Category Archives: Innovation

74% of Companies Will Die in 10 Years Without Business Transformation

According to Executives

74% of Companies Will Die in 10 Years Without Business Transformation

GUEST POST from Robyn Bolton

One day, an architect visited the building site of his latest project. There he saw three people all laying bricks. He asked each what they were doing. “I’m laying bricks,” the first responded. “I’m building a wall,” said the second. “I’m building a cathedral,” exclaimed the third.

The parable of the Three Bricklayers is a favorite amongst motivational speakers, urging their audiences to think beyond today’s tasks and this quarter’s goals to commit to a grandiose vision of eternal success and glory.

But there’s a problem.

The narrative changed

The person who had a vision of building a cathedral? They now believe they’re building ruins.

Is the C-Suite Quietly Quitting?

Recently published research found that three out of four executives believe that “without fundamental transformation* their organization will cease to exist” in ten years. That’s based on data from interviews with twenty-four “current or former CEOs who have led successful transformations” and 1,360 survey responses from C-Suite and next-generation leaders.

And, somehow, the news gets worse.

While 77% of C-suite executives report that they’re committed to their companies’ transformation efforts, but 57% believe their organization is taking the wrong approach to that transformation. But that’s still better than the 68% of Next-Gen executives who disagree with the approach.

So, it should come as no surprise that 71% of executives rate their companies’ transformation efforts as not at all to moderately successful. After all, it’s hard to lead people along a path you don’t agree with to a vision you don’t believe in.

Did they just realize that “change is hard in human systems?”

We all fall into the trap of believing that understanding something results in commitment and change.

But that’s not how humans work.

That’s definitely not how large groups of humans, known as organizations, work.

Companies’ operations are driven only loosely by the purpose, structures, and processes neatly outlined in HR documents. Instead, they are controlled by the power and influence afforded to individuals by virtue of the collective’s culture, beliefs, histories, myths, and informal ways of working.

And when these “opaque dimensions” are challenged, they don’t result in resistance,

They result in inertia.

“Organizational inertia kills transformations”

Organizations are “complex organisms” that evolve to do things better, faster, cheaper over time. They will continue doing so unless changed by an external force (yes, that’s Newton’s first law of motion).

That external force, the drive for transformation, must be strong enough to overcome:

Insight Inertia stops organizations from getting started because there is a lack of awareness or acceptance amongst leaders that change is needed.
Psychological Inertia emerges when change demands abandoning familiar success strategies. People embrace the idea of transformation but resist personal adaptation, defaulting to comfortable old behaviors.
Action Inertia sets in and gains power as the long and hard work of transformation drags on. Over time, people grow tired. Exhausted by continuous change, teams progressively disengage, becoming less responsive and decisive.

But is that possible when 74% of executives are simply biding their time and waiting for failure?

“There’s a crack in everything, that’s how the light gets in.”

Did you see the crack in all the doom and gloom above?

43% of executives believe their organizations are taking the right approach to transformation.
29% believe that their organizations’ transformations have been successful.
26% believe their company will still be around in ten years.

The majority may not believe in transformation but only 33% of bricklayers believed they were building a cathedral, and the cathedral still got built.

Next week, we’ll explore how.

Image credit: Pixabay

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Top 10 Human-Centered Change & Innovation Articles of October 2025

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Top 10 Human-Centered Change & Innovation Articles of October 2025 Drum roll please…

At the beginning of each month, we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. Did your favorite make the cut?

But enough delay, here are October’s ten most popular innovation posts:

AI, Cognitive Obesity and Arrested Development — by Pete Foley
Making Decisions in Uncertainty – This 25-Year-Old Tool Actually Works — by Robyn Bolton
The Marketing Guide for Humanity’s Next Chapter – How AI Changes Your Customers — by Braden Kelley
Don’t Make Customers Do These Seven Things They Hate — by Shep Hyken
Why Best Practices Fail – Five Questions with Ellen DiResta — by Robyn Bolton
The Need for Organizational Learning — by Mike Shipulski
You Must Accept That People Are Irrational — by Greg Satell
The AI Innovations We Really Need — by Art Inteligencia
Three Reasons You Are Not Happy at Work – And What to Do to Become as Happy as You Could Be — by Stefan Lindegaard
The Nuclear Fusion Accelerator – How AI is Commercializing Limitless Power — by Art Inteligencia

BONUS – Here are five more strong articles published in September that continue to resonate with people:

McKinsey is Wrong That 80% Companies Fail to Generate AI ROI — by Robyn Bolton
The Most Challenging Obstacles to Achieving Artificial General Intelligence — by Art Inteligencia
The Great American Contraction – Population, Scarcity, and the New Era of Human Value — by Art Inteligencia
Three Steps from Stuck to Success – Managing Uncertainty — by Robyn Bolton
Building a Learning Organization — by Stefan Lindegaard

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last four years:

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Cutting-Edge Ways to Decouple Data Growth from Power and Water Consumption

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The Sustainability Imperative

LAST UPDATED: November 1, 2025 at 8:59 AM

Cutting-Edge Ways to Decouple Data Growth from Power and Water Consumption

GUEST POST from Art Inteligencia

The global digital economy runs on data, and data runs on power and water. As AI and machine learning rapidly accelerate our reliance on high-density compute, the energy and environmental footprint of data centers has become an existential challenge. This isn’t just an engineering problem; it’s a Human-Centered Change imperative. We cannot build a sustainable future on an unsustainable infrastructure. Leaders must pivot from viewing green metrics as mere compliance to seeing them as the ultimate measure of true operational innovation — the critical fuel for your Innovation Bonfire.

