We all arrive at leadership with certain preconceptions about what makes a successful leader.
Sometimes we form an idea of what great leaders do based on historical leaders or modern-day leaders who are always getting media attention. Other times we form a picture of great leadership based on our own past experiences—both leaders we’ve worked under and even what attributes got us promoted into leadership. But those are often anecdotes.
And the plural of anecdote is not data. When you look at the data on effective leaders, pretty quickly you notice that some of these notions are misconceptions or outright leadership myths.
In this article, we’ll outline six leadership myths that are holding you back as a leader and may even be ruining your team—if you believe them of course.
Myth 1: Your Title Is Your Power
The first leadership myth is that your title is your power. It’s great that you’ve been promoted into a leadership role, but the mere title of leader doesn’t actually give you a lot of power over the team. Sure, your name is one box higher than your team members on the organizational chart. But if you work for a large organization, you may not actually have much ability to fire or punish people without getting approval from your boss or from human resources. Instead of trying to gain “legitimate power,” new leaders are better served by gaining rapport or respect from their team (what’s often called referent power and expert power respectively). When your team feels connected to you and respects your expertise, they’re much more likely to be influenced by you than if you’re merely trying to command them.
Myth 2: You Need To Have The Answers
The second leadership myth is that you need to have all the answers. This myth is most common in new leaders. Often, it’s the individual contributors who are hugely productive and who often have all the answers that get promoted into leadership roles. You were promoted for your expertise, so you protect your expertise at all costs. But the longer you stay in a leadership role, the more likely it is that your people know how to do the work better than you do. Pretending you know better may actually trigger their disrespect. In addition, leaders gain a lot of trust among their team when they’re willing to say, “I don’t know” and then look to the team for answers or commit to finding the answers and bringing them back. You don’t need to have all the answers, you just need to be committed to helping your find them.
Myth 3: Your Style Works For Everyone
The third leadership myth is that your style works for everyone. This myth is most common with middle managers. In the first leadership role, you often develop your preferred leadership style. And it often works because you’re leading a team of people who do a lot of the same work. But as you move up in an organization, and as your “team” starts to be a collection of different roles with different preferences, your preferred style becomes less important. It stops being about how you want to lead and starts being about how they want to be led—and led on an individual level. The best leaders understand the motivations and skillsets of each of their people individually and adjust their leadership style accordingly.
Myth 4: Disagreement Equals Disrespect
The fourth leadership myth is that disagreement equals disrespect. When someone on a team speaks up and disagrees with your idea, it can be easy to become defensive and see their disagreement as an act of defiance. And while some people can be downright belligerent, most disagreement on a team is healthy. The best teams are marked by a sense of psychological safety where everyone feels free to speak up, to express themselves, and even admit failure. And when team members disagree respectfully with you, how you respond affects how much psychological safety the team feels. Treat conflict as collaboration and remember that task-focused disagreement not only helps improve your idea, it helps everyone on the team know their opinions are valued.
Myth 5: Silence Signals Consent
The fifth leadership myth is that silence signals consent. This myth is the reverse of the previous one. Disagreement does not equal disrespect but at the same time, no one saying anything doesn’t mean everyone agrees with you. It could be that they have disagreements, but don’t yet feel safe to share them. (Or it could mean that everyone agrees…which means your team might not get much independent thinking.) When you feel your team reaching consensus early, or when no one is pushing back on your ideas, you’ll have to look harder for disagreements and encourage more candor on the team. Be willing to wait in silence for someone to speak up. Then treat that conflict as collaboration and over time your team will be less and less silent.
Myth 6: Performance Is Personal
The sixth leadership myth is that performance is personal. This final myth is less of a leadership myth and more of an organizational one. For most organizations, performance is measured individually and performance reviews conducted individually. But great leaders know it takes a team effort, and a growing body of research suggests that most of individual performance is better explained by the resources and collaboration of the team as a whole—whether high performance or low. So, when coaching members of your team, remember to take into consideration that much of their performance isn’t something they can fix, but rather something in the system or on the team that they need you to fix.
