Category Archives: Innovation

Thin Lizzy – An Innovation Miracle from a Monster

Gila monster in the Southwest desert - a source of bio-inspired innovation and GLP-1 medical breakthroughs

GUEST POST from Pete Foley

The pejoratively named Gila monster is a protected and borderline endangered species that inhabits my adopted Southwest.  It is the only venomous lizard in the USA, but while its venom can be deadly, human deaths are extremely rare.  It’s generally a shy, slow moving creature that spends much of its time underground.  It presents little danger unless you try to handle it, and if you are lucky enough to see one, it’s pink and black colors make it quite stunning to look at.

Monsters and Weight Loss: But whether you perceive it as beauty or beast, it has recently played a surprisingly important and beneficial role in human health.  As many reading this will already know, it’s venom is the origin of GLP-1’s. These are the ‘miracle ingredient’ found in diabetes and weight loss drugs like Ozempic and Wegovy.  GLP-1’s were initially isolated from Gila Monster venom about 30 years ago. These ‘Thin Lizzy’ drugs are now manufactured synthetically, but it’s unlikely that we’d have discovered them without the help of this maligned ‘monster’

A Benevolent Monster. Type-II diabetes and obesity are deadly diseases, and GLP-1’s have helped many patients live longer, better quality lives. I sometimes worry about over and unsupervised use, and long term effects of such a widely used new drug.  But there is no question around the benefits it has brought to the human race.  Gila is a benevolent monster, and we owe it our thanks for saving countless lives.  

Bio-Inspired Innovation:  In a broad sense, this is a great example of biomimicry, or at least copying innovation from nature.  Nature is a huge untapped resource of largely pre-cooked innovations.  Pretty much any problem we face, somewhere nature has already solved. It’s not always easy to find or adapt those solutions, but sometimes when we do, we get miracles like GLP-1. We can find innovations anywhere in nature, but marginal environments often have disproportionately more. They force evolution, as nature has to solve more difficult problems.  Often we hear biodiversity expressed in terms of ‘number of species’. That is a valid claim. There is no question, for example, that the density of species and fierce competition in the Amazon make it a rich source of biodiversity, and hence bio-derived innovation. But the huge number and diversity of species there also adds to the ‘needle in a haystack’ challenge we find with seeking innovation in nature. But the extremely harsh, hot, dry, environment of Southwest Deserts can also drive unusual adaptations.  In the case of GLP-1’s, their metabolism and glucose management help the Gila monster navigate an environment where food and water is scarce, and feeding sporadic.   Perhaps more importantly, given the harshness of the environment here, it’s likely that GLP-1’s are the tip of the ice-berg, and that our desert contains a reservoir of many more useful secrets waiting to be unlocked, especially around metabolism and water management.

Destruction of Wilderness:  But marginal environments are often also where species are most fragile and under threat.  In the desert southwest, the Gila’s habitat (and that of other marginalized species like the desert tortoise) is being squeezed from all directions.  An historic drought has gripped much of the area for decades.  And we are now compounding that with massive housing developments, even bigger industrial scale solar farms, and the massive infrastructure needed to transmit the energy those farms create. Even more recently, we are further compounding that ’squeeze’ with data centers, increased mining for rare metals and more.  These ‘developments’ not only destroy massive swathes of wilderness, and put additional pressure on already endangered species, but also compound drought and climate change by piling rapidly accelerating heat island effects on top of a warming climate.

Don’t Shoot Yourself in the Foot. As an innovator I embrace change, and recognize that progress inevitably comes with trade-offs.  But change needs to be managed thoughtfully, especially the inevitable trade offs that change creates in a complex system. Speed is often important, but it needs to be weighed against the need to have some basic understanding of the broad impact we have beyond the narrow, core objective. To use a ‘western’ analogy, in a gunfight it’s important to fire first, but not so fast that you shoot yourself in the foot.

The Desert is an Ocean with its Life Underground: In my last article I talked about the need for more scientists in leadership positions. One of the reasons for this is that our leaders today often appear unable, or perhaps unwilling to look at the big, complex picture, but instead over-simplify issues.  Nowhere is this more evident than in the southwest United States, where in the rush for growth, ‘renewable’ energy, raw material independence and AI development is destroying huge swathes of wilderness. While well intentioned, this is often driven by leaders who are focused on narrow goals, and ignore collateral damage by simplistically regarding the Mojave and as ‘s ‘only a desert’. But that desert is really an extremely complex and fragile system. GLP-1’s are likely the tip of the iceberg. We don’t know what else lies below the surface, but we need to be careful that we don’t destroy it before we have a chance to find out

The Pros and Cons of Solar Energy in the Desert: Just taking mass solar as an example of well intentioned but overly simplistic thinking.  Our deserts are rapidly getting littered with massive industrial scale solar farms, together with the equally massive infrastructure needed to transport the electricity they create to population centers, and/or AI data centers.

At a basic level, the concept of solar is a good one; what’s not to love about pollution free energy independence?  But if we look at the bigger, far more complex picture, it’s nowhere near that simple.

Too Hot For Solar? For example, a hot sunny desert is a superficially obvious place to build solar infrastructure.  But that’s until we realize that surface temperatures are so hot cells operate far below optimum efficiency.  Meanwhile dust further reduces efficiency, and remote locations make building, maintaining and connecting these farms difficult, expensive and environmentally damaging.

Collateral Damage: Solar farms and their infrastructure do extensive damage to our desert wilderness. They remove habitat for endangered species, and block migration roots for others.  Their installation and maintenance uses scarce water, and creates significant CO2 emissions (the thing they were supposed to prevent).  Much of the technology is shipped from China, posing a question around true energy independence, and that shipping and manufacture also creates CO2.  Climate change is a global issue, and while shifting CO2 emission for solar manufacture from the US to China may look good on some spreadsheets, it does nothing to solve the actual problem. 

These solar farms also create enormous amounts of dust.  Installing them requires removing of both surface crust and vegetation whose slow growing root systems hold the desert surface together (and ironically store CO2 via a symbiotic relationship with a mycelium).  That dust not only reduces the efficiency of the solar panels themselves, but also presents a hazard to traffic, and can even be quite toxic.  Mojave desert dust contains both natural asbestos and potentially deadly valley fever.  Its why all construction has to be constantly sprayed with increasingly scarce water.

With industrial scale desert solar, the narrow view of ‘renewable and ‘clean’ solar energy’ is highly attractive.  The reality is more complex, and full of trade offs that pit a green core technology against the environmental cost of construction, maintenance, eventual decommissioning, destruction of habitat and unintended consequences such as toxic dust. This makes a superficially simple choice far more complex. Some trade offs are alignable. For example, we can probably calculate actual net CO2 savings over the lifetime of a solar farm after manufacture, shipping, installation and decommissioning are taken into account.  But I’m not even sure if we can truly compare some of the other trade offs.  How do we quantify the trade off between toxic dust and reduced CO2 emissions?  Or how do we quantify and compare the impact of water usage, or loss of habitat to endangered species? 

Simplistic Focus: The result is a very complex calculation. But what is clear is that our leaders today typically ignore this, and instead remain simplistically focused on the narrow view.  Maybe if we could get more scientists into leadership positions we might do a better job of understanding trade offs, and the cost benefit of new technologies.  Today politicians all too often line up in favor of, or in opposition to projects based on overly simplistic, partisan frames, when really we need to manage complex trade offs. 

Calculating the Cost of Change in Complex Systems: Now, although I believe we need to do much better at managing complex systems, that doesn’t mean the pendulum needs to swing to far in the other direction. Complexity and uncertainty should not become an excuse for procrastination, inaction, or what I like to call the tyranny of data. The later is when we get stuck generating data and reports in increasing detail that add so much complexity, we never make a decision. As an innovator I embrace change, and recognize that progress inevitably comes with trade-offs.  But it’s about balance, and its critical to understand those trade offs at a systems level before charging ahead with initiatives, but still be willing to move forward embracing some uncertainty. All innovation comes with some risk, but smart innovators minimize those risks and balance them against timely progress.  And scientists are trained to learn as they go. That’s a balance I’d argue our leaders are struggling with today, swinging between inaction, and massive investments based on limited knowledge.

Solar is one example. But there are many more. In my home city of Las Vegas we are already facing a severe water crisis and extreme heat island effects.  In light of that, the mass destruction of wilderness to build 250,000 new MacMansions in the desert seems to lack even minimal big picture thinking.  Data centers, the innovation de jour are a more complex challenge. There is certainly a demand for them, and there is  a powerful, albeit US centric argument for keeping the US at the head of the AI innovation curve.  That means we do need data centers, but the cost in water and energy, two resources that are in relatively short supply here, arguably makes the SouthWest a poor choice of location.  Although I’ll acknowledge that data centers are rapidly becoming a somewhat universal ‘good idea as long as it’s not here’ technology.

Embracing Complexity and Solving Trade Offs:  But embracing complexity and looking at these at a systems level does not mean stopping innovation or progress. Quite the opposite, it should ultimately help us to innovate more effectively, and maybe face-plant less often. Identifying and challenging trade offs had long been a source of innovation, and is at the core of many innovation processes.  For example, with AI, could the US stay ahead of the AI curve by focusing data centers on more useful tasks, while cutting out less useful and energy expensive ‘slop’ such as action figures and/or caricatures?  That is maybe where regulation comes in, but as I mentioned in my last article, regulation without understanding risks both being ineffective, or creating unintended collateral damage. So this all supports the need for more technical ‘savvy’ in leadership.  
 
We Don’t Know What We Don’t Know.  When we try to evaluate trade off’s associated with innovation, what we don’t know is always one of the biggest challenges.  Who would have guessed 30 years ago that the Gila monster would provide the cure for obesity, and significantly reduce Type -II diabetes.  As mentioned before, we can be fairly sure that our desert wilderness holds many more untapped innovations, but we just don’t know what they are.  That harsh environment drove the evolution of tools for metabolism and glucose management that today treat obesity and diabetes management.  Longer term, could they also be a source of chemistry with efficacy against cancers, where glucose restriction and differentiation between the kinetics of healthy and cancer cell replication are effects we have, and will likely continue to exploit?  That’s speculation, but it highlights that we often don’t know all of the trade offs, and so those complex models need to be monitored and updated.  Narrow focus on a simplistic model means we miss so many potential opportunities. We also risk destroying the sources of the innovations and breakthroughs we haven’t found yet

Image credits: Google Gemini

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Innovation Frameworks

A Practitioner’s Guide to the Most Important Models

Innovation Frameworks

by Braden Kelley and Art Inteligencia

Every organization wants to innovate. Few do it consistently. The difference is almost never creativity — most organizations have more ideas than they can act on. The difference is structure: a repeatable way of thinking about innovation that aligns effort with strategy, channels creative energy toward real opportunities, and builds the organizational capability to innovate continuously rather than occasionally.

