Category Archives: Innovation

Shifting Mindset to Lead with Innovation in Mind

GUEST POST from Art Inteligencia

In our fast-paced world, continuous adaptation and innovation are crucial for success. The cornerstone of driving meaningful change is a shift in mindset. By fostering a culture of innovation, leaders can effectively guide their organizations through evolving landscapes. In this article, we explore the concept of human-centered change, focusing on two powerful case studies that illuminate the impact of mindset shifts in leading with innovation.

Understanding Human-Centered Innovation

The essence of human-centered innovation lies in recognizing the central role of people—employees, customers, and stakeholders—within the change process. By prioritizing empathy and inclusivity, organizations can design solutions that are not only innovative but also aligned with human needs and aspirations.

Case Study 1: Microsoft – Transforming Culture through Empathy

The Scenario

Microsoft, once perceived as a software behemoth lagging in innovation, needed a transformative approach to regain its competitive edge. Under the leadership of CEO Satya Nadella, a profound mindset shift was initiated with empathy at its core.

The Mindset Shift

Nadella prioritized a cultural transformation, encouraging a growth mindset across the organization. By valuing learning from failures and embracing diverse perspectives, Microsoft fostered a supportive and inclusive workplace.

The Impact

This cultural overhaul led to groundbreaking advancements, such as the remarkable success of Azure, Microsoft’s cloud computing platform. The focus on empathy and collaboration not only reinvigorated innovation but also greatly improved employee satisfaction and retention.

Case Study 2: Airbnb – Designing with Users in Mind

The Scenario

Airbnb faced significant challenges as it sought to expand globally while facing regulatory hurdles and increasing competition. The company needed to innovate its approach to maintain its unique value proposition.

The Mindset Shift

Airbnb embraced a human-centered design approach. By deeply understanding their hosts’ and guests’ experiences, they crafted solutions that addressed key pain points, enhancing trust and safety within their platform.

The Impact

Implementing these insights led to the introduction of features like “Superhost” and improved review systems, which bolstered user confidence. This user-centered innovation enabled Airbnb to solidify its market position and drive sustainable growth.

Key Takeaways

These case studies highlight the transformative power of mindset shifts rooted in human-centered principles. From fostering empathy to prioritizing user experiences, organizations can cultivate a culture of innovation by embracing these practices:

Encourage a Growth Mindset: Nurture an environment where failures are viewed as learning opportunities, fostering innovation and resilience.
Empathize with Stakeholders: Actively engage with the needs and concerns of employees and customers to ensure solutions are impactful and human-centric.
Promote Collaborative Environments: Break down silos and encourage cross-functional collaboration to harness diverse perspectives and drive creative solutions.

Conclusion

The journey toward human-centered innovation requires a fundamental shift in mindset. By leading with empathy, inclusivity, and a commitment to understanding human needs, organizations can unlock new levels of creativity and competitive advantage. As demonstrated by Microsoft and Airbnb, such transformation not only fuels innovation but also strengthens connections with those who matter most—people.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pixabay

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Learning from the Failure of Quibi

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Learning from the Failure of Quibi

GUEST POST from Greg Satell

In 2018, Steve Blank wrote a piece in Harvard Business Review questioning the viability of the “lean startup” model. Given that Steve had pioneered lean startup techniques, I was intrigued. Why would he, all of a sudden begin, to doubt an idea that had been so successful and, to me at least, still seemed so relevant, even for large enterprises.

As it turned out, what made Steve hesitate was a new venture called “New TV” that was headed up by the dream team of legendary Hollywood producer Jeffrey Katzenberg and star Silicon Valley CEO Meg Whitman. Beyond talent and cache, it had raised almost $2 billion. With that much money, how could it lose?

Now we know. The venture, which eventually came to be known as “Quibi,” recently announced it was shutting down, less than seven months after its product launch. It’s become an all too familiar tale. Multi-billion-dollar washouts, including WeWork, Better Place and others, have become all too common. We need to learn from their mistakes.

You Can Have Too Much Money

At the heart of Steve Blank’s argument against his own idea was that The Lean Startup “was an answer to a specific problem,” namely that startup companies face a limited runway due to scarce capital. In effect, he sees launching a new company as a race to identify a viable business model before you run out of money.

In Quibi’s case, however, there seemed to be unlimited capital. Its nearly $2 billion in funding would give it the ability to roll out a full-fledged business and, if things didn’t work as planned, still have the option to pivot. With access to that much money plus, presumably, ample access to even more, how could Katzenberg and Whitman go wrong?

To be honest, I never found the argument to be persuasive. I’ve launched countless businesses in my career and one thing I’ve learned is that you need to keep capital scarce in the early days. Limiting the amount of money you have around forces people to face up to problems and solve them. You can’t ignore warning signs when you’re close to broke.

Quibi, on the other hand, failed because it did ignore signals. With almost limitless programming budgets, producers knew they could sell Quibi their worst work. Infighting between Katzenberg and Whitman was ignored. Potential snafus, such as the inability for consumers to screenshot and share memes or to watch on TV screens, were overlooked.

Identify the Hair on Fire Use Case

Conventional marketing strategy dictates that you identify the largest addressable market for your product. That, after all, is where you can reach the most people, scale your business and earn the most money. So it made sense for Quibi to target Millennials in search of “quick bites” to watch while on line at Starbucks.