As you review this list, one myth in particular probably stood out to you—depending on your style and your leadership journey. That reaction is a good signal that the particular myth is one to focus your attention on and work on improving. But keep a lookout for the other myths as well. You may not believe them, but you may need to defend your team from other leaders who do. And as you move from myth to reality, your team will move toward greater performance until eventually they, and you, are doing their best work ever.
One of the ways innovators—and everyone else—so often get tripped up is by our own hidden assumptions. Beliefs we aren’t consciously aware of. These assumptions can make our challenges much harder than they need to be.
I’m sure you’ve heard the expression “Thinking outside the box.” That most overused creativity cliché. You probably don’t know where that metaphor came from, but I suspect you’ve seen it, and it illustrates this difficulty.
It’s called the nine-dot problem. Remember this? You’re supposed to connect all nine dots with four straight lines that are all connected and continuous. One long line with four segments. See if you can figure out (or recall) the solution.
What makes the nine-dot problem hard to solve—when you haven’t seen it before—is an assumption you probably made without realizing it. You assumed that those lines must fall within the space defined by those dots, that they’re inside the box. Once you realize you can go outside the box, it becomes much easier. You can see the solution HERE.
This puzzle goes back at least to the 1970s. Since then, many variations have been suggested. Here’s one that prompts a different hidden assumption.
Think of it as perhaps a new assignment for your team:
Folks, we hit it out of the park on that nine-dot program. That solution gave us a very profitable competitive advantage. But it’s been a while. Our competitors are catching up. We need an update. Our new challenge is similar. There are still nine dots, but they’re a little bigger, a little closer together, and our budget is smaller. We can only afford three lines instead of four. Otherwise, our challenge is the same—connect all the dots. So how do we do that with just three straight lines that are connected and continuous?
Can you figure this one out? If you don’t immediately see the solution, it’s probably because you’re making another assumption that you don’t realize you’re making. You’re assuming that you must go through the centers of the dots. But that’s not required. The lines can tilt. Without that limitation—that hidden assumption—the solution is much more straightforward. That solution is HERE.
I’m not telling you to stop making assumptions. That’s a fool’s errand. We all make assumptions, every moment of our lives, or we couldn’t function. You can’t check out everything. What I recommend instead is that you recognize that you’re always making assumptions and get better at identifying them. That way you can decide whether they’re appropriate. In other words, you need to be willing to challenge your own thinking—your own mindset.
Innovators are willing to question even what may seem obvious, because that’s how you gain new insights and make discoveries. And because learning how to understand this world in some new way is the first step toward making it better.
View this post as a video on YouTube here:
Image Credit: Unsplash
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Today, technology has become central to how every business competes. Futuristic advancements like artificial intelligence, big data and cloud computing are no longer pie-in-the-sky propositions, but mission critical initiatives that leaders are racing to implement within their organizations.
Unfortunately, most of these initiatives fail. In fact, McKinsey found that fewer than a third of organizational transformations succeed. That’s incredibly sobering. Imagine any other initiative with that type of expected return not only getting consistently funded, but enthusiastically viewed as a smart bet on the future.
Yet digital transformation doesn’t have to be a sucker’s bet. The truth is that digital transformation is human transformation and that’s where you need to start. Initiatives fail because organizations habitually get blinded by the “gee-whiz” aspects of technology, don’t focus on clear business objectives, scale too fast and then declare victory way too early.
1. Focus on People First, Technology Second
At first, digital transformation seems fairly straightforward. There are many capable vendors that can competently implement cloud technology, automation, artificial intelligence applications or whatever else you’re looking for. So, theoretically at least, a standard procurement process should be effective in sourcing and executing a project.
Yet consider how the the nature of work has changed has changed over the last few decades, due to technological shifts. We spend far less time quietly working away at our desks and far more interacting with others. Much of the value has shifted from cognitive skills to social skills and collaboration has increasingly become a competitive advantage. New technologies such as the cloud and AI will only strengthen and accelerate these trends.