The single greatest drain on resources in any data center is cooling, often accounting for 30% to 50% of total energy use, and requiring massive volumes of water for evaporative systems. The cutting edge of sustainable data center design is focused on two complementary strategies: moving the cooling load outside the traditional data center envelope and radically reducing the energy consumed at the chip level. This fusion of architectural and silicon-level innovation is what will decouple data growth from environmental impact.

The Radical Shift: Immersive and Locational Cooling

Traditional air conditioning is inefficient and water-intensive. The next generation of data centers is moving toward direct-contact cooling systems that use non-conductive liquids or leverage natural environments.

Immersion Cooling: Direct-to-Chip Efficiency

Immersion Cooling involves submerging servers directly into a tank of dielectric (non-conductive) fluid. This is up to 1,000 times more efficient at transferring heat than air. There are two primary approaches: single-phase (fluid remains liquid, circulating to a heat exchanger) and two-phase (fluid boils off the server, condenses, and drips back down).

This method drastically reduces cooling energy and virtually eliminates water consumption, leading to Power Usage Effectiveness (PUE) ratios approaching the ideal 1.05. Furthermore, the fluid maintains a more stable, higher operating temperature, making the waste heat easier to capture and reuse, which leads us to our first case study.

Case Study 1: China’s Undersea Data Center – Harnessing the Blue Economy

China’s deployment of a commercial Undersea Data Center (UDC) off the coast of Shanghai is perhaps the most audacious example of locational cooling. This project, developed by Highlander and supported by state entities, involves submerging sealed server modules onto the seabed, where the stable, low temperature of the ocean water is used as a natural, massive heat sink.

The energy benefits are staggering: developers claim UDCs can reduce electricity consumption for cooling by up to 90% compared to traditional land-based facilities. The accompanying Power Usage Effectiveness (PUE) target is below 1.15 — a world-class benchmark. Crucially, by operating in a closed system, it eliminates the need for freshwater entirely. The UDC also draws nearly all its remaining power from nearby offshore wind farms, making it a near-zero carbon, near-zero water compute center. This bold move leverages the natural environment as a strategic asset, turning a logistical challenge (cooling) into a competitive advantage.

Case Study 2: The Heat Reuse Revolution at a Major Cloud Provider

Another powerful innovation is the shift from waste heat rejection to heat reuse. This is where true circular economy thinking enters data center design. A major cloud provider (Microsoft, with its various projects) has pioneered systems that capture the heat expelled from liquid-cooled servers and redirect it to local grids.

In one of their Nordic facilities, the waste heat recovered from the servers is fed directly into a local district heating system. The data center effectively acts as a boiler for the surrounding community, warming homes, offices, and water. This dramatically changes the entire PUE calculation. By utilizing the heat rather than simply venting it, the effective PUE dips well below the reported operational figure, transforming the data center from an energy consumer into an energy contributor. This demonstrates that the true goal is not just to lower consumption, but to create a symbiotic relationship where the output of one system (waste heat) becomes the valuable input for another (community heating).

“The most sustainable data center is the one that gives back more value to the community than it takes resources from the planet. This requires a shift from efficiency thinking to regenerative design.”

Innovators Driving the Sustainability Stack

Innovation is happening at every layer, from infrastructure to silicon:

Leading companies and startups are rapidly advancing sustainable data centers. In the cooling space, companies like Submer Technologies specialize in immersion cooling solutions, making it commercially viable for enterprises. Meanwhile, the power consumption challenge is being tackled at the chip level. AI chip startups like Cerebras Systems and Groq are designing new architectures (wafer-scale and Tensor Streaming Processors, respectively) that aim to deliver performance with vastly improved energy efficiency for AI workloads compared to general-purpose GPUs. Furthermore, cloud infrastructure provider Crusoe focuses on powering AI data centers exclusively with renewable or otherwise stranded, environmentally aligned power sources, such as converting flared natural gas into electricity for compute, tackling the emissions challenge head-on.

The Future of Decoupling Growth

To lead effectively in the next decade, organizations must recognize that the convergence of these technologies — immersion cooling, locational strategy, chip efficiency, and renewable power integration — is non-negotiable. Data center sustainability is the new frontier for strategic change. It requires empowered agency at the engineering level, allowing teams to move fast on Minimum Viable Actions (MVAs) — small, rapid tests of new cooling fluids or localized heat reuse concepts — without waiting for monolithic, years-long CapEx approval. By embedding sustainability into the very definition of performance, we don’t just reduce a footprint; we create a platform for perpetual, human-driven innovation.

