welcome.
After some time, this came up in discussions, and while it was a trivial thing to fix, it still made a huge impact on morale. And not only did that help them adapt to local habits, but the changes also enriched the culture of the organization globally.
So, the takeaway here is that it’s important to pay attention to cultural differences and discuss them openly. Usually, the issues are easy enough to fix, but when they aren’t discussed, you easily miss them, and that’s what leads to many challenges down the road. The reality is that most people won’t be familiar with everyone else’s culture by default and expecting that to be the case just isn’t realistic.
Have core values and some norms, be flexible on the rest
Each organization’s culture is a result of its background. A sum of its parts, if you will. Be it the nationality of the company, past strategic and hiring decisions, and even simple practices and ways of working that have stuck around for one reason or another.
A few of these factors are core for the identity and competitiveness of the organization, and it’s these core values that you should hold on to. However, most of these factors are simple habits that are inconsequential in the grand scheme of things.
Making the difference between the two is key.
The core values and norms are something you simply need to succeed as an organization, and those you simply can’t compromise on. New employees, whatever their background or experience, do need to adhere to these few essentials. And for that to happen, you need to train them on these values and principles and tell why that is so important for your organization.
You should be adamant about upholding your core values, but be flexible and willing to give up or change the more inconsequential parts of your culture so that it can evolve and improve
On the other hand, the rest of inconsequential norms and habits you should be willing to give up or change when needed so that everyone can feel welcome and be the best version of themselves. Everybody doesn’t have to be a carbon copy of one another.
But there’s more to it than just that. The right changes can, in fact, make your culture better. This is essentially what “hire for culture add, not culture fit” means in practice.
Let’s again use the Netflix lunch example. Was it crucial for the company to have employees to eat at their desks? Of course not. It was just an inconsequential habit. However, it was vital to have the new Brazilian employees feel welcome, not just because it’s the right thing to do, but also because it improved the company’s performance. Plus, introducing this conscious habit globally helped have a positive impact elsewhere too!
The same can be applied even within the scope of your innovation work. For example, if you’re working on a new medical device, quality and safety are much more important than absolute speed to market. On the other hand, for a consumer web app, it’s probably the other way around. The exact values mentioned here aren’t important, it’s that they should support your strategy and innovation capabilities.
Figure out what the true core values and norms are for your innovation efforts, and make sure to reinforce these – and then be flexible on the rest.
Push decision-making down whenever possible
We’re a strong advocate for decentralized innovation. I won’t recap the whole topic here, but in a nutshell, it’s people who are closest to the market and the real work that often come up with the best ideas. Also, a decentralized approach allows you to dramatically scale your innovation work, which is key for long-term results.
While we’d argue that this is usually the preferable approach, it’s even more important when you’re operating in a multicultural and international environment, as we pointed out earlier.
Not only is this likely to lead to better decisions, but it’s guaranteed to improve the accountability and motivation of the employees making those decisions, which will lead to better results.
This is a key characteristics of the Netflix culture, and CEO Hastings prides himself in doing as few decisions as possible. And, at large, it’s seemed to work really well for them.
However, a market where they are struggling is India. And, at least on the surface, it looks like the problem has been that they’ve tried to adapt the same success formula to India as most other markets: using local top talent to produce new hit TV shows. The problem is that apparently Indians value sports and movies much more than they do TV shows, which has led to competitors focused on those areas dominating the market and a big commercial disappointment for Netflix. From the outside, it’s hard to say if they didn’t really live up to their values here, or if the mistake happened regardless of that. Still, I’m sure there were people on the ground in India that knew of these cultural preferences beforehand.
In practical terms, there are naturally some opportunities and capabilities that make sense to work on centrally, but in an international organization there are also plenty that would be best tackled by empowering people further down the organization to make decisions that best drive the key interests of the organization.
For example, some of our customers have launched big international innovation campaigns or other initiatives and struggled. They might find it difficult to engage people in the field because the centralized effort just doesn’t feel relevant for many of these people, or they might not be able to implement enough good ideas with that same centralized approach.
While there are others that have succeeded in similar centralized efforts, our most successful and advanced customers have nearly without exception evolved the way they work to really embrace innovation at the scale of the organization at large.
…and make sure innovators have the support they need
However, for that decentralized approach to work, you need to guide and support the people innovating across the organization. This is of course not specific to just an environment where there are cultural differences, but for innovation in general.
You likely have plenty of smart and capable people working for you who’d be more than capable of driving innovation, but if they don’t have the right resources, tools, and mindset, they might struggle.
So, in practice, you should:
share strategic priorities, and make sure people continue to work towards those
provide tools and resources that help people with the innovation process
communicate and oversee the above-mentioned core cultural values and norms of the organization
help people with challenges in being heard, understood, or taken seriously by others
help facilitate discussions and share innovation best practices between different parts of the organization
Often, the most convenient way to accomplish the above goals is to make these efforts a priority of your centralized innovation team, instead of having that small team try to drive innovation themselves.
The right approach and specific methods, tools, and frameworks obviously depend on the situation, but the point is that with the right support, you’ll find that people will often surprise you with the innovations that they’re able to create. The key to success with this model is to proactively invest in improving capabilities and supporting innovators across the organization.
Anyway, with this kind of an approach, you can move from just trying to manage cultural differences, to embracing and using them to drive value for your organization.
Conclusion
The topic of cultural differences is such a complex and nuanced topic that we’ve barely scratched the surface on here, even though this has been a pretty long article.
But to summarize, if ignored, cultural differences can become a big challenge for innovators. Yet, if embraced and properly managed, it can turn out to be a real advantage for you.
The first step is to understand that these differences exist in the first place, and that teams and people from different backgrounds are likely to have certain strengths, but also certain weaknesses, when it comes to innovation.
Then, reflect on what the ideal culture for innovation looks like in your specific business, and discuss these differences openly with your team.
And finally, try to approach the whole process systematically, with the help of tools like our Innovation Culture Scorecard, one by one addressing challenges that are holding your team back from reaching its true innovation potential.
As mentioned, when embraced and properly managed, cultural differences can turn out to be a real competitive advantage for an innovator.
This article was originally published in Viima’s blog.
Image credits: Viima, Pixabay, Unsplash, Pexels
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I recently read a couple of excellent articles by Nick Skillicorn, and Prof. Rita McGrath where both discuss the challenges and intricacies involved in structuring and governing innovation within a large organization.
This is a classic topic that every corporate innovator has without a doubt come across, and it’s also one where “the right approach” is often quite elusive.
Inspired by those articles, we’ll present the most common archetypes and then dig a little deeper on the topic and share our thoughts and experiences to help you figure out how innovation should be structured within your organization.
Why organizing innovation is challenging
Before we dive into the different models for governing and organizing innovation, it’s important to understand why this is such a challenging topic to begin with.
That’s of course quite a lengthy and nuanced topic, but in short, there is no such thing as a perfect organizational structure or governance model. The bottom line is that a large organization is simply such a complex entity that structuring everything perfectly so that there aren’t any kind of bottlenecks, misaligned incentives, or any duplication of work just isn’t very realistic. If you’ve ever worked in large organization, you’ve certainly come across some of these challenges.
Now, most of these challenges are likely to be worse with innovation than with “business as usual” as, by definition, innovation means introducing changes. And most organizations simply aren’t designed for constant change.
What’s more, businesses are naturally very different from one another. A structure that works for a single product software company probably isn’t ideal for a CPG manufacturer or a house of brands because not only are their industries different, so are the innovations they are going after. So, what works well for some organization probably won’t be ideal for you.
