How to Calculate the ROI of Customer Experience

Announcing the Launch of a Free CX ROI Calculator

by Braden Kelley

Most executive teams already believe that customer experience (CX) matters. Almost none of them can say, in dollars, what a specific improvement found in a Customer Experience Audit is worth — and that gap is usually the real reason a CX investment stalls before it reaches a budget conversation. Here’s a framework for closing it, backed by the research, plus a free calculator to run the numbers on your own business.

Why “CX matters” isn’t a business case

“Customer experience drives loyalty” is true, and it convinces almost no one holding a budget. What moves a budget conversation is a specific number: this metric, moved by this much, produces this many retained customers, worth this much revenue. Most CX teams never make that translation, so the initiative competes for funding against proposals that speak fluent finance while CX speaks fluent satisfaction score.

The good news is that the translation isn’t guesswork. There’s two decades of published research connecting experience metrics to financial outcomes — the trick is applying it to your own numbers instead of citing it as an abstract principle.

The research behind the number

Bain & Company, the originator of the Net Promoter Score, has found that NPS explains roughly 20% to 60% of the variation in organic growth rates between competitors in the same market, and that the NPS leader in a given industry typically outgrows competitors by more than double. In an early, widely cited analysis, Bain found that Dell’s detractors made up about 15% of its customer base and represented roughly $68 million in lost revenue — and estimated that converting just 2% to 8% of those detractors into promoters could add approximately $167 million in annual revenue.

7 pts → ~1%NPS increase → revenue growth (London School of Economics)
10 pts → 3.2%NPS increase → B2B upsell revenue (CustomerGauge)
5 pts → 25–95%Retention increase → profit increase (Reichheld / Bain)

A separate study from the London School of Economics found that a 7-point increase in NPS corresponds to roughly 1% revenue growth, while CustomerGauge’s research in B2B contexts found a tighter, more immediate link: a 10-point NPS increase correlating with a 3.2% increase in upsell revenue among existing accounts. Underneath all of it sits Fred Reichheld’s original retention research at Bain, which found that a 5-point improvement in customer retention increases profits by 25% to 95%, depending on the industry and business model.

The wide range in that last figure isn’t a weakness in the research — it’s the whole point. The financial return on a CX improvement depends on your margin structure, your customer lifetime value, and how much of the retained revenue is truly incremental. A generic industry number can’t answer that. Your own numbers can.

The four-step value chain

Here’s the framework that turns the research above into a number specific to your business:

  1. Experience Metric — the number you already track: NPS, CES, or CSAT.
  2. Behavioral Outcome — the specific customer behavior that metric predicts: renewing, referring, buying again, or churning.
  3. Financial Outcome — that behavior’s dollar value: retained revenue, reduced cost-to-serve, lower acquisition cost.
  4. The Intervention — what actually has to change to move Box 1 in the first place: a redesigned onboarding flow, a fixed billing process, a retrained support tier.

CX ROI 4 Box Framework

This is also the fastest way to diagnose why a past CX initiative didn’t show up in revenue: almost always, it moved Box 1 (the score) without a demonstrated effect on Box 2 (a specific behavior), so it was never going to reach Box 3. The fix isn’t more CX effort in general — it’s picking an intervention with a direct, traceable line to a named behavior, and measuring that behavior directly.

What “typical” looks like, by industry

Churn rates and cost-to-serve vary meaningfully by industry — and by methodology, which is worth naming honestly rather than smoothing over. Here are representative midpoints reconciled across several published benchmark studies:

Industry Typical annual churn Cost per service contact
SaaS / Software 5–14% $18–$35
Retail / eCommerce 20–37% $2.70–$12
Financial Services / Insurance 15–20% $15–$25
Healthcare 7–9% $50–$60
Telecom / Utilities 15–25% $20–$30
B2B Professional Services ~10–13% $30–$60

These are starting points for a company with no internal baseline yet — not universal constants. The strongest version of any business case replaces these with your own churn rate, revenue per customer, and service cost the moment that data exists.

CX ROI Calculator
Want to skip straight to your own number? Use the free CX ROI Calculator →
It runs this exact framework against your own customer count, revenue per customer, and churn rate, and gives you a business-case-ready total in under two minutes.

How to build the business case, step by step

1. Start with your own numbers

Current churn rate, average revenue per customer, and cost per service contact — pulled from finance or CS systems — will always be more persuasive than an industry benchmark. Use published figures only where internal data doesn’t exist yet.

2. Separate the well-established link from your company-specific estimate

The macro relationship between experience and growth (Bain, LSE, CustomerGauge) is well documented and easy to defend by name. The precise dollar impact for your company is always a modeled estimate — say so explicitly, and the business case gains credibility rather than losing it.

3. Model conservatively, then show the range

A single point estimate invites a single objection. A modeled range — conservative, moderate, optimistic — tends to survive scrutiny far better, because it demonstrates the thinking rather than just the output.

4. Tie the number to a specific intervention

Executives fund actions, not scores. Pair the projected financial impact with the specific initiative expected to produce it, rather than presenting the improvement as if it happens on its own.

Try it on your own numbers

The fastest way to see this framework in action is to run it against your own business. The CX ROI Calculator uses the same four-step chain described above — enter your customer count, revenue per customer, and current churn rate (or start from an industry benchmark), and it estimates the annual revenue and cost-to-serve impact of a defined experience improvement, along with a summary you can paste straight into a slide.

Get the CX ROI Benchmark Report — the full industry benchmark table with sources, the CX Value Chain framework, and answers to the five objections a CFO is most likely to raise. Enter your email and we’ll send it straight to your inbox.


If after exploring the ROI calculator you would like to explore unlocking revenue opportunities for your business with a Customer Experience Audit, contact me directly or call (206) 349-8931. I’m happy to have a no-obligation conversation about whether an audit makes sense for your current situation.

Image Credit: Gemini

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

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People Pools Provide a Different Lens for Collaboration

People Pools Provide a Different Lens for Collaboration

GUEST POST from Stefan Lindegaard

Most organizations and networks talk about stakeholder management. That usually means mapping interests, aligning priorities, and balancing influence (and even power). Useful, yes – but limited.

With People Pools, I suggest a different lens. Instead of treating stakeholders as roles or entities to manage, we see them as groups of people with strengths, constraints, and motivations. And people show up with mindsets that shape how they collaborate.

So what do People Pools look like in practice?

  • Startups and SMEs bringing agility and fresh ideas
  • Corporates contributing scale and resources
  • Investors scanning for opportunities and deal flow
  • Universities and knowledge institutions adding talent and long-term perspective
  • Municipalities and government shaping growth, jobs, and reputation
  • Advisors, mentors, and hidden contributors (like nurses, data managers, or regulators) who often decide whether ideas succeed in practice

This matters for hubs, ecosystems, and networks. Innovation doesn’t flow because interests are mapped – it flows when diverse pools are activated, connected, and bridged.