You can learn more about how the industry is adapting to these challenges in the face of rising heat from AI in the video:

This video discusses the limitations of traditional cooling methods and the necessity of liquid cooling solutions for next-generation AI data centers.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

UPDATE: Apparently, Microsoft has been experimenting with underwater data centers for years and you can learn more about them and progress in this area in this video here:

Image credit: Google Gemini

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Four Pillars of Innovation

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People, Learning, Judgment and Trust

Four Pillars of Innovation

GUEST POST from Mike Shipulski

Innovation is a hot topic. Everyone wants to do it. And everyone wants a simple process that works step-wise – first this, then that, then success.

But Innovation isn’t like that. I think it’s more effective to think of innovation as a result. Innovation as something that emerges from a group of people who are trying to make a difference. In that way, Innovation is a people process. And like with all processes that depend on people, the Innovation process is fluid, dynamic, complex, and context-specific.

Innovation isn’t sequential, it’s not linear and cannot be scripted.. There is no best way to do it, no best tool, no best training, and no best outcome. There is no way to predict where the process will take you. The only predictable thing is you’re better off doing it than not.

The key to Innovation is good judgment. And the key to good judgment is bad judgment. You’ve got to get things wrong before you know how to get them right. In the end, innovation comes down to maximizing the learning rate. And the teams with the highest learning rates are the teams that try the most things and use good judgement to decide what to try.

I used to take offense to the idea that trying the most things is the most effective way. But now, I believe it is. That is not to say it’s best to try everything. It’s best to try the most things that are coherent with the situation as it is, the market conditions as they are, the competitive landscape as we know it, and the the facts as we know them.

And there are ways to try things that are more effective than others. Think small, focused experiments driven by a formal learning objective and supported by repeatable measurement systems and formalized decision criteria. The best teams define end implement the tightest, smallest experiment to learn what needs to be learned. With no excess resources and no wasted time, the team wins runs a tight experiment, measures the feedback, and takes immediate action based on the experimental results.

In short, the team that runs the most effective experiments learns the most, and the team that learns the most wins.

It all comes down to choosing what to learn. Or, another way to look at it is choosing the right problems to solve. If you solve new problems, you’ll learn new things. And if you have the sightedness to choose the right problems, you learn the right new things.

Sightedness is a difficult thing to define and a more difficult thing to hone and improve. If you were charged with creating a new business in a new commercial space and the survival of the company depended on the success of the project, who would you want to choose the things to try? That person has sightedness.

Innovation is about people, learning, judgement and trust.

And innovation is more about why than how and more about who than what.

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Image credit: Unsplash

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Reduce Innovation Risk with this Nobel Prize Winning Formula

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Reduce Innovation Risk with this Nobel Prize Winning Formula

GUEST POST from Robyn Bolton

As a kid, you’re taught that when you’re lost, stay put and wait for rescue. Most executives are following that advice right now—sitting tight amid uncertainty, hoping someone saves them from having to make hard choices and take innovation risk.

This year’s Nobel Prize winners in Economics have bad news: there is no rescue coming. Joel Mokyr, Philippe Aghion, and Peter Howitt demonstrated that disruption happens whether you participate or not. Freezing innovation investments doesn’t reduce innovation risk. It guarantees competitors destroy you while you stand still.

They also have good news: innovation follows predictable patterns based on competitive dynamics, offering a framework for making smarter investment decisions.

How We Turned Stagnation into a System for Growth

For 99.9% of human history, economic growth was essentially zero. There were occasional bursts of innovation, like the printing press, windmills, and mechanical clocks, but growth always stopped.

200 years ago, that changed. Mokyr identified that the Industrial Revolution created systems connecting two types of knowledge: Propositional knowledge (understanding why things work) and Prescriptive knowledge (practical instructions for how to execute).

Before the Industrial Revolution, these existed separately. Philosophers theorized. Artisans tinkered. Neither could build on the other’s work. But the Enlightenment created feedback loops between theory and practice allowing countries like Britain to thrive because they had people who could translate theory into commercial products.

Innovation became a system, not an accident.

Why We Need Creative Destruction

Every year in the US, 10% of companies go out of business and nearly as many are created. This phenomenon of creative destruction, where companies and jobs constantly disappear and are replaced, was identified in 1942. Fifty years later, Aghion and Howitt built a mathematical model proving its required for growth.

Their research also lays bare some hard truths:

Creative destruction is constant and unavoidable. Cutting your innovation budget does not pause the game. It forfeits your position. Competitors are investing in R&D right now and their innovations will disrupt yours whether you participate or not.
Competitive position predicts innovation investments. Neck-to-neck competitors invest heavily in innovation because it’s their only path to the top. Market leaders cut back and coast while laggards don’t have the funds to catch-up. Both under-invest and lose.
Innovation creates winners and losers. Creative destruction leads to job destruction as work shifts from old products and skills to new ones. You can’t innovate and protect every job but you can (and should) help the people affected.