The truth is that value never disappears it just shifts to another place. Consider the case of bank tellers. There are more than twice the number of bank tellers today than there were before ATM machines, but the work they do is vastly different. They are no longer there to execute transactions, but to advise, solve problems and up-sell. That takes very different skills.
So the first step towards a successful digital transformation is not the technology itself, but thinking about how you can empower your people through it. Where do you expect value to shift to? What new skills will your people need to learn in order to succeed? How can technology help them get where they need to be to serve your customers well?
2. Establish Clear Business Outcomes
Another common mistake executives make when implementing new technology is to focus on the capabilities of the technology itself, rather than the business outcome you hope to achieve. Are you trying to drive transactions, improve service and customer experience or something else entirely? You need to determine that before you can even think about a technical approach.
That’s why every transformational effort should involve operational managers, partners and front-line workers from the start. You need also to talk to customers and see what they actually value, rather than what would simply help operations to run smoother. From there, you can begin to develop a vision for how your business can function differently.
For example, when Barry Libenson first arrived at the data giant Experian as Global CIO in 2015, he spent his first few months talking to customers and the business units that served them. Everywhere he went, he found the same thing: what customers valued most was access to real-time data, which his company’s existing infrastructure could not provide.
From there, the path forward was fairly simple, but not easy. He needed to shift his company from a traditional on-site server architecture to the cloud. That took him three years to accomplish, but it transformed Experian’s business, empowered new business models and led to new revenue streams.
3. Identify A Keystone Change
Once the vision is in place, the tendency, all too often, is to embark on what becomes a “five-year death march” to achieve it. In the end, everybody ends up frustrated, angry and, inevitably, it turns out that by the time the vision is achieved, the technology is out of date.
So instead of trying to swallow the entire vision whole, it’s best to start out with a keystone change. Think about a clear and tangible goal you can achieve in the near term that would require the involvement of multiple stakeholders and pave the way for future, more complex initiatives in the future.
One way to do this is to choose a solution that will help people with tedious, mundane tasks rather than create a new capability. It’s much easier to get people excited by reducing the time and effort they have to expend on something they hate then it is to push them to adopt something new. You always want to attract and empower, rather than bribe or coerce.
For example, in Experian’s case, Libenson started out by creating internal API’s rather than building customer facing features. These didn’t create an enormous impact, but they showed what was possible and built momentum for the larger vision.
4. Treat Transformation As A Journey, Not A Destination
Perhaps the most dangerous part of any transformation is when the initial objectives have been achieved. That’s when motivation begins to weaken and complacency sets in. In my book, Cascades, I call this problem surviving victory and it is a crucial element of every transformational effort.
The key to surviving victory is to plan for it from the start. In Experian’s case, the journey was never about the cloud. That was merely a destination. The vision was always to serve customers better and to develop new business models. That’s why Libenson focused not only on implementing technology, but indoctrinating new values and beliefs.
“Having gone through this transformational process over the past three years and seeing concrete business results, we are much better positioned to adopt those technologies,” he told me. “We’ve made the changes in culture, our organizational structure and skills to be able to adopt new technologies quickly, completely and with better collaboration with our customers.”
That emphasis on values is key, because to change fundamental behaviors you first have to change fundamental beliefs and digital transformation is always about empowering action. Keep your eye on that and you will be likely to succeed where most others fail.
— Article courtesy of the Digital Tonto blog and an earlier version appeared on Inc.com
— Image credit: Pixabay
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A good idea will sell itself, right? Unfortunately not — Emerson was spectacularly wrong when he suggested that all you needed to do was build that better mousetrap to have the world beating a path to your door.
History is full of examples of innovations that, whilst being good and proven solutions, more than just a gleam in their inventor’s eye, stubbornly refused to scale. They failed to have impact on a widespread basis.
Think about Earl Tupper and his alchemical miracle, creating an award-winning product out of the unpromising raw material of black sludge waste from oil refining. Tupperware eventually made it as an innovation which scaled but it was only after Brownie Wise teamed up with him and pioneered the social marketing which brought the product into the homes of key influencers.