Working with People Pools means:

  • Surfacing what each pool brings (and what holds them back).
  • Balancing WIIFM (what’s in it for me) with WIIFUS (what’s in it for us).
  • Empowering connectors who move between pools.
  • Building bridges and learning loops instead of silos.

Traditional stakeholder management is about alignment and control. People Pools are about activation and mindset.

That shift creates ecosystems where people don’t just sit in the same room – they actually learn, connect, and innovate together.

Image Credit: Stefan Lindegaard

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The Edge of Intelligence

How Neuromorphic Engineering Will Humanize the Future of Innovation

The Edge of Intelligence

GUEST POST from Art Inteligencia


The Hidden Friction of Modern AI

We are living through an era of breathtaking algorithmic capability. Generative AI, large language models, and autonomous agents are reshaping organizational agility and rewriting the rules of experience design. Yet, behind every seamless digital interaction lies an unsustainable truth: our most advanced intelligence is kept on an exceptionally short leash. It remains tethered to hyperscale data centers drawing massive, environmentally costly energy footprints from the power grid.

The root of this problem isn’t the software; it is the physical architecture of the silicon itself. For decades, computing has relied on the traditional von Neumann architecture — a system where data must constantly shuttle back and forth between a separate memory unit and a processing unit. This structural bottleneck means that as AI tasks grow more complex, chips run hotter and consume more power simply moving data around, creating an architectural barrier to true scaling.

For innovation leaders and strategists, this creates a profound friction. True, human-centered innovation cannot be fully realized if every localized tool, remote system, or smart device requires a high-bandwidth, high-carbon umbilical cord tied to a centralized cloud. To design experiences that are genuinely resilient, ambient, and privacy-first, we must liberate intelligence from the server farm. Neuromorphic engineering represents the paradigm shift that will finally break this bottleneck, moving us away from centralized computing and toward a decentralized future where intelligence lives efficiently at the edge.

What is Neuromorphic Architecture? (The Organic Paradigm Shift)

To break the computational logjam, engineering is turning to the most efficient computer ever created: the human brain. The brain operates on roughly 20 watts of power — barely enough to illuminate a dim closet bulb — yet it manages complex cognitive tasks, sensory processing, and real-time learning simultaneously. Neuromorphic engineering sheds the rigid rules of traditional computing to mirror this organic brilliance, designing physical silicon structures that mimic the nervous system’s architecture.

Synapses in Silicon: The Structural Shift

Standard processors think in binary code, executing instructions sequentially according to a strict, rhythmic system clock. This means every component is continuously running and drawing power, whether it is actively processing fresh data or not. Neuromorphic computing fundamentally upends this design through two key innovations:

  • Asynchronous Processing: Instead of relying on a system clock, neuromorphic chips operate based on event-driven spikes. Individual artificial neurons remain quiet and consume virtually zero energy until a change in data triggers them to “fire” and transmit information. If nothing is happening, no energy is wasted.
  • Co-located Memory and Processing: By embedding hardware-based artificial synapses directly next to artificial neurons, these chips eliminate the structural divide that defines traditional processing. The hardware does the thinking and the remembering in the exact same physical space, completely bypassing the data-shuttling bottleneck.

By shifting from sequential logic to a highly parallel, brain-inspired network, we achieve a massive leap in energy efficiency. This architecture transitions our systems from energy-hungry server clusters down to self-contained edge components operating on mere milliwatts of power.

The Futurology & Scaling Angle: Unlocking Edge Intelligence

The true value of neuromorphic engineering lies far beyond a simple reduction in corporate utility bills. For innovation strategists and futurologists, this hardware pivot represents a massive scaling catalyst: the liberation of artificial intelligence from centralized cloud architectures. When advanced cognitive processing requires milliwatts instead of megawatts, the mathematical parameters governing system design, operational reach, and deployment speed change overnight.

Decentralizing the Innovation Landscape

For years, the industry trajectory has favored hyper-centralization. Complex models required massive, multi-billion-dollar data centers to function, creating a digital dependency that stifles agility. Neuromorphic computing completely flips this model, enabling advanced AI agents to execute local, highly sophisticated inference tasks on small, distributed devices for weeks at a time on a single milliwatt of power. Intelligence ceases to be a destination you connect to; it becomes an ambient feature embedded directly into the environment.

Breaking the Umbilical Cord: The Strategic Advantages

By migrating intelligence directly to the physical edge, organizations can unlock three critical operational advantages that traditional cloud setups simply cannot match:

  • Zero Latency for Real-Time Action: Eliminating the round-trip journey to a distant cloud server means local systems can analyze, decide, and act in milliseconds. In time-sensitive scenarios, this immediate local processing makes the difference between predictive success and operational failure.
  • Absolute, Privacy-First Security: Because data is processed entirely on the local neuromorphic chip without ever being transmitted over a network, the attack surface shrinks dramatically. This architecture allows organizations to build deep, trust-based customer experiences that naturally safeguard sensitive personal and enterprise data.
  • Carbon Dematerialization: Moving computing workloads away from massive, grid-dependent server farms and onto ultra-low-power edge silicon provides a tangible, measurable path to reducing an organization’s digital carbon footprint, aligning aggressive technology scaling with vital sustainability goals.

Human-Centered Design Scenarios: Neuromorphic in Action

To fully appreciate the impact of brain-inspired computing, we must look past the spec sheets and examine how it alters human experiences. Moving advanced intelligence to the edge allows us to design environments and tools that are profoundly responsive, resilient, and unobtrusive. By removing the constraints of power consumption and cloud connectivity, we can bring three critical, human-centered design frontiers to life:

1. Extreme Field Resilience

Traditional digital tools fail the moment they lose their connection to the network. Neuromorphic engineering allows us to design industrial and humanitarian tools that maintain full cognitive capabilities in the most isolated environments on Earth. Imagine search-and-rescue drones or deep-sea research equipment operating autonomously for weeks on a tiny battery, analyzing complex visual data and making critical safety decisions entirely offline. By placing autonomous decision-making directly into the hands of field teams, we build operational resilience where it matters most.

2. Privacy-First Smart Cities

Current smart city frameworks frequently rely on a centralized “surveillance state” model, shuttling massive streams of public data back to central servers for analysis. Neuromorphic chips allow us to redesign urban infrastructure from the street corner up. Traffic signals, public utility grids, and safety systems can process visual and environmental changes locally and instantly. A street corner camera can identify a traffic hazard or an emergency situation and adjust local systems immediately — all while completely discarding the raw footage locally to preserve citizen anonymity.

3. Ambient and Intimate Experience Design

The next generation of wearables, medical tech, and smart home systems must integrate naturally into the background of daily life without demanding constant attention, frequent charging, or invasive data sharing. Neuromorphic architecture enables sub-milliwatt, continuous contextual awareness. Medical implants can monitor heart or neurological patterns and predict adverse events locally in real time. Consumer devices can subtly adapt to user habits, preferences, and physiological states over time, delivering deeply customized experiences without draining battery life or transmitting personal routines to a corporate mother ship.