Ultimately, creative destruction drives sustained growth. It is painful and scary, but without it, economies and society stagnate. Ignore it at your peril. Work with it and prosper.

From Prize-winning to Revenue-generating

Even though you’re not collecting the one million Euro prize, these insights can still boost your bottom line if you:

Connect your Why teams with your How teams. Too often, Why teams like Strategy, Innovation, and R&D, chuck the ball over the wall to the How teams in Operations, Sales, Supply Chain, and front-line operations. Instead, connect them early and often and ensure the feedback loop that drives growth
Check your R&D and innovation investments. Are your R&D and innovation investments consistent with your strategic priorities or your competitive position? What are your investments communicating to your competitors? It’s likely that that “conserving cash” is actually coasting and ceding share.
Invest in your people and be honest with them. Your employees aren’t dumb. They know that new technologies are going to change and eliminate jobs. Pretending that won’t happen destroys trust and creates resistance that kills innovation. Tell employees the truth early, then support them generously through transitions.

What’s Your Choice?

Playing it safe guarantees the historical default: stagnation. The 2025 Nobel Prize winners proved sustained growth requires building innovation systems and embracing creative destruction.

The only question is whether you will participate or stagnate.

Image credit: Wikimedia Commons

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Bridging the Gap Between Strategic Ambition and Innovation Delivery

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Why Long-Range Planning and Product Development Rarely Align — And What Companies Can Do About It

Bridging the Gap Between Strategic Ambition and Innovation Delivery

GUEST POST from Noel Sobelman

Across industries, executive teams craft long-range plans (LRPs) with confident projections for revenue growth, market expansion, and innovation impact. But when it comes time to deliver, product development pipelines often tell a different story. This misalignment, between the top-down assumptions embedded in strategic plans and the bottom-up reality of new product development (NPD), is one of the most persistent and under-addressed risks in corporate planning.

The consequences are serious: growth targets are missed, credibility erodes, and shareholder confidence wanes. And yet, many organizations continue to treat this disconnect as inevitable, rather than solvable.

The Illusion of Alignment

On paper, LRPs typically assign a portion of future revenue to innovation — new products, new markets, new business models. This makes sense. In competitive, fast-moving sectors, sustaining growth depends on a constant stream of successful launches.

But few companies take the next step: validating whether their actual innovation pipeline supports those ambitions. The top-down LRP rarely connects meaningfully with the bottom-up details of project timelines, product margins, development risks, or resource constraints.

Leadership may assume, for instance, that new product contributions will ramp up in years three through five of the plan. Yet the NPD pipeline might only be populated with early-phase projects, with no clear line of sight to commercialization in that time frame. Or worse, it might be filled with low upside sustaining efforts that do little to drive long-term growth.

This isn’t just a data problem — it’s an accountability problem.

A Blind Spot in Strategic Execution

Unlike sales or operations, which are frequently forced to reconcile their contributions to the LRP through tangible metrics and quarterly reviews, product development is often allowed to operate in a parallel universe. Project business cases get approved on a rolling basis, disconnected from aggregate targets. Teams work diligently, but no one steps back to ask: Do the numbers add up?

In many organizations, this analysis is simply never done. When questioned about how the pipeline contributes to the LRP, the answers range from vague optimism (“We’ll figure it out”) to manual workarounds (“We added 5% to last year’s numbers to cover new product upside”).

Such informal planning approaches might have been acceptable in a slower, less competitive world. But in today’s environment, where innovation cycles are compressed, capital is scrutinized, and every function is expected to deliver ROI, they fall short.

Interestingly, other parts of the business, particularly operations, already have a model for how to approach this. Manufacturing teams routinely perform network strategy exercises to determine whether they have the physical capacity to meet future demand. They map projected sales to factory utilization, labor capacity, CapEx, and throughput. If there’s a gap, they create an actionable plan.

Yet in most organizations, this rigor stops at the walls of the plant. There is no equivalent exercise on the R&D side to ask: Do we have the innovation pipeline, product plans, and resources required to meet our revenue commitments? Working with our clients, we’ve seen how powerful it is when this same network strategy logic is applied to product development. The exercise shifts the conversation from hope to confidence, from general intent to measurable plans.

The Case for a Unified Growth Strategy

The path forward requires a more integrated, data-driven approach, a growth strategy that spans both the strategic and executional layers of the business.