That’s what an innovation framework provides. And after two decades of working with organizations on innovation and change — and developing my own frameworks including the Eight I’s of Infinite Innovation, the Value Innovation Framework, and the Human-Centered Innovation Toolkit™ — I’ve developed strong views on which frameworks work, which ones fall short, and how to choose the right one for your situation.

This guide covers the most important innovation frameworks in use today, what each one does well, where each one is limited, and how to choose the right framework for your organization’s specific innovation challenge.

What is an Innovation Framework?

An innovation framework is a structured approach that helps organizations systematically identify opportunities, generate and evaluate ideas, and move from concept to implemented value. A good innovation framework does three things: it provides a common language that aligns leaders, teams, and stakeholders around what innovation means and how it works in your context; it sequences the activities of innovation so that effort is directed toward the highest-value opportunities; and it builds repeatable capability — so that innovation becomes a way of working rather than a periodic event.

The most important thing to understand about innovation frameworks is that no single framework covers all types of innovation equally well. Frameworks that excel at incremental product improvement are not designed for disruptive business model innovation. Frameworks built for startup environments don’t always transfer to large, complex organizations. The first step in choosing a framework is understanding what type of innovation challenge you are actually facing.

The Most Important Innovation Frameworks

McKinsey’s Three Horizons Framework

Developed at McKinsey and popularized in the book The Alchemy of Growth, the Three Horizons Framework helps organizations balance their innovation portfolio across three time horizons:

  • Horizon 1 — Extending and defending the core business. Incremental improvements to existing products, services, and business models. Typically 70% of innovation investment.
  • Horizon 2 — Building emerging businesses. Adjacent opportunities that leverage existing capabilities in new markets or segments. Typically 20% of innovation investment.
  • Horizon 3 — Creating genuinely new options. Transformative innovations that may cannibalize the core business or create entirely new markets. Typically 10% of innovation investment.

Strengths: The most useful framework for having conversations about innovation investment allocation at the executive level. Forces organizations to acknowledge that they need different innovation approaches for different time horizons, and that Horizon 3 work requires protection from the short-term pressures that dominate Horizon 1 management.

Limitations: The 70-20-10 split is a guideline, not a rule — and organizations in different competitive situations need different allocations. The framework also doesn’t tell you how to innovate within each horizon, just how to allocate investment across them. And the original framework assumed horizons of roughly 0-2, 2-5, and 5+ years — in fast-moving industries today, those timeframes may be compressed significantly.

Best for: Portfolio strategy, investment allocation conversations, and helping leadership teams understand why protecting Horizon 3 work from Horizon 1 pressures is essential.

Jobs to Be Done (JTBD)

Developed by Clayton Christensen and refined by Tony Ulwick and Bob Moesta, Jobs to Be Done reframes the innovation question from “what product should we build?” to “what job are customers hiring this product to do?” The insight is that customers don’t buy products — they hire them to make progress in specific circumstances, and understanding the underlying job opens innovation opportunities that product-focused thinking misses entirely.

Strengths: The most powerful framework available for identifying genuinely unmet customer needs and generating breakthrough product and service concepts. The “milkshake marketing” insight — that people hired McDonald’s milkshakes for a morning commute job, not a dessert job — is one of the most cited examples in innovation literature because it illustrates how different JTBD thinking is from conventional market research. JTBD consistently surfaces opportunities that product roadmaps and voice-of-customer surveys miss.

Limitations: Requires significant qualitative research skill to apply well. The interviews and observation needed to surface real jobs-to-be-done are more demanding than standard customer research. JTBD also doesn’t provide a framework for the full innovation process — it’s an insight methodology, not an end-to-end innovation system.

Best for: Product and service innovation, identifying white space opportunities, and challenging assumptions about why customers actually use your products.

Lean Startup

Developed by Eric Ries and drawing on Toyota’s lean manufacturing principles, the Lean Startup framework centers on the Build-Measure-Learn loop: build a minimum viable product (MVP), measure how real customers respond, and learn whether to persevere with the current direction or pivot to a different approach. The core insight is that the biggest risk in innovation is building something nobody wants — and that risk is best mitigated through rapid, cheap experimentation rather than elaborate upfront planning.

Strengths: The most influential innovation framework of the past two decades in the startup world, and increasingly in corporate innovation. The MVP concept has genuinely changed how organizations think about early-stage development. Lean Startup’s emphasis on validated learning — testing assumptions with real customers before significant investment — reduces the waste that kills most innovation programs.

Limitations: Developed for startup environments and doesn’t fully account for the complexity of large organization constraints — governance requirements, brand risk, organizational politics, and the need to coordinate across functions. “Move fast and break things” works differently when you are breaking an established brand or regulatory relationship. Also focuses primarily on product and technology innovation rather than business model or organizational innovation.

Best for: New product development, digital product and service innovation, and any context where rapid experimentation and validated learning are possible.

Disruptive Innovation Framework

Clayton Christensen’s theory of disruptive innovation describes how new entrants typically begin by serving overlooked, over-served, or non-consuming segments with simpler, cheaper solutions — and then move upmarket over time, eventually displacing established players who were focused on serving their most profitable customers. The framework provides a lens for understanding competitive threats that conventional competitive analysis misses.

Strengths: The most powerful framework for understanding how industries are disrupted and for identifying both threats and opportunities from disruptive dynamics. Helps established organizations avoid the innovator’s dilemma — the tendency to dismiss disruptive threats as irrelevant to their core market until it is too late.

Limitations: Better as a diagnostic and strategic lens than as a practical innovation process. The framework tells you where disruption is likely to come from and why, but doesn’t tell you what to do about it. Also, the theory has been misapplied so frequently — with “disruptive” used as a synonym for any significant innovation — that it has lost some of its precision.

Best for: Competitive analysis, strategic planning, and helping leadership teams understand the threats they are systematically underestimating.

Open Innovation

Coined by Henry Chesbrough, open innovation describes a model in which organizations use both internal and external ideas and paths to market to advance their innovation. Rather than relying solely on internal R&D, open innovation deliberately leverages external partners — startups, universities, customers, suppliers, and even competitors — to access capabilities and ideas that would take too long or cost too much to develop internally.

Strengths: Dramatically expands the innovation surface area available to an organization. Companies like Procter & Gamble, whose Connect + Develop program targeted sourcing 50% of innovations from outside the company, demonstrated that open innovation can transform both the scale and velocity of an innovation program. Particularly powerful for organizations that need to access rapidly evolving technology capabilities.

Limitations: Requires significant organizational capability to manage external relationships, evaluate external ideas, and integrate external technologies without destroying their value. The “not invented here” syndrome — the organizational immune system’s tendency to reject external ideas — is a powerful force that many open innovation programs underestimate. Also raises complex IP and partnership issues.

Best for: Technology-intensive industries, organizations seeking to accelerate innovation velocity, and any context where the external innovation ecosystem is moving faster than internal R&D can match.

Design Thinking

Formalized at Stanford’s d.school and popularized by IDEO, design thinking is a human-centered, iterative problem-solving methodology built around five stages: Empathize, Define, Ideate, Prototype, and Test. At its core, design thinking insists that innovation must begin with deep understanding of the people being served — not with technology capabilities or product roadmaps.

Strengths: The best framework available for ensuring that innovation addresses real human needs. Design thinking’s emphasis on empathy and prototyping has genuinely changed how organizations approach product and service development. The methodology transfers well beyond product design to organizational change, service design, and public policy — anywhere that complex human-centered problems need to be solved creatively. For a full treatment, see our guide to the design thinking process.

Limitations: The Empathize and Define stages require significant time investment that organizations under delivery pressure often shortcut — producing the tool’s use without its value. Design thinking also doesn’t address the full innovation pipeline beyond concept validation: scaling, organizational alignment, and change management are outside its scope.

Best for: Product and service innovation, organizational change design, and any context where the problem is not fully understood and human needs are the primary design constraint.

Braden Kelley’s Innovation Frameworks

After applying and observing the frameworks above across hundreds of organizations, I developed my own frameworks to address the gaps I consistently encountered — particularly the absence of frameworks designed for building continuous innovation capability rather than managing individual innovation projects.

The Eight I’s of Infinite Innovation

The Eight I’s of Infinite Innovation is a continuous innovation framework built around eight interconnected elements: Inspiration, Insight, Ideation, Invention, Implementation, Illumination, Improvements, and Infinity. Unlike project-based innovation frameworks, the Eight I’s is designed to be a perpetual cycle — the outputs of one round become the inputs for the next, creating a self-reinforcing engine of continuous innovation rather than a series of discrete projects.

The framework is particularly suited to organizations transitioning from a product-centered to a customer needs-centered structure — where innovation must be ongoing and adaptive rather than periodic and planned. The Eight I’s is most powerful when combined with the Value Innovation Framework, which provides the strategic lens for determining which opportunities are worth pursuing. Read more about the Eight I’s of Infinite Innovation →

Eight I's of Infinite Innovation

The Value Innovation Framework

The Value Innovation Framework addresses the question that most innovation frameworks leave unanswered: will this innovation actually succeed in the market? Most frameworks focus on generating and validating ideas, but provide little guidance on predicting whether an innovation will achieve real-world adoption. The Value Innovation Framework fills that gap with a simple but powerful equation:

Innovation = Value Creation × Value Access × Value Translation

The components are multiplicative, not additive — which is the key insight. Do two of the three brilliantly and one poorly, and the innovation can still fail. All three must be executed well for an innovation to succeed:

Value Creation — The innovation must create incremental or entirely new value large enough to overcome the switching costs of moving from the old solution (including the “Do Nothing” option). New value can be created by making something more efficient, more effective, possible that wasn’t possible before, or by creating new psychological or emotional benefits. If the value created doesn’t exceed the friction of switching, adoption won’t happen regardless of how well the other two components are executed.