The Commercial Landscape: Market Leaders and Startups to Watch

Neuromorphic engineering has officially breached the perimeter of academic research and entered the commercial fast lane. As traditional silicon hits the physical and economic boundaries of Moore’s Law, a vibrant ecosystem of semiconductor giants and venture-backed startups has emerged to productize brain-inspired architecture. For strategists building long-term roadmaps, these are the key players shaping the hardware landscape:

The Semiconductor Giants (Research & Infrastructure Scale)

  • Intel (Loihi Platform): Intel remains a primary institutional driver of neuromorphic development. Their massive Hala Point system uses Loihi 2 processors to pack over one billion artificial neurons into a single research chassis, demonstrating up to 100× the energy efficiency of conventional hardware for complex optimization workloads.
  • IBM (TrueNorth & NorthPole): A true pioneer in the space, IBM’s foundational neurosynaptic research continues to push the boundaries of ultra-low-power digital image and sensory recognition, aiming squarely at defense, aerospace, and high-performance computing (HPC) environments.
  • Samsung & Qualcomm: Both tech giants are aggressively integrating neuromorphic mixed-signal IP and co-processors into their commercial system-on-chip (SoC) portfolios, targeting the next generation of smartphones, advanced driver assistance systems (ADAS), and consumer electronics.

The Pure-Play Pioneers & Edge Disruptors

  • BrainChip (Akida): As one of the few publicly traded pure-play neuromorphic companies, BrainChip has achieved widespread commercial traction. Their Akida event-based neural processor brings ultra-low-power, on-device machine learning to millions of IoT systems, smart sensors, and autonomous vehicles via recent integrations with LiDAR and edge perception platforms.
  • Innatera: This European innovator is making major waves at the micro-scale. Having commercialized their Pulsar neuromorphic microcontroller, Innatera has partnered with original design manufacturers (ODMs) to drive mass production of always-on intelligent consumer wearables, health tech, and smart home sensors that operate entirely within sub-milliwatt power budgets.
  • SynSense: Specializing in the fusion of sensing and computing, SynSense designs mixed-signal processors that process sparse, real-time data streams instantly. Their chips are purpose-built for ultra-low-latency processing in smart cameras, bio-signal analysis tools, and auditory devices.
  • Rain AI & Unconventional AI: Representing the heavy-hitting venture tier, these companies are building analog-in-memory neuromorphic architectures leveraging memristors to simulate biological synapses. Backed by massive financing rounds from top-tier technology visionaries, they are actively aiming to scale these brain-inspired chips into the billions of units required for the future edge economy.

The Change Management & Strategic Roadmap for Leaders

The transition to neuromorphic architecture will not be a passive hardware upgrade handed down by IT; it requires a fundamental recalibration of enterprise strategy. Innovation leaders who fail to adapt their roadmaps now risk locking their organizations into rigid, energy-intensive architectures just as the rest of the market decentralizes. Embracing edge intelligence demands proactive change management across infrastructure, design philosophy, and team capabilities.

Rethinking the Ecosystem and Auditing Roadmaps

The immediate task for strategists is to audit current digital transformation initiatives for cloud dependency. Are you over-indexing on centralized architectures that will soon become costly technical debt? Leaders must begin identifying where ultra-low-power, offline inference can replace grid-dependent processing, actively pivoting investment toward distributed systems that scale without exponential carbon costs.

Designing for the Peripheral

For decades, experience design has been built around active engagement: prompting the user to look at a screen, click a button, or initiate a connection. Edge intelligence requires a shift in design mindset from “active center-stage interaction” to “ambient, peripheral support.” When a device can process context constantly on less power than a digital watch, the goal is to build systems that anticipate and resolve friction quietly in the background, minimizing the cognitive load on the human.

Bridging the Skills Gap

The move to asynchronous, spike-based processing breaks many of the traditional rules of software engineering. To fully leverage this technology, organizations must rebuild their cross-functional teams, bridging the historically siloed worlds of hardware design, software engineering, and customer experience. Preparing teams for this shift requires cultivating new competencies in event-driven programming and decentralized systems, ensuring your workforce is ready to build the next generation of human-centered tools.

Conclusion: Designing a Smarter, Sustainable Tomorrow

Neuromorphic engineering is far more than a technical upgrade for hardware developers or an efficiency metric for infrastructure teams. It represents the foundational unlocking mechanism for the next grand era of human-centered innovation. By fundamentally breaking the decades-old von Neumann bottleneck, brain-inspired processors allow us to shift from a world where intelligence is an expensive, centralized luxury to one where it is a cheap, ubiquitous, and sustainable utility.

The ultimate goal of any transformative technology must be to blend seamlessly into the fabric of daily life, amplifying human potential while strictly respecting the ecological boundaries of our planet. Centralized cloud reliance has carried us far, but its massive power grids and high latency are reaching their natural scaling limits. Embracing an asynchronous, edge-first architecture is our path forward to resolving that tension.

For innovation leaders, futurologists, and experience strategists, the challenge ahead is clear: stop designing exclusively for the cloud and start preparing for the perimeter. By liberating artificial intelligence from the data center and bringing it directly to the edge, we finally gain the architectural freedom to build an enterprise landscape — and a society—that is as profoundly resilient as it is impactful.

FutureHacking™ Is Coming

FutureHacking™ is Braden Kelley’s strategic foresight methodology — and a paid download and training program is launching soon. Register your interest now to be the first to know when it’s available, and get early access pricing.

Frequently Asked Questions

To help both human readers and search indexing engines quickly parse the foundational concepts of brain-inspired computing, here is a clear breakdown of the most common questions regarding neuromorphic engineering.

What is the difference between traditional von Neumann architecture and neuromorphic engineering?

Traditional von Neumann architecture separates memory and processing, requiring data to constantly shuttle back and forth, which creates an energy-intensive bottleneck. Neuromorphic engineering co-locates memory and processing on physical artificial synapses and neurons, allowing the chip to process information asynchronously only when data spikes or changes occur, drastically cutting energy consumption.

Why is neuromorphic computing critical for the future of decentralized AI and edge devices?

Modern AI requires massive, centralized data centers to handle complex processing workloads due to high power demands. Neuromorphic chips require orders of magnitude less power — often operating on just a single milliwatt — allowing advanced AI agents to run locally and independently on small edge devices for weeks at a time without cloud connectivity or high carbon footprints.

What are the primary real-world use cases for neuromorphic engineering in experience design?

Key use cases include extreme field tools that operate entirely offline in remote areas, privacy-first smart city infrastructure that processes data locally to protect citizen anonymity, and ambient consumer wearables or medical implants that seamlessly adapt to human behavior in real time without draining battery life.


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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Thoughts on Selling

Thoughts on Selling

GUEST POST from Mike Shipulski

Like most things, selling is about people.

The hard sell has nothing to do with selling.

Just when you think you’re having the least influence, you’re having the most.

When – ready, sell, listen – has run its course, try – ready, listen, sell.

Regardless of how politely it’s asked, “How many do you want?” isn’t selling.

If sales people are compensated by sales dollars, why do you think they’ll sell strategically?