At the core is a disciplined feedback loop: reconciling the LRP’s innovation-driven revenue expectations with the actual new product roadmap, resource plan, and market assumptions. This means:

Bottom-up modeling of product-level forecasts (volumes, ASPs, margins, launch dates) that aggregate to a portfolio view of expected revenue. Our benchmarks show that without this discipline, overstatements of new product contributions can widen to 20–40% or more in the outer years of the LRP. Modeling helps identify these gaps early, enabling timely course corrections.
Scenario analysis that tests different mixes of existing and in-development products to identify gaps and prioritize high-leverage opportunities.
Risk adjustment grounded in performance benchmarks and realistic probabilities of technical and commercial success, not wishful thinking. Companies that formalize these assumptions often uncover significant overstatements in expected revenue from early-stage projects.
Cross-functional transparency between R&D, finance, operations, and commercial teams to ensure the entire organization is planning from a shared reality.

Working with our clients, we’ve helped build models that mirror this approach, combining innovation pipeline data, financial assumptions, and market insights into a unified view of expected contribution to growth. The result? Greater visibility into how future revenue will be earned and higher confidence in investment decisions. For some organizations, this alignment has helped redirect 10–15% of R&D spend toward higher-value opportunities without increasing total investment.

In nearly every case, the analysis reveals significant gaps between what leadership believes the innovation engine will deliver and what’s realistically in flight. But once exposed, those gaps become manageable. They become actionable.

This isn’t about punishing innovation teams for uncertainty. It’s about giving them, and the organization, an honest view of what’s likely to be delivered and where targeted adjustments are needed.

Building the Capability (Not Just the Model)

Organizations that do this well don’t just build a single model — they build the capability. They embed portfolio management processes that continually evaluate whether innovation plans are aligned with strategic goals. They invest in tools and talent that can translate project business cases into forward-looking financial impact. And critically, they elevate the conversation from “project selection” to “portfolio impact.”

This approach can also shift the internal conversation away from politics and gut feel, and toward clarity and confidence. CFOs, for example, are increasingly demanding to know what they’re getting for the annual increases in R&D spend. A connected, data-rich view of how new product drives future cash flows goes a long way in strengthening that case. We’ve seen how quickly these conversations mature when companies adopt a planning discipline that brings product development onto the same strategic playing field as operations and sales.

The Strategic Imperative

Ultimately, reconciling innovation with the LRP isn’t a nice-to-have. It’s a fiduciary responsibility. Companies make commitments to their boards and investors based on the assumption that R&D investment will deliver a meaningful share of future growth. When that assumption is built on loosely connected plans and unvalidated forecasts, the entire strategy is at risk.

Bridging that gap can unlock substantial value. In our experience, we see organizations with tightly aligned portfolio and strategy processes outperform their peers by as much as 40% in terms of new product ROI and time-to-market.

The good news? The gap is measurable. The tools, models, and methods to close it exist. What’s often missing is the mandate.

Organizations that seize this opportunity will be better equipped to make confident trade-offs, accelerate high-potential initiatives, and pivot early when plans drift off course. They’ll be able to tell a coherent story, not just about where they want to go, but how they plan to get there.

And that story, told with numbers and backed by action, is what distinguishes companies that plan for growth from those that actually deliver it.

If you’re interested in exploring how to better align your product development plans with long-range strategic goals or want to assess the credibility of your innovation pipeline, we’d be happy to share what we’ve learned from working with companies in similar situations.

Image credits: Pexels

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Picking Innovation Projects in Four Questions or Less

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Picking Innovation Projects in Four Questions or Less

GUEST POST from Mike Shipulski

It’s a challenge to prioritize and choose innovation projects. There are open questions on the technology, the product/service, the customer, the price and sales volume. Other than that, things are pretty well defined.

But with all that, you’ve still go to choose. Here are four questions that may help in your selection process:

1. Is it big enough?

The project will be long, expensive and difficult. And if the potential increase in sales is not big enough, the project is not worth starting. Think (Price – Cost) x Volume. Define a minimum viable increase in sales and bound it in time. For example, the minimum incremental sales is twenty five million dollars after five years in the market. If the project does not have the potential to meet those criteria, don’t do the project. The difficult question – How to estimate the incremental sales five years after launch? The difficult answer – Use your best judgement to estimate sales based on market size and review your assumptions and predictions with seasoned people you trust.

2. Why you?

High growth markets/applications are attractive to everyone, including the big players and the well-funded start-ups. How does your company have an advantage over these tough competitors? What about your company sets you apart? Why will customers buy from you? If you don’t have good answers, don’t start the project. Instead, hold the work hostage and take the time to come up with good answers. If you come up with good answers, try to answer the next questions. If you don’t, choose another project.

3. How is it different?

If the new technology can’t distinguish itself over existing alternatives, you don’t have a project worth starting. So, how is your new offering (the one you’re thinking about creating) better than the ones that can be purchased today? What’s the new value to the customer? Or, in the lingo of the day, what is the Distinctive Value Proposition (DVP)? If there’s no DVP, there’s no project. If you’re not sure of the DVP, figure that out before investing in the project. If you have a DVP but aren’t sure it’s good enough, figure out how to test the DVP before bringing the DVP to life.