Yet when you’re launching something new and different, you don’t want the largest addressable market which, almost by definition, already has a lot of companies serving it. Instead, you want to identify a hair-on-fire use case—a problem that somebody needs solved so badly that they almost literally have their hair on fire. That’s where you’ll find customers to put up with the inevitable bugs and glitches that always come up.

For example, with Tesla, Elon Musk didn’t target the largest addressable market—a mid-market family model—but rather Silicon Valley millionaires who liked the idea of a high performance, eco-friendly car. Those customers weren’t price sensitive and didn’t need to depend on the car to pick the kids up at soccer practice, but did give the company a foothold in the luxury market. The mass market product, the Model 3, would come years later.

I’m sure there is a “hair-on-fire” use case for a short form video platform. Unfortunately, these things are never obvious, if they were, they would already be large addressable markets. Presumably, Katzenberg and Whitman considered themselves to be so smart that they could get it right on the first try.

Train The Monkey First

At Google’s X division, the company’s “moonshot factory,” the mantra is #MonkeyFirst. The idea is that if you want to get a monkey to recite Shakespeare on a pedestal, it’s best to start by training the monkey, not building the pedestal, because training the monkey is the hard part. Anyone can build a pedestal.

Returning to the example of Tesla and Elon much, in the case of electric cars, the “monkey” was always making a battery powerful enough to achieve an acceptable range. In the early years, that’s what the company focused on and, with an affluent customer base, they could do so without worrying too much about costs.

Once Tesla had customers, it could begin to focus on learning from them and adapting to what they wanted from an electric car. At the same time, it was able to develop manufacturing and operational capability that allowed it to scale. All of this went very slowly at first, but then accelerated at a pace that took incumbent car companies by surprise.

In the media business, the “monkey” is always to build an audience. Yet Katzenberg and Whitman chose to plow money into content, assuming that they knew what their (at that point nonexistent) audience wanted. Essentially, they blew through all of their money building the pedestal and assumed the monkey would train itself.

Your Strategy Is Always Wrong

In their letter announcing the closure of Quibi, the founders wrote, “And yet, Quibi is not succeeding. Likely for one of two reasons: because the idea itself wasn’t strong enough to justify a standalone streaming service or because of our timing… Unfortunately, we will never know but we suspect it’s been a combination of the two.”

Yet the point isn’t that Quibi got it’s strategy wrong or that the pandemic altered its chances of success, but rather that your strategy is always wrong. Everybody gets disrupted sooner or later and every business model eventually fails. The art of managing a venture isn’t to execute the “right” strategy, but to make the strategy less wrong over time.

Katzenberg and Whitman, it seems, allowed their previous success to blind them. They appear to have simply assumed that they were so smart that they could get it all right out of the gate. They didn’t allow room for error, to make mistakes or to pivot. When things didn’t go as planned, there was nowhere else to go. They had to pack it in.

Probably the most important thing we can learn from Quibi’s failure is to not believe your own PR. Plan for and prepare things to go wrong. Nobody really knows anything until it can be observed in the real world. Or, as Steve Blank might put it, no business plan ever survives first contact with a customer.

— Article courtesy of the Digital Tonto blog
— Image credit: Unsplash

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Good Design Makes Technology Disappear

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Good Design Makes Technology Disappear

by Braden Kelley

The late Clayton Christensen wrote a little book called The Innovator’s Dilemma that many of you I’m sure have read. Many people think of it as a book about disruptive innovation, but it can be much more than that if you shift your perspective.

The Classic Disruptive Innovation Example

One of the case study examples is that of mini-mills disrupting the rolled steel producers in the steel industry by starting at the bottom of the food chain with the production of low margin re-bar and then moving upwards into higher margin steel products. This is seen as the blueprint for how you disrupt an industry. You go first where the incumbents are least likely to be concerned about new entrants – low margin products – a market that incumbents might actually be happy to lose, because their average margins will actually increase and wall street will potentially reward them in the short-term with higher stock prices.

But if you shift your perspective on this case study and apply it to emerging technology, something new emerges.

Learning and Adoption Require a Compelling Use Case BEFORE They Can Occur

I’ve been listening to a lot of podcasts while I work lately. Podcasts with leading scientists from around the world. One of the core themes that continuously emerges is that innovation is really hard and takes a long time. I was really struck by iRobot co-Founder Rodney Allen Brooks speaking about how they had a target of launching the Roomba at $200 and this meant that he had FIFTY CENTS per unit to spend on a piece of silicon to power their invention. He told the story of running around Taiwan looking for a chip that was cheap enough and was handicapped in ways that wouldn’t matter for their particular application – as ALL chips in that price range are going to have severe limitations. This is a great story for highlighting some of the unexpected challenges in turning an invention into an innovation.

Another interesting innovation case study – on the failure side – is that of Google Glass. The smart glasses arrived as an overhyped and underwhelming product and died on the vine in a very short period of time. One of the key reasons for their failure was the lack of a compelling use case, and another was that technology was too front and center – so much so that Google Glass seemed like a creepy invention.

“Making access to information just instant and intuitive. By doing that, technology fades into the background, and we’re more connected with the people and things around us.”

This quote is pulled directly from the video below about Google’s reboot of their smart glasses initiative:

Google’s Live Translation Glasses arrive this time without a product page, without a formal product name and promising much less.