This means that benchmarking and then applying “best practices” likely won’t work too well. Unfortunately, there just isn’t a single correct way to organize innovation.
Exploring the organizational archetypes for innovation
Having said that, there are a handful of common approaches, which we like to call archetypes, that most organizations use as the foundation for their efforts to organize and govern innovation.
Both McGrath and Skillicorn have done an excellent job in presenting many of these approaches, so a lot of credit for the following descriptions goes to them and I’d warmly recommend you read their takes too. Regardless, we’ve summarized their main points and combined them with our own experiences to create the following archetypes.
We’ll next explain each of these briefly, along with a quick summary of the key strengths and weaknesses for each.
No in-house innovation
The first and simplest way to organize innovation is to not do it, or to completely outsource it. Perhaps the most common method here is to simply keep tabs on promising startups and then acquire them, or to have tight collaboration with universities and other research institutions.
While this obviously keeps things simple organization-wise and minimizes fixed costs, it also means that you no longer have control over your own destiny, and are instead reliant on third parties, which puts you in a very vulnerable position long term. Furthermore, in the last decade, we’ve seen a huge inflow of capital to fund startups, which means that valuations for promising startups have skyrocketed and acquiring them on the cheap is simply no longer a very feasible strategy.
Suffice to say, if you want to build an organization that thrives in the long run, I wouldn’t recommend this approach.
Pros
Low fixed costs
Structurally simple
Cons
Lack of strategic control and ability to build the future of the organization
Lack of differentiation
Reliance on third parties for both execution and especially exploration
Acquisition of promising innovations has become expensive
Centralized
Perhaps the most common way large organizations set up innovation is by creating a centralized department that serves the innovation needs of the entire organization including each business unit and support functions, such as IT or HR. This can be a subdivision within R&D, but these days it’s typically a separate cross-departmental unit serving the innovation needs of business units.
Either way, such a unit is quick and easy to set up, and the approach has some other obvious advantages too, such as innovation expertise being built and managed centrally, which speeds up learning, as well as management and reporting being easy to organize.
It’s these advantages that make centralization the obvious choice for many who are just starting out with innovation. This is also an especially common approach for large industrial companies that typically have a strong R&D tradition.
If all of the innovation has to go through a single team, that team will inevitably become a bottleneck for innovation, no matter how skilled or large it is.
However, in the long run, this approach is also one that is likely to significantly limit your innovation potential. The reason is simple: if all of the innovation has to go through a single team, that team will inevitably become a bottleneck for innovation. No matter how large or skilled the team, they’ll never have enough resources. What’s more, this will also disincentivize everyone else in the organization from innovating and that prevents you from creating a true culture of innovation.
Pros
Quick, easy, and cheap to set up
Dedicated resources for working on innovation
Easy to govern, manage, and report on the overall innovation portfolio
Centralization can speed up learning
Cons
Poor scalability as centralized team will inevitably become a bottleneck for innovation
Likely to be pulled into too many projects, which leads to poor execution
High risk of degenerating into a support function serving business unit requests instead of strategically building the future of the organization
Likely to disincentivize others in the organization from innovating
Conflicting interests between business units can make prioritization difficult
Typically lack authority to make important, hard decisions
Dedicated
Popularized by Clayton Christensen as a solution to the Innovators’s Dilemma, dedicated business units for innovation have become increasingly popular in large organizations that are looking for the next stage of their growth. Sometimes these units have proper P&L responsibility, and they might even report directly to the CEO or others in senior management, but at times they can also be innovation labs responsible primarily for testing and piloting new ideas before they are to be integrated into the core business.
Regardless of the particularities, these approaches have some specific strengths, but also clear weaknesses. The good thing is that because the unit is independent, it can usually avoid being held back by the restrictions of the business as usual and can build their talent and approaches from scratch.
If innovation is the job of a select few, it will be incredibly hard to build a pro-innovation culture.
The downside is that they also don’t necessarily play to the strengths that the organization has already built. Without strong and clear leadership, these kinds of innovation efforts are likely to have an equally poor success rate as your average startup – but without the asymmetric upside.
The reason is simple: if you already have hundreds of millions or billions in revenue, most new businesses just don’t move the needle enough – unless they can quickly grow to a massive size or be combined with the strengths and competitive advantages of the core business.
And just like with the centralized model, this model again limits innovation to one part of the organization. As before, that will likely prevent you from creating a true culture of innovation, and thus lead to the unit becoming a bottleneck down the road.
Pros
Freedom to operate independently from processes of existing business units, which is essential for trying new things and creating disruptive innovations
Ability to hire and organize specifically for innovation
If led well, ability to focus on the long-term instead of short-term performance
High profile innovation unit can also be used for marketing and employer branding purposes
Cons
Conflicts of interest and lack of cooperation between core business and innovation unit likely to lead to politics, tension, and other challenges in integrating innovations into core business
Independence and lack of communication between business units might hurt strategic alignment and prevent the innovation unit from benefiting from the existing strengths of the organization
Can easily degenerate into a cost center performing innovation theaterwithout a clear strategic focus, strong leadership, and evidence-based processes
Likely to disincentivize innovation in other parts of the organization and thus prevent the creation of an innovation culture
High initial investment with lots of uncertainty can make the business case for investing in innovation look bad
Embedded
Many organizations have relatively independent business units or product and brand teams, and for them it can often make sense for innovation to be embedded within these units.
Traditional examples of such an approach are companies like P&G and other CPG companies with strong brands. These companies are working hard to keep up to date with evolving trends and consumer needs to innovate and create new products for the consumer. However, the same can also be true for many other kinds of businesses, such as software companies with multiple products.
Depending on the industry and organization, these units might have varying levels of control over their innovations once they are on the market. For example, in CPG companies manufacturing, logistics and many other functions would likely be managed by core business operations instead of this unit.
Pros
Better able to focus innovation on things that matter for each business, be they strategic projects or emerging customer needs
More control over innovation resources and ability to get talent that meets specific needs
Parallelization over different units can increase innovation throughput of the organization overall
Easier to align innovation with business needs and plans within the unit
The business case for investing in innovation is typically easy to make as you can start from low-hanging fruits that provide immediate value
Cons
Innovation likely to be biased towards more applied and incremental projects due to focus on immediate business needs
Some efforts may be duplicated between teams, especially if more long-term R&D work is being done
Can lead to a silo-effect, extra need to focus on facilitating knowledge transfer between units
Ambidextrous
Our fifth approach is usually referred to as the ambidextrous organization. We’ve also seen it be referred to as the Hybrid model, and it’s quite a natural evolution from the previous archetypes as it seeks to combine the best of both worlds.
In a nutshell, the idea is that innovation should happen across the organization with existing business units focused on exploiting their current position through incremental innovation, and a separate dedicated unit being responsible for exploring and building the future of the organization through more radical or disruptive innovation.
In the ambidextrous model, existing units use incremental innovation to exploit the current position and new units are set up to explore and build future.
In practice, a new P&L responsible division will be setup for new non-core businesses, and the more incremental innovation will then be organised either as Embedded or Centralized.
If an organization does successfully implement such an approach, it can lead to exceptional long-term performance, but that’s of course easier said than done. For most organizations, this is likely to require a significant transformation, and it can be challenging to get everyone onboard, build the right processes, as well as to align goals and incentives the right way across the organization.