Or Toshiba, investing close to a billion dollars in the technology underpinning what they saw as the next generation of high quality DVD recording, only to fall in the final straight as the market opted instead for Sony’s Blu Ray system. This was a fascinating echo of the story which Sony had encountered decades earlier when its Betamax video tape format lost the battle to the VHS standard, despite having many technical advantages over its rival.
Or Better Place, an ambitious green start-up that offered to make the world more sustainable by introducing battery swap technology for electromobility. Despite raising a huge amount of venture finance and gaining the backing of world leaders and CEOs prepared to set up factories the vision fell apart after three years.
These are not the failures of foolish and unprepared entrepreneurs; they all had much to offer and had proven their technologies worked. But they each stumbled over one or other of the many rocks strewn in the way of those trying to make the journey to scale. There are plenty of them in the world of commercial innovation — and in the field of social change, innovations designed to have an impact and change the world, it’s even more difficult.
Evidence and scale
One of the challenges is around the role of evidence. At its simplest we adopt new things because we see some benefit in them, they make our lives easier, more comfortable or better in some way. That’s what gives rise to the S-curve shape which you can find associated with any innovation — it isn’t a case of all or nothing, adoption takes time. And one of the key influences on that is the role of evidence.
For early adopters it’s a matter of being convinced enough by data or demonstrations that the innovation has real advantages to offer — they’re looking for hard and measurable facts to underpin their decision. But as we move along the road diffusion becomes more of a social process as well.
The more we see others getting benefit, the more we’re prepared to take the risk. Shaping our perceptions of new things so that we adopt them sooner is a huge part of what advertising does and it plays on our desire for evidence. Being persuaded — by facts, figures, demonstrations or simple observation accelerates the process.
Think of Washington Carver’s famous attempts to get rural farmers in the southern USA (a sceptical breed) to adopt new strains and methods. Simply giving them the hard facts wasn’t enough — his success came when he could show that the crops in his demonstration fields grew higher or thicker than those around. Seeing is believing — and it reminds us that evidence comes in many forms and can be communicated in different ways.
It’s also a matter of who is offering us the evidence — can we trust it, can we believe it? The advertising industry has played this tune for a long time, persuading us about the virtues of better toothpaste or headache pills by invoking the (eminently trustworthy) authority of medical practitioners. We also listen to key influencers, opinion leaders whose perceptions we trust — and we’re much more likely to adopt something if it is recommended by ‘people like us’.
All of these factors help shape the familiar S-curve pattern which we see over the life of innovations whereby adoption accelerates after the initial first wave. There’s a kind of snowball effect with the accumulation of evidence (especially the experience of satisfied adopters) driving up the pace of adoption. (Or not — negative evidence or word of it can quickly stop adoption in its tracks).
So if we are concerned with trying to scale our innovation it’s worth looking a little more closely at the role evidence plays, at the monitoring and evaluation processes which build that evidence base, and at how evidence is communicated. We could do a lot worse than break our review down into some key question areas — the who, what, when and how of scaling evidence.
Who?
Who needs evidence? Well, self-evidently (!) adopters, as we’ve seen, it’s a key part of the innovation decision process. But we often use proxies — opinion leaders — to influence our decisions — whether it’s the Jones’s we try to keep up with or our favourite social media influencer. Adoption is based on trusting others judgment and we assume they have reviewed the ‘evidence’ in coming to their decision.
Beyond that there’s another group — investors. Whether it is donors funding social innovation or government promoting a new technology or individual investors in a crowdfunding campaign those investors are looking for evidence to shape their behaviour. Is the innovation worth doing — is there evidence of demand and potential impact? Is there evidence downstream of actual impact, and along the way are the trends in the right direction? And afterwards, was the investment worthwhile, was it done well, could it have been done better, what have we learned? All questions which require evidence.
And then there’s the innovators themselves, the teams growing and scaling their innovation. Their core approach in coming up with their original solution will have been based on prototyping and experimenting, pivoting as they learn from the market what works and what doesn’t. And that experimental learning cycle doesn’t stop once the solution is established. If anything the journey to scale requires even more of this pivoting and adaptation to suit different contexts and situations on the scaling journey. Once again what the team needs is evidence.