The time horizon for selling defines the selling.

When people think you’re selling, they’re not thinking about buying.

Selling is more about ears than mouths.

Selling on price is a race to the bottom.

Wanting sales people to develop relationships is a great idea; why not make it worth their while?

Solving customer problems is selling.

Making it easy to buy makes it easy to sell.

You can’t sell much without trust.

Sell like you expect your first sale will happen a year from now.

Selling is a result.

I’m not sure the best way to sell; but listening can’t hurt.

Over-promising isn’t selling, unless you only want to sell once.

Helping customers grow is selling.

Delaying gratification is exceptionally difficult, but it’s wonderful way to sell.

Ground yourself in the customers’ work and the selling will take care of itself.

People buy from people and people sell to people.

Image credits: Pixabay

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Showing Respect for Your Customer’s Time

Showing Respect for Your Customer's Time

GUEST POST from Shep Hyken

This article answers the question: How do you prove to customers that you value and respect their time throughout the customer experience?

A customer takes the time to buy a product, which could include research, visits to a store, calls to a salesperson, and many other tasks that go into a pre-purchase routine. So once they buy it, the work should be over. But sometimes it’s not. Something goes wrong, or the customer may have a question. Regardless of what it is, they are about to spend more time related to their purchase that isn’t just the actual use of the product.

My point is that when the customer has to spend more time than they should, make it so easy and reasonable that they have confidence that if there is ever another problem, you’re a company that is easy to do business with and respects the customer’s time.

Superhero Customer Service Shep Hyken

When you show respect for a customer’s time, it pays dividends in the form of repeat business, customer loyalty, and word-of-mouth referrals. So, how can you prove this to the customer? As I was preparing for an upcoming customer experience keynote speech, I created an acronym for the word TIME.

  • T is for Timely: Respect the clock and quickly respond. Return calls, emails, and messages when promised – or sooner. Fast response shows respect, and the longer the wait, the less customers trust you. Every unnecessary minute equals disrespect.
  • I is for Individualized: Make efficiency personal. Know and remember your customer. Use information and data on the customer to anticipate needs and create a more time-efficient experience.
  • M is for Minimal Effort: This is about being easy. Reduce transfers, logins, and redundant questions, and streamline processes. Two words sum this one up: eliminate friction.
  • E is for Efficiency: Efficiency is the combination of the T, I, and M. Solve issues in one interaction. Use technology to accelerate an experience, not complicate it. More efficient also means “more easier.” (I know, that’s poor English, but it makes the point.) Efficient means easy, and easy creates confidence.

Time is the one resource your customers can never get back. Every minute they spend navigating your phone system, repeating information, or chasing down answers is a minute stolen from their day. When you make things easy and fast, you’re not just solving a problem, you’re proving that your customers’ time matters to you.

So, here’s a homework assignment. Look at your customer touchpoints. Find if there is friction that’s wasting their time. What process can be streamlined? What one step can be eliminated? Create the experience that’s easy and saves your customer’s time. In a world where everyone is stretched thin, being the company that values time is more than just good service. It’s a competitive advantage.

Image Credit: Shep Hyken, Pexels

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Is Your Customer Experience Costing You Customers?

A Free 12-Point Diagnostic

by Braden Kelley and Art Inteligencia

Most organizations don’t know they have a customer experience problem until it shows up as churn they can’t explain, growth that’s stalled despite strong acquisition investment, or a competitor quietly pulling ahead in a market they thought they owned.

By the time those signals are visible in the numbers, the experience failures causing them have usually been accumulating for months — sometimes years. The customers who left didn’t file complaints. They just left. The friction that drove them away wasn’t measured because nobody thought to measure it. The competitive gaps weren’t visible because nobody had walked the competitor’s journey recently enough to know they existed.

This is the fundamental challenge of customer experience management: the experiences that cost organizations the most are almost never the ones they’re already measuring.

How Do You Know If You Need an Experience Audit?

That’s the question I hear most often from leaders who are considering an Experience Audit — and it’s exactly the right question to ask before committing to any significant diagnostic investment.

The honest answer is that many organizations don’t need a full Experience Audit right now. Some have genuinely strong experience fundamentals, solid visibility into their journey gaps, and active improvement programs already addressing the right things. For those organizations, an audit would confirm what they already know — valuable, but not urgent.

Other organizations are flying blind — relying on satisfaction scores that measure the wrong touchpoints, competitive assumptions that haven’t been tested in years, and internal perspectives that have long since lost the ability to see what new customers and employees actually experience. For those organizations, an audit isn’t a nice-to-have. It’s the prerequisite for every other improvement investment they’re considering.

The challenge is that it’s genuinely difficult to know which situation you’re in — from the inside.

Introducing the Free Experience Audit Readiness Checklist

I’ve developed a simple 12-point diagnostic — the Experience Audit Readiness Checklist — that helps leaders answer the “do we need an audit?” question honestly, in about five minutes, without any outside perspective required.

The checklist covers four areas:

  • Visibility & Awareness — Do you actually know what customers or employees experience, or are you relying on internal assumptions? When did anyone on your leadership team last go through your own journey end-to-end?
  • Performance Signals — Are churn, attrition, or satisfaction scores moving in the wrong direction despite investments meant to improve them?
  • Organizational Readiness — Do different departments have conflicting views of what the experience looks like? Have improvement initiatives failed to move the numbers you expected?
  • Strategic Stakes — Is a competitor improving their experience in ways starting to affect your market position? Are you considering a major investment and want to know where it will have the most impact?

A few questions that tend to generate the most honest conversation:

“Nobody on our leadership team has personally gone through our own customer or employee journey end-to-end in the last 12 months.”

“We’ve launched improvement initiatives before that didn’t move the numbers we expected them to move.”

“We’ve never formally compared our experience, touchpoint by touchpoint, against our top competitors.”

In my experience, leadership teams that read those statements and immediately think of one or two colleagues who would answer them differently have found some of their most useful conversations.

What Your Score Means

The checklist produces a simple score based on how many of the 12 items apply to your organization:

  • 0–2 checked — Strong foundation. Keep monitoring proactively.
  • 3–5 checked — Early warning signs worth a closer look.
  • 6–8 checked — Meaningful blind spots likely costing you revenue.
  • 9–12 checked — High risk. An audit should be a near-term priority.

Download the Free Checklist

Experience Audit Readiness ChecklistThe Experience Audit Readiness Checklist is available as a free PDF download — two pages, five minutes, and a clearer picture of whether your experience gaps are a background concern or a front-burner priority.

Download the free checklist on the Experience Audit page →

If you check six or more boxes and want to talk through what an Experience Audit would look like for your specific situation,
contact me directly or call (206) 349-8931. I’m happy to have a no-obligation conversation about whether an audit makes sense for where you are right now.