Value Access — Also thought of as friction reduction. How easy is it for people to access, use, and do business around the new solution? A highly valuable innovation that is difficult to access, purchase, integrate, or use will fail. Value Access covers the full spectrum of friction that stands between a customer and the value an innovation creates — distribution, pricing, integration complexity, learning curve, and switching costs.

Value Translation — How well does the innovation communicate its value in terms that resonate with the people it is designed for? Apple’s iPad launch illustrates this perfectly: the initial announcement failed to translate the value clearly, putting the launch at risk — until a single Out of Home advertisement showing a person relaxing with an iPad on their lap communicated in seconds what no amount of technical specification could. Value Translation is about helping people understand how the innovation fits into their lives, not just what it does.

The Value Innovation Framework is an innovation success prediction tool — it can be applied to evaluate existing innovations, diagnose why past innovations failed, and guide the development of new ones. It is most powerful when combined with the Eight I’s of Infinite Innovation – which can be downloaded as an 11″ x 17″ reference for free here. Read the full treatment in Innovation Is All About Value →

Value Innovation Framework

The Human-Centered Innovation Toolkit™

The Human-Centered Innovation Toolkit™ is the most comprehensive of my innovation frameworks — a complete system for building innovation capability inside organizations. It draws on the best of design thinking, jobs to be done, and lean startup while adding the organizational change management dimension that none of those frameworks adequately address.

The central insight driving the toolkit is that innovation programs fail most often not because of insufficient creativity or inadequate process, but because the organizational change required to implement innovations is underestimated and under-managed. The Human-Centered Innovation Toolkit™ integrates the innovation process with the change management process — giving organizations a single system for generating validated concepts and successfully implementing them.

How to Choose the Right Innovation Framework

The right framework depends on your innovation challenge, organizational context, and where you are in the innovation process. Use this guide to match your situation to the most appropriate approach:

Your situation Best framework(s)
Deciding how to allocate innovation investment across time horizons Three Horizons Framework
Identifying unmet customer needs and white space opportunities Jobs to Be Done
Validating new product concepts quickly and cheaply Lean Startup
Understanding competitive disruption threats Disruptive Innovation Framework
Accessing external innovation capabilities and ideas Open Innovation
Solving complex human-centered problems Design Thinking
Building continuous innovation capability across the organization Eight I’s of Infinite Innovation + Value Innovation Framework
Integrating innovation and change management into a single system Human-Centered Innovation Toolkit™
Full-spectrum innovation from insight to implementation Human-Centered Innovation Toolkit™ + Change Planning Toolkit™

Most organizations benefit from combining frameworks rather than selecting one exclusively. The Three Horizons gives you the portfolio lens. Jobs to Be Done gives you the customer insight. Design Thinking gives you the problem-solving process. Lean Startup gives you the validation methodology. The Human-Centered Innovation Toolkit™ ties them together with the organizational change capability that determines whether any of them actually produce results at scale.

The Most Common Reasons Innovation Frameworks Fail

Even the best innovation framework will fail if applied poorly. Here are the most common failure modes I’ve observed across organizations:

Selecting frameworks based on trend rather than fit. Design thinking is enormously popular. That doesn’t mean it’s the right framework for every innovation challenge. Before selecting a framework, diagnose your actual situation — what type of innovation are you pursuing, what is your primary constraint, and what organizational capability do you most need to build?

Treating frameworks as one-time events. A design thinking workshop is not a design thinking capability. A Lean Startup bootcamp is not a Lean Startup organization. Frameworks only build organizational capability when they are practiced repeatedly, supported by leadership, and embedded in how work actually gets done — not when they are run as standalone events.

Ignoring the organizational change dimension. Every significant innovation requires organizational change to implement — changes to processes, structures, skills, culture, and resource allocation. Most innovation frameworks are silent on this dimension, which is why so many validated concepts never get implemented. Building an innovation framework without a corresponding change management approach is the single most common reason innovation programs produce learning but not results.

Applying corporate constraints to startup frameworks. Lean Startup and Design Thinking were developed for environments where speed, flexibility, and risk tolerance are high. Large organizations often apply these frameworks while maintaining governance structures, approval chains, and risk management processes that fundamentally undermine the methodologies’ core principles. The frameworks need to be adapted for corporate environments, not applied verbatim.

Under-investing in the human side. The best innovation frameworks are collaborative, not expert-driven. They are designed to be used with the teams and stakeholders who will implement innovations, not by consultants or innovation functions who deliver conclusions to leadership. Organizations that use frameworks as expert tools rather than collaborative platforms consistently get lower-quality insights, lower ownership, and lower implementation rates.

Top Reasons Innovation Frameworks Fail

Frequently Asked Questions About Innovation Frameworks

What is an innovation framework?

An innovation framework is a structured approach that helps organizations systematically identify opportunities, generate and evaluate ideas, and move from concept to implemented value. It provides a common language for talking about innovation, a sequence of activities for managing the innovation process, and a set of principles that reflect how successful innovation actually works. The best innovation frameworks are adapted to the specific type of innovation challenge an organization faces — there is no single framework that is right for all situations.

What are the most widely used innovation frameworks?

The most widely used innovation frameworks include McKinsey’s Three Horizons Framework (for portfolio allocation), Jobs to Be Done (for identifying unmet customer needs), Lean Startup (for rapid concept validation), Disruptive Innovation (for competitive strategy), Open Innovation (for accessing external ideas and capabilities), and Design Thinking (for human-centered problem solving). Most experienced innovation leaders use multiple frameworks in combination rather than relying on any single approach, selecting frameworks based on the specific innovation challenge at hand.

What is the difference between an innovation framework and an innovation process?

An innovation framework is a broader conceptual structure — a set of principles, lenses, and approaches that guide how an organization thinks about and pursues innovation. An innovation process is more specific — a defined sequence of steps, activities, and decision points for managing innovation from idea to implementation. Most innovation frameworks include or imply a process, but the framework encompasses more than the process: it includes the mindsets, organizational capabilities, and strategic logic that determine whether the process produces results.

How do you build an innovation framework for your organization?

Building an innovation framework for your organization involves four steps. First, diagnose your actual innovation challenge — are you trying to improve the core business, explore adjacent opportunities, or develop transformative new capabilities? Different challenges require different frameworks. Second, select the frameworks that best fit your challenge and organizational context. Third, adapt those frameworks to your specific environment — accounting for your governance requirements, risk tolerance, and organizational culture. Fourth, build the organizational capability to use the frameworks consistently over time, not just as one-time events. This requires leadership support, training, embedded practice, and the organizational change management capability to implement what the frameworks reveal.

Why do innovation frameworks fail in large organizations?

Innovation frameworks fail in large organizations most often for four reasons: they are applied as one-time events rather than ongoing practices; they are selected based on trend rather than fit; they ignore the organizational change dimension required to implement innovations; and they are applied by expert consultants rather than collaboratively with the teams who will execute the work. The organizations that get the most value from innovation frameworks are those that adapt them to their specific context, practice them consistently, and invest equally in the change management capability needed to turn innovation concepts into implemented results.

For real-world examples of each framework in action, see our guide to innovation framework examples.

Content Authenticity Statement: The topic area, key elements to focus on, etc. were decisions made by Braden Kelley, with a little help from Claude and Google Gemini to clean up the article, add images and create infographics.

Image credits: Google Gemini

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Just Say No to Innovation

Just Say No to Innovation

GUEST POST from Greg Satell

Pundits tell us that the world is increasingly volatile, uncertain, complex and ambiguous. It’s the VUCA gospel. Under the banner of “innovate or die,” massive transformation projects are being kicked off constantly. Executives around the world scramble to reorganize and reinvent their organizations, only to reorganize and reinvent them again.

It gets worse, consider a 2014 report by PwC that revealed 65% of respondents in corporations complained about change fatigue, 44% of employees complained they don’t understand the change they’re being asked to make, and 38% say they don’t agree with it. A more recent study by Gartner in 2020 found that propensity for change fatigue doubled during the pandemic.

Executives, wanting to be seen as dynamic leaders, are launching too many initiatives, very few of which lead to positive impact, while at the same time the rest of the workforce struggles with increasing mental health challenges. The answer is less, not more. We need to focus on fewer initiatives, with more commitment to ensure their success.

Why Change Fails

It’s a familiar story we’ve seen time and time again. An ambitious new leader comes in and launches a transformational initiative. There’s a kickoff meeting and a massive internal communication campaign to rally the troops for the multi-year program. Consultants are hired and employees are told, in no uncertain terms, they must get on board.

Two years later, the leader moves on, having sold another company on the myth of his transformational leadership. Another, equally ambitious executive comes in with their own idea for change. The old initiative is dropped, there is a kickoff meeting, an internal communication campaign, consultants are hired and employees are told to get on board.

Rinse and repeat.

There’s plenty of blame to go around. But let’s face it, there is a tendency to glorify the kickoff more than genuine results. Part of this is cultural and part of it reflects other trends. An excessive adherence to quarterly benchmarks puts too much focus on short term impact. Combine this with a general decline in executive tenure means that leaders often leave before transformation projects can be completed.

All of this comes at a cost. Take a look at the economic data and you will inevitably find that productivity growth is significantly lower than in earlier generations. In the US in particular, the White House has found that competition, across a wide variety of metrics, has declined significantly in the past few decades.

The Power Of No

When people remember Steve Jobs’ tenure at Apple, they remember the products that were launched. Yet arguably, the most important thing he did at Apple was kill products. When he returned to the company in 1997, he found that years of undisciplined management led to a bloated product line. The first thing Jobs did was not to launch new innovations, but to do an extensive review in which he cut 70% of the product line.

“One of Jobs’s great strengths was knowing how to focus.” Walter Isaacson, his biographer, would later write. “Deciding what not to do is as important as deciding what to do,” he quotes the legendary CEO saying. “That’s true for companies, and it’s true for products.”