One of the things that really struck me in this short video is that while it is super easy to anchor on the value of the translation piece – displaying Mandarin on screen from an English voice for example – they have several other powerful uses cases, including:

People who have single-sided deafness
People who don’t want to wear hearing aids, or for whom hearing aids don’t work
People who are fully deaf
People who are trying to learn a new language

Do One Thing Really Well and Build From There

Google’s Live Translation Glasses remind me of another pair of smart glasses launched a little while back in the glow of the Google Glass failure – Amazon’s Echo Frames.

Amazon’s Echo Frames build themselves around the compelling use case of hands-free searching and calling. They have speakers and a microphone, connect to your iOS or Android smartphone, and can even be fitted with prescription lenses.

Amazon Echo Frames

Don’t Strip the Gears on Your Innovation Machine

Our ability to imagine usually outpaces our ability to execute and it can be a challenge to rein in our imagination to match our ability to not just execute, but to do so profitably and at a pace that our customers can see their way to adopt it.

When we look at my Innovation is All About Value methodology, we can also see that companies fail less often at value creation, and more frequently at value access and value translation.

When your start small and build around a compelling use case it is easier to get the value translation right and it is easier to build the key value access components to support your value creation.

Timing matters…

Price matters…

Compelling use cases matter…

What’s yours?

Keeping the end in mind and the future in sight – is important – but it is more valuable to identify where to start and add value as you go.

Don’t strip the gears on your innovation machine and keep innovating!

Image credit: The Verge, Amazon

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Surfacing Your Hidden Assumptions

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Successful strategy and innovation are about how fast you can become aware of your assumptions.

Surfacing Your Hidden Assumptions

GUEST POST from Soren Kaplan

When it comes to strategy and innovation, success depends on how fast you become aware of your assumptions and then modify them. But it’s a paradox: You can’t see your most fundamental assumptions until you overcome them. This means that you can only understand your mindsets that were barriers retrospectively.

Let’s look at how this works. I have a quick story for you, then a question.

A bus driver was heading down Van Ness Avenue in my hometown of San Francisco. He went through a stop sign without even slowing down, then turned onto a one-way street going the opposite direction as the rest of the traffic. A police officer saw the whole thing but he didn’t stop him or issue a ticket because no laws had been broken. The question for you is this: How can this scenario be possible?

If you answered that the bus driver was walking down the street, you are correct. This is a very simple example to illustrate how we all make assumptions. Most people just assume that a bus driver is always driving a bus. But of course, that’s not the case. The most important part of this exercise isn’t to point out that an assumption may have been made in the first place – it’s only natural to do so. It’s to show that most of us only recognize that we’ve made an assumption after we’ve discovered that our thinking was invalid or that it led us astray. And by then, it can often be “too late.”

Let’s go back to the bus driver for a moment. What if I had framed things up in the scenario a little differently and included another statement up front that said “In San Francisco, people use cars, take the bus, or walk down the street to get where they’re going.” How would this have impacted your assumptions? For most people, the idea that it’s possible the bus driver could be walking down the street would have been planted in their brains as they read the rest of the scenario – and they would have more easily overcome their limiting assumption that bus drivers only drive buses. The goal is to continually broaden your perspective so that you can overcome your assumptions before they limit your options or slow you down.

Here are a couple of tried and true approaches I’ve used to challenge and expand mindsets.

Identify Areas of Intrigue

When it comes to developing your strategy or innovating, get clear on what you need to know and learn. List up to 4-5 topics. Examples might include things like board games children like most, the healthiest yet best tasting desserts, or the most successful social media influencers. For each topic, create a list of guiding questions that, if answered, would really give you a solid understanding of the area. For instance, using the board games children like mostexample, you could come up with questions like: What are the most popular children’s board games? How long do the best games take to play? Do adults usually play with the children? What does it take to win? This exercise will help you better understand what’s most important to further explore so you can broaden your perspective.

Adapt a Business Model

Find a company completely outside of your industry or market and look at what makes them different and what they do really well. Then adapt their model to your cause. Use the format “I want to be the ____________ of ____________” by putting a company name into the first blank and the area of your target market or innovation area into the second blank. For example, if you want to transform the fashion industry, you might try “I want to be the Netflix of fashion”, which could lead you down the path of high-end evening gown rental services like Rent the Runway. Consider companies like Starbucks, Twitter, Domino’s, NIKE, Home Depot, or any other innovative company you can think of.

Your mindsets naturally constrain your ability to consider alternatives and possibilities that go beyond the boundaries of your thinking. Your limiting assumptions can be about personal skills, team knowledge and abilities, organizational capabilities, market needs, technology, financial limitations, partnership possibilities, competition, or just about anything else. The goal is to recognize you hold assumptions and then act to surface them.

As the writer John Seely Brown once said, the harder you fight to hold on to specific assumptions, the more likely there’s gold in letting go of them.

Image credit: Pexels

This article was originally published on Inc.com and has been syndicated for this blog.

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Prototyping with Users is a Human-Centered Approach to Testing Ideas

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Prototyping with Users is a Human-Centered Approach to Testing Ideas

GUEST POST from Chateau G Pato

In the rapidly evolving landscape of innovation, creating solutions that resonate with users is more crucial than ever. The key to successful innovation is not just creating value but delivering it in a way that meets users’ needs and expectations. This is where human-centered prototyping comes into play. By involving users in the prototyping process, organizations can gather valuable insights, refine their ideas, and ultimately create more meaningful products and services.