Pros
Easier to build a balanced innovation portfolio with both strong short and long-term performance
Enables building an innovation-oriented culture across the organization
Enough resources for key projects across the organization
Makes it easier to communicate the innovation strategy with clear roles and responsibilities for each part of the organization
Can customize governance models to meet the needs of different types of innovation in different parts of the organization
Cons
Expensive and difficult to build, as well as to maintain
Requires clear leadership and a commitment to a transformation from the top
Can demotivate innovation-oriented employees that are in the core business
Usually requires extensive changes to processes and the re-skilling of managers and employees across the organization
While easier than with most other models on paper, prioritization and division of responsibilities can still be challenging in practice
Decentralized
Our final model is the decentralized approach. If you look at any of the best innovators in the world, be it Apple, Tesla, SpaceX, or Amazon, this is closest to the model they use. None of these organizations has a centralized or dedicated team responsible for all innovation in the organization.
Instead, the organization decentralizes the responsibility for innovation to happen in individual teams (which are typically cross-functional and relatively small) across the organization. Each team is focused on figuring out how they could help the organization better reach their strategic goals, and innovation is just one of the key tools in that process.
If a team (or an individual leader or employee) comes across a big idea that shows promise but would require significant additional investments, they’ll apply for additional resources from management via a quick and streamlined process. If approved, that typically leads to another team being set up to pursue that idea.
This approach is sometimes called the permissionless model due to the significant freedom each team possesses to make decisions affecting their own work. The obvious advantages are that they usually know the problems intimately and have the resources, incentive, and know-how to solve them, and have fewer dependencies to other parts of the organization. That leads to an extremely high pace of innovation and innovation throughput for the organization, which together create a tremendous competitive advantage.
Having said that, this too isn’t exactly an easy model to implement for most organizations. Typically, this would require a fundamentally different mindset, leadership philosophy, and a significantly higher talent density. For the average organization, that means a full-blown transformation where most fundamentals in the organization would need to change, which of course isn’t feasible for many.
Pros
Extremely high throughput and pace of innovation
Ability to adapt, re-organize and meet changing demands quickly
Strong focus on execution and value creation
Clear roles and responsibilities
Cons
Would require a fundamental transformation for most organizations
Requires strong communication and strategic clarity from management
Active management involvement required to remove barriers and to organize teams so that the portfolio remains balanced
Requires high talent density across the organization, which can be very challenging to achieve in practice
Continuously evolving and rapidly changing landscape might be too intensive for some employees
Some work often initially duplicated across teams, but can be managed by creating horizontal support teams
Choosing the right approach for your organization
As you can see, every approach has their benefits, but also their disadvantages.
In our experience, the Hybrid and especially Decentralized are the likeliest approaches to lead to sustained levels of high innovation performance in the 21stcentury but implementing either isn’t exactly a walk in the park for a large organization. If you have the luxury of meeting (or are close to meeting) the prerequisites, these are the models I’d personally go for.
However, for many, that just isn’t the reality. Even if you’re like most organizations and don’t quite have the talent, leadership, or other prerequisites needed for these approaches, I’d keep either the Hybrid or Decentralized approach as your eventual goal to build towards.
Move control and decision-making down in the organization to be able to move faster, make more informed decisions, respond to changes quicker, and to simply innovate more.
However, instead of a major overnight transformation, you should be prepared for a set of smaller, gradual steps that build your capabilities and culture towards that future while solving the current problems with your processes and structures.
While not ideal in theory, in practice the journey towards becoming a mature top innovator typically first leads towards centralization for most incumbent organizations. They need to build their innovation strategy, knowledge and capabilities before they can successfully decentralize and move control and decision-making down in the organization to be able to move faster, make more informed decisions, respond to changes quicker, and to simply innovate more.
With that background, if such an approach is used, it’s crucial that this centralized innovation function understands and embraces their temporary role so that they are willing to relinquish control and power over innovation to others. All too often we see these leaders clinging on to the team, budget and power they’ve built long after it would’ve been in the organizations’ best interest to re-organize.
Best practices for organizing innovation
As we’ve discussed, if you’re planning to make changes to the way you organize innovation, most decisions will depend on your context. Still, there are a few things that are good to keep in mind regardless of the approach you end up choosing. Here’s my top three:
The best innovators continuously evolve
The first, and perhaps the most important point to remember is that the best innovators continuously evolve and improve the way they work. They don’t just pick one organizational structure and go with that forever. Instead, they are constantly looking for ways to re-organize their efforts so that they work on whatever is likely to best help them reach their goals. This is of course one of the fundamental strengths of the Decentralized model but applies to other approaches too.
This is also in line with how the most successful organizations approach re-organizations in general. They don’t just wait until the old structure is burning, they act proactively to position themselves for the future they want to create.
Clear roles and decision-making structures
It’s pretty obvious, but if people don’t know who can make a decision on an idea that they may have, or even who’s responsibility it would fall under, odds are that not a lot of innovation will happen.
The reality is that there will always be some ambiguity and overlap, especially in fast moving environments, but clear roles and decision-making structures are regardless important for an organization that wants to innovate.
If projects or decisions seem to get stuck, or turf battles seem to consistently pop up in your organization, unclear roles and ambiguous decision-making are likely to be the main culprits.
Organize according to strategy and plan for the execution
Again, it might sound obvious, but especially with innovation, the differences can be dramatic. Organization is the link between your strategy and your execution, so make sure it isn’t detached from the realities of what it will take to reach your goals with innovation.
To use a bit of a simplified example, if your strategy is focused on creating new business from emerging disruptive technologies, then the Embedded model probably won’t cut it as your innovators will be kept busy by the priorities from the core business.
Plan for the execution, on the other hand means that each team should have the resources and the freedom needed to reach your goals. If, using our previous example, you allocate just a few engineers to the team and then hope that sales will magically turn those technologies into booming businesses, odds are very much against you.
In other words, try to allocate resources so that the team has everything they need to reach their goals. While this sounds super basic, we still see these mistakes frequently when innovation is a bit of an afterthought for management.
Conclusion
As is probably evident by now, no structure or approach to governing innovation is ever going to be perfect, at least for long. As your goals change or your business and industry keep evolving, you will need to change and evolve too.
Even though organizing innovation doesn’t seem to get the same kind of attention as innovation strategy or culture, it’s extremely important, nevertheless. Get it wrong, and it will be almost impossible for your organization to succeed at innovation. Get it right, and you’ll at the very least have a realistic shot at that.
Hopefully this article has provided you with more thoughts on the topic, and some views on what to do and not-to-do.
Most large organizations are led and managed very systematically, and they pride themselves on that. Managers and leaders within those organizations are usually smart, educated, and want to make data-driven, evidence-based decisions.
However, when it comes to innovation, that can be a part of the problem as Clayton Christensen famously pointed out.
Many leaders these days are well aware of the problem, but even if they are, they may still have a hard time leading innovation because the approach is so different from what most of them are used to in their day-to-day. The mindset, mental models and frameworks needed are just fundamentally different.
So, to get it right, you need to pick out the right frameworks and mental models and use those to lead both your own thinking, as well as your teams. Because innovation has become such a hot topic, there’s been an explosion in the number of these. So, how do you know which ones to adopt?
Well, in these situations, it’s often beneficial to take a step back and go to the roots of the phenomenon to figure out what the timeless fundamentals are, and what’s just part of the latest fad.
So, in this article, we’ll look at arguably the oldest innovation framework in the world, the scientific method. We’ll first explore the concept and briefly compare it to more modern frameworks, and then draw some practical takeaways from the exercise.
What is the scientific method and how does it relate to innovation?
Most of us probably remember hearing about the scientific method, and it’s generally seen as the standard for proving a point and for exploring new phenomena. Having said that, given that even to this day, there still isn’t a clear consensus on what the scientific method actually is, it’s probably a good idea to explore the term.
The scientific method is a systematic, iterative, and primarily empirical method of acquiring knowledge.