At one point a frustrated Jobs simply said, “Stop!” He grabbed a magic marker, went to the whiteboard, made a classic two by two matrix with “Consumer” and “Pro” making up the columns and “Desktop” and “Portable” making up the rows. He then declared that Apple would make four great products, one for each quadrant and that would be it.

He maintained the same discipline throughout his tenure. Over the next decade, he would launch the iMac, the iPod, the iPhone and the iPad. A handful of products was all it took to create the most valuable company in the world. Becoming an innovation-led company is not about launching a lot of ideas, but focusing on the ones that matter and figuring out how to make them work.

The Time To Commit

While we talk about transformation more and more, we seem to be doing it less and less. This is no accident. Change and transformation aren’t about coming up with the idea and doing a fancy kickoff event followed by an extensive communication campaign, it’s about converting those ideas into impactful solutions to problems people care about.

There’s far too much talk and not nearly enough impact. Change should be an inspiration, not one more burden in an otherwise exhausted workplace. It’s time to refocus our efforts on change that matters. In most enterprises, that will mean committing to fewer initiatives, but seeing them through.

To do that effectively, leaders need to learn to say, “no.” Every organization needs to maximize the impact of limited resources and that means we need to make choices. Pursuing one thing means that we need to give up something else. We can’t just spin our wheels and expect to get anywhere, we need to pick a direction and get going.

That’s not as easy as it sounds. Committing to a specific objective means we limit our options. Sticking with a project when things get tough takes courage and resilience. That’s why so few leaders are able to do it consistently. But the evidence is clear. If you want to compete successfully, that’s what you need to do.

— Article courtesy of the Digital Tonto blog
— Image credit: Google Gemini

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Dan Toma is an innovation thought leader and co-author of the award-winning books ‘The Corporate Startup’ (2017) and ‘Innovation Accounting’ (2022). Puzzled by the question ‘Why are innovative products mainly launched by startups?’, together with his colleagues at the London-based consultancy company OUTCOME, he focuses on enterprise innovation transformation. Specifically on the changes blue-chip organizations need to make to allow for new ventures to be built in the corporate setting.

What is an Innovation Keynote Speaker?

Innovation Keynote Speaker Braden Kelley

Most organizations know they need to innovate. Far fewer know how to build the conditions that make innovation actually happen — consistently, at scale, across teams and functions. This is the gap that a great innovation keynote speaker is uniquely positioned to close.

But the term gets used loosely. Not every speaker who mentions disruption or design thinking qualifies as an innovation keynote speaker in the meaningful sense. Understanding what the role actually involves — and what separates genuinely useful speakers from entertaining but forgettable ones — is worth your time before you commit budget to a booking.


What Is an Innovation Keynote Speaker?

An innovation keynote speaker is a subject matter expert who helps organizations understand, develop, and apply innovation capabilities through live presentations, workshops, and masterclasses. Unlike a generic motivational speaker, an innovation keynote speaker brings deep expertise in how organizations create new value — and the cultural, structural, and human factors that determine whether innovation efforts succeed or fail.

The best innovation speakers don’t just inspire. They equip. Audiences leave with frameworks they can apply, mental models that reframe stubborn problems, and a clearer sense of the specific actions that will move their organization forward.

A strong innovation keynote typically addresses some combination of:

  • Innovation strategy — how organizations choose where and how to innovate
  • Innovation culture — the leadership behaviors, structures, and norms that enable or block creative thinking
  • Human-centered design — building solutions around the real needs of real people
  • Change management — navigating the human side of transformation
  • Emerging technology and trends — understanding which forces are reshaping your industry and how to respond

What Does an Innovation Keynote Speaker Actually Do?

The format varies significantly depending on your event’s needs, budget, and goals. Here’s how the most common engagements work in practice.

Keynote Presentations

A 45 to 75-minute keynote is the most common format — typically delivered at a conference, leadership summit, or annual meeting. A well-designed innovation keynote sets the intellectual agenda for the event, gives attendees a shared language and framework, and creates the momentum that carries into breakout sessions and hallway conversations.

The best innovation keynotes challenge assumptions rather than confirming them. They introduce ideas the audience hasn’t encountered before, reframe familiar problems in ways that open new solutions, and leave people with a clear sense of what they can do differently starting Monday morning.

Workshops and Masterclasses

Workshops extend the keynote into active application. Rather than a one-way presentation, a workshop engages participants in using innovation frameworks on their own real challenges — building skills through practice rather than passive listening.

Innovation workshops are particularly valuable for leadership teams that need to move beyond general awareness into genuine capability building. A half-day or full-day workshop with the right facilitator can accomplish more than months of internal training on the same topics.

Webinars and Virtual Keynotes

Virtual formats have expanded access to innovation speakers significantly. A well-produced virtual keynote can reach distributed teams across multiple locations simultaneously, making innovation thinking accessible to organizations that couldn’t previously justify the investment in an in-person event.

Custom Research and Advisory

The deepest engagement level involves an innovation speaker working with your organization over time — developing custom frameworks, conducting research specific to your industry, and helping build internal capabilities rather than delivering a single keynote.


Innovation Keynote Speaker vs. Motivational Speaker — What’s the Difference?

This distinction matters more than most event planners realize when they’re making a booking decision.

A motivational speaker primarily works on mindset and emotional energy — leaving audiences feeling inspired, capable, and energized. That’s genuinely valuable in the right context. But motivation without a map doesn’t produce innovation. If your audience leaves feeling great but can’t articulate a single new framework or specific action they’ll take, the investment hasn’t generated a return.

An innovation keynote speaker works on both energy and capability. The best ones are genuinely inspiring — but the inspiration is grounded in substance. The audience doesn’t just feel differently, they think differently. They have new tools. They see their organization’s challenges through a new lens.

If your event goal is to energize your team before a busy quarter, a motivational speaker may be exactly right. If your goal is to build organizational capability, shift culture, or equip leaders with frameworks they’ll actually use, you need an innovation speaker.


What to Look for When Booking an Innovation Keynote Speaker

The speaking industry makes it easy to find charismatic presenters. It’s harder to find innovation speakers with genuine depth. Here’s what to look for.

Proprietary Frameworks and Original Thinking

Any speaker can summarize research and present trend lists. What distinguishes an exceptional innovation keynote speaker is original intellectual contribution — frameworks they’ve developed, models they’ve tested, insights that aren’t available in any business book. Ask what frameworks the speaker brings that are uniquely theirs. Look for powerful tools like Braden Kelley’s Nine Innovation Roles and Innovation Maturity Assessment. Look for comprehensive methodologies like Braden’s Human-Centered Innovation and frameworks like those in Stoking Your Innovation Bonfire.

Real-World Application Experience

Innovation theory is easy to talk about. Innovation practice is significantly harder. Look for speakers who have actually led innovation initiatives inside organizations — who understand the politics, the resource constraints, the cultural resistance, and the messy reality of trying to make new things happen inside existing institutions.

Genuine Customization

An innovation keynote that could be delivered identically to any audience in any industry is a warning sign. Strong innovation speakers invest real time understanding your organization’s specific challenges, your industry’s dynamics, and your audience’s level of sophistication before they set foot on stage. The best keynotes feel like they were written specifically for your people — because they were.

A Body of Work That Demonstrates Commitment

Books, frameworks, tools, research, years of consistent contribution to the field — these signal that a speaker has genuinely earned their expertise rather than recently rebranding as an innovation speaker because the label is in demand. Look at what they’ve built, not just how well they present.

Outcomes, Not Just Content

Ask what the speaker wants your audience to be able to do differently after the keynote. The answer tells you everything. Vague answers about inspiration or awareness signal a speaker focused on their own performance. Specific answers about behavioral changes, new frameworks the audience will apply, or decisions they’ll make differently signal a speaker focused on your organization’s outcomes.


Questions to Ask Before You Book

Use these in your vetting conversations to quickly identify the right fit:

  • What original frameworks do you bring that aren’t available elsewhere? Listen for genuine intellectual property, not trend summaries. Look for powerful frameworks like The Eight I’s of Infinite Innovation and FutureHacking.
  • How do you customize your content for different industries and audiences? A strong answer involves a discovery process. A weak one describes the same talk delivered everywhere.
  • What do you want our audience to be able to do differently after your keynote? Look for specific behavioral outcomes, not emotional ones.
  • Can you share an example of an insight you’ve delivered that wasn’t obvious at the time? This tests whether their thinking is genuinely ahead of the curve.
  • What formats beyond the keynote do you offer, and when are they most valuable? This helps you understand whether a workshop or masterclass would serve your goals better than a standalone keynote.
  • How do you measure whether a keynote has been successful? Speakers who think about impact tend to deliver it.

Why Organizations Hire Innovation Keynote Speakers

The specific reasons vary, but the most common situations where an innovation keynote speaker adds the most value include:

Annual conferences and leadership summits — where the right keynote sets the intellectual agenda for the year and gives distributed teams a shared framework to work from.

Culture change initiatives — where an external voice can say things internal leaders can’t, create psychological safety for new conversations, and help an organization see itself differently.

Strategy offsites — where a keynote or workshop challenges the assumptions underlying the current strategy before the planning process begins in earnest.

Industry conferences — where an innovation speaker positions your organization as a thought leader by association and delivers genuine value to attendees.

Learning and development programs — where innovation capability needs to be built systematically across a leadership population rather than inspired in a single event.


Ready to Book an Innovation Keynote Speaker?

Braden Kelley is an innovation keynote speaker and futurist who has spent decades helping organizations build the mindsets, frameworks, and capabilities to thrive through change. His human-centered approach to innovation and change management has been applied by organizations worldwide, and his proprietary frameworks — including the Human-Centered Change methodology — give audiences tools they can use immediately.

Whether you need a keynote that re-frames how your leadership team thinks about innovation, a workshop that builds practical capability, or a masterclass that equips your people with frameworks for navigating change, Braden brings the substance and the delivery to make your event memorable and genuinely useful.

Explore ten reasons to hire an innovation keynote speaker — then book Braden Kelley for your next event.


Explore more on innovation strategy, change management, and human-centered thinking at Human-Centered Change and Innovation.