Why Human-Centered Prototyping?

Traditional approaches to prototyping often place the design team at the center, focusing on functionality and technical feasibility. However, this can lead to solutions that miss the mark in terms of user experience and desirability. Human-centered prototyping, on the other hand, involves users as co-creators. This approach ensures that prototypes are not only technically sound but also aligned with users’ real-world needs and behaviors.

1. Engaging Users Early and Often

By bringing users into the prototyping phase from the start, organizations can uncover unexpected use cases, validate assumptions, and identify pain points that may not be immediately obvious. Engaging users early helps to build empathy and ensures that the final product is grounded in reality.

2. Rapid Iteration and Feedback Loops

Human-centered prototyping emphasizes rapid iteration. By quickly creating and testing prototypes with users, teams can learn and adapt in real-time. This continuous feedback loop encourages experimentation and reduces the risk of costly failures further down the line.

Case Studies

Case Study 1: Airbnb’s Evolution through User Insights

Airbnb’s rise to prominence is a testament to the power of user-centered prototyping. In its early days, the founders faced significant challenges in gaining traction. To address this, they adopted a strategy of directly engaging with their users — both hosts and guests.

By conducting in-person visits and creating low-fidelity prototypes of their platform, Airbnb’s team gathered firsthand insights into the needs and pain points of their users. This process revealed critical aspects, such as the importance of trust and the need for detailed property descriptions and quality photos.

Through continuous iteration based on real user feedback, Airbnb was able to refine their platform, resulting in a user experience that feels both personalized and intuitive. This approach was instrumental in transforming their business model into a globally recognized brand.

Case Study 2: IDEO’s Design Thinking in the Healthcare Sector

IDEO, a leader in applying design thinking to solve complex problems, utilized human-centered prototyping to innovate in the healthcare sector. They partnered with a hospital to improve patient experience in the emergency department.

IDEO’s process involved shadowing medical staff, interviewing patients, and creating role-playing scenarios to simulate the patient journey. By developing storyboards and low-fidelity prototypes, they could quickly test and iterate on different aspects of the experience.

One of the key insights was the importance of communication in alleviating patient anxiety. IDEO’s prototypes included redesigned information boards and communication tools that made processes more transparent and efficient. The result was a significant improvement in patient satisfaction and operational efficiency.

Conclusion

Human-centered prototyping is more than just a method; it’s a mindset that prioritizes empathy, collaboration, and real-world validation. By involving users throughout the prototyping process, organizations can create solutions that are not only innovative but also relevant and impactful.

As we move forward, the organizations that embrace this human-centered approach will be best positioned to navigate the complexities of innovation in the digital age. The lessons from companies like Airbnb and IDEO provide a compelling roadmap for leveraging user insights to drive meaningful change and deliver exceptional products and services.

Extra Extra: Because innovation is all about change, Braden Kelley’s human-centered change methodology and tools are the best way to plan and execute the changes necessary to support your innovation and transformation efforts — all while literally getting everyone all on the same page for change. Find out more about the methodology and tools, including the book Charting Change by following the link. Be sure and download the TEN FREE TOOLS while you’re here.

Image credit: Pixabay

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Creating Innovation with Hardcore Soft Skills

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Creating Innovation with Hardcore Soft Skills

Recently I had the opportunity to speak with Yadira Caro on the Hardcore Soft Skills Podcast.

In the episode I define what innovation really is, how people, process and technology come together to create innovation and where people go wrong.

The conversation includes a discussion of how to craft successful innovation teams because it’s such a crucial factor for successful innovation.

I also speak about the peril of idea fragments and the importance of respecting your employees by putting funding and execution capabilities in place BEFORE you ask your employees for even a single idea.

We talk about top-down innovation…

We talk about bottom-up or middle-out innovation…

And, we also speak about many different innovation misconceptions.

So, I encourage you to check out the episode!

You can listen to the embedded podcast above or click this link to go to the podcast page.

Image credit: Pixabay

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OKRs vs. KPIs: Choosing the Right Framework for Innovation

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OKRs vs. KPIs: Choosing the Right Framework for Innovation

GUEST POST from Art Inteligencia

In the world of innovation, measuring success is as crucial as the innovation process itself (a powerful one being The Eight I’s of Infinite Innovation from Braden Kelley). Among the most popular tools for tracking progress are OKRs (Objectives and Key Results) and KPIs (Key Performance Indicators). Though they often appear interchangeable, each serves distinct purposes and can significantly impact the direction and success of innovation initiatives. So, how do we choose the right framework for fostering innovation?

Understanding OKRs and KPIs

OKRs are a framework that sets ambitious objectives linked with quantifiable key results. Invented by Intel and popularized by Google, OKRs encourage stretching beyond comfort zones to achieve groundbreaking advances.

“OKRs are not about spreadsheets. They are about focused and inspired work.” – John Doerr

KPIs, on the other hand, are metrics used to evaluate the performance of organizations, employees, or particular activities. They are generally well-defined and are used to track targets and processes that are stable and need consistency.