In the field of social innovation there are other stakeholders to consider, all of whom have influence on whether or not an innovation can scale. Research on innovation scaling in the humanitarian sector suggests that there are many different players involved, each of whom have different evidence needs, as shown here.
So what kind of evidence do we need? And, in a world increasingly plagued by fake news and unreliable facts ,what constitutes ‘good’ evidence? There isn’t one size fits all, different players (as we’ve just seen) look for different kinds of evidence.
During a recent webinar Lydia Tanner of The Research People showed a helpful graphic which underlines this point; evidence is very much a matter of horses for courses.
Of course we’re looking for evidence of impact, of relative advantage. But in the field of social innovation where donors and funders may be asking the question there’s also a need to provide evidence that the problem is important and the ‘right’ one to address, and that the solution has real value to end-users. Is there real advantage to the solution, is it compatible with the context into which it will operate?
And on the left had side of the diagram there are considerations of how well the solution is delivered. This involves reflecting and collecting data on the innovation process itself and how well it is working , alongside the nature and experience of the solutions being offered.
There’s also something important about the quality and reliability of the evidence we assemble. Clearly our aim should be to provide proof, facts which can be verified — not for nothing does the healthcare sector place so much weight on randomised control trials as a gold standard to help determine whether a new medicine is effective or not. RCTs are all about assembling an evidence base of reliable and robust data. The trouble is that getting at good evidence is difficult, not least because there are so many kinds of information we can assemble as ‘evidence’ — not least those vanity metrics which tell us that ‘20,000 people can’t be wrong…..’!
When?
And then there’s the when question. When should we start to assemble our evidence base and when does it have most impact? The simple answer is ‘always’ — throughout the innovation and scaling process.
At the start of the scaling journey we want evidence to reassure us that there is potential demand, that our innovation will be solving a big enough and important enough problem and that what we have developed represents a robust solution which is capable of being scaled. Without this to back up our claims we’re unlikely to get very far in trying to convince others to buy into or support our solutions.
During the process it’s all about pivoting, using evidence of success and failure to help shape and adapt our innovation to suit different contexts. In the social innovation field the ‘market’ may involve a number of different players but the principle is the same. We can use the different kinds of evidence outlined above to help us get a better fit between innovation and context. Which will increase our chances of successfully scaling it.
A simple example might be the case of Netflix. Early on in their innovation journey Netflix realised that their entertainment supply model based on shipping DVDs by post was not the way to go; whilst their model worked there was increasing evidence that people were turning to online streaming of music and the same was likely to happen to video as high speed internet bandwidth became available. So they pivoted to a streaming approach, learning with the newly-emerging market while at the same time maintaining their video-by-post approach.
(And contrary to popular myth Blockbuster didn’t simply plough on with its old bricks and mortar solution using shops as rental hubs. They saw the evidence of Netflix ‘s successful new online model and developed their own solutions to emulate it. But their wider value network had too much invested in the original model and was reluctant to let go. So in spite of the evidence they couldn’t change their business model with the resulting collapse of their operations).
And at the end of the process there’s an opportunity for collecting a different kind of evidence, around learning. If we succeeded, why and what can we do more of next time? And if we failed, what can we change? Smart organizations concerned with learning to repeat the innovation trick develop ‘routines’ — embedded patterns of behaviour which become ‘the way we do things’ around innovation. These routines find their way into polices , procedures and processes — but not by accident. There’s a need for post-project reviews, set down meetings and other devices to capture learning. The trouble is , particularly with projects which have not gone so well, that there’s a tendency to cover up and disguise things — obscuring the evidence we need so badly to help us improve things next time.
How?
Which brings us to the how? How do we set up robust and flexible monitoring and evaluation so we can collect the different kinds of evidence needed to by different stakeholders? What frameworks and tools are available? What different approaches might be needed under different circumstances? Not surprisingly there is no simple answer to this but a clear need to put an evidence strategy in place at the outset of the innovation scaling journey. Since evidence will play such a key role we need to allow time and resources and develop or bring in expertise to work on this aspect of our project in parallel with rolling out our solution.