Top 10 Human-Centered Change & Innovation Articles of April 2026

Top 10 Human-Centered Change & Innovation Articles of April 2026Drum 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 April’s ten most popular innovation posts:

  1. Why an AI Soft Landing Might Look Like Victorian England — by Braden Kelley
  2. The Four Psychological Disruptions of AI at Work — by Braden Kelley
  3. Liberated to Care – How AI Can Restore Humanity in Healthcare — by Kellee M. Franklin, PhD.
  4. The Consumption Collapse – When the Feedback Loop Bites Back — by Art Inteligencia
  5. Four Steps to the Future – Announcing the Newest FREE Addition to the FutureHacking™ Toolkit — by Braden Kelley
  6. Which of the Nine Innovation Roles do you play? (A Quiz) — by Braden Kelley
  7. How to Consciously Develop More Courage — by Tullio Siragusa
  8. Does Planned Obsolescence Fuel the Fire or Just Burn the House Down? – The Innovation Paradox — by Braden Kelley
  9. Misunderstanding Big Ideas is Very Dangerous — by Greg Satell
  10. Artificial Intelligence Powered Teamwork — by David Burkus

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

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!

Build a Common Language of Innovation on your team

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 five years:

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Participatory Design Meets Diversity

GUEST POST from Douglas Ferguson

A few years ago Voltage Control surveyed nearly 100 leaders on culture, mental health, DEI, experience, hybrid, leadership, facilitation, collaboration, technology, and change (in case you missed it). Within the findings, we explore leaders’ challenges, capability gaps, and opportunities to adapt to the current workplace ecosystem more effectively, in order to define a working maturity model. The maturity model is a snapshot of trends across the nearly 100 leaders we heard from, combining quotes with findings from a survey. Download Work Now 2023 here. (you can also get the 2022 edition at the same time)

Workplace culture and diversity are essential to co-creation and participatory design.  

Participatory Design Meets Diversity

Participatory design is a driving force in our innovation practice. The unique design methodology opens the door to rich conversations and remarkable collaboration. With this approach to design, participants are invited into the process of investigating, reflecting, developing, and essentially co-creating your products or services.

With engaging design processes in place, these sessions capture the needs of all participants in a hierarchical manner. As opposed to us telling customers or clients what they need, this approach allows for key stakeholders to show us what matters to them.

We’ve spent years practicing and incorporating this methodology into workshops and design sprints. What we discovered is invaluable: diversity is key.

Our team at Voltage Control found that in order to truly overcome bias and move industries forward, diversity in participants throughout this design process is vital. Hosting inclusive spaces in this co-design experience will lend itself to more ideas and fuel irreplicable growth.

Straight from our Work Now 2023 findings, here’s what leaders should ask themselves regarding their workplace culture before leading inclusive participatory design sessions:

  • How might we support organizations in (re)defining their cultures at this moment and beyond?
  • How might we enable leaders to communicate and co-create a shared culture—including to remote and hybrid teams?
  • For leaders who created a culture of “increased transparency of communication and encouraged virtual gatherings”—what would need to change so these practices are not lost as remote work time decreases?
  • How might we enable leaders to co-create meaningful work experiences with their teams in order to enhance environments of trust and collaboration?

For more on cultivating diverse teams and improving your participatory design strategy, join us in our Liberating Structure series where we will lead you in unleashing creativity in your meetings through maximum participation.

Douglas Ferguson | President, Voltage Control

Image credit: Pexels

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If Inertia is Not Your Friend Then Time is Your Enemy

If Inertia is Not Your Friend Then Time is Your Enemy

GUEST POST from Geoffrey A. Moore


As managers, because we are always in the middle of something, we can easily forget how much our operating model depends on inertia for its success. We count on our supply chain to deliver more or less as promised, we expect our quarterly bookings to be pretty much as forecasted, and we count on our customer churn to be within its normal range. This is the world of the Performance Zone and the Productivity Zone, one we measure largely based on its financial performance, something that is made possible by inertia, the tendency of objects in motion to continue in motion, albeit with well-timed well-directed boosts from ourselves and our partners.

Disruptive innovation breaks this pattern. When successful, it can generate spectacular momentum with early adopters, but that fizzles out when things hit the chasm. The whole point of crossing the chasm is to restart the engine of inertia, first around a single compelling use case in a single beachhead target market, then building out to adjacent use cases and segments. Wherever inertia can get established, reliable supply chains, forecastable bookings, and manageable churn will follow.

But here is the thing to keep in mind while this effort is underway: the clock is ticking! That’s why we say, when inertia is not your friend, time is your enemy. As a consequence, whenever you are managing anything disruptive, be that an external offering to customers or an internal revamping of your business model, operating model, or infrastructure model, you must prioritize time to tipping point over all other variables.

The single most valuable tactic for staying on top of your time budget is establishing a cadence of weekly commits. Each commit is tied to a change in state that will be brought about within the next seven days, each change in state representing a meaningful step towards the tipping point. You can’t afford to ignore your finances, but do not let financial metrics distract you from prioritizing time to tipping point. Until you have established inertial momentum, financial performance is ephemeral, and not a good predictor of business health.

Finally, because weekly commits is a challenging discipline, it is critical to enlist your team in the higher cause that warrants extraordinary efforts on their behalf. It does no good to shame people who have missed a commit. Rather the motto is win or learn. Either make the commit and take the next step, or understand the root cause of why you missed the commit and adjust accordingly. Do not get discouraged. Be resilient.

That’s what I think. What do you think?

Image Credit: Gemini, Geoffrey Moore

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Leveraging Multi-Agent Orchestration Frameworks for Innovation

Orchestrating the Human-Centered Future

LAST UPDATED: May 7, 2026 at 7:10 PM

Leveraging Multi-Agent Orchestration Frameworks for Innovation

GUEST POST from Art Inteligencia


From Solitary Bots to Orchestrated Teams

The current innovation landscape is hitting a ceiling. While single-model AI has provided significant individual productivity gains, it often fails when faced with the multifaceted complexity of enterprise-scale digital transformation. We are witnessing the transition from isolated AI interactions to a paradigm of integrated digital ecosystems.

The Innovation Bottleneck

Relying on a single “jack-of-all-trades” model often leads to context collapse and a lack of depth. For true innovation to thrive, we need diverse perspectives and specialized expertise. Multi-Agent Orchestration (MAO) addresses this by moving us away from “chatting with AI” toward orchestrating outcomes through a coordinated digital workforce.

Defining the MAO Shift

MAO is the connective tissue that allows multiple AI agents — each with specific roles, tools, and personas — to collaborate on complex goals. It turns a series of prompts into a dynamic workflow, ensuring that the right “expert” agent is handling the right task at the right time, while maintaining a persistent thread of strategic intent.

The Human-Centered Lens

In this new era, the human role evolves rather than diminishes. An orchestrated framework still requires a conductor. Our focus remains on the human-centered design principles that ensure these agent swarms are aligned with real human needs, ethical guardrails, and the overarching vision of the organization.

The Anatomy of an Innovation-Ready MAO Framework

Building an orchestration framework for innovation requires more than just connecting APIs; it requires a structural design that mirrors high-performing human teams. To move beyond simple automation and toward true creative problem-solving, an MAO framework must balance three core pillars: specialization, communication, and persistence.

Specialization vs. Generalization

The era of the “Generalist Bot” is yielding to the Specialized Agent Swarm. In an innovation context, this means deploying distinct agents with narrow, deep mandates. You might have “The Researcher” scanning global patent databases, “The Devil’s Advocate” specifically programmed to find flaws in business models, and “The Rapid Prototyper” generating code or wireframes. This role-based approach prevents the cognitive dilution often seen in large, single-model prompts.

The Orchestration Layer: Solving “Context Collapse”

The true power of MAO lies in the orchestration layer — the “manager” that handles agent hand-offs. This layer uses standardized communication protocols to ensure that when a task moves from a researcher to a designer, the strategic intent isn’t lost. This solves the “broken telephone” problem, allowing for complex, multi-step innovation cycles that can run autonomously while remaining aligned with the initial human vision.

State Management and Shared Memory

Innovation is rarely linear; it is an iterative journey. A robust MAO framework utilizes persistent state management. By maintaining a “shared memory” across the swarm, agents can reference earlier pivots, discarded ideas, and customer feedback from previous sessions. This ensures the digital workforce isn’t just reacting to the latest prompt, but is learning and evolving alongside the project’s lifecycle.

Strategic Applications in the Innovation Lifecycle

Multi-Agent Orchestration (MAO) transforms innovation from a series of manual tasks into a scalable, high-velocity engine. By embedding intelligent agents across the innovation funnel, organizations can move from reactive problem-solving to proactive future-shaping.

FutureHacking and Trend Spotting

Traditional trend scanning is often limited by human bandwidth. Using MAO, we can deploy Agent Swarms to scan disparate data sources — from patent filings to social sentiment — simultaneously. These agents act as “Signal Pickers,” synthesizing weak signals into cohesive foresight scenarios. This allows leaders to “hack” the future by identifying emerging opportunities months or years before they become mainstream.

Rapid Concept Validation via “Digital Personas”

One of the most powerful applications of MAO is the ability to stress-test ideas before investing significant capital. We can create Synthetic Customer Personas — digital agents programmed with specific demographic data, behaviors, and pain points. These “synths” provide immediate, iterative feedback on new experience designs, ensuring that human-centered design principles are baked into the concept from the very first draft.

Closing the XLM Gap

While traditional metrics focus on system performance, Experience Level Measures (XLMs) focus on human outcomes. MAO frameworks can be configured to monitor these XLMs in real-time across digital and physical touchpoints. When friction is detected, agents don’t just alert a dashboard; they can autonomously propose friction-lessening interventions or prototype alternative workflows, ensuring the experience remains seamless and human-centric.

Managing the Change: The Human-Agent Work Collaboration

The successful integration of Multi-Agent Orchestration (MAO) isn’t just a technical deployment; it is a profound organizational shift. To leverage these frameworks effectively, we must redesign our workflows to treat AI agents as collaborative partners rather than just automated scripts.