Image Credit: Gemini

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

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After the Chasm – Scaling Beyond the Beachhead

After the Chasm - Scaling Beyond the Beachhead

GUEST POST from Geoffrey A. Moore

Crossing the chasm is the single most important goal for a B2B application that seeks to disrupt the status quo. The playbook has held up for more than 30 years because it continues to just work. That said, it does not say anything about what to do if you’re stuck in the mud on the other side. So, let’s suppose your enterprise has successfully crossed the chasm, achieved tens of millions of dollars in ARR, but is no longer growing at a rate to keep pace with the Rule of 40 percent (the sum of your profit and your growth rate). Your investors are getting antsy. Now what?

First of all, know your place. You are still sub-scale for a customer CFO to consider you desirable as a go-to vendor. Same goes for a CIO who is trying to consolidate rather than expand the list of vendors they are working with. So, as with crossing the chasm, your only ally will be a process owner with a problem process that is not getting the IT support they need. This time, however, you are looking for an adjacent process owner, someone for whom your chasm-crossing sponsor would make a good reference. This lowers the bar for how problematic the use case may be because there is already some proof that the solution will work.

Note that we are still at the departmental level, still a point-product app, not a platform, not a suite. Those are all worthy ambitions for the future, but if you try to activate them now, the CFO and the CIO will get involved, and you will get bogged down in proof-of-concept exercises that will take forever to scale.

That said, it is not too early to recruit ecosystem partners to help secure your beachhead and expand your reach. The key here is to engage with companies that are big enough to help but small enough to give you their full attention—not Tier 1 systems integrators, more like outsourced service providers to small and medium businesses or specific departmental functions. You don’t need a lot of these, but the ones you do recruit have to lean in, so make sure that there is enough trapped value in the target use case to pay both you and them a premium for resolving it. To accelerate this effort, ask your professional services team to package up their hard-won knowledge and make it available to the partners who can expand your beachhead market. You want your team to be plowing in the adjacent field, not harvesting in the initial one.

On the go-to-market side, you still need to be disciplined in deploying most of your resources into the target market segment and not letting them get distracted by chasing one-off opportunities elsewhere. That said, you can relax a bit from the laser focus of chasm-crossing as long as, say, two-thirds of the marketing and sales resources are directly aligned with your current goal. Remember at this point that marketing is still a territory capture game, so you want to go after targets that are big enough to matter but small enough to lead, and as always, a good fit with your crown jewels.

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

Image Credit: Geoffrey Moore

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The Personal AI Renaissance

Finding the Human Premium in an Automated World – An AI Soft Landing Scenario

LAST UPDATED: July 5, 2026 at 11:58 AM

The Personal AI Renaissance

by Braden Kelley and Art Inteligencia


The Death of the “Average” Knowledge Worker

We are living through a profound transition in the nature of work, yet we continue to measure productivity with the yardsticks of the past. The greatest inequality of the AI era may not be access to information — the internet solved that decades ago — but rather access to intelligence amplification. We are witnessing the arrival of a new, distinct class of augmented individuals, and the divide between those who embrace this evolution and those who resist it is widening by the day.

For a brief moment, we viewed AI merely as a better search engine — a way to get faster, slightly more polished answers. That was the “chatbot” phase. We have now moved into the era of the Personal AI Renaissance. This is not about a tool that generates text; it is about the integration of a persistent, personalized intelligence layer into our daily cognitive workflows. This layer knows your strategic priorities, understands your communication style, and tracks your long-term goals.

The implications for the labor market are seismic. The traditional dichotomy of “human versus AI” is a false framing that distracts from the real competitive shift. The true divide in the coming years will not be between machines and people, but between the unaugmented human and the AI-amplified human. In this new landscape, professional obsolescence is no longer a function of your education level or your years of experience, but of your capacity to effectively manage and leverage your personal intelligence layer. The era of the “average” knowledge worker has ended; the era of the amplified individual has begun.

Beyond “Better Answers”: The Shift to Personalization

To grasp the true power of this shift, we must abandon the notion of AI as a generalized utility. The primary value of the latest generation of models is not merely their ability to generate faster responses; it is their capacity for deep, persistent personalization. When AI moves from being a standalone tool to an integrated intelligence layer, it fundamentally transforms from a search engine into a multifaceted collaborator.

In this Personal AI Renaissance, the individual is supported by a dynamic system that evolves alongside them. We see this intelligence layer manifesting in several key, high-value roles:

  • The Strategist: Beyond simple task management, the AI functions as a partner that aligns your daily decisions with your long-term strategic objectives, helping you maintain focus amidst complexity.
  • The Coach: By providing personalized feedback loops and constructive friction, the AI pushes you to refine your thinking, challenge your biases, and improve your cognitive performance over time.
  • The Researcher/Assistant: This role involves offloading the heavy cognitive load of data synthesis and information retrieval, allowing the human to focus on higher-order decision-making.
  • The Teacher: The AI acts as a bespoke educator, translating complex, dense information into the specific mental models and language that make the most sense for your unique perspective.

By delegating these varied roles to a personalized AI layer, the worker gains a form of cognitive leverage previously unavailable. This isn’t about replacing human input; it is about delegating the friction of execution so that the human can devote more energy to creativity, empathy, and the nuanced judgment required for meaningful innovation.

Personal AI Renaissance Infographic

The Productivity Gap: Capability Over Credentials

We are entering a period where the traditional signals of professional worth — degrees, job titles, and years of tenure — are being rapidly decoupled from actual output. As the AI-amplified human becomes the new standard for high-performance, the competitive landscape is shifting from what you know to how you augment your intelligence.

The productivity gap is no longer dictated by education level, but by augmentation capability — your fluency in integrating AI into your specific workflow to solve problems faster and more creatively. An employee with a strong command of their personalized intelligence layer can now outperform peers who, by conventional standards, might be more “qualified” but remain unaugmented.

This represents a true “soft landing” for human potential. Rather than being replaced, the worker who learns to harness these tools is liberated from the drudgery of rote cognitive tasks. This allows them to pivot their focus toward the activities that require fundamentally human traits: empathy, complex system orchestration, and the high-level judgment required to navigate ambiguity in a digital transformation journey.

However, we must also acknowledge the inherent risk for those who remain static. The danger is not that AI will take your job; the danger is that an AI-amplified human — someone who has learned to partner with this intelligence layer to increase their speed, quality, and strategic focus — will become the new baseline for organizational success. In this high-velocity environment, the ability to rapidly integrate and adapt to new augmentation capabilities is the ultimate professional skill.

The Human-Centered Implication: Agency in the Age of Amplification

The transition to an integrated intelligence layer invites a necessary introspection regarding our own agency. When we delegate synthesis, research, and strategic sparring to an AI partner, the fundamental nature of our cognitive work changes. The risk is not that we lose control, but that we become overly reliant on the convenience of the tool, potentially allowing our critical thinking muscles to atrophy if we treat the output as gospel rather than a starting point for deeper investigation.

True agency in this new era requires a shift in mindset: we must view the AI not as an oracle, but as a mirror — a tool that reflects and expands our own intellectual curiosity. We remain the architects of intent, the ones who define the “why” and the “what,” while the AI provides the “how” and the “how fast.” Maintaining this distinction is essential for preserving the human-centered elements of our work, such as ethical reasoning and the intuitive leaps that often drive true innovation.