And we need to think hard about how we communicate the evidence we acquire to a variety of different audiences. How do we build on good evidence to tell the innovation story? Adoption of innovation is a social process which is accelerated or retarded by more than facts; it depends on perceptions and on social influence. That’s a lesson which comes through repeatedly in the work of Everett Rogers, the ‘godfather’ of innovation diffusion research and it continues to play a key role according to current research findings. It’s also clear from the experience of would-be innovators trying to scale their solutions
There was nothing wrong with Earl Tupper’s product innovation except that no-one was particularly interested in buying it. That all changed when he switched his marketing from in store sales to doorstep selling and through that to the in-home party. Brownie Wise was one of the early demonstrators and quickly proved her facility at persuading home-makers to adopt the product. Her sales pitch was essentially around changing the way in which the core evidence — that the product worked and was a viable better food storage solution than traditional glass jars — was communicated and perceived.
She had great attention-grabbing skills — for example one of her ‘party tricks’ involved filling a Tupperware bowl with tomato soup and throwing it across the room where it landed, seal still intact and without spilling and staining the carpet. But she accompanied these tricks with a much more powerful approach which was to engage the party hostesses as sales agents. Their ‘source credibility’ — the degree of trust and respect in which they were held by their peers — meant that they were powerful opinion leaders, able to accelerate adoption across social networks. These days we’d call them ‘influencers’ but whatever the label the way in which they could amplify the positive perception of evidence played a key role on the successful scaling.
So what lessons can we take from this? First we should remind ourselves that scaling innovation is not automatic it’s a long and difficult Journey — and one in which evidence makes a difference. Evidence is what drives and accelerates (or retards) that S-curve around adoption.
But we need to consider an evidence strategy — it’s not just that we need evidence but we need to think about who’s it for and their different needs, what form it can take that will be convincing, how are we going to communicate the story, etc.?
Innovation is what what’s helped us as a species to survive and grow in what is still a very hostile, turbulent and uncertain world. But that innovation process hasn’t been a matter of simply adopting every new idea because it’s new. That’s a very dangerous approach, not least because many innovations may take us in the wrong direction. We’re actually quite cautious about adoption; we’re not risk averse but we’ve evolved to be careful about the risks we take. Having credible evidence occupies a place centre stage in that adoption decision. Which means that if we’re serious about scaling our innovation then we need to take the evidence question seriously.
It’s not uncommon to hear leaders say, “We must put the customer at the core of everything we do.” What does that really mean? I had a chance to interview Howard Moodycliffe for Amazing Business Radio. Moodycliffe is the CEO of TimeToReply, a SAAS company that empowers employees to deliver fast, efficient, consistent responses through email. In our interview, he gave his take on the “customer at the core of your business” comment and more. Below are some of what he believes goes into a successful customer experience, followed by my commentary.
1. Put the Customer First – According to Moodycliffe, this is the most important strategy. Here’s his advice. Answer the question, “What experience do you want your customers to have?,” and then create it. Customers must feel valued and respected, and their needs must always be at the forefront of anything you design—both the product or service and the process of doing business with you.
2. Be Proactive and Respond Quickly – A fast response is good, but proactive communication is better. Moodycliffe can look at customers’ data and determine if they should receive a proactive email. He’s also quick to point out that email is just one channel. His concept applies to customer communication regardless of the channel. In our interview, I shared that one of my clients in the internet/cable industry gathers all of the ways a customer can be contacted: email, phone, social channels, WhatsApp, etc. If there is a problem, they send announcements to all channels, hoping to reach the customer on one of them. As for responding quickly, knowing what the customer expects is essential. He quotes Jay Baer, who says, “Response time should be just a little faster than the customer expected.” And realize that each channel has different expectations. You probably won’t be happy if you call customer support and are put on hold for fifteen (15) minutes. But you would be elated if you sent an email and received a response within two hours.
3. Personalize the Experience – Moodycliffe says, “Customers want to feel like they are more than just a number.” Our customer experience research found that 71% of customers believe a personalized experience from an agent or company employee is important. However, making a customer feel as if they are an account or a number could be worse than a generic, un-personalized experience.