Case Study 1: Google – The Triumph of OKRs

Google’s remarkable growth and innovation can, in part, be attributed to its successful use of OKRs. Larry Page and Sergey Brin adopted OKRs from Intel, aiming to balance daunting aspirations with precise actions.

In a pivotal instance, Google aimed to “organize the world’s information and make it universally accessible and useful.” The associated key results included increasing the number of pages indexed and enhancing user satisfaction through a streamlined user interface. This clear alignment of bold objectives and tangible results spurred innovation without stifling creativity, showcasing the transformative power of OKRs.

Case Study 2: A Traditional Manufacturer – The Stability of KPIs

Consider a traditional manufacturing company focused on operational efficiency and quality control. Here, KPIs are indispensable for maintaining precision and reliability in production.

The company aimed to reduce waste and improve product quality. By utilizing KPIs such as scrap rate, production downtime, and customer defect rate, they implemented incremental improvements that led to significant cost savings and enhanced quality.

This structure allowed them to consistently meet customer expectations and stay competitive, showcasing how KPIs serve businesses prioritizing stability and incremental innovation.

When to Use OKRs

OKRs shine in environments where transformative change is sought. Think of startups, tech firms, or any company looking to disrupt the status quo. OKRs encourage risk-taking, freeing teams to explore uncharted territories. They are ideal for organizations that embrace experimentation and are willing to pivot based on insights and discoveries.

When to Use KPIs

KPIs are optimal for situations that require reliability, consistency, and precise tracking. They fit well in established processes where steady improvement and performance monitoring are crucial. Industries like manufacturing, logistics, or healthcare, where the margin for error is minimal, benefit greatly from KPIs.

Integrating OKRs and KPIs for Holistic Innovation

Rather than choosing between OKRs and KPIs, consider blending them. Organizations can leverage the ambitious spirit of OKRs while grounding them with the stable, measurable metrics of KPIs.

For instance, a tech company could set ambitious OKRs to innovate a new product line with radical features, using KPIs to monitor development timelines, budget adherence, and defect rates. Such integration ensures a balance between aspiration and accountability, driving sustainable innovation.

Conclusion

The choice between OKRs and KPIs ultimately hinges on your organizational objectives, industry demands, and desired outcomes. Understanding their intrinsic differences and strategic applications is paramount in optimizing innovation effectiveness.

By carefully considering your framework choice and exploring the potential of combining these tools, businesses can foster an innovative culture that is both adventurous and accountable, paving the way for sustained success.

Innovation thrives on clarity, ambition, and measurable outcomes. Whether through OKRs, KPIs, or a tailor-made blend, harnessing the right framework is key to nurturing the next breakthrough.

Image credit: Pexels

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Challenges and Opportunities in Scaling Social Innovations

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GUEST POST from Chateau G Pato

In today’s fast-paced world, social innovations are pivotal in addressing complex societal challenges. These are novel solutions that meet social needs more effectively or efficiently than existing alternatives. However, scaling these innovations to achieve broader impact is fraught with challenges. In this article, we’ll explore both the obstacles and opportunities inherent in scaling social innovations, supported by real-life case studies.

Understanding the Challenges

One of the primary challenges in scaling social innovations is the resource constraint. Many social enterprises operate on tight budgets, limiting their capacity to expand. Furthermore, impact measurement is often complex and multifaceted, making it difficult to prove efficacy to potential stakeholders.

Another significant challenge is maintaining the integrity and core values of the innovation during scaling. As organizations grow, they risk diluting their mission and losing the elements that made their innovation successful initially.

Seizing Opportunities

Despite these challenges, there are several opportunities to scale social innovations successfully. First, leveraging partnerships and collaborations can provide access to additional resources, networks, and expertise. Second, advancements in technology facilitate wider reach and efficient impact tracking, proving invaluable for scaling efforts.

Moreover, policy support and favorable regulatory environments can create conducive ecosystems for scaling. Building strong stakeholder relationships and continuous learning loops also enhance scalability.

Case Study 1: Grameen Bank

The Grameen Bank, founded by Muhammad Yunus in Bangladesh, is an outstanding example of a social innovation that successfully scaled. It introduced microcredit as a way to alleviate poverty by providing small loans to impoverished entrepreneurs without requiring collateral.

Challenges: Initially, the bank faced skepticism from traditional financial institutions and cultural barriers. The idea of lending without collateral was perceived as risky and unconventional.

Opportunities and Success: The Grameen Bank’s scaling success can be attributed to its innovative group lending model, which fostered community accountability. The bank also prioritized local partnerships and trained its staff to understand community dynamics, ensuring a deep-rooted presence. Today, the Grameen Bank model is replicated in over 100 countries, proving the impact and scalability of microfinance.

Case Study 2: Teach For All

Teach For All is a global network of independent organizations working to expand educational opportunity by enlisting talented graduates and professionals to teach in high-need communities for at least two years.

Challenges: A significant challenge Teach For All faced was adapting its model to different countries with varying educational needs, cultures, and policies. Additionally, recruitment and training at scale presented logistical hurdles.

Opportunities and Success: Teach For All overcame these challenges by adopting a flexible, locally-adaptive model. By empowering local partners to customize implementation to their unique context, Teach For All maintained its core mission while respecting local nuances. The organization leveraged global learnings and cross-border partnerships, enhancing both scope and depth of impact. Today, Teach For All operates in over 60 countries, impacting millions of students globally.