For leaders and organizations, this requires a fundamental shift in the management mandate. The focus must move away from top-down efforts to “automate processes” or eliminate roles, and toward the deliberate nurturing of amplified talent. The most successful organizations of the future will be those that foster an ecosystem where human judgment is elevated, not replaced, by these new intelligence layers. It is about creating a culture where the combination of human empathy and machine-augmented speed becomes a source of sustainable, long-term competitive advantage.

Conclusion: Embracing the Renaissance

We are standing at the threshold of a new way of working, one where the boundaries of individual capability are being fundamentally redrawn. Viewing the adoption of a personal AI layer merely as a “tech upgrade” misses the broader, more critical reality: this is a strategic professional imperative. Those who integrate these capabilities into their daily lives are not just working differently; they are working at a velocity and depth that was previously impossible for a single individual to sustain.

The future does not belong to the AI, nor does it belong to the unaugmented human. It belongs to the amplified human — the professional who masters the synergy between human intuition and machine-driven speed. This Renaissance is an invitation to offload the cognitive friction that has historically slowed our most important work, leaving us more space to do what humans do best: ideate, empathize, and lead.

As you step into this new era, ask yourself: How will you curate your own intelligence layer, and where will you focus the newfound capacity you gain? The revolution is already here, and the choice to participate is yours. Choose to amplify.

Frequently Asked Questions

What is the primary difference between a chatbot and a personal AI intelligence layer?

While a chatbot typically provides isolated, one-off answers to queries, a personal AI intelligence layer maintains deep context, understands your unique strategic priorities, and tracks your long-term goals to function as an integrated, persistent collaborator.

Why is “augmentation capability” more important than education level in the AI era?

In the current professional landscape, the productivity gap is driven by an individual’s ability to effectively integrate and leverage AI to enhance their output. Augmentation capability allows professionals to transcend traditional education-based limitations by dramatically increasing their speed, quality, and capacity for complex work.

Does the rise of AI-amplified humans mean the end of human-centered work?

No. The rise of AI-amplified humans actually shifts the focus of work toward inherently human traits. By delegating rote cognitive tasks and information synthesis to the AI, humans are freed to devote more energy to empathy, complex system orchestration, and the high-level judgment required for innovation.


Operationalize Organizational Empathy

Ready to Bridge the Gap Between Technology and Human Experience?

Technology only provides capability; human adoption creates the value. If you want to move past cold operational metrics and design fear out of your transformation, let’s connect. Get expert guidance on architecting impactful Experience Level Measures (XLMs) or establishing a dedicated Experience Management Office (XMO) tailored to your culture.

Explore the AI Soft Landing Series

This article is part of a broader exploration into architecting optimistic socioeconomic transitions for the AI era. Dive deeper into the series below:

EDITOR’S NOTE: This is a visualization of but one possible future. I will be publishing other possible futures as they crystallize in my mind (or as you suggest them for me to explore).

Image credits: Google Gemini

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

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Storytelling Determines the Change We Can Achieve

Storytelling Determines the Change We Can Achieve

GUEST POST from Greg Satell

At some level, change is always about the stories we tell. We like to think that we humans are objective arbiters of the facts, but that’s not really true. We think in narratives. In one study, juries considered experts that shared stories far more credible than those that merely offered an analysis of the relevant facts.

Every organization tells stories, some deliberate, some not. General Electric was able to tell successful stories for decades, yet it was a false narrative and, when the facts caught up, the firm collapsed. On the other hand, Satya Nadella was able to change the narrative at Microsoft even though, objectively, the company was already doing well.

Hollywood mogul Peter Guber describes stories as “emotional transport” and that’s why we need to be purposeful about the ones we tell if we are to bring about genuine transformation. Stakeholders need to be able to see themselves as heroes in the stories we tell, working within shared values to achieve a common purpose. Our story needs to be their story.

What Makes A Story?

The first element of any story is its exposition, which is the world you build around the story and includes the setting, the characters and other background information. This often comes at the beginning of the story, but it doesn’t have to. Sometimes, elements of the setting or details about the characters are leaked out as the plot develops.

The most important aspect of any story is the tension or conflict to be resolved. That’s what keeps the audience’s interest. Will the hero survive? Does the boy end up with the girl? Will justice prevail? It is the uncertainty surrounding the tension that makes a story interesting. A preordained story is a bore.

Another way to look at a story is an intention or ambition and an obstacle. If you can identify an ambition that people actually have—even if they aren’t aware of their intention—and then show that you can overcome the obstacles preventing them from achieving that ambition, you have a powerful narrative. Steve Jobs was a master at telling those kinds of stories (and then adding “one more thing.”)

There are, essentially, two ways to start a story. The first and less effective way is with the exposition, laying out the setting and characters, like when your mom tells the tale of meeting someone at the drug store and 10 minutes later you’re still hearing about their grandchildren. The other is to start with the tension, like when a James Bond movie begins with him hanging off a helicopter and you only find out why only later.

Start with the tension. Identify a problem to be solved. Learning how the obstacles to solving the problem will be overcome is what makes a story interesting.

The Hero’s Journey

One of the most common narrative devices is the “hero’s journey“, which involves different variations of a departure, an initiation, and a return. For example, in the Star Wars trilogy, we met Luke Skywalker as a restless boy on Tatooine. The hologram he unlocked in R2D2 kicked off his departure on a journey, in which he learned about “The Force.”

In a hero’s journey, the primary struggle is internal and the righteousness of your cause is your salvation. In order for Luke to prevail against Darth Vader and the evil empire, he first needed to conquer himself. Once he was able to do that, victory became, in some sense, inevitable.

We often like to think about change in this way because, in some sense, it’s easy. After all, who can oppose us when we are so diligently working for the greater good? If we are just good and pure and true, then success should become inevitable. We just need to keep the faith, endure any hardship that comes our way and we will be rewarded in the end.

Unfortunately, like Star Wars, this story is a fantasy and the stubborn belief in it will almost guarantee failure. Some people mistakenly see nobility in this type of defeat, because they can tell themselves that they fought “the good fight” and the deck was simply stacked against them. That way, they can blame everyone else instead of their “heroic” selves.

Change As A Strategic Conflict

The true story of change is that of strategic conflict between a future vision and the status quo. There are sources of power keeping the status quo in place and that’s where the battle lies. If you can remove—or even mitigate—those sources of power the status quo cannot survive and transformation can take place. As long as they remain, nothing will change.

Once you understand this story, you can begin to build an effective strategy. Power always lies in institutions and you can begin to identify which ones support the status quo, which support the future vision and which are on the fence. Those institutional targets will determine how you develop tactics.

Notice how differently the two stories affect actions. If you believe that you’re on a hero’s journey, then your primary goal is to communicate your virtue. You assume that once everyone understands your idea, they will embrace it. So you spend your time coming up with slogans, convinced that the right message will help others see the light.