4. Use Technology to Your Advantage – Tech in customer service can be used to automate tasks, provide self-service options, and collect/analyze feedback. Moodycliffe emphasizes that technology should empower employees when they interact with customers. If you bring in a new program that is cumbersome and difficult for employees, that pain will eventually be felt by customers on the outside.
5. Empower Your Employees – While this has been preached by many (including myself), we all need to be reminded of the importance of letting your employees do what they are being paid to do: take care of customers (or other employees). In addition, another advantage of using technology is that it empowers users (employees) to be more effective and efficient at what they do.
6. Measure and Improve – The first thing that came to mind when Moodycliffe mentioned this was the quote often attributed to Peter Drucker: “You can’t manage what you don’t measure.” You can’t assume that you’re delivering a great customer experience. You must measure your performance and use feedback to create a better experience.
In today’s competitive landscape, the notion of putting customers at the core of your business isn’t a cliché — it’s a mandate for success. From Moodycliffe’s insights to research-backed strategies, it’s clear that proactive, personalized, and technology-empowered strategies are not an option but essential.
Image Credit: Pexels
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Fall is in the air, which brings to mind the season’s favorite sport—no, not football, strategic planning! Let’s face it, 2023 has been a tough year for most of us, with few annual plans surviving first contact with an economy that was not so much sluggish as simply hesitant. With the exception of generative AI’s burst onto the scene, most technology sectors have been more or less trudging along, and that begs the question, what do we think we can do in 2024? Time to bring out the strategy frameworks, polish up those crystal balls that have been a bit murky of late, and chart our course forward.
This post will kick off a series of blogs about framing strategy, all organized around a meta-model we call the Hierarchy of Powers:
The inspiration for this model came from looking at how investors prioritize their portfolios. The first thing they do is allocate by sector, based primarily on category power, referring both to the growth rate of the category as well as its potential size. Rising tides float all boats, and one of the toughest challenges in business is how to manage a premier franchise when category growth is negative. In conjunction with assessing our current portfolio’s category power, this is also a time to look at adjacent categories, whether as threats or as opportunities, to see if there are any transformative acquisitions that deserve our immediate attention.
Returning to our current set of assets, within each category the next question to answer is, what is our company power within that category? This is largely a factor of market share. The more share a company has of a given category, the more likely the ecosystem of partners that supports the category will focus first on that company’s installed base, adding more value to its offers, as well as to recommend that company’s products first, again because of the added leverage from partner engagement. Marketplaces, in other words, self-organize around category leaders, accelerating the sales and offloading the support costs of the market share leaders.
But what do you do when you don’t have company power? That’s when you turn your attention to market power. Marketplaces destabilize around problematic use cases that the incumbent vendors do not handle well. This creates openings for new entrants, provided they can authentically address the customer’s problems. The key is to focus product management on the whole product (not just what your enterprise supplies, but rather, everything the customer needs to be successful) and to focus your go-to-market engine on the target market segment. This is the playbook that has kept Crossing the Chasm on entrepreneur’s book lists some thirty years in, but it is a different matter to execute it in a large enterprise where sales and marketing are organized for global coverage, not rifle-shot initiatives. Nonetheless, when properly executed, it is the most reliable play in all of high-tech market development.
If market power is key to taking market share, offer power is key to maintaining it, both in high-growth categories as well as mature ones. Offer power is a function of three disciplines—differentiation to create customer preference, neutralization to catch up to and reduce a competitor’s differentiation, and optimization to eliminate non-value-adding costs. Anything that does not contribute materially to one of these three outcomes is waste.
Finally, execution power is the ability to take advantage of one’s inertial momentum rather than having it take advantage of you. Here the discipline of zone management has proved particularly valuable to enterprises who are seeking to balance investment in their existing lines of business, typically in mature categories, with forays into new categories that promise higher growth.
In upcoming blog posts I am going to dive deeper into each of the five powers outlined above to share specific frameworks that clarify what decisions need to be made during the strategic planning process and what principles can best guide them. In the meantime, there is still one more quarter in 2023 to make, and we all must do our best to make the most of it.