Conclusion

While scaling social innovations presents distinct challenges, the examples of Grameen Bank and Teach For All demonstrate that it is indeed possible to expand impact effectively and sustainably. By recognizing and addressing scaling barriers such as resource limitations, execution risks, and impact measurement difficulties, social innovators can unlock immense potential.

Through strategic partnerships, use of technology, and adaptive frameworks, social innovations can not only grow but thrive, continuously transforming and uplifting communities worldwide.

Image credit: Pixabay

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Managing Knowledge Spaghetti

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How collaboration platforms can help turbocharge your innovation efforts

Managing Knowledge Spaghetti

GUEST POST from John Bessant

Say the word ‘innovation’ and many people quickly conjure in their mind the wonderful ‘lightbulb moment’. But of course, innovation isn’t like this — that flash of inspiration is only the start of what will be a long journey trying to create value from that initial idea. It’s all about navigating our way through a landscape of uncertainty, learning to deal with a variety of roadblocks, potholes and other unexpected barriers.

And if we want to be able to repeat the trick, to give our ideas a fighting chance then the evidence is clear; we need some kind of a process. Over a hundred years of research has fed our understanding to the point where the kind of system we need to make innovation happen can be specified as an international standard. And in terms of pictures what we’re really looking for is less a lightbulb moment than a reproduceable process, something like this.

Clear Innovation Strategy Bessant

Which is fine, as long as we bear in mind one important truth. Innovation doesn’t happen like that.

Back in 1931 the mathematician Alfred Korzybski presented a paper to a meeting in New Orleans on mathematical semantics. It was pretty complex stuff but one phrase which he used has stuck in the wider popular memory. He pointed out that ‘the map is not the territory…’ In other words, a description of something is not the thing itself. The model is not reality. Which has some pretty important implications for the way we work with innovation.

Process models, however detailed, are simplifications, ways of representing how innovation might take place. But — like any map, be it a crumpled sketch someone has drawn or a sophisticated Google Maps picture — it is a guide, it isn’t the place itself. The map is, by its nature, a reduction of the process and in reducing it we lose some important information.

The reality, of course, is that innovation is more complex. And it’s all about knowledge spaghetti.

Just like a plate of pasta innovation involves many different strands. Only this time we are talking about knowledge — technical knowledge, market knowledge, legal knowledge, financial knowledge and so on. They need to be woven together to create value.

And these knowledge strands are held by different people, inside and outside the organization. We have to find them and connect them, link them together to enable us to innovate. Whilst we can superimpose structures on it to help us with this task, we shouldn’t forget that we’re really working with knowledge spaghetti.

Spaghetti solutions

So how do we work with it? Just like recipes for spaghetti there are many variations. One approach is to employ specialists and create cross-functional teams which bring together the relevant strands and align them towards a focused target. That’s proved to be a good model for developing new products and services, especially if we can find ways to bring in all the relevant players including users.

We can use a similar cross-functional approach to the design and implementation of major internal process innovations — things like introducing a new IT system or reorganizing to become more customer-focused. And a third approach involves carefully constructed strategic collaborations, bringing knowledge partners together with complementary strands of knowledge spaghetti.

One very powerful model is based on the idea that everyone in the organization has something to contribute to the innovation story — high involvement innovation. Here we’re working on the belief that even small strands of knowledge can be important and if we could bring them in to the story, we’d make significant progress.

Which history tells us we can. In countless embodiments the principle of high involvement has been shown to pay dividends. Ask people for what they know that might help solve problems around quality, cost, delivery, etc — and there’s no shortage of good ideas in response. The challenge has, historically, been one of working with such high volumes of knowledge and keeping the flywheel going by responding to employee suggestions and giving feedback on progress towards their implementation.

Suggestion boxes and schemes work but until recently had their limitations. Two recent trends have changed all that. The first, borne on the waves of total quality thinking and then the whole ‘lean’ movement, has shown us that in any context people are very effective innovators, well able to improve on what they are doing on a continuing basis.

And the second has been the emergence of collaboration platforms on which they can deploy their innovation skills. Today’s collaborative innovation platform resembles its suggestion box predecessor in outline only; it’s still a way of collecting ideas from employees. But it does so in an interactive space in which challenges can be posed, ideas suggested, comments added and shaping, and welding together multiple knowledge sets and experience enabled. And in doing so they open up the very real possibilities of high involvement innovation — getting everyone to contribute to the innovation story.

Emergent properties

But it’s not just the raw return on investment which collaboration platforms offer — though these benefits are impressive. Their real value lies in the way they enable ‘emergent properties’ — the innovation whole becomes much greater than the sum of its parts.

They give us new and powerful ways of working with the knowledge spaghetti. Not only can we handle the sheer scale of the knowledge challenge and focus it towards key objectives but we can do so in ways which yield surprising additional benefits. They effectively turbocharge our innovation system.

In particular they contribute in the following ways:

Reach — one of the obvious ways in which platforms can help is that they create a network which even remote users can connect to. We can spread the innovation net far and wide, can reach the parts other innovation approaches don’t. For example, recruiting ideas from people on ships at sea or working on an off-shore oil platform would have been impossible until recently. Now they can join the innovation conversation as simply as placing a phone call. Working under extreme conditions like in a humanitarian disaster area can now also be a space for crowdsourcing new and urgent solutions to problems. (We’ve seen this in Ukraine where the problems of getting urgent supplies in and vulnerable people out of a war zone are being addressed by many people sharing ideas across makeshift collaboration platforms based on mobile phone networks). We’re now able to involve people in innovation anywhere on the planet and on a 24/7 timescale.