Some of the key ideas behind the scientific method actually date back to ancient times and several different cultures, perhaps most famously to Ancient Greece. The initial principles evolved gradually throughout the years, but it took until the Enlightenment before the term “scientific method” began to be used, and these principles became popularized.
With that background we can safely call the scientific method the oldest innovation framework in the world. In the end, applying this method is where most of the big technological innovations and breakthroughs we all now know and benefit from every day, have come from throughout history.
But enough about history, what does the process actually look like? Well, as mentioned, that depends on whom you ask, but the key principles everyone agrees on are that it is a systematic, iterative, and primarily empirical method of acquiring knowledge.
Again, there’s no consensus on the exact steps used in the process, and there are also minor variances in terminology, but the four steps practically every version seems to have can be seen from the chart below.
While traditionally the scientific method has been used primarily for basic research, it’s been the inspiration for many recent, popular processes and frameworks around business innovation.
At a high level, most of these are very similar to the scientific method, just applied to a more specific domain, and that come with some practical guidelines for applying said methods in practice.
With so many similarities, there’s clearly something there that’s worth paying attention to. Let’s next dive deeper to understand why that is the case.
Why are the frameworks so similar?
By definition, innovation is about creating and introducing something new. Sometimes that can mean small, incremental changes, but often we’re talking something much bigger.
And, in today’s globalized, hyperconnected and rapidly moving world, a lot of volatility, uncertainty, complexity and ambiguity (VUCA) will always be involved, especially when you’re moving into these uncharted waters.
This leads to two fundamental problems:
You usually can’t have all the information before making a decision
Whatever plans and assumptions you initially make will likely be wrong
What that in turn means is that many of the practices and frameworks leaders have applied for years in managing people and projects as they’ve risen through the ranks of the business, will not be applicable here. In fact, they can even be counterproductive as we pointed out in the introduction. Some leaders have a hard time accepting this and adapting to the new reality, and that usually doesn’t end well.
On the other hand, some leaders that have realized this have decided to go to the other extreme. They’ve heard stories of these great visionaries and innovators that had a dream of the future and just refused to take no for an answer. While there is a lot to like in that approach, the mistake that often happens is that once these leaders embark on that journey, they refuse to adapt their vision to meet the reality.
Finding the right balance is always tricky, but what helps with that is adapting the iterative, exploratory, and empirical approach of the scientific methodand the other frameworks and processes we mentioned before.
This doesn’t mean that it would be a free-for-all, on the contrary. These processes are in fact systematic and usually quite structured.
The purpose of the scientific method is to create structure and understanding from what seems like an incomprehensible mess.
To put it in another way, the purpose of the scientific method is actually to create structure and understanding from what initially seems like an incomprehensible mess – and that is the foundation that most great innovations are built on.
What can we learn from that?
Let’s now reflect on what that means for the day-to-day job of innovators and leaders managing innovation.
For me, it essentially boils down to three main takeaways. We’ll next cover each of them briefly.
Innovation is a learning process, just like the scientific method
As we just covered, most innovation processes abide by the same key principles as the scientific method. They are iterative, empirical, and exploratory. But they are also systematic, evidence-based, and most importantly, focused on learning and solving problems.
With innovation, your first priority is always to be skeptical of your initial plan and question your assumptions. When you do that and look at the data objectively to try figure out how and why things work the way they do, you’ll unlock a deeper level of understanding, and that level of understanding is what can help you solve problems and create better innovations that make a real difference for your customers and your organization.
To sum up, when you’re trying to build the future, don’t assume you’re right. Instead, ask how you’re wrong, and why. Often the hardest part about learning is to unlearn what you’ve previously learned. This is what’s often referred to as first principles thinking.
“Trying things out” isn’t unscientific or non-evidence-based
We still see leaders in many organizations struggle to admit that they, either as a leader or as an organization, don’t know something.
There’s often resistance to admitting a lack of understanding and to “trying things out” because those are seen as amateurish and unscientific or non-evidence-based, approaches. Rational leaders naturally want to do their homework before choosing a direction or committing significant resources to an initiative.
However, with innovation, often doing your homework properly means that you understand that you don’t know all the answers and need to figure out a way to find out those answers instead of just trusting your gut or whatever market research you might have been able to scrape together.
“Trying things out” is how more or less every meaningful innovation has ever been created. By definition, there’s always an amount of trial and error involved in that process.
So, if you recognize yourself struggling to embrace the uncertainty, take a hard look in the mirror, be more pragmatic and have the courage to make yourself vulnerable. If you have the right talent in your team, being vulnerable is actually a great way to gel the team together and improve performance.
On the other hand, if you understand all of this, but your boss doesn’t, it might be a good idea to politely remind them of how the scientific method works. While it’s not a silver bullet that would be guaranteed to convert everyone into a believer at once, I’ve found this to be a good way to remind leaders how science and progress really gets made.
Essentially, you need to convince them that you know what you’re doing and have a rational, evidence-based plan purpose-built to combat the VUCA we already talked about.
It requires a different management style
As you’ve probably come to understand by now, all of that requires a very different style of management than what most managers and leaders are used to.
To make innovation happen in an organization, leaders do need to provide plenty of structure and guidance to help their teams and employees operate effectively. Without that structure and guidance, which good innovation processes naturally help provide, you’re essentially just hoping for the best which isn’t exactly an ideal strategy.
However, managing innovation is more about setting direction and goals, questioning assumptions, as well as removing obstacles and holding people accountable, than it is about the way most people have learned to manage as they’ve risen in the ranks, which is by breaking a project or goal into pre-defined tasks and then simply delegating those down in the organization.
The traditional approach works well when you have a straightforward problem to solve, or job to accomplish, even if it’s a big and complicated project like building a bridge. These days, the laws of physics related to that are well understood. But if you’re entering a new market or innovating something truly novel, the dynamics probably won’t be as clear.
Also, when it comes to capital allocation for innovation, you can certainly try to create a business plan with detailed investment requirements and a thorough project plan along with precise estimates for payback times, but because odds are that all of your assumptions won’t be right, that plan is likely to do more harm than good.
Instead, it’s usually better to allocate capital more dynamically in smaller tranches, even if your goals are big. This can help stay grounded and focus work on solving the next few problems and making real progress instead of executing on a grandiose plan built on a shaky or non-existent foundation.
Conclusion
The scientific method is arguably the oldest innovation framework in the world. While it has naturally evolved, it’s largely stood the test of time.
The scientific method has allowed mankind to significantly accelerate our pace of innovation, and as an innovator, you’d be wise to keep the key principles of the method in mind and introduce processes that institutionalize these within your organization.
Innovation is an iterative process of learning and solving problems, and succeeding at it takes a lot of humility, pragmatism, and even vulnerability. With innovation, you just can’t have all the answers beforehand, nor can you get everything right on the first try.
When you’ve been successful on your career, it’s sometimes easy to forget all of that. So, make sure to remind yourself, and the people you work with, of these principles every now and then.
Fortunately, there’s nothing quite like putting your most critical assumptions to test and learning from the experiment to bring you down to earth and remind yourself of the realities!
Innovation is, without a doubt, a creative endeavor. However, many people still think it’s all about creativity. There’s a magical a-ha moment, and the rest is history.
Well, as we’ve explained before, that’s just not true. Those that have really been trying to innovate know that there is much more hard work than there is fun and games in the process of creating and scaling an innovation.
Thus, discipline plays a huge role in innovation. In fact, I’d argue that discipline is one of the least spoken about, yet most important factors determining whether individuals and organizations succeed at creating innovations.
So, in this article, we’ll dive deeper into the topic and discuss the role discipline plays in innovation to hopefully help you and your organization do a better job at it.
