Category Archives: Innovation

Why Amazon Paid $3.9 Billion to Get into the Healthcare Business

GUEST POST from Shep Hyken

Amazon is known for its amazing customer experience, despite most customers never talking to an Amazon employee. How does this digital experience — with no human interaction—drive so much loyalty? The short answer is confidence. There is very little that goes wrong with an Amazon experience, and if by chance it does, its system takes care of almost all problems—again, without human interaction. That said, if a customer does need to talk to a human, which is very seldom, the customer support team is there.

But what happens if you combine technology with a high-touch business, like a doctor’s office? You get One Medical, which Amazon bought in 2023 for $3.9 billion. One Medical’s founder, Dr. Tom Lee, is a Harvard-trained primary care physician who then went on to Stanford to get an MBA. Before opening his first clinic, he asked himself, “Why do we do these in healthcare like we’ve always done them? Why does every waiting room look like some sterile IKEA? Why do I wait in a reception area and then wait again in the exam room?” It was questions like these that caused Lee to tinker with and disrupt the traditional medical visit model.

Starting with one clinic, Lee created a different experience. He built an app and charged patients an $89/year subscription that gave them access to doctors. He focused on simple things like getting an appointment without making a call. Those little things were the start of what turned out to be a stellar experience that allowed him to expand, ultimately catching the eye of Amazon.

When the Amazon deal was completed, HealthCare Dive reported that Amazon now had a network of more than 220 medical offices in 27 U.S. markets with more than 836,000 members plus 9,000 enterprise clients. Neil Lindsay, SVP of Amazon Health Services said, “We’re on a mission to make it dramatically easier for people to find, choose, afford and engage with the services, products and professionals they need to get and stay healthy, and coming together with One Medical is a big step on that journey.” That’s what Amazon does. They make it easy for customers.

Joseph Michelli, bestselling business author of numerous books that tell the stories of iconic brands like The Ritz-Carlton, Starbucks, Mercedes and others, recently released a new book, All Business Is Personal: One Medical’s Human-Centered, Technology-Powered Approach to Customer Engagement, that tells the One Medical story. I had a chance to interview him on Amazing Business Radio, and here are the highlights that will give you some insight into why Amazon became interested in acquiring this amazing company.

Question Everything

Just ask, “Why?” It doesn’t matter what type of business you are in, there are reasons for everything. Often the reason a company or person does something is because “We’ve always done it this way.” So, question everything. Maybe you’ll still do it the way you’ve always done it, but at least you will have tried to find a better way.

Create a Stellar Customer (Patient) Experience

As Lee created a Customer Experience (CX) that drove impressive ratings, he looked at the friction most patients experienced. He started with an obvious pain point, the waiting room, which is, as the name implies, a room for people to wait. Some patients in traditional medical practices are forced to wait for unreasonable amounts of time. But not at One Medical. In addition to being easy to get a same-day or next-day appointment, Michelli shared that 95% of patients are seen within three minutes of their scheduled times. As already mentioned, Lee questioned every aspect of the patient’s experience, and he found many ways to make it better.

Blend Technology with the Human Touch

Technology, like apps and AI, makes life more convenient for customers by allowing things like easy online scheduling or getting immediate answers from AI chatbots. Often, technology can feel cold and impersonal, especially in healthcare. The best use of technology is to make things faster and simpler, but smart businesses, like One Medical, know to offer human backup when a customer/patient needs it. Finding the right balance between tech and the human touch keeps your business from being a commodity—just “another faceless service.”

Convenience Is King

People love doing business with companies that create convenient experiences. For One Medical, this means offering same-day appointments, speedy callbacks or handling many issues online versus the phone, so the patient doesn’t have to wait on hold or wait for a callback. Research shows that 73% of customers will pay more for a convenient experience. The easier you make someone’s experience, the more likely they will come back as well as tell others about you.

Make It Personal, Not Just Personalized

It’s great to remember a customer’s name or recall past purchases. That’s personalization. To take it a step further, make it personal. Make the customer feel that you care about them. That means when the customer (or patient) talks to an employee, they feel cared for, listened to and valued. Personal connections build trust and confidence, which leads to repeat business and potential loyalty.

The Effort Is Worth It

These five reasons (and a few more) are what gave Amazon 3.9 billion reasons (as in dollars) to acquire One Medical. Even if you were to practice these reasons flawlessly, you may never catch Amazon’s attention, but you will catch your customers’ (and potential customers’) attention. And that will make the effort worthwhile.

Image Credits: Pixabay

This article originally appeared on Forbes.com

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Eight Types of Innovation Executives

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Revisited

Eight Types of Innovation Executives

GUEST POST from Stefan Lindegaard

Over the past few decades working with corporate innovation, I’ve noticed recurring patterns especially when it comes to executive leadership.

True, we shouldn’t put leaders in boxes. But we also can’t afford to ignore the signs. That’s why I created this visual: an overview of eight (8) types of innovation executives.

It’s a simple ‘tool’ to help you recognize behaviors, traits, and (in)actions that influence your organization’s innovation capabilities.

By spotting these patterns, you can better understand where your executive team stands and how to move forward with initiatives that strengthen your ability to innovate.

I am curious: which of these types do you recognize in your organization? Or maybe even in yourself? Feel free to drop a comment.

1. No Problem

Best-case scenario: executives who understand innovation and get personally involved.

Hint: Leverage their support to upgrade other key leaders.

2. No Need

“We don’t need innovation.” If that’s the belief, you’ve got a deeper issue.

Hint: Understand the mindset. If change isn’t possible, consider walking away.

3. No Results

“We tried, it didn’t work.” Past failures lead to present resistance.

Hint: Deliver quick wins, back up with data, and rebuild credibility.

4. No Time

“Too busy.” Innovation gets pushed aside by daily demands.

Hint: Integrate with existing priorities—show how everyone wins without adding work.

5. No Money

There’s no budget for innovation, capabilities, or execution.

Hint: Shift the focus to people. Show impact. Demonstrate ROI.

6. No Walk

They say the right things, but take no real action.

Hint: Test for walk vs. talk. Use respectful confrontation to prompt real commitment.

7. No Responsibility

“Not my job. Go ask someone else.” Ownership is missing.

Hint: Innovation is everyone’s responsibility — starting at the top. Align ownership.

8. No Clue

“I’ve never been trained in this.” A lack of understanding, not resistance.

Hint: This is workable. Provide support, context, and practical tools.

Image Credit: Stefan Lindegaard

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

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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.

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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.

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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.

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This article was originally published on Forbes.com

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