The New Org Chart: Integrating Digital Agents

As we move toward hybrid teams, our organizational structures must evolve to include “digital coworkers.” This requires moving beyond traditional silos to create Human-AI Work Collaboration models. In this setup, digital agents are assigned specific roles — such as data synthesis or rapid iteration — allowing human team members to focus on high-level strategy, creative direction, and empathy-driven decision-making.

Avoiding the Trap of “Automated Austerity”

A critical challenge in the age of MAO is avoiding a race to the bottom. Organizations must resist the “Vicious Cycle of Automated Austerity,” where AI is used solely to cut costs and displace human labor. Instead, the focus should be on augmentation — using agent swarms to expand our capacity for innovation and to create new forms of value that were previously impossible to achieve.

Governance and “Escalation Gates”

Trust is the foundation of any collaborative system. To maintain this, MAO frameworks must include Escalation Gates — predefined points where autonomous processes must pause for human review. Whether it’s an ethical check, a brand alignment review, or a strategic pivot, these gates ensure that the “digital workforce” remains accountable to human leadership and organizational values.

The Skill Shift: From Prompting to Orchestration

The core competency for future leaders is shifting from “Prompt Engineering” to Orchestration Leadership. This involves the ability to design complex workflows, define agent personas, and manage the hand-offs between human and digital actors. It’s about being the conductor of the orchestra, ensuring every “player” is in sync to produce a harmonious and innovative outcome.

The Ecosystem: Leading Frameworks and Players to Watch

The shift toward Multi-Agent Orchestration (MAO) is supported by a rapidly maturing ecosystem of enterprise-grade platforms and agile, open-source frameworks. For innovation leaders, selecting the right stack is about balancing the need for governance with the requirement for creative flexibility.

The Infrastructure Giants: Enterprise-Grade Orchestration

The “Big Three” have moved beyond simple model hosting to provide full-lifecycle agent runtimes.

  • Microsoft (Azure AI Foundry & Semantic Kernel): The primary choice for organizations heavily invested in the .NET and Microsoft 365 stacks. Azure AI Foundry (formerly AI Studio) provides hierarchical orchestration, allowing a “manager” agent to delegate tasks to role-specific sub-agents with built-in SOC 2 and HIPAA compliance.
  • Google Cloud (Gemini Enterprise Agent Platform): Launched at Next ’26, this platform features a re-engineered Agent Runtime with sub-second cold starts and an Agent Memory Bank that allows agents to recall high-accuracy details for long-term project context.
  • AWS Bedrock (AgentCore): A serverless powerhouse that excels in model diversity. Its AgentCore platform is designed for production-scale autonomous agents, offering a 25-30% cost-performance advantage for inference-heavy innovation workloads.
  • IBM (watsonx Orchestrate): Remains the leader for highly regulated industries, focusing on sovereign AI and “hard” governance where every agentic action must be auditable and tied to legacy systems like SAP or Salesforce.

The Agile Frameworks: The Innovator’s Toolkit

For teams building bespoke innovation workflows, these frameworks offer the most granular control.

  • LangGraph (by LangChain): The “gold standard” for stateful, controllable workflows. It treats agent interactions as directed cyclic graphs, making it the best choice when you need precise control over branching, retries, and human-in-the-loop “time travel” debugging.
  • CrewAI: Known for its role-based paradigm. It is the most “human-centered” framework, allowing you to define a “crew” (e.g., Researcher, Writer, Reviewer) that mirrors real-world team dynamics. It is currently the fastest path from a conceptual “innovation roles” model to a working prototype.
  • Pydantic AI: A newcomer that has gained rapid adoption for its focus on “Type-Safe” Python agents. It is essential for projects where data integrity is non-negotiable, such as financial modeling or technical engineering simulations.

Startups to Watch: The Next Wave of “Agentic” Innovation

These private companies are defining specialized niches within the orchestration space.

  • Sierra: Led by Bret Taylor, Sierra is at the forefront of autonomous customer experience orchestration, moving beyond chatbots to agents that can actually execute complex transactions and resolutions.
  • Decagon & Maven AGI: These players are transforming support and operations into “proactive experience management,” using multi-agent systems to anticipate friction before it occurs.
  • XBOW: A critical player in the security and compliance layer, ensuring that as your agent swarms grow, they remain within legal and ethical guardrails.
  • Cognition AI & Anysphere (Cursor): While focused on coding, their “agentic” approach to software development provides a blueprint for how AI can handle complex, multi-step creative projects from start to finish.

Conclusion: Stoking the Digital Bonfire

We stand at a pivotal moment in the evolution of work and creativity. Multi-Agent Orchestration is not merely a “tech stack” upgrade; it is the infrastructure for a new era of human-augmented intelligence. By moving away from siloed tools and toward an orchestrated digital workforce, we can finally overcome the bottlenecks that have long slowed the innovation lifecycle.

However, the technology is only as effective as the vision behind it. As we deploy these frameworks, our guiding principle must remain human-centered. We don’t build agent swarms to replace the “magic maker” or the “conscript”; we build them to amplify the impact of every role within the innovation team.

The Call to Action: Don’t just build a bot; build a capability. Start by identifying the “Experience Level Measures” that matter most to your customers, and then design an orchestration framework specifically to move those needles.

MAO is the connective tissue that allows human creativity to scale. By offloading the coordination, data synthesis, and rapid prototyping to an orchestrated framework, we free up human innovators to do what they do best: dream, empathize, and decide. It’s time to stop managing software and start conducting the future.

Frequently Asked Questions

1. What is the difference between an AI Agent and Multi-Agent Orchestration (MAO)?

A single AI agent is a tool designed to perform a specific task or conversation. Multi-Agent Orchestration (MAO) is the framework that manages a “team” of these agents, handling the hand-offs, memory, and strategy required to complete complex, multi-step innovation projects without manual human intervention at every step.

2. How does MAO improve the innovation process?

MAO accelerates the innovation lifecycle by automating the “busy work” of research, prototyping, and validation. By deploying specialized agents (like a digital “Devil’s Advocate” or “Trend Spotter”), teams can stress-test more ideas in less time, ensuring only the most viable, human-centered concepts move forward.

3. Is MAO intended to replace human innovation teams?

No. In a human-centered framework, MAO is designed for augmentation. It offloads data-heavy and repetitive tasks to digital agents so that humans can focus on high-value roles—providing strategic vision, ethical oversight, and the emotional intelligence necessary to create meaningful experiences.

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.

Image credits: Gemini

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Winning with Artificial Intelligence in 90 Days

Winning with Artificial Intelligence in 90 Days

Exclusive Interview with Charlene Li

The rapid evolution of artificial intelligence (AI) has shifted the technology from a futuristic curiosity to the primary engine of modern organizational growth. In an era defined by data-driven decision-making, the ability to effectively harness machine learning and predictive analytics is no longer just a competitive advantage; it is a fundamental requirement for long-term viability. However, the path to integration is rarely linear. Many organizations find themselves caught between the urgent need for transformation and the daunting reality of legacy infrastructure, talent shortages, and the cultural shifts required to move beyond small-scale pilots toward true enterprise-wide intelligence.

While the potential for increased efficiency and innovation is clear, the execution remains a significant hurdle.

The organizations that thrive in this new landscape are those that treat AI as a core strategic pillar rather than a plug-and-play software update. This requires a rethink of how human talent and machine intelligence coexist, ensuring that the technology enhances human capability rather than simply automating existing inefficiencies. Overcoming these challenges involves not just technical prowess, but a disciplined approach to change management and a clear vision for how intelligence will redefine the value the organization provides to its customers.

Today we will dive deep into what it takes to quickly achieve success with artificial intelligence with our special guest.

Creating a 90-Day Blueprint to Win with Artificial Intelligence

Charlene LiI recently had the opportunity to interview Charlene Li, a New York Times bestselling author, keynote speaker, and AI transformation strategist. Her latest book, Winning with AI: The 90-Day Blueprint for Success, co-authored with Dr. Katia Walsh, gives senior leaders a practical framework for moving from AI experimentation to measurable business value. Her prior books include The Disruption Mindset, Open Leadership, and Groundswell. Fast Company named her one of the most creative people in business, and she has worked with global organizations including 14 of the Dow Jones Industrial 30 companies. She is the founder of Altimeter Group (acquired by Prophet) and currently leads Quantum Networks Group.

Below is the text of my interview with Charlene and a preview of the kinds of insights you’ll find in Winning with AI: The 90-Day Blueprint for Success presented in a Q&A format:

1. What confusion is being created by speaking of “AI” as one thing when there are different kinds of AI, and how does this hold back AI adoption?

When people say “AI,” they’re usually thinking ChatGPT. But ChatGPT is generative AI — and that’s just one of three types of AI showing up in business today. There’s also predictive AI, which has been quietly running in your CRM, your fraud detection, and your streaming recommendations for years. And there’s agentic AI, which takes autonomous action toward a goal rather than waiting for a prompt.

The Oracle (predictive), the Creator (generative), and the Agent (agentic) — that’s how Katia and I describe them in Winning with AI. They do fundamentally different things, and they require fundamentally different things from you.

The conflation matters because it leads to bad decisions. Leaders see a generative AI demo, get excited, and ask their teams to “do something with AI” — when the actual business problem might be better solved with predictive AI (and probably already could’ve been three years ago). Or they hear “agentic AI” and assume their organization is ready to deploy autonomous agents when they haven’t even gotten generative AI into their workforce yet.

The winners aren’t choosing among types — they’re using all three strategically, in combination. A customer care transformation might use predictive AI to route inquiries, generative AI to draft responses, and agentic AI to handle routine cases autonomously. Once you can see the three distinctly, the question stops being “what can I do with AI?” and starts being “what can AI do for me?” That’s the question that actually unlocks value.

2. What are some of the key characteristics of AI inertia and some of the best ways to break free?

We call it pilot purgatory — and almost every organization we work with is stuck there. The signs are easy to spot: dozens of disconnected pilots, lots of conference attendance, lots of slide decks, no measurable financial impact. An MIT study found 95% of AI initiatives fail to scale. That’s not a technology failure. It’s a failure of leadership and culture.