What is discipline?
As a term, discipline is commonly used to just refer to being strong-willed enough to put in a lot of hard work. In other words, self-discipline.
However, if we look at a dictionary, there are a few distinct but connected uses for the word. One refers to it as a branch of science, skill or type of work, another as the practice of regulating the behavior of people in a system, and the third as a synonym for punishing people for undesirable behavior in that system.
Well, innovation is certainly a discipline in the first meaning of the word, but it’s also one that takes a lot of discipline to succeed at, in the second meaning of the word.
“Innovation is a discipline that takes a lot of discipline to succeed at.”
Let’s dive a bit deeper on that second meaning for the word. For our purposes, we can further divide that it into two categories:
Self-discipline
Organizational discipline
There’s obviously a lot these have in common, but for an organization to succeed at innovation, you need both.
In a nutshell, you need self-disciplined individual willing to put their head down and persist. But you also need organizational discipline to focus on what matters, and to create the incentive structures needed to reinforce all of that.
Why is discipline so important for innovation?
So, with that covered, we can dive deeper into why discipline is so important for innovation and how that happens in practice.
We’ll next cover each of the main points briefly.
It takes hard work, persistence, and focus to create an innovation
Because our software is centerer around ideas, we often have to explain that while every innovation starts from an idea, an idea is maybe 1% of the way there towards a real innovation. It still needs development, refinement, implementation, scaling, and so on.
Going through that whole process takes a lot of hard work for pretty much every idea, even if the idea might seem trivial at first. The fact is that by the time you get an idea, hundreds, thousands or maybe even millions of people have probably had the same idea before. Most have just never bothered to implement it, or at least haven’t succeeded at it.
“Every innovator will face plenty of challenges on the way, and there will be plenty of times when things look dire, and you could give up.”
Every innovator will face plenty of challenges on the way, and there will be plenty of times when things look dire, and you could give up. Most do. But to succeed, you need to persevere and persist through these hardships.
To do that, you’re going to need a lot of discipline to avoid potential distractions, keep your head down and focus on what matter.
Trust the process and keep going
If you’ve ever been following a challenging fitness program, you know the feeling when it looks like you’re working your butt off and not making any progress.
The weights feel even heavier than they did the last time. That’s because you’ve been accumulating stress on your body, and it hasn’t yet had the opportunity to respond. Once you get some rest and recover from that stress caused by the exercise, the body will react to the stress and make you stronger.
Well, the journey is the same with innovation: facing those stressors will feel challenging, but if you don’t give up, that’s what will make both you and the innovation better.
To keep using the same metaphor, if you’d like to run a 3-hour marathon, your fitness program will obviously look very different from if you instead wanted to squat 500 pounds. Similarly, if your strategy calls for incremental innovation, your innovation processes will look very different from those aiming for disruptive innovation, but more on that here.
Regardless, the key in each of these situations is to just trust the process and keep going. Even when things don’t look great. The challenges you face will shape your innovation for the better, and the results will follow – or you’ll run out of money. Regardless, you just need the discipline to persist and stay on track.
While following the process is what will eventually get you there, you of course need to make sure you’re on the right path in the first place, and that is where disciplined thinking comes into play.
It’s easy to fool yourself without disciplined thinking
Our brain has a natural tendency to take mental shortcuts. We have an ability to recognize patterns and use those to make quick decisions efficiently and thus save energy. In most everyday situations, that ability is obviously very beneficial.
However, with innovation, this is often problematic. It’s these mental shortcuts that lead to many of the root causes behind issues that prevent organizations from innovating. This is perhaps easiest captured in common sayings like “This is how we’ve always done it” and “There’s no way that could work”.
“Our brain has a natural tendency to take mental shortcuts, which is the root cause behind many obstacles for innovation. Disciplined thinking is how you combat that.”
What’s more, if you’re an optimistic person, as most people working on innovation usually are, it’s easy to fool yourself to think that you have created something valuable even when you really haven’t. We often prematurely fall in love with that solution, instead of the problem.
Remaining highly analytical and rational in your decision-making while still being creative and aspirational is a tough combination for any person, or even for a team, to have.
Achieving that balance takes a lot of disciplined thinking. You need to stay grounded in reality, be willing to question yourself, and go back to first principleswhile still relentlessly moving forward. It’s a mindset anyone can learn, but that requires constant discipline to maintain.
Most organizations lack discipline
However, even if you are a good innovator, and have a great team that ticks all the boxes we’ve talked about above, it doesn’t mean that you’re automatically going to succeed.
One of the big barriers for that is the lack of organizational discipline. This is common for both startups and large organizations alike.
The idea is simple to understand. Just like an individual must remain focused to become great at something, so does an organization.
You need to make tough choices to have a clear strategy. That means saying no to a lot of things, so that you can focus on the things that will truly make a difference.
Sometimes you might have to keep investing in these truly strategically important areas, even if there’s no quantifiable ROI in the near term. Again, at the same time, the organization needs the discipline to not think about sunk costs and ruthlessly kill innovation projects that have proven to not be able to live up to their potential to free up resources for the ones that have the best odds of success.
That might sound like a paradoxical combination, and to a certain extent, it is. But that’s what makes it interesting.
On the execution side, you need a lot of discipline to have clear roles and set clear goals so that people have the prerequisites for succeeding, but also leave innovators with enough freedom to explore the best way to reach those goals. Again, that is a difficult combination to achieve. It requires a lot of discipline at all levels of the organization.
In our experience, most organizations just aren’t there yet, even if many individuals within the organization would be, and that is a big barrier for innovation.
As a result, corporate innovators often end up burning out or losing their motivation just trying to navigate the maze of organizational hierarchy for one permission and approval after another before they even get to start working on an innovation. That is a clear sign of an organization that isn’t disciplined – or alternatively has chosen to not innovate.
Discipline in practice
We’ve covered a lot of ground, and most of that has been pretty abstract, so before we wrap up, I’ll share a more practical example with you.
It’s a cliché to use Steve Jobs and Apple as an example for innovation, so I don’t usually like to do that. However, for this specific topic, I think it’s the perfect illustration because people usually see Jobs as this creative visionary and the ultimate ideas guy who couldn’t care less about processes or discipline.
But in fact, the first thing he did when coming back to Apple in 1997 wasn’t to come up with cool new products. It was to introduce a ton of discipline in everything they did and ruthlessly cut back on anything that didn’t truly help them innovate and create better products going forward.
Jim Collins does a great job summarizing some of the other actions in his book Great by Choice:
“They cut perks, stopped funding the corporate sabbatical program, improved operating efficiency, lowered overall cost structure, and got people focused on the intense ‘work all day and all of the night’ ethos that’d characterized Apple in its early years. Overhead costs fell. The cash-to-current-liabilities doubled, and then tripled.”
That provided Apple with the financial stability needed to invest in innovation and allowed them to focus their leadership and top talent purely on creating new innovations that ended up shaping the future of the company.
Also, from the Walter Isaacson biography of Jobs (which I highly recommend), it becomes obvious how diligent and disciplined Jobs and the rest of the team at Apple were in perfecting every little detail of their products, processes, and even the look of their stores (sometimes to a fault).
Conclusion
To conclude, it takes a lot of discipline to succeed at innovation. That discipline is at least as important as the creativity we usually associate with the term innovation. And, because it’s so underrated, I’d argue it’s the part most of us need to focus on.
After all, it is that disciplined execution of an idea that usually makes the difference between those that succeed and fail.
Thomas Edison did a great job in summarizing discipline when asked about his failed attempts at a lightbulb:
“I have not failed. I have successfully found 10,000 ways that will not work.”