That’s what I think. What do you think?
Image Credit: Pixabay, Geoffrey A. Moore
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Half a decade ago, I laid out a perspective on the evolution of innovation. Now, I return to these reflections with a sentiment of both awe and unease as I observe the profound impacts of AI on innovation and business at large. The transformation unfolding before us presents a remarkable panorama of opportunities, yet it also carries with it the potential for disruption, hence the mixed feelings.
1. The Reign of R&D (1970-2015): There was a time when the Chief Technology Officer (CTO) held the reins. The focus was almost exclusively on Research and Development (R&D), with the power of the CTO often towering over the innovative impulses of the organization. Technology drove progress, but a tech-exclusive vision could sometimes be a hidden pitfall.
2. Era of Innovation Management (1990-2001): A shift towards understanding innovation as a strategic force began to emerge in the ’90s. The concept of managing innovation, previously only a flicker in the business landscape, began its journey towards being a guiding light. Pioneers like Christensen brought innovation into the educational mainstream, marking a paradigm shift in the mindsets of future business leaders.
3. Business Models & Customer Experience (2001-2008): The millennium ushered in an era where simply possessing superior technology wasn’t a winning card anymore. Process refinement, service quality, and most critically, innovative business models became the new mantra. Firms like Microsoft demonstrated this shift, evolving their strategies to stay competitive in this new game.
4. Ecosystems & Platforms (2008-2018): This phase saw the rise of ecosystems and platforms, representing a shift from isolated competition to interconnected collaboration. The lines that once defined industries began to blur. Companies from emerging markets, particularly China, became global players, and we saw industries morphing and intermingling. Case in point: was it still the automotive industry, or had the mobility industry arrived?
5. Corporate Transformation (2019-2025): With the onslaught of digital technologies, corporations faced the need to transform from within. Technological adoption wasn’t a mere surface-level change anymore; it demanded a thorough, comprehensive rethinking of strategies, structures, and processes. Anything less was simply insufficient to weather the storm of this digital revolution.
6. Comborg Transformation (2025-??): As we gaze into the future, the ‘Comborg’ era comes into view. This era sees organizations fusing human elements and digital capabilities into a harmonious whole. In this stage, the equilibrium between human creativity and AI-driven efficiency will be crucial, an exciting but challenging frontier to explore.
I believe that revisiting this timeline of innovation’s evolution highlights the remarkable journey we’ve undertaken. As we now figure out the role of AI in innovation and business, it’s an exciting but also challenging time. Even though it can be a bit scary, I believe we can create a successful future if we use AI in a responsible and thoughtful way.
Image Credit: Stefan Lindegaard, Unsplash
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Innovations don’t have to be complicated to be impactful. They just need to deliver enough additional value that existing solutions become widely replaced, or flipped around, for the new solution to be widely adopted.
Recently I have been seeing a new simple, yet elegant, solution driving around the streets of Seattle.
It’s pictured in the photo above and it is quite simply the delivery of a temporary license for a newly purchased vehicle that can be printed and installed in a license plate holder in the same way that the eventual traditional license plate will be.
Now, perhaps your state or country already has this, but for me, every vehicle I have ever purchased was instantly defiled by a piece of paper and tape or tape residue that could be difficult remove after a couple months baking in the sun (especially in the summer).
This instant cheapening of a brand new vehicle is now a thing of the past!
Some may say that this is not really that big of a deal because you’re just moving the temporary registration from the back window to now live in the license plate frame, but there are several tangible benefits for multiple parties from this seemingly small change:
Car Owner – improved aesthetics – the car just looks better!
Car Owner – improved safety from increased visibility while driving
State and Car Owner – increased toll revenue so everyone is paying their fair share
Car Owner – improved safety – easier to identify hit and run drivers
Police – improved safety – easier to identify vehicle during traffic stops
Car Owner – improved convenience – easier to quickly find license number when it’s requested
What is your favorite simple innovation that you’ve seen or experienced recently?
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