4. Is it possible?

Usually, this is where everyone starts. But I’ve listed it last, and it seems backward. Would you rather spend a year making it work only to learn no one wants it, or would you rather spend a month to learn the market wants it then a year making it work? If you make it work and no one wants it, you’ve wasted a year. If, before you make it work, you learn no one wants it, you’ve spent a month learning the right thing and you haven’t spent a year working on the wrong thing. It feels unnatural to define the market need before making it work, but though it feels unnatural, it can block resources from working on the wrong projects.

Conclusion

There is no foolproof way to choose the best innovation projects, but these four questions go a long way. Create a one-page template with four sections to ask the questions and capture the answers. The sections without answers define the next work. Define the learning objectives and the learning activities and do the learning. Fill in the missing answers and you’re ready to compare one project to another.

Sort the projects large-to-small by Is it big enough? Then, rank the top three by Why you? and How is it different? Then, for the highest ranked project, do the work to answer Is it possible?

If it’s possible, commercialize. If it’s not, re-sort the remaining projects by Is it big enough? Why you? and How is it different? and learn if It is possible.

Image credit: Pexels

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Are You Getting Your Fair Share of $860 Billion?

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Are You Getting Your Fair Share of $860 Billion?

GUEST POST from Shep Hyken

According to Qualtrics, there is an estimated $860 billion worth of revenue and cost savings available for companies that figure out how to create an improved Customer Experience (CX) using AI to better understand and serve their customers. (That includes $420 billion for B2B and $440 billion for B2C.) Qualtrics recently released these figures in a report/eBook titled Unlock the Potential through AI-Enabled CX.

I had a chance to interview Isabelle Zdatny, head of thought leadership at Qualtrics Experience Management Institute, for Amazing Business Radio. She shared insights from the report, including ways in which AI is reshaping how organizations measure, understand and improve their relationships with customers. These ideas are what will help you get more customers, keep existing customers and improve your processes, giving you a share of the $860 billion that is up for grabs. Here are some of the top takeaways from our interview.

AI-Enabled CX Represents a Financial Opportunity

The way AI is used in customer experience is much more than just a way to deflect customers’ questions and complaints to an AI-fueled chatbot or other self-service solution. Qualtrics’ report findings show that the value comes through increased employee productivity, process improvement and revenue growth. Zdatny notes a gap between leadership’s recognition of AI’s potential and their readiness to lead and make a change. Early adopters will likely capture “compounding advantages,” as every customer interaction makes their systems smarter and their advantage more difficult for competitors to overcome. My response to this is that if you aren’t on board with AI for the many opportunities it creates, you’re not only going to be playing catch-up with your competitors, but also having to catch up with the market share you’re losing.

Customers Want Convenience

While overall CX quality is improving, thanks to innovation, today’s customers have less tolerance for friction and mistakes. A single bad experience can cause customers to defect. My customer experience research says an average customer will give you two chances. Zdatny says, “Customers are less tolerant of friction these days. … Deliver one bad experience, and that sends the relationship down a bad path more quickly than it used to.”

AI Takes Us Beyond Surveys

Customer satisfaction surveys can frustrate customers. AI collects the data from interactions between customers and the company and analyzes it using natural language processing and sentiment. It can predict churn and tension. It analyzes customer behavior, and while it doesn’t look at a specific customer (although it can), it is able to spot trends in problems, opportunities and more. The company that uses this information the right way can reap huge financial rewards by creating a better customer experience.

Agentic AI

Agentic AI takes customer interactions to a new level. As a customer interacts with AI-fueled self-service support, the system can do more than give customers information and analyze the interaction. It can also take appropriate action. This is a huge opportunity to make it easier on the workforce as AI processes action items that employees might otherwise handle manually. Think about the dollars saved (part of the $860 billion) by having AI support part of the process so people don’t have to.

Customer Loyalty is at Risk

To wrap this up, Zdatny and I talked about the concept of customer loyalty and how vulnerable companies are to losing their most loyal customers. According to Zdatny, a key reason is the number of options available to consumers. (While there may be fewer options in the B2B world, the concern should still be the same.) Switching brands is easy, and customers are more finicky than ever. Our CX research finds that typical customers give you a second chance before they switch. A loyal customer will give you a third chance — but to put it in baseball terms, “Three strikes and you’re out!” Manage the experience right the first time, and keep in mind that whatever interaction you’re having at that moment is the reason customers will come back—or not—to buy whatever you sell.

Image Credits: Pexels

This article was originally published on Forbes.com

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How Tangible AI Artifacts Accelerate Learning and Alignment

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Seeing the Invisible

By Douglas Ferguson, Founder & CEO of Voltage Control
Originally inspired by “A Lantern in the Fog” on Voltage Control, where teams learn to elevate their ways of working through facilitation mastery and AI-enabled collaboration.

Innovation isn’t just about generating ideas — it’s about testing assumptions before they quietly derail your progress. The faster a team can get something tangible in front of real eyes and minds, the faster they can learn what works, what doesn’t, and why.