When you begin to internalize the story of change as a strategic conflict it becomes clear that it’s more important to make a difference than to make a point. Demonstrating the righteousness of your cause is not nearly as important as letting others see their place in your story, how they too can be heroes in it and how they will be better off for it.

Creating A Shared Journey

Change always begins with a grievance. There’s something people don’t like and they want it to be different. We like to see ourselves as heroes fighting for everything that’s right and we question the motivations of those who oppose our cause. When we believe in something passionately, it’s hard to see how anyone, in good conscience, can see it another way.

Yet consider recent research that finds our conceptions of even something so simple as a penguin vary so widely that the mention of the word evokes very different associations in all of us. Clearly, more emotionally-laden content, such as a policy issue or a business strategy is going to spark vigorous debates.

The stories we tell need to create a sense of safety around transformation, to emphasize a shared future. Yet all too often we begin our stories with silly talk about “disruption” or burning platforms. The storytellers seek to ennoble themselves as champions and demonize others who see things differently.

Yet if we truly care about change, we need to hold ourselves accountable to be effective messengers. That’s why the narratives we build about change need to focus on shared values and establish common ground upon which we can build a shared future.

The stories we tell are important. We need to choose them wisely and tell them well.

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

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Innovation Framework Examples: 7 Real-World Cases That Show How They Work

Innovation Framework Examples: 7 Real-World Cases That Show How They Work

by Braden Kelley and Art Inteligencia

The most common question I get after presenting on innovation frameworks is not “which framework is best?” — it’s “can you show me what this actually looks like inside a real organization?” That question is exactly right. Frameworks are only valuable when you can see how they translate from theory to practice, and the translation is rarely as clean or obvious as the textbook version suggests.

What follows are real examples of organizations applying specific innovation frameworks — what the framework gave them, what it required of them, and what the outcomes looked like. For a complete guide to the major frameworks themselves, see our comprehensive innovation frameworks reference guide.

Design Thinking: IDEO and Bank of America’s “Keep the Change”

Bank of America’s “Keep the Change” savings program is one of the most cited design thinking success stories for good reason — it demonstrates what happens when you apply genuine customer empathy rather than product-feature thinking to a business problem.

The challenge: Bank of America wanted to help customers save more money, but conventional savings products were failing to attract adoption among their target segment of working-age adults. IDEO was brought in to apply design thinking to the problem.

The empathy research revealed something that no amount of market data had surfaced: people found saving difficult not because they lacked discipline, but because saving felt like a deliberate sacrifice that required conscious decision-making every time. The insight was behavioral, not financial.

The solution that emerged from this insight was counterintuitive: make saving automatic and invisible. Every time a customer made a debit card purchase, the amount was rounded up to the nearest dollar, and the difference was automatically transferred to savings. No decision required. No sacrifice felt.

The result: 2.5 million new customers enrolled in the first year, and Bank of America customers saved more than $1 billion through the program in its first year of operation. The program succeeded because the design thinking process surfaced a genuine behavioral insight — that the friction to saving was psychological, not financial — that product-focused thinking had systematically missed.

The framework lesson: Design thinking’s empathy stage is not market research. It surfaces the behavioral and emotional dimensions of a problem that quantitative data can’t see. The “Keep the Change” insight — that automatic saving removes the psychological friction that makes conscious saving feel like sacrifice — was only discoverable through direct human observation.

Jobs to Be Done: McDonald’s Milkshake Story

Clayton Christensen’s milkshake story is the most famous example of Jobs to Be Done thinking in practice — and it’s worth revisiting in detail because it illustrates exactly how differently JTBD reframes a business problem.

McDonald’s wanted to increase milkshake sales. Conventional market research asked customers what they wanted in a milkshake — thicker? sweeter? more flavors? The answers were inconclusive and the improvements they prompted didn’t move the sales needle.

A JTBD researcher took a different approach: instead of asking customers what they wanted in the product, he asked what job they were hiring the milkshake to do. The finding was completely unexpected. The majority of morning milkshake purchasers were buying for the commute — they needed something that would keep them full through a long, boring drive, that they could consume one-handed without making a mess, and that would last long enough to feel like an event rather than a transaction. The milkshake — thick, slow to consume, and easy to hold — was uniquely suited for this job. The alternatives (a banana, a bagel, a coffee) all failed on at least one dimension of the commute job.

The implication was immediately actionable: make the morning commute milkshake even better at its actual job — thicker, available faster at the drive-through, with a thinner straw to make it last longer. Don’t change the flavor. The job, not the product attribute, was the unit of analysis.

The framework lesson: JTBD reframes the competitive set entirely. McDonald’s wasn’t competing with Burger King for milkshake customers — it was competing with bananas and bagels for the morning commute job. That reframe opens completely different improvement directions than conventional competitive analysis would ever produce.

Lean Startup: Dropbox’s Minimum Viable Product

Dropbox’s founding story is the canonical example of Lean Startup’s MVP principle applied to its fullest effect — and what makes it particularly instructive is that the MVP wasn’t even a product. It was a video.

In 2007, Drew Houston had built a working prototype of Dropbox but faced a fundamental challenge: file synchronization is a problem that requires a significant user base to be meaningful, and building that base requires persuading investors and early users that the problem is real and the solution works. The conventional path — build, launch, market, iterate — would require substantial capital for a product whose value proposition was genuinely hard to communicate without experiencing it.

The Lean Startup approach: before investing further in the product, validate that people actually wanted it. Houston created a simple three-minute demo video explaining what Dropbox would do. No working product. No technical demonstration. Just a clear explanation of the problem and how Dropbox would solve it. He posted it on Hacker News.

The waitlist went from 5,000 to 75,000 overnight. The demand signal was unambiguous. The MVP — in this case, a video rather than a product — had validated the core assumption (that people wanted effortless file synchronization across devices) at a cost of hours rather than months of development.

The framework lesson: The point of an MVP is to test the most important assumption at the lowest possible cost, not to build the simplest functional version of the product. In Dropbox’s case, the most important assumption was demand, not technical feasibility — so the MVP was a demand test, not a product prototype.

Three Horizons Framework: Amazon Web Services

Amazon’s development of AWS is the most instructive example of McKinsey’s Three Horizons Framework in practice — partly because Amazon’s leaders almost certainly weren’t thinking about Three Horizons when they built it, but the strategic logic maps perfectly onto the framework.

Amazon’s Horizon 1 business in the early 2000s was e-commerce — the core retail operation that was generating revenue and requiring continuous improvement. The challenge every e-commerce business faces is infrastructure: you need enormous computing capacity to handle peak periods (holiday shopping), but that capacity sits idle for most of the year. Amazon had solved this problem for itself through massive internal infrastructure investment.

The Horizon 2 insight — building an adjacent business from existing capabilities — came from recognizing that the infrastructure Amazon had built to run its own e-commerce operation was itself a valuable product that other companies needed. The capability was already built. The extension was to offer it externally.

The Horizon 3 bet was that computing infrastructure as a service would become a foundational utility — that the long-term market was enormous and that Amazon’s early investment would produce compounding advantages as the market developed. In 2024, AWS generated over $100 billion in annual revenue and represented the majority of Amazon’s operating profit.