The classic characteristics:

    • Use cases as a strategy. Many use cases equals procrastination. A long list of pilots is how organizations look busy without committing to anything.
    • Diffused accountability. When the CIO, CFO, and CMO all “share” responsibility for AI, no one owns the outcome.
    • Waiting for the foundation to be perfect. Clean data, the right platform, the perfect org structure — these become reasons to delay rather than constraints to solve through.
    • Confusing motion with progress. Running pilots feels like progress. It isn’t, unless those pilots are tied to your most important business problems.

To break free: pick your biggest strategic problems, figure out how AI solves them, invest heavily in those solutions, and move with urgency. Appoint one AI value owner who lives, breathes, and dreams AI outcomes. Kill pilots that aren’t on a path to scale. And replace “fail fast” with “learn fast” — nobody actually rewards failure, and the language of failure lets people walk away from things that should be pushed through.
Speed is the new moat. The companies that win aren’t the ones with the best technology. They’re the ones that adapt faster than their competitors.

3. There are still a lot of people out there not using AI (or not realizing that they are). What are some of the best ways for people to get started with AI?

Most people are already using AI — every spam filter, every Google Maps route, every recommendation on a streaming service is AI. So the real question is: how do you get started with the kind of AI that’s reshaping work right now, which is generative AI?

My advice is genuinely simple. Pick one of the major tools — Claude, ChatGPT, Gemini, Copilot — and start using it for one real task you do every week. Not a toy task. A real one. Drafting an email. Prepping for a meeting. Summarizing a long document. Brainstorming an approach to a problem you’re stuck on.

Two practical tips that make a big difference:

Write better prompts. A good prompt has a role (“Act as a marketing strategist”), instructions (what you want done), context (the background the AI needs), and an output format (memo, table, slide outline). Then refine through dialogue. Most people give AI two sentences and judge it on the result. Give it two paragraphs and you’ll be amazed.

Try the flipped interaction. Instead of asking AI for an answer, ask it to ask you questions until it has enough context to give a good answer. For example, at the end of a prompt, add this sentence: “Ask me any clarifying questions you may have.” It turns your prompt into a conversation.

I think of AI fluency as learning to eat with chopsticks: at first you’re concentrating on every motion, and eventually it’s just how you eat. You won’t get there by reading about it. You get there by using it. Every day. On real work.

4. Does AI safety really matter? It seems like all of the major AI players are just focused on speed and getting to AGI before China, am I wrong?

You’re not wrong about what the AI players are doing. But you’re probably not playing that game – more on that below. First, I’d push back on the framing that safety and speed are opposites.

Think of Formula 1. The drivers who win championships have absolute confidence in their brakes, their crash structures, their fire suppression systems. That’s why they can push so hard on speed. Safety is what makes speed possible. The companies moving fastest on AI adoption aren’t the ones cutting corners on responsibility — they’re the ones with the highest ethical standards, because trust eliminates friction. When your team knows where the guardrails are, when your customers trust your intentions, when your board has confidence in your approach, you can move at the speed AI demands.

The 2024 Edelman Trust Barometer found that 43% of people would reject AI in products and services if they don’t believe the innovation has been thoroughly scrutinized. That’s not a PR problem — it’s a revenue and competitive position problem.

On the AGI race specifically, the geopolitical framing oversimplifies what’s actually a much more textured conversation about how AI is deployed within companies, governments, and communities. Most leaders I work with aren’t worrying about AGI — they’re worrying about whether their AI customer service tool is treating customers fairly, whether their AI-driven hiring screen is introducing bias, and whether their data is being used in ways customers didn’t consent to. Those are the safety questions that matter for the next five years, regardless of what the frontier players are doing.

5. Where is the government being too hands off with AI and its impacts, and what conversations should governments and societies be having about AI and its impacts that they’re not?

I’ll be careful here because I’m not a policy person — I work with the leaders implementing AI inside organizations. But from that vantage point, a few things stand out.

The conversation we aren’t having enough is about workforce transition. Not “will AI take jobs” — we’ve been arguing about that abstractly for three years. The real question is what happens to the millions of people whose roles will substantially change in the next five years, and who’s responsible for helping them adapt. Right now, that’s mostly being left to individual employers, and the gap between what enlightened employers are doing and what the median employer is doing is enormous. That gap will become a societal problem long before regulators catch up.

The second underdiscussed conversation is about education. We’re training a generation of students with curricula designed for a pre-AI world. By the time we figure out what AI fluency looks like in K–12, the kids who needed it most will be in the workforce.

Third — and this is where I’d actually like to see governments lean in more — is data. Most AI regulation focuses on the models. The leverage is in the data: who owns it, how it can be used, what consent looks like in a world where data collected for one purpose can be repurposed for AI training that wasn’t imagined when it was collected.

That said, regulations always lag technology. Anchoring your responsible and ethical AI policy in your organization’s values rather than waiting for rules is the right move, regardless of what governments do.

6. What are the key pillars that form the basis of a strong AI foundation for those who seek to take full advantage of AI in their organization?

In Winning with AI, Katia and I lay out four building blocks. They develop together, not sequentially.

Mindset — the cultural ability to move at AI’s speed. Speed, focus, customer-centricity, experimentation, and learning from setbacks rather than treating them as evidence that the technology doesn’t work. Without the right mindset, you can have the best tools in the world, and they’ll sit unused.

Skillset — AI fluency across the workforce, not just in IT. Everyone needs to understand what AI can and can’t do, how to use it responsibly, and how to apply it to their actual work.

Toolset — the technical foundation. We tell leaders to build with LEGO, not cathedrals. Modular, interchangeable components you can swap as the technology evolves, sitting on top of data that’s good enough to start with.

Decision-set — the governance and decision-making structures that let you move fast without breaking things. Who decides what, how quickly, with what oversight.

The mistake organizations make is treating these as a sequence — first we’ll fix the data, then we’ll train people, then we’ll deploy. That sequence will take you a decade. The right approach is to build the blocks while delivering value, using each AI application to strengthen multiple blocks at once.

And one piece that wraps all four: leadership. Without active, visible commitment from the top, the four building blocks don’t compound. With it, they accelerate.

7. Of all the outcomes that the different types of AI can achieve, which activities create the most value for organizations?

Winning with AIWe frame the value AI creates in three areas: engagement, efficiencies, and reinvention.

Engagement is about deepening relationships with customers and employees through personalization, prediction, and proactive service. Anticipating what someone needs before they articulate it.

Efficiencies are about doing what you already do, faster and cheaper. This is where most organizations start — and where most get stuck. Efficiency gains are real, but they’re easy for competitors to replicate, which means they don’t create lasting advantage.

Reinvention is the most transformational and the most uncomfortable. It’s not asking “how can we do what we do faster?” — it’s asking “what becomes possible now that the old constraints are gone?” New business models. New revenue streams. New markets that were never economical before.

The trap is thinking efficiency is AI’s value. We call it the efficiency trap. Companies that limit themselves to efficiency are using a strategic weapon as a cost-cutting tool. The real competitive advantage comes from engagement and reinvention.

A great example: Coursera. Translation used to cost about $10,000 per course, which made global expansion economically impossible at the scale of their 5,000+ course catalog. Generative AI eliminated that constraint overnight. CEO Jeff Maggioncalda saw it immediately and launched Project Genesis by the end of 2022. That’s reinvention — AI removing a constraint that defined the business model.

If I had to pick one activity that creates the most value, it would be: using AI to remove a constraint that has shaped your industry’s economics for so long that nobody questions it anymore.

8. There was a lot of talk for a while about becoming an AI-first organization. Is this something that companies should be trying to do?

No. Be AI-ready instead.

“AI-first” is a technology company’s framing. It puts the technology in the driver’s seat, which sounds visionary but in practice produces dozens of disconnected pilots with no strategic impact. You end up chasing AI because it’s shiny rather than because it solves a real problem.

“AI-ready” is a business leader’s framing. It puts strategy in the driver’s seat. You’re building the culture, the skills, the decision systems, and the technical foundation that let AI create real value against the strategic priorities you already have.

Said simply: AI-first is a technology mindset. AI-ready is a business mindset.

You don’t actually need an AI strategy. You need a business strategy that uses AI. Anyone selling you on an AI strategy is selling you the wrong thing.

9. What should people be doing as individuals to maintain their value to their organizations and to grow their careers?

Three things, in order.

One: develop genuine AI fluency. Not “I’ve used ChatGPT a few times” fluency. Real fluency — the kind where AI is woven into how you think, prepare, decide, and communicate. The people and organizations who get to AI fluence in 2026 will pull dramatically ahead of those who don’t, and the gap will be very hard to close once it opens.

Two: deepen what’s uniquely human. AI can amplify cognition at speeds and scales no individual can match. What it can’t do is exercise empathy, self-reflection, intuition, judgment, and wisdom. These five traits — the foundation of what Katia and I call “superhumans” in the book — become more valuable, not less, as AI handles more of the cognitive work. The leaders who pair AI’s reach with these distinctly human capacities are the ones creating the most value.

Three: build a lifelong learning practice. The shelf life of any specific skill is shrinking. The skill that doesn’t depreciate is the ability to learn — quickly, repeatedly, with intellectual humility. Normalize not knowing. Embed reflection into how you work. Treat curiosity as a professional asset, not a side hobby.

If you do those three things, you’ll be more valuable in the future than you are today, regardless of what happens to your specific role.

10. What have organizations gotten wrong about rolling out AI and what can the early adopters do to recover from botched initial rollouts?

The biggest things organizations get wrong:

  • Treating AI as a technology project. It’s a business initiative for value creation that happens to use technology. When IT owns it, it stays small.
  • Use cases instead of strategy. A laundry list of pilots is procrastination dressed up as progress.
  • Diffused accountability. Without a single AI value owner, the work fragments.
  • Skipping the people work. Throwing tools at employees without addressing the fear underneath. Until fear is replaced by trust, no amount of training will change behavior.

If you’ve already botched the rollout, here’s the recovery path:

Stop and audit. What’s actually scaling, what’s not, what’s draining resources without producing value? Be honest. Sunset the dead ends.

Appoint one accountable AI leader. If no single person is accountable for AI value creation across the enterprise, fix that this quarter. Not part-time, not committee-led — one person whose performance is measured on the value that AI creates.

Pick one strategically meaningful problem and go after it. Not the easiest problem. The one whose solution would matter most to the business.