Yet many teams stay stuck in abstraction for too long. They debate concepts before they draft them, reason about hypotheses before they visualize them, and lose energy to endless interpretation loops. That’s where AI, when applied strategically, becomes a powerful ally in human-centered innovation — not as a shortcut, but as a clarifier.

How Tangible AI Artifacts Accelerate Learning and Alignment

At Voltage Control, we’ve been experimenting with a practice we call AI Teaming — bringing AI into the collaborative process as a visible, participatory teammate. Using new features in Miro, like AI Flows and Sidekicks, we’re able to layer prompts in sequence so that teams move from research to prototypes in minutes. We call this approach Instant Prototyping — because the prototype isn’t the end goal. It’s the beginning of the real conversation.

Tangibility Fuels Alignment

In human-centered design, the first artifact is often the first alignment. When a team sees a draft — even one that’s flawed — it changes how they think and talk. Suddenly, discussions move from “what if” to “what now.” That’s the tangible magic: the moment ambiguity becomes visible enough to react to.

AI can now accelerate that moment. With one-click flows in Miro, facilitators can generate structured artifacts — such as user flows, screen requirements, or product briefs — based on real research inputs. The output isn’t meant to be perfect; it’s meant to be provocative. A flawed draft surfaces hidden assumptions faster than another round of theorizing ever could.

Each iteration reveals new learning: the missing user story, the poorly defined need, the contradiction in the strategy. These insights aren’t AI’s achievement — they’re the team’s. The AI simply provides a lantern, lighting up the fog so humans can decide where to go next.

Layering Prompts for Better Hypothesis Testing

One of the most powerful aspects of Miro’s new AI Flows is the ability to layer prompts in connected sequences. Instead of a single one-off query, you create a chain of generative steps that build on each other. For example:

Synthesize research into user insights.
Translate insights into “How Might We” statements.
Generate user flows based on selected opportunities.
Draft prototype screens or feature lists.

Each layer of the flow uses the prior outputs as inputs — so when you adjust one, the rest evolves. Change a research insight or tweak your “How Might We” framing, and within seconds, your entire prototype ecosystem updates. It’s an elegant way to make hypothesis testing iterative, dynamic, and evidence-driven.

Seeing the Invisible

In traditional innovation cycles, these transitions can take weeks of hand-offs. With AI flows, they happen in minutes — creating immediate feedback loops that invite teams to think in public and react in real time.

(You can see this process in action in the video embedded below — where we walk through how small prompt adjustments yield dramatically different outputs.)

The Human Element: Facilitating Sensemaking

The irony of AI-assisted innovation is that the faster machines generate, the more valuable human facilitation becomes. Instant prototypes don’t replace discussion — they accelerate it. They make reflection, critique, and sensemaking more productive because there’s something concrete to reference.

Facilitators play a critical role here. Their job is to:

Name the decision up front: “By the end of this session, we’ll have a directionally correct concept we’re ready to test.”
Guide feedback: Ask, “What’s useful? What’s missing? What will we try next?”
Anchor evidence: Trace changes to specific research insights so teams stay grounded.
Enable iteration: Encourage re-running the flow after prompt updates to test the effect of new assumptions.

Through this rhythm of generation, reflection, and adjustment, AI becomes a conversation catalyst — not a black box. And the process stays deeply human-centered because it focuses on learning through doing.

Case in Point: Building “Breakout Buddy”

We recently used this exact approach to prototype a new tool called Breakout Buddy — a Zoom app designed to make virtual breakout rooms easier for facilitators. The problem was well-known in our community: facilitators love the connection of small-group moments but dread the logistics. No drag-and-drop, no dynamic reassignment, no simple timers.

Using our Instant Prototyping flow, we gathered real facilitator pain points, synthesized insights, and created an initial app concept in under two hours. The first draft had errors — it misunderstood terms like “preformatted” and missed saving room configurations — but that’s precisely what made it valuable. Those gaps surfaced the assumptions we hadn’t yet defined.

After two quick iterations, we had a working prototype detailed enough for a designer to polish. Within days, we had a testable artifact, a story grounded in user evidence, and a clear set of next steps. The magic wasn’t in the speed — it was in how visible our thinking became.

Designing for Evidence, Not Perfection

If innovation is about learning, then prototypes are your hypotheses made tangible. AI just helps you create more of them — faster — so you can test, compare, and evolve. But the real discipline lies in how you use them.

Don’t rush past the drafts. Study what’s wrong and why.
Don’t hide your versions. Keep early artifacts visible to trace the evolution.
Don’t over-polish. Each iteration should teach, not impress.

When teams treat AI outputs as living evidence rather than final answers, they stay in the human-centered loop — grounded in empathy, focused on context, and oriented toward shared understanding.

A Lantern in the Fog

At Voltage Control, we see AI not as a replacement for creative process, but as a lantern in the fog — illuminating just enough of the path for teams to take their next confident step. Whether you’re redesigning a product, reimagining a service, or exploring cultural transformation, the goal isn’t to hand creativity over to AI. It’s to use AI to make your learning visible faster.