The framework lesson: The Three Horizons Framework is most valuable not as a planning tool but as a diagnostic: it forces explicit conversations about whether the organization is investing appropriately across all three time horizons, and whether Horizon 1 pressures are crowding out the Horizon 2 and 3 investments that produce long-term competitive advantage. Amazon’s willingness to invest in and protect Horizon 3 bets — including AWS, Prime, and Alexa — while competitors focused primarily on Horizon 1 optimization is a significant part of why it has compounded value so effectively.

Open Innovation: Procter & Gamble’s Connect + Develop

Procter & Gamble’s Connect + Develop program, launched in 2000 under CEO A.G. Lafley, is the most cited example of open innovation at enterprise scale. Lafley set an ambitious and specific goal: source 50% of P&G’s innovations from outside the company. This was not aspirational language — it was a specific, measurable target that required fundamentally restructuring how P&G approached innovation.

The program built explicit infrastructure for external idea sourcing: a dedicated team for identifying and evaluating external innovations, partnerships with universities and research institutions, a public submission portal for independent inventors, and acquisition strategies that brought external technologies inside P&G’s commercialization machinery.

The results were significant. Spin-off toothbrush innovations, the Swiffer product line, and the Pringles printing technology all came through open innovation channels. By 2006, P&G reported that more than 35% of its new products had elements that originated from outside the company, up from about 15% in 2000. Productivity in R&D improved substantially.

What made Connect + Develop work where most open innovation programs fail was the investment in internal absorption capability — the processes, relationships, and organizational structures that allowed P&G to actually use external ideas rather than just collect them. The “not invented here” syndrome that kills most open innovation programs was addressed through deliberate cultural and process design, not just aspiration.

The framework lesson: Open innovation requires two-sided capability development — not just the ability to attract external ideas, but the organizational capacity to evaluate, integrate, and commercialize them. P&G’s investment in internal absorption capability was as important as its investment in external sourcing.

The Value Innovation Framework: Apple iPad Launch

The Apple iPad launch in 2010 illustrates the Value Innovation Framework’s three components — Value Creation, Value Access, and Value Translation — and specifically demonstrates what happens when Value Translation fails even when the other two are strong.

The iPad’s Value Creation was genuinely significant: a device that made web browsing, email, media consumption, and light content creation dramatically more convenient than a laptop for a large set of use cases. Value Access was strong: the price point was lower than expected, distribution through Apple Stores and carriers was immediate, and the device worked out of the box without configuration.

The initial launch, however, struggled with Value Translation — helping people understand what job the device was actually for. The early marketing positioned it as a larger iPhone or a smaller laptop, both framings that made it seem like a compromise rather than a genuine innovation. Reviews were mixed. The initial sales trajectory was uncertain.

The Value Translation breakthrough came not from a product change but from a single advertising image: a person relaxing on a couch with an iPad in their lap. That image communicated in seconds what no amount of specification comparison could: this is the device for the relaxed, casual computing moment — not the desk, not the commute, but the couch. Sales accelerated dramatically after that visual translation clicked.

The framework lesson: Innovation = Value Creation × Value Access × Value Translation is multiplicative, not additive. The iPad had strong Value Creation and Value Access from day one. The Value Translation gap almost cost Apple the launch. Fixing the translation — not the product — unlocked the market.

Disruptive Innovation: Netflix vs Blockbuster

The Netflix/Blockbuster story has become the defining example of disruptive innovation theory in practice — perhaps because it is unusually clean as a case study, with a visible incumbent, a clear disruption pattern, and a decisive outcome.

Netflix’s initial DVD-by-mail service in 1998 entered the video rental market from exactly the position Christensen’s theory predicts: serving an overlooked segment (frequent renters who resented late fees and found the trip to the store inconvenient) with a simpler, different model that the incumbent (Blockbuster) had no interest in responding to. Blockbuster’s most profitable customers were the casual renters who came into stores and paid late fees — the customers Netflix was serving were not Blockbuster’s priority.

As Netflix improved, it moved upmarket — expanding its library, improving delivery speed, and eventually transitioning to streaming. By the time the threat was obvious to Blockbuster, the incumbent’s response was structurally constrained: its entire business model (physical stores, late fees, walk-in customers) was incompatible with the direction the market was moving. Blockbuster filed for bankruptcy in 2010. Netflix is now a global media company with over 300 million subscribers.

The framework lesson: Disruptive innovation theory’s most valuable practical application is identifying threats that conventional competitive analysis will dismiss. Blockbuster’s leadership could see Netflix’s numbers for years and rationally conclude that the threat was manageable. The framework reveals why that rational conclusion was wrong: the disruption was coming from a direction Blockbuster’s financial incentives prevented it from defending.

Frequently Asked Questions

What are some real-world examples of innovation frameworks in action?

Real-world innovation framework examples include: Bank of America’s “Keep the Change” savings program (design thinking applied to behavioral finance); McDonald’s milkshake insight (Jobs to Be Done reframing the competitive set); Dropbox’s video MVP (Lean Startup demand validation before product development); Amazon Web Services (Three Horizons Framework applied to infrastructure-as-a-service); Procter & Gamble’s Connect + Develop (open innovation at enterprise scale); the Apple iPad launch (Value Innovation Framework showing the importance of Value Translation); and Netflix’s disruption of Blockbuster (Disruptive Innovation theory playing out over a decade). Each example illustrates how frameworks translate from theory to specific, actionable decisions in real organizations.

Which innovation framework is most widely used by large companies?

McKinsey’s Three Horizons Framework and Design Thinking are the most widely adopted innovation frameworks among large organizations. Three Horizons is particularly prevalent in corporate strategy and portfolio management contexts because it provides a common language for conversations about innovation investment allocation. Design Thinking has been widely adopted across industries — from product development to healthcare to public policy — because its human-centered, iterative approach applies to virtually any type of complex problem. In practice, most sophisticated innovation programs use multiple frameworks in combination rather than selecting one exclusively.

How do you choose the right innovation framework for your organization?

Choosing the right innovation framework depends on your primary challenge: if you need to allocate innovation investment across time horizons, use Three Horizons; if you need to identify unmet customer needs, use Jobs to Be Done; if you need to validate a new concept quickly, use Lean Startup; if you need to understand competitive disruption threats, use Disruptive Innovation theory; if you need to access external capabilities, use Open Innovation; if you need to solve a complex human-centered problem, use Design Thinking. Most organizations benefit from using multiple frameworks in combination — each addresses a different dimension of the innovation challenge. For a complete framework selection guide, see our comprehensive innovation frameworks guide.

Want to go deeper on any of these frameworks? Our complete guide to innovation frameworks covers each one in detail — what it does well, where it falls short, and how to choose the right approach for your specific situation.




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Braden Kelley is a LinkedIn Top Voice, bestselling author, and innovation keynote speaker who helps organizations get to the future first and build sustainable innovation cultures.

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Image Credit: Gemini

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

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