Discipline is, without a doubt, about putting in the work, but there’s a bit more to it than that. It’s also about staying focused and grounded in reality, both of which are well displayed in that quote.
“Being disciplined, both as an individual as well as an organization can be very challenging. The good thing is that it is a muscle that you can develop.”
Even if it might not be immediately obvious, lack of discipline either as an individual or as an organization, is the root cause behind a significant portion of challenges organizations face when trying to innovate.
To be frank, being disciplined, both as an individual as well as an organization, for extended periods of time can be very challenging. The good thing is that it is, figuratively speaking, a muscle that you can develop. Most would-be innovators and leaders just aren’t quite there yet.
If you recognize yourself or your organization from this article, there’s no need to hide that – and there’s nothing to be ashamed of. We’ve all been there. Each of us has areas in our life where we lack discipline, or at the very least, times when we’ve failed to keep that up.
In fact, as an individual or organization, you need to be honest and admit that this is a problem for you. Once you do, you can take steps to address that, and you’ll be much closer to becoming a successful innovator.
This article was originally published in Viima’s blog.
Image credits: Unsplash, Viima
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Given that innovation is responsible for roughly 85% of economic growth, it’s without a doubt a pretty big deal for the success of both individual organizations, as well as for the society at large.
However, to achieve the level of impact that many are looking for from innovation, you can’t simply “create something new”, and then just hope the results will come. You will need to commit to systematically pursuing those results by scaling viable ideas into products or businesses that create value – at scale.
That is of course easier said than done. If you think it’s hard to come up with innovations, just try scaling one up. In this article, we’ll explore the topic in more detail and provide you with actionable tips on how to actually scale an innovation.
What does it mean to scale an innovation?
To explain what it means to scale an innovation, let’s first take a step back and look at the lifecycle of an innovation.
To begin, every innovation starts from a rough idea or concept. Often you may have a specific goal in mind, or a problem to be solved, but sometimes it can just be a cool idea that you think could really make an impact. From there, you first need to validate that the idea makes sense, and then build a product or a service that meets a real need in the market.
With these steps taken care of, the next part is to scale the innovation. At this point, we have all the pieces in place to create value, but we haven’t yet unlocked that value for the vast majority of the available market.
So, as you may see from the chart above, scaling is the part where most of the value creation and impact comes from. With that said, we can define scaling an innovation as the process of expanding the presence and the use of the innovation to be as widespread as possible to maximize that impact.
Scaling innovation is the process of expanding the presence and the use of the innovation to be as widespread as possible to maximize the impact the innovation can have.
While on paper that sounds straightforward enough, it’s extremely important to first clarify the vision of what successful scaling looks like for your innovation, and what metrics you will use to measure your success here. For some, it might just be revenue or profit, for others it could be the number of customers or users, the impact you’ve delivered, and so on.
Most of these metrics are of course related, but when you start with the end in mind and gradually work backwards from there, you are much more likely to succeed because everyone in the organization will know what it actually is that you’re aiming for.
With that goal in mind, you can start narrowing in on the methods required to get there, which is what we’ll be focusing on next.
Dimensions of scaling an innovation
Traditionally, scaling innovation is seen as a matter of advancing the adoption, or the diffusion, of innovation. This is best visualized with a chart depicting the adoption curve, which you’ll find below.
The idea is that to scale an innovation, you need to cross that chasm and go from a few early adopters to the mainstream market where the volumes are significantly higher.
While that is certainly true, we can dig a bit deeper to understand scaling in a more nuanced, and more practical, way.
In reality, there are three dimensions to scaling an innovation.
Let’s look at each of them a little closer.
Scaling Up
First, scaling up is about creating the preconditions for scaling effectively.
Before we start talking about scaling up, we’ll assume that the basic prerequisites for scaling are in place, namely that there’s a clear vision and a product-market fit for your innovation, and that the market potential is large enough for there to be something to scale to, even if the market isn’t there today.
Assuming those prerequisites are there, you need to ensure that:
you can produce enough of the innovation to scale
you can do that efficiently enough to be financially and operationally viable
For some products, such as software and other immaterial goods, that first part is pretty straightforward. For others, such as most complex manufactured goods, even the first one will be a real challenge.
Having said that, the second part of being efficient enough will prove to be a challenge for virtually every innovation. Even for a software product, acquiring, serving, and retaining customers profitably at scale is often more difficult than people realize. For other, fundamentally less scalable goods and services, this is often excruciating.
In addition to these two more practical aspects, there’s a third and more ambiguous component to scaling up, and that is the social and institutional adoption of the innovation.
How well you scale up affects how large of a scale you can ultimately reach.
For example, with an innovation as mundane as the modern umbrella, men who used it were initially ridiculed. So, before the umbrella could really take off as an innovation, societal norms needed to change. In other cases, there may be regulatory hurdles or other institutional considerations that might need to be addressed before an innovation can ultimately scale.
Regardless of the specifics, scaling up is necessary for every innovation that wants to reach significant scale.
However, what many people don’t pay enough attention to is that how well you scale up affects how large of a scale you can ultimately reach. If you can’t produce the goods at volume, and at low enough of a price while still being profitable at a unit economics level, there’s an obvious limit to your potential to scale.
Scaling Out
Scaling out is what most people think of when it comes to scaling an innovation. It’s the geographical or demographical expansion of the innovation to a larger audience.
In its simplest form, scaling out simply means getting a wider market share and audience for the innovation within an existing market. As we covered earlier, this typically means moving from those early adopter market segments towards the mainstream.
Scaling out is what most people think of when it comes to scaling innovation as it’s where you expand the innovation to a larger audience.
However, it doesn’t have to be limited to just that. Sometimes the same products or services can be sold and used in other geographical areas, or even in other industries or entirely different use cases, both of which unlock new markets and additional demand, and thus lead to a larger impact for the innovation. A well-known example of this is Tesla using their experience and innovations in electric car batteries to expand to stationary energy storage.
Regardless of which path you choose, often these efforts to scale out to new segments or industries do require additional work to adapt the innovation or its positioning to the differing characteristics of these new segments, markets, and audiences.
Scaling out to new market segments can increase complexity a lot, so be mindful of the operational implications of your strategic decisions here.
This naturally adds complexity, which makes the scaling up part we covered earlier more challenging. So, be mindful of how you scale out and what the operational implications of your strategic decisions here will be.
Scaling Deep
The third, and the least well-known method for scaling innovation is scaling deep. This essentially means that you unlock more impact for your innovation by expanding and maximizing the use of it, typically for the people who already have access to it.
This usually requires you to either change people’s behavior to increase usage, or alternatively come up with innovative means for improving the utilization rate by enabling more people to make use of the same assets. Scaling deep is partly a matter of culture and mindset, and partly a more practical matter of having the right components in place for enabling and encouraging active use of the innovation.
A classic, albeit somewhat controversial example of the first type would be social media algorithms. They are designed to provide users with engaging content to keep them entertained and thus stay in the service for longer, which leads to more revenue from the same number of users.
An example of the second type would be cloud computing. By adding network, virtualization, and software layers on top of the computing hardware, cloud providers can get more use out of the same hardware, which unlocks value for both the service provider and the customers.
This is how Amazon not just significantly reduced costs in one of their major cost centers, IT infrastructure, but actually turned that into Amazon Web Services (AWS), an additional growth business that now accounts for the majority of the profits for the entire organization.
Scaling deep is about unlocking more impact for your innovation by expanding and maximizing the use of it. This can help reduce the need to scale up or out, or alternatively maximize the impact from doing so.