High involvement innovation has always worked well in teams — that’s been at the heart of the success of lean approaches. But until recently that depended on the team being physically together, exploring and co-creating solutions — not easy if you’re working with a distributed team. Platforms solve this challenge, enabling virtual team meetings and collaboration and asynchronous collaboration.

Organizations like Conoco-Philips employ around 10,000 people, globally distributed and often in hard to access places like off-shore oil platforms. Airbus has around 130,000, again globally distributed and engaged in multiple activities. And Bombardier have over 15,000 ‘knowledge workers’ around the world with whom they want to engage. Through the use of collaboration platforms organizations like these are able to achieve sustained high involvement and significant traction on their innovation challenges.

Richness — successful high involvement innovation isn’t just about assembling lots of people. By their nature people are different and diversity matters in innovation. They bring different perspectives, different ways of framing and working with the problem being explored. Plus they are not just cardboard cut-outs, they have a rich history of different experiences — their origins, their education, their work experience. All of this represents potentially useful strands of knowledge spaghetti, and platforms help us draw on this.

Subsea7, a major player in the world of offshore services for the oil and other industries has used a platform approach to great effect. In one example a long-running concern with turnaround times for fitting out ships was solved when someone on the platform identified a solution which he had originally seen in action at a previous employer. The resulting savings ran into millions of dollars.

People also bring with them networks of connections; knowledge is socially distributed and connecting to these networks can yield surprising possibilities. It means the innovating organization can access different skills and specialized knowledge inside and outside the organization. It’s classic open innovation, building on the idea that in even the largest organization ‘not all the smart people work for you’.

Refining — one of the powerful features of collaboration platforms is that they enable — well, collaboration! They make it possible to comment, criticize (constructively), modify and refine ideas, setting up a process of true co-creation. This fits well with recent research which argues that there’s a fundamental flaw in the model of ‘brainstorming’ used by many organizations to source ideas. The principle of postponing judgment has been replaced by a ‘no criticism’ approach in which every idea is accepted. But the reality is that good ideas need to be tempered, hammered into shape, worked on — and processes of constructive criticism are really important. Pixar, for example, has made this a core feature of its daily ideation process.

And having access to the diversity of perspectives which platforms allow means that there is real potential for shaping and developing interesting ideas into great and value-adding ones. They provide a way of creating those magical ‘water-cooler’ moments in an online and distributed world.

Requiring — using focused campaigns to draw out ideas in particular directions. One of the limits of the old model of suggestion schemes is that they operate in ‘bottom up’ fashion, solving problems which are important and visible at a local level. But the real power of HII lies in mobilizing it to work on ‘top-down’ strategic challenges. The campaign model sits at the heart of many collaboration platforms and allows short intense ‘sprints’ focusing the innovation energy on a key problem area, rather like a laser beam.

Conoco Philips Alaska have been using a process targeted at continuous improvement of their extensive operations; they run between 6 and 8 campaigns every year, involve around 1500 employees and generate savings running into millions of dollars annually.

But it depends on several things — not least spending time to ensure the ‘right’ question is being asked. Simply setting ‘how can we improve productivity’ as a target is too vague, a bit like using that medieval weapon, the blunderbuss. Chances are some of your shots will hit the target but there’s an awful lot of waste involved.

So it’s important to ensure we’re asking the ‘right’ question; a key feature of successful collaboration platforms is the amount of effort which goes in to this kind of front-end problem exploration. The sharper the question the better the quality of answers and the chance that new creative pathways can be opened up.

Recombination — ‘ if only our organization knew what it knows’ is a source of concern for anyone concerned with innovation. So much knowledge which might be useful is locked up inside silos and not shared. Worse, we don’t always know what’s inside those silos or whether and how it could be relevant to someone else. Platforms have the power to make this visible, not least by drawing it out in response to focused and challenging campaigns.

There’s also the possibility that someone else in the organization may have experienced a similar type of problem even if they don’t recognize the relevance of their experience. A powerful principle in creativity is looking for analogous solutions — for example, the challenge of cutting turnaround times in airports for low-cost carriers was solved by applying principles originally developed for Formula 1 pitstops. And the same approach was then adopted by surgeons in London looking to improve the utilization of operating theatres.

Reverse reinvention — lots of effort is often wasted by reinventing wheels, solving the same problem in different places. Platforms offer a way of reversing this process, highlighting solutions which have been tried elsewhere and also inviting creative improvisation around those solutions, extending their applicability and effectiveness. A kind of creative re-iteration.

The Canadian engineering company Bombardier have been using a collaboration platform approach for over ten years and one of the biggest benefits they have seen is a significant increase in the amount of knowledge being shared across their organization.