Learn from Ally Bank. When generative AI emerged, Ally’s CIO Sathish Muthukrishnan deliberately chose the most resistant audience — customer service agents — and a low-stakes problem: summarizing customer calls. The result was so valuable that the agents who’d been most skeptical became the loudest advocates: “Don’t take this away from me.” Targeting the skeptics with a real win is one of the most powerful change strategies we’ve seen.

A botched rollout isn’t a death sentence. It’s actually a useful clearing of the underbrush — assuming you learn from it.

11. Several studies have come out recently about the negative effects of AI on human cognition. Any tips for how to best use AI without degrading your brain?

This is a real concern and worth taking seriously. The risk isn’t AI itself — it’s lazy AI use. Using AI to skip thinking rather than to enhance it.

A few habits I’ve found useful:

Think first, then prompt. Before going to AI for an answer, write down what you think. Coursera’s Jeff Maggioncalda calls this cognitive bootstrapping — write your perspective on a decision, then ask AI to challenge it: “What are the strengths and weaknesses of this view? What are my blind spots? What would you recommend I improve?” AI sharpens your thinking instead of replacing it.

Treat AI outputs as drafts, not deliverables. Read critically. Push back. Ask why. Verify facts. The moment you stop questioning AI’s outputs is the moment your thinking starts to atrophy.

Protect deep work. Schedule time for thinking that doesn’t involve AI at all. Reading, writing, reflecting, walking — the unstructured time where your brain consolidates what it knows. AI can compress research, but it can’t compress wisdom. That still has to come from lived experience, integrated over time.

Notice the difference between using AI to accelerate something you understand and using AI to substitute for understanding. Acceleration is healthy. Substitution erodes you.

The promise of AI isn’t to do our thinking for us. It’s to help us think better. The discipline is staying on the right side of that line.

12. Any question you wish I had asked but didn’t?

Yes — I’d love a question about the human possibility on the other side of this.

Most AI conversation is about risk, displacement, and disruption. Those are real. But the conversation Katia and I get most excited about is what becomes possible when AI handles the cognitive work that has been depleting people for decades — the synthesis, the routing, the routine analysis — and frees up human capacity for what only humans can do.

We call those people “superhumans” — not because they’re enhanced by technology in some sci-fi sense, but because they finally have the room to be more deeply human. To exercise empathy, self-reflection, intuition, judgment, and wisdom at a level that’s been crowded out by cognitive overload.

The first companies to deliberately develop and organization filled with superhumans won’t just have a competitive advantage. They’ll be creating an entirely new form of value — one we haven’t fully named yet. That’s the future I want leaders thinking about. Not “how do I survive AI?” but “what becomes possible for my people on the other side of this?”

Dream it. Then build it.

Conclusion

Thank you for the great conversation Charlene!

I hope everyone has enjoyed this peek into the mind of one of the women behind the insightful new title Winning with AI: The 90-Day Blueprint for Success!

Image credits: Charlene Li, Pexels

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Go Beyond SLAs and Measure Human Success with the New XLM Matrix (free download)

LAST UPDATED: April 29, 2026 at 12:03 PM

Go Beyond SLAs and Measure Human Success with the XLM Matrix

by Braden Kelley


The Crisis of the “Efficient but Empty” Experience

In our current landscape of rapid digital transformation, we have achieved unprecedented levels of speed and automation. Organizations have mastered the “how” of delivery, yet many find themselves facing a growing paradox: processes are becoming more efficient while human satisfaction is simultaneously declining. We are successfully building faster systems that often leave the user feeling more like a cog in a machine than a valued participant.

The root of this issue lies in our reliance on traditional Service Level Agreements (SLAs). For decades, SLAs have served as the gold standard for operational success, measuring technical markers like system uptime, response times, and throughput. While these metrics are essential for maintaining infrastructure, they are fundamentally “cold” metrics. They can tell you that a system is functioning, but they cannot tell you if the person using that system is thriving, frustrated, or merely exhausted by the interaction.

To innovate effectively in a human-centered future, we must look beyond technical availability and begin measuring the actual quality of the human encounter. We need a shift in perspective—moving from monitoring system performance to measuring human success. This evolution requires a new framework: Experience Level Measures (XLMs). By focusing on how an innovation impacts the user’s cognitive load, sense of agency, and emotional resonance, we can move past “efficient but empty” outputs and toward solutions that deliver genuine value.

Introducing the XLM Matrix

To bridge the gap between technical output and human success, we developed the XLM (Experience Level Measure) Matrix. This visual framework is designed to help innovation teams move beyond abstract empathy and toward concrete, measurable experience improvements. By visualizing the relationship between friction, measurement, and action, teams can align their efforts with the outcomes that actually move the needle for their users.

The matrix is structured as a series of concentric rings, requiring teams to work from the “inside out” to ensure every innovation is rooted in a real-world human need:

  • The Inner Circle (The Friction Point): This is the starting line. Here, teams identify the specific “ugh” moment—the point in the journey where the user currently feels confused, slowed down, or disempowered.
  • The Middle Ring (The XLM): This layer transforms qualitative frustration into a quantitative metric. It asks: “How do we measure the absence of that friction?” An XLM isn’t about system uptime; it’s about the user’s success rate in reaching their goal without cognitive fatigue.
  • The Outer Ring (The Innovation Lever): Once the friction is identified and the metric is set, the outer ring focuses on the solution. It identifies the specific change in the product, service, or workflow that will directly influence the XLM and eliminate the friction point.

By using this “Target Logic,” teams ensure that they aren’t just innovating for the sake of novelty, but are strategically pulling levers that have a measurable impact on the human experience.

The XLM (Experience Level Measure) Matrix

The Four Pillars of Human-Centered Innovation

To provide a comprehensive view of the user experience, the XLM Matrix is divided into four critical quadrants. Each quadrant represents a fundamental pillar of how humans interact with technology and services. By examining an innovation through these four lenses, teams can uncover hidden friction points and prioritize improvements that resonate most deeply with their audience.

1. Cognitive Load

“Does this make the user’s life simpler or more complex?”

In an age of information abundance, mental energy is a finite resource. This pillar focuses on the mental effort required to complete a task. Innovation here is about reducing noise, simplifying navigation, and ensuring that the “cost of thinking” is kept to an absolute minimum.

2. Time-to-Value

“How quickly does the user reach their ‘Aha!’ moment?”

Success is often determined by the distance between a user’s first interaction and their first realization of value. This quadrant measures the speed of relevance. Effective innovation in this space removes barriers to entry and streamlines the path to a meaningful outcome.

3. Agency

“Does the user feel in control, or like a cog in the process?”

As systems become more autonomous, maintaining human agency is vital. This pillar explores whether a tool empowers the user or forces them into a rigid, predetermined path. High-agency innovations provide the user with the autonomy to make meaningful choices and direct the outcome.

4. Emotional Resonance

“Does the interaction build trust or cause frustration?”

Every interaction leaves an emotional footprint. This quadrant assesses the “vibe” of the experience. It looks beyond function to ask if the solution feels reliable, empathetic, and aligned with the user’s values, transforming a transactional moment into a relational one.

How to Use the Matrix with Your Team

The XLM Matrix is most effective when used as a collaborative workshop tool. By gathering cross-functional perspectives—from product and design to engineering and customer success—you can ensure a 360-degree view of the human experience. Follow these three steps to run your first experience audit:

Step 1: The Empathy Audit

Focus on the Inner Circle. Select one of the four quadrants and ask the team to identify the most persistent “ugh” moment currently facing the user. Be specific. Instead of saying “the checkout process is slow,” identify the exact friction point, such as “the user feels overwhelmed by the number of form fields.”

Step 2: Defining the Metric

Move to the Middle Ring. Once the friction point is clear, brainstorm how you would measure its absence. This is your Experience Level Measure (XLM). If the friction is cognitive overload from form fields, your XLM might be “reduction in time spent on the checkout page” or “a 20% increase in completion rate without support intervention.”

Step 3: Pulling the Innovation Lever

Reach the Outer Ring. Now, identify the specific technical or design change that will move that metric. This is your “Innovation Lever.” It could be an AI-driven auto-fill feature, a progress bar to improve the sense of agency, or a “save for later” option to reduce immediate emotional pressure.

Repeat this process for each quadrant to build a robust, human-centered innovation roadmap that prioritizes meaningful outcomes over simple feature checklists.

Conclusion: Creating a Human-Centered Future

The transition from measuring system performance to measuring human success is not just a technical shift; it is a cultural one. As we move deeper into an era of agentic AI and rapid digital acceleration, the organizations that thrive will be those that prioritize the human experience as their primary north star. Innovation is no longer defined solely by what we can build, but by how effectively we enable people to feel, act, and succeed.

The XLM Matrix provides a structured, repeatable path to this future. By moving from the friction of the “ugh” moment to the strategic clarity of the innovation lever, your team can ensure that every project delivers meaningful, human-centered value. It is time to stop guessing how our users feel and start building for their success.

Start Your Experience Transformation Today

Ready to move beyond SLAs? Download the high-resolution, 11″x17″ (works as A3 too) printable version of The XLM Matrix and begin identifying the measures that truly matter for your innovation team. You can also use it virtually by uploading it and locking it down as a background in Miro, Mural, LucidSpark, Figjam or the FREE Microsoft Whiteboard or Google Jamboard.


Download the Free XLM Matrix Canvas

Frequently Asked Questions

What is the difference between an SLA and an XLM?

A Service Level Agreement (SLA) measures technical system performance, such as uptime or response speed. An Experience Level Measure (XLM) focuses on human outcomes, measuring how effectively an innovation reduces cognitive load, increases user agency, or builds emotional resonance.

How does the XLM Matrix help innovation teams?

The XLM Matrix provides a visual framework to move from identifying user friction (“ugh” moments) to defining specific metrics and identifying the technical or design “levers” required to improve the human experience.

Can the XLM Matrix be used for internal digital transformation?

Yes. The matrix is highly effective for internal projects. By measuring the cognitive load and time-to-value for employees using new internal tools, organizations can ensure their digital transformation efforts actually increase productivity rather than just adding complexity.

Image credits: Braden Kelley, Google Gemini

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