Scaling Deep can reduce the need to scale up or out, or alternatively, maximize the impact from doing so. As such, it’s an excellent compliment for most innovations. However, it’s just that: a compliment. Your primary method of scaling should always be either to Scale Up or Scale Out depending on whether your bottleneck is more on the supply or demand side.
Even in the case of AWS, which has created entirely new vectors for scaling out and has dramatically subsidized their costs for scaling up, it obviously wouldn’t have been possible without Amazon already being at significant scale.
What’s the takeaway? These dimensions are distinct but very much intertwined.
If you can scale on all three of these dimensions in a coordinated way, you will not only be much more likely to achieve significant scale with your innovation in the first place, but also maximize the potential for scale and impact from those efforts. If you build momentum on one of the dimensions, some of that momentum will carry over to the other dimensions, which again helps you accelerate change going forward.
As such, pay attention to each of these dimensions and try to consider all of them in your plans to scale innovation. That doesn’t mean you should focus on all three from the get-go, on the contrary, but planning with the big picture in mind can allow you to make much more educated decisions.
Scaling innovation in practice
As we’ve established above, there unfortunately isn’t a one-size fits all solution to scaling innovation.
Achieving breakthrough success with an innovation, which is the goal of scaling innovation, always requires many related and adjacent (usually more incremental) innovations.
This is an extremely common pattern that you will see happening over and over again if you just start paying attention to it. Square co-founder Jim McKelvey has done a great job in describing that in more detail in his recent book called the Innovation Stack.
A well-known example is the lightbulb. Edison patented his famous design back in 1879, but most households didn’t yet have access to electricity, so it wasn’t something they could benefit from. It took countless other innovations and another 45 years before even half of US homes had one, even though the benefits were obvious.
In practice, scaling an innovation is simply an iterative and exploratory process where you focus on eliminating whatever bottleneck is preventing you from scaling, one by one. And, as we saw in the example of the lightbulb, sometimes these can be much bigger and more fundamental than you may think at first.
Often you can just copy solutions other people have already used for the same or a similar problem (which you should always go for if you can), but many times you will also need to innovate something completely new and occasionally even go beyond your core product.
With that said, there are some common patterns that can be helpful for structuring your thinking when faced with some of these bottlenecks. However, as each innovation is ultimately new, and thus unique, these won’t necessarily fit every case.
Having said that, we’ll share one framework for each dimension of scaling below. We’ve also created a toolkit that includes the frameworks as editable templates, along with some examples and other supporting material, which you can download here.
Demand side
For most organizations and innovations, the demand side is likely the source of most bottlenecks.
The way we see it, this is not just about drumming up interest and demand for your product, but also about making sure that it fits the needs and budgets of the buyers in your market. And of course, you need to make sure you’re in a market, or at least one that has the potential to become, large enough to accommodate your scaling efforts.
Unlike what people often think, product-market fit isn’t enough for a business to be scalable. You also need to have the right business and operating models, as well as use the right channels.
In other words, scaling out isn’t just about product-market fit, as people often mistakenly think. You also need to have the right business and operating models and use the right channels. Brian Balfour has written an excellent five-part series about this, which I highly recommend you read.
The basic idea is pretty simple: your business needs to align all of these aspects in a cohesive manner to be able to scale. If even one of them is wrong, growth will feel like, as Balfour puts it, “pushing a boulder uphill”. It will take way too much capital, effort, and time. However, get the four elements right together, and the growth will come naturally.
What’s important to understand here is that the model isn’t a static picture you just do once. If the market changes, or you run into challenges that force you to change one of these elements, you’ll need to review each element and make sure the big picture still works.
Supply side
For some products and businesses, especially those with physical products, the supply side often becomes a key consideration.
Here, the bottlenecks can be extremely varied, and dependences on external suppliers can lead to challenges that are hard to overcome.
In general, what top innovators do differently from the rest of the companies is that they almost always vertically integrate their value chain as they are working towards scaling up.
There are many benefits to this approach, such as reduced overhead, but the key differences are in increased quality, and most importantly, the company’s ability to control their own destiny and innovate more freely because they’re not being constrained by their supply chain.
Top innovators vertically integrate their value chain to address bottlenecks and turn cost centers into additional sources of growth and profit.
The classic example is Apple, and the way that they control both the hardware and software of their products. In recent years, they’ve been increasing that integration in both directions. They’re moving upstream to offer more services on top of their operating systems, as well as downstream by designing their own processors, which has provided them with a big performance advantage.
However, there are many others. Amazon, Microsoft, Tesla, Google, Netflix, Nvidia, and pretty much every innovative company is trying to do the same in the scope of their own business.
The basic idea is again simple: if a part of your supply chain becomes a major bottleneck, or is a major cost center, you should try to take control of those parts to address the bottlenecks and turn cost centers into additional sources of growth and profit, just like Amazon has done with AWS, but also warehousing and shipping.
That isn’t to say that vertical integration wouldn’t be challenging or have downsides. It certainly is and does. Because of these limitations, it’s generally advisable to only vertically integrate to the parts of your supply chain that either are a clear bottleneck or could become a key competitive advantage for you. However, top innovators often have little choice but to take these steps if they want to move fast enough and have enough control to be able to scale their innovation to its full potential.
Another key consideration on the supply side is simply the architecture of your products and services, and the process you have for delivering them. It’s obviously much easier to have a scalable architecture and automated processes for purely software or content focused businesses, but how you craft these does play a huge role for complex physical products too.
This is again a very extensive topic on its own, but the goal should be to try to make the manufacturing, delivery, and service of your products as seamless and scalable as possible. As with everything else we’ve discussed so far, this too is an iterative process.
However, to provide you with a slightly more practical framework to get started, here’s Elon Musk explaining how he’s learned to approach this topic after his early struggles of trying to do that with the extremely complex products at SpaceX and Tesla.
While Musk specifically talks about the process in the scope of engineering for scale, these same principles also apply to your organization and internal processes too.
And, as Musk explained in the video, it’s easy to get tempted by the promises of optimizing for efficiency and automation, but if you haven’t addressed the big picture first, these will often end up just being a big waste of time and money.
So, make sure to start by first eliminating those unnecessary requirements and parts or tasks, and try to simplify the design before you focus too much on optimizing for efficiency and automating.
Utilization
In addition to supply and demand, we still have the third dimension of utilization to cover. The idea with this “scaling deep” part is to find creative ways to make the most out of existing supply to either unlock new demand, maximize the utilization of those assets, or simply to increase your customer retention by finding ways to get more value for them from your products.
As you may have guessed by now, the specifics vary quite a lot on a case-by-case basis, but the flowchart below can hopefully serve as a starting point for your efforts in this area.
To summarize, there are three common paths you may take here.
The first is to find ways to increase the usage of assets that are only being used a fraction of the time through practices such as asset sharing and virtualization.
The second is to move from one-off purchases to a subscription to eliminate friction and increase the usage of the services.
The third is to find additional ways to expand the use of the product. This is usually done either by finding new value-adding uses for the same product, or simply by activating usage through means such as improved quality, usability, better communication etc.
However, sometimes it might even be necessary to work around tougher and more pervasive issues, such as regulatory considerations or even the changing of societal norms.
While increased utilization isn’t often that glamorous or exciting, it can really make a difference in making your business and operating models efficient enough to allow you to scale volume faster and more sustainably.
Conclusion
Scaling an innovation won’t be easy. It will always take years, and an endless amount of hard work with an extreme focus on solving each and every bottleneck standing in your way.