Because once the team can see it, they can improve it. And that’s where innovation truly begins.

🎥 Watch the Demo: How layered AI prompts accelerate hypothesis testing in Miro

Join the waitlist to get your hands on the Instant Prototyping template

Image Credit: Douglas Ferguson, Unsplash

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Why Best Practices Fail

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Five Questions with Ellen DiResta

Why Best Practices Fail

GUEST POST from Robyn Bolton

For decades, we’ve faithfully followed innovation’s best practices. The brainstorming workshops, the customer interviews, and the validated frameworks that make innovation feel systematic and professional. Design thinking sessions, check. Lean startup methodology, check. It’s deeply satisfying, like solving a puzzle where all the pieces fit perfectly.

Problem is, we’re solving the wrong puzzle.

As Ellen Di Resta points out in this conversation, all the frameworks we worship, from brainstorming through business model mapping, are business-building tools, not idea creation tools.

Read on to learn why our failure to act on the fundamental distinction between value creation and value capture causes too many disciplined, process-following teams to create beautiful prototypes for products nobody wants.

Robyn: What’s the one piece of conventional wisdom about innovation that organizations need to unlearn?

Ellen: That the innovation best practices everyone’s obsessed with work for the early stages of innovation.

The early part of the innovation process is all about creating value for the customer. What are their needs? Why are their Jobs to be Done unsatisfied? But very quickly we shift to coming up with an idea, prototyping it, and creating a business plan. We shift to creating value for the business, before we assess whether or not we’ve successfully created value for the customer.

Think about all those innovation best practices. We’ve got business model canvas. That’s about how you create value for the business. Right? We’ve got the incubators, accelerators, lean, lean startup. It’s about creating the startup, which is a business, right? These tools are about creating value for the business, not the customer.

R: You know that Jobs to be Done is a hill I will die on, so I am firmly in the camp that if it doesn’t create value for the customer, it can’t create value for the business. So why do people rush through the process of creating ideas that create customer value?

E: We don’t really teach people how to develop ideas because our culture only values what’s tangible. But an idea is not a tangible thing so it’s hard for people to get their minds around it. What does it mean to work on it? What does it mean to develop it? We need to learn what motivates people’s decision-making.

Prototypes and solutions are much easier to sell to people because you have something tangible that you can show to them, explain, and answer questions about. Then they either say yes or no, and you immediately know if you succeeded or failed.

R: Sounds like it all comes down to how quickly and accurately can I measure outcomes?

E: Exactly. But here’s the rub, they don’t even know they’re rushing because traditional innovation tools give them a sense of progress, even if the progress is wrong.

We’ve all been to a brainstorm session, right? Somebody calls the brainstorm session. Everybody goes. They say any idea is good. Nothing is bad. Come up with wild, crazy ideas. They plaster the walls with 300 ideas, and then everybody leaves, and they feel good and happy and creative, and the poor person who called the brainstorm is stuck.

Now what do they do? They look at these 300 ideas, and they sort them based on things they can measure like how long it’ll take to do or how much money it’ll cost to do it. What happens? They end up choosing the things that we already know how to do! So why have the brainstorm?”

R: This creates a real tension: leadership wants progress they can track, but the early work is inherently unmeasurable. How do you navigate that organizational reality?

E: Those tangible metrics are all about reliability. They make sure you’re doing things right. That you’re doing it the same way every time? And that’s appropriate when you know what you’re doing, know you’re creating value for the customer, and now you’re working to create value for the business. Usually at scale

But the other side of it? That’s where you’re creating new value and you are trying to figure things out. You need validity metrics. Are we doing the right things? How will we know that we’re doing the right things.

R: What’s the most important insight leaders need to understand about early-stage innovation?

E: The one thing that the leader must do is run cover. Their job is to protect the team who’s doing the actual idea development work because that work is fuzzy and doesn’t look like it’s getting anywhere until Ta-Da, it’s done!

They need to strategically communicate and make sure that the leadership hears what they need to hear, so that they know everything is in control, right? And so they’re running cover is the best way to describe it. And if you don’t have that person, it’s really hard to do the idea development work.”

But to do all of that, the leader also must really care about that problem and about understanding the customer.

We must create value for the customer before we can create value for the business. Ellen’s insight that most innovation best practices focus on the latter is devastating. It’s also essential for all the leaders and teams who need results from their innovation investments.

Before your next innovation project touches a single framework, ask yourself Ellen’s fundamental question: “Are we at a stage where we’re creating value for the customer, or the business?” If you can’t answer that clearly, put down the canvas and start having deeper conversations with the people whose problems you think you’re solving.

To learn more about Ellen’s work, check out Pearl Partners.

To dive deeper into Ellen’s though leadership, visit her Substack – Idea Builders Guild.

To break the cycle of using the wrong idea tools, sign-up for her free one-hour workshop.

Image credit: 1 of 950+ FREE quote slides available at http://misterinnovation.com

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