Retaining and recording — making sure ideas are retained even if they can’t be applied right now. One of the challenges of mobilizing collective intelligence is that we may well attract thousands of ideas. Some can be shaped and refined for immediate implementation, some require further work and investment. And for some there is the problem of being the right idea at the wrong time. In the past organizations hitting this problem would probably lose sight of the idea, leaving it buried in a file somewhere or gathering dust. But platforms allow for effective curation of ideas, not only tracking and recording all suggestions but also retaining them to match against future campaigns and challenges.
Rewiring — organizations are like people — they have ‘predictive minds’ . They are inclined to take a lazy approach, picking tried and tested solutions off the shelf when they confront a problem. But being forced to redefine, to reframe, can trigger a search for new approaches to those old problems. We see this effect often under crisis conditions where traditional solution pathways may not be available and we have to think differently — to make new neural connections across the collective mind. Creating novel campaigns to provide this challenge can open up new idea space — they can help us ‘get out of the box’.
Refreshing — at heart high involvement innovation is about people and the key ingredient to its long term success is finding ways to keep the motivation high. People are brilliant problem solvers but they’re only going to give their ideas if they see some benefit. Research has shown that money isn’t a strong motivator — but having your voice heard and having the opportunity to create the change you’d like to see around your organization is. There’s a wealth of research to support this going right back to the early years of organization studies; the message on employee engagement remains the same but the question is then raised about how to achieve this. Collaboration platforms by their inclusive and open nature offer a powerful new tool to help and organizations like Liberty Global consider this motivational aspect to be a key factor in helping build a culture of innovation across a large organization.

Knowledge Spaghetti Success

So whether it is an upgrade to continuous improvement activity, harvesting employee suggestions for doing what we do but better, or pushing the frontiers to create novel products and services, there’s real scope for using this turbocharged approach.

But powerful though they are, collaboration platforms are at heart still software. It’s not a case of ‘plug and play’ — getting the best out of these systems requires hands-on management, something we’ll look at in a future blog.

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Edge Computing and Its Implications for Innovators

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Edge Computing and Its Implications for Innovators

GUEST POST from Chateau G Pato

In the rapidly evolving landscape of technology, edge computing stands out as a promising frontier that amplifies the potential for innovation. By processing data closer to the source, edge computing reduces latency and enhances the speed and reliability of data transfer. This paradigm shift presents an array of opportunities for innovators looking to redefine industries. In this article, we will explore edge computing and its implications for innovators through two compelling case studies.

The Essence of Edge Computing

Edge computing represents a distributed computing architecture where data processing occurs near the data source rather than relying solely on centralized cloud environments. This approach minimizes latency, reduces bandwidth usage, and allows for more immediate responses, crucial for applications demanding real-time data processing. As we delve deeper into edge computing’s implications, let’s consider two case studies that highlight its transformative impact.

Case Study 1: Smart Cities and Intelligent Traffic Management

Innovators in urban planning and transportation are leveraging edge computing to enhance traffic management systems in smart cities. By integrating edge devices in traffic lights, road sensors, and connected vehicles, cities can gather and analyze traffic data in real-time.

For instance, a forward-thinking municipality deployed edge computing devices at multiple intersections across the city. These devices continuously collect data on vehicle flow, pedestrian movement, and even weather conditions. The edge processing allows the system to adaptively change traffic light patterns to minimize congestion and reduce accidents, without the delay inherent in cloud-only solutions.

Outcome: The implementation resulted in a 20% reduction in average commute times and a 15% decrease in traffic-related accidents, showcasing how edge computing can improve urban living while contributing to sustainability by reducing fuel consumption.

Case Study 2: Manufacturing and Predictive Maintenance

In the manufacturing sector, edge computing is revolutionizing predictive maintenance processes. A leading industrial equipment manufacturer introduced edge computing to monitor machinery health using IoT sensors. Traditionally, data from these sensors would be sent to the cloud for analysis, causing delays in detecting potential issues.

With edge computing, data is processed at the equipment level. Real-time analysis enables the identification of anomalies and deviations from normal operating conditions. Maintenance alerts can be raised instantaneously, allowing for timely interventions before equipment failures occur.

Outcome: This strategic innovation led to a 25% reduction in downtime and a 30% increase in equipment lifespan, translating to substantial cost savings and enhanced operational efficiency.

Implications for Innovators

Edge computing empowers innovators with several distinct advantages:

Real-Time Decision Making: By facilitating immediate data processing and analysis, edge computing allows innovators to implement real-time decision-making processes critical in dynamic environments.
Enhanced Privacy and Security: Processing data at the edge can enhance security and privacy by minimizing the amount of data sent to external servers, reducing exposure to potential breaches.
Scalability and Flexibility: Edge computing supports scalable and flexible system designs, enabling innovators to deploy solutions that adapt to changing demands and expand functionality over time.
Cost Efficiency: By reducing the reliance on constant cloud connectivity and bandwidth, edge computing can lead to significant cost reductions, particularly in data-intensive applications.

Embracing the Edge

The future of innovation lies in the effective integration of edge computing across various sectors. For innovators ready to embrace this cutting-edge technology, the potential is immense. From enhancing urban living to optimizing industrial processes, edge computing is a catalyst for transformative change.

As we continue to explore the vast potential of edge computing, innovators must remain focused on designing human-centered solutions that not only leverage technological advancements but also address the real needs and challenges of users. By doing so, we can unlock unprecedented levels of efficiency, sustainability, and progress.

Edge computing is not just a technological paradigm shift; it is an invitation for innovators to pioneer a new era of intelligent, responsive, and sustainable solutions. The future is at the edge—let’s innovate together.

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