Hopefully you’ll find some of the frameworks and playbooks we’ve introduced in this article useful for shaping your thinking, and for building your organization and processes, but you’ll inevitably come across plenty of challenges where you’ll just need to figure out the solutions yourself. Still, if you want to truly succeed with innovation, that’s what you’re in for.
So, be prepared for those challenges, and be realistic with your expectations and timelines. For example, the “growth gap” can easily sneak up on your organization if top management has unrealistic expectations for the financial returns of innovation.
In general, large organizations have some disadvantages, but they also have huge advantages when it comes to scaling an innovation, so look for ways to leverage those advantages to your benefit.
And finally, make sure to surround yourself with top talent that’s prepared for the ride. Scaling innovation is teamwork, and it takes a special kind of a team to pull it off. You need people that are used to constant change, have a growth mindset, and the skills needed to solve whatever problems your domain may have.
As mentioned, scaling innovation is a journey that happens in small increments, and at times, it will feel frustrating. But if your team persists, keeps on learning and solving problems, you can eventually close in on whatever the full potential of your innovation is.
Image credits: Pexels, Viima
This article was originally published in Viima’s blog.
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Innovation strategy is a common source of anxiety for many innovation managers: they always want one, but few think their organization has a clearly defined one.
However, the good news is that innovation strategy is just a set of decisions on how to best fulfill the company’s overall strategic goals related to creating something new or improved. So, even if your organization doesn’t yet have a clearly defined innovation strategy, it’s often a surprisingly straightforward task to derive it from the overall corporate strategy.
Having said that, there still are a handful of ways in which innovation strategies often go wrong. In this article, we’ll explore some of these more common mistakes, and seek to provide you with some actionable tips for avoiding them.
The Classic Strategy Mistakes
Let’s start by covering the five classic strategy mistakes. These are not specific to innovation strategies but are by far the most common problems in those too.
At first glance, these classic mistakes seem like very basic rookie mistakes that no senior leader worth their salt will make. However, they are actually very difficult to avoid completely in a large organization. Most strategies, even some of the best, thus usually include some of these elements.The point is that if you start to see more than one or two of these, or if they’re obvious issues, odds are that your strategy will run into challenges down the road. Let’s next cover each of these mistakes briefly.
Daydreaming. This is the classic case of management coming up with a big, bold vision but not having any idea on how to get there, and no concrete plans for figuring that out. For front-line employees and managers, it’s immediately obvious that the strategy just isn’t rooted in reality.
Alignment is a related, but more nuanced challenge, and one that almost every large organization struggles with. Bridging the gap between the big picture goals and the day-to-day across the entire organization is just a very difficult task that is nearly impossible to get right from the get-go. The key is getting most of the way there, and then actively working to further improve alignment as you execute on the strategy.
Hoping for the best is a classic mistake for the big-picture style of leaders who think that their job is to get the big picture right, and its’ then other people’s job to make things happen. In reality, as Professor Martin well put it, it just doesn’t work like that. If your strategy doesn’t consider the execution, you’re just hoping for the best and usually that won’t happen. There’s a reason for the CEO being the Chief Executive
Not deciding is probably the second most common challenge right after alignment. We’ve all seen strategies that are basically a variation of “we do everything for everyone because that’s the biggest market”, and that lack of focus can only lead to spectacular failure when it comes time to execute the strategy. Another variation of this is strategies like “we focus on growth”, “we will become a market leader”. These aren’t meaningful choices; they are the end results, and very abstract ones at that. Nevertheless, growth can be made into an effective strategy if it’s focused on a very specific area, and the strategy includes the compromises you’re willing to make to achieve that growth, for example profitability. However, that’s just not what most companies are doing when they say their strategy is growth.
The 5-year plan is our nickname for running an extremely intensive one-off strategy process where a detailed roadmap is created for the next five (or however many) years. The problem is that no matter how well you know the business and do your research, no one gets it right from the get-go, and even if you theoretically would, there are very few markets that are so stagnant that nothing significant will change in the next five years. Good strategies are always a result of an iterative, on-going process.
In a nutshell, innovators plan for the long-term and towards specific goals – but remain flexible on the ways to get there and make strategy an iterative learning process focused on getting things done and continuously moving in the right direction. There are many good frameworks for this. Be it Future-Back, Discovery-Driven Planning, Blue Ocean Strategy, or the Lean Startup, they all essentially talk about variations of the same thing.
The Real Challenge is Implementation
Let’s say you get the big picture right and avoid the classic mistakes we’ve just covered. The good news is that you’re now in the game! The bad news is that you’re still a long way from successfully pulling off your strategy.
The implementation is the hard part, and the part that makes all the difference. In essence, a great strategy, be it an innovation strategy or any other kind of strategy, sets the upper limit for the performance of the organization. A poor strategy, even when executed perfectly, will still lead to poor performance. But so does a perfect strategy when implemented poorly.
Reliable figures for the failure rate of strategy execution are hard to come by, but the consensus seems to be in the range of 60-90%. I haven’t seen research on the same figures for innovation focused strategies but based on the stats that are available, I’m quite confident they aren’t much better.
Anyone can, after all, say that they want to change the world or become a global leader at something, but few can make that happen.
So, a great innovation strategy is built on a nuanced understanding of an organization’s operating environment and is built on choices that give the organization the best possible odds of success. And, in that, keeping the implementation and the day-to-day realities top of mind during each phase of the strategy work is key.
A great innovation strategy is built on a nuanced understanding of an organization’s operating environment and is built on choices that give the organization the best possible odds of success.
The details will naturally vary depending on the business and industry, but before we wrap up, we’ll briefly cover some of the key principles that most organizations pursuing an innovation focused strategy should pay attention to.
Getting Implementation Right
1. Tell the What, focus on the Why, and leave room for the How
The first of our principles is to understand that you as a leader don’t have all the answers. Whatever plan you create will need to be adjusted, and it should be done by the people executing the strategy. So, make sure your strategy tells the big picture mission and key choices you’ve made (the What), but focuses especially on the rationale behind them (the Why) while leaving room for people to figure out what the best methods are for achieving those goals (the How).
Statistically speaking, no one will remember your strategic goals, but with a couple of well-chosen examples, you can get your employees to remember the rationale behind key choices, which has far reaching consequences throughout the organization. If you get that right, alignment and execution will become dramatically easier.
2. Speed is key, systematically seek out and remove barriers for it
As we’ve covered earlier, executing an innovative strategy is an iterative learning process. The faster you can move, the faster you will learn, and the more you can accomplish. This leads to compounding returns, and that’s why I think pace of innovation is the ultimate competitive advantage any organization may have.
There are a number of things that can help make an organization more agile, innovative, and faster, but in the end it comes down to systematically seeking out and removing any and all barriers that prevent people from executing the strategy – and innovating. Sometimes this is straightforward if you just keep an ear to the ground, but often you may need to resolve more complex structural issues.
3. Decentralize
While it’s been shown that an extraordinary CEO can temporarily get an organization to execute well with sheer will of force, things will unravel the moment they leave if capabilities and responsibilities aren’t spread out across the organization. Thus, smart leaders will focus on controlled decentralization and capability building from the get-go.
The same principle applies for both strategy execution and innovation. Simply put, decentralization will help your organization make more informed decisions and move even faster.
Conclusion
As we all know, strategy plays a big role in determining the success of any organization. It essentially sets the upper limit for their performance, and a poor one will prevent the organization from ever reaching its full potential.
But, in any industry, there are likely dozens if not hundreds of companies with great, often even nearly identical strategies. Some just seem to pull it off, where others don’t.
Thus, it’s the implementation that makes the difference and really determines the success of an organization, and planning for execution and adapting to a changing reality must be crucial parts of your strategy from the get-go.
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