Don’t Underestimate the Power of Identity

Don't Underestimate the Power of Identity

GUEST POST from Greg Satell

In the 1990s, western-style liberal democracy was triumphant. The Berlin Wall had fallen and the Cold War had been won. Teams of diplomats and consultants rushed to spread the Washington Consensus, an agreed upon set of reforms that poor countries were pressured to undertake by their richer brethren.

Francis Fukuyama noted at the time that we had reached an endpoint in history, when one model had achieved dominance over all others. Yet even as he laid out the rational case, he invoked the ancient Greek concept of thymos, or “spiritedness,” to warn that even at the end of history, there would be some who would insist on going their own way, no matter the consequences.

That’s why any change, even if provably good, noble and just, will inevitably incur resistance. It’s a simple truth that humans form attachments to people, ideas and other things and, when those attachments are threatened, we see it as an outright attack on our identity and lash out. That’s why identity needs to be at the center of any change strategy, if it is to succeed.

Triggering Resistance

Imagine this scene. You are in a meeting to discuss a proposal. There is an active discussion and, over the course of an hour, the group steadily moves toward a consensus. Then, as you’re moving on to discuss next steps and close the meeting, somebody who hadn’t spoken up during the entire hour suddenly has a hissy fit and melts down in the middle of the conference room.

Let’s think about what happened underneath the surface. When the idea was initially proposed, that person had such a visceral reaction to it that they couldn’t even articulate why they were so against it. Nevertheless, being such a disastrous idea in their eyes, they were sure it would get derailed along the way. When that didn‘t happen, it triggered an explosion.

There’s a couple of things that we can take away from this. The first is that it was the initial success of moving to next steps that triggered them and their inappropriate reaction pretty much assured that the proposal would move forward. So one way of dealing with resistance is to quietly build traction. If you can resist the urge to engage or attack your opposition directly, they will eventually feel the need to lash out and discredit themselves.

Another important point to consider is that people rarely melt down in conference rooms. Usually, they hold their composure and then go quietly sabotaging the idea in the hallways. Your most active opposition are usually not the ones voicing concerns. Most often, they find more surreptitious ways of undermining an initiative.

Shifting Identities

Just because people build attachments doesn’t mean that they need to be permanent aspects of their identity. In Immunity to Change, Harvard’s Robert Kegan and Lisa Lahey, discuss their decades of research into how people build competing commitments based on how they see themselves. Their work shows that greater self awareness can break the spell.

For example, “David” was a senior leader who had taken on greater responsibility and desperately needed to delegate. As much as he saw how important it was for him to hand off projects and give more autonomy to his people, he just couldn’t bring himself to do it. It was almost as if he was actively sabotaging his own objective.

As it turned out, David had a somewhat hardscrabble upbringing and identified himself as a “hands on” manager. It was important for him to do “real work” and not just be “overhead.” So every time he tried to delegate, it felt like he was getting away from his hardworking roots and becoming something he didn’t want to be.

The problem was solved once David saw that the change he needed to undertake involved his own identity. He began to see another role for himself, that of an enabler and a coach, empowering his people. He was able to see their accomplishments as his own. It didn’t happen automatically—it took real work—but it can be done.

Designing A Dilemma

I once had a six-month assignment to restructure the sales and marketing operations of a troubled media company and the sales director was a real stumbling block. She never overtly objected, but would just nod her head and then quietly sabotage progress. For example, she promised to hand over the clients she worked directly with to her staff, but never seemed to get around to it.

It was obvious that she intended to slow-walk everything until the six months were over and then return everything back to the way it was. As a longtime senior employee, she had considerable political capital within the organization and, because she was never directly insubordinate, creating a direct confrontation with her would be risky and unwise.

So rather than create a conflict, I designed a dilemma. I arranged with the CEO of a media buying agency for one of the salespeople to meet with a senior buyer and take over the account. The sales director had two choices. She could either let the meeting go ahead and lose her grip on the department or try to derail the meeting. She chose the latter and was fired for cause. Once she was gone, her mismanagement became obvious and sales shot up.

Dilemma actions have been around for at least a century. One early example was Alice Paul’s Silent Sentinels, who picketed the Woodrow Wilson’s White House with his own quotes in 1917. More recently, the tactic has been the subject of increasing academic interest. What’s becoming clear is that these actions share clear design principles that can be replicated in almost any context.

Key to the success of a dilemma action is that it is seen as a constructive act rooted in a shared value. In the case of the Sales Director, she had agreed to give up her accounts and setting up the meeting was aligned with that agreement. That’s what created the dilemma. She had to choose between violating the shared value or giving up her resistance.

Creating A Larger, Integrated Identity

Our identity and sense of self drives a lot of what we see and do, yet we rarely examine these things because we spend most of our time with people who are a lot like us, who live in similar places and experience similar things. Our innate perceptions and beliefs seem normal and those of outsiders strange, because our social networks shape us that way.

That’s why we often see so much resistance to change. People get invested in the status quo. They work within it, follow its rules and achieve some things. Those achievements become part of their identity and to reject the means in which their present self arose is, in some sense, to reject a part of themselves.

Yet our identities aren’t fixed. They grow and evolve over time. We routinely choose to add facets to our identity, while shedding others, changing jobs, moving neighborhoods, breaking off some associations as we take on others. “Identity can be used to divide, but it can and has also been used to integrate,” Francis Fukuyama wrote in his book on the subject.

It is at this nexus of identity and purpose that creativity and innovation reside, because when we learn to collaborate with others who possess knowledge, skills and perspectives that we don’t, new possibilities emerge to achieve greater things. To make that possible, however, we need to support the identities of those around us, so that we can build the shared purpose upon which we can build a shared future.

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

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Constrained Innovation is Beating Unconstrained Innovation – Again

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Constrained Innovation is Beating Unconstrained Innovation - Again

by Braden Kelley and Art Inteligencia

Every few years, Silicon Valley rediscovers a lesson the rest of the innovation world already knows: constraints don’t kill breakthroughs — they focus them.

This week’s AI headlines make the point again. Moonshot’s Kimi K3 and Thinking Machines Lab’s Inkling are not “unlimited compute with unlimited budget” stories. They are constrained-innovation stories — open-weight models built to compete with (and sometimes beat) far richer, closed frontier systems from OpenAI and Anthropic on the tasks that matter to builders. At the same time, a quieter race is packing surprising capability into models small enough to live on a smartphone with 6 GB of RAM or less.

If you lead change, product, or experience design, this is not just a model-release week. It is a reminder of how innovation actually works when resources are scarce, goals are clear, and “more” is not allowed to substitute for “better.”

The unconstrained myth

Unconstrained innovation sounds romantic: infinite GPUs, infinite capital, infinite permission to chase every benchmark.

In practice, unconstrained environments often produce:

Feature sprawl instead of sharp value
Capability inflation instead of usable outcomes
Vendor dependence instead of organizational learning
Status races (who has the biggest model) instead of customer impact

Constrained innovation does the opposite. It forces tradeoffs. Tradeoffs force clarity. Clarity forces design.

We’ve seen this movie before — in lean startups, in frugal engineering, in design-to-cost product development, in wartime R&D. The pattern is durable:

When you cannot buy your way to “more,” you must invent your way to “enough.”

AI is now teaching that lesson at planetary scale.

Kimi K3: open weight, frontier pressure

China’s Moonshot AI released Kimi K3 in mid-July 2026 as what it calls the world’s first open ~3T-class model — roughly 2.8 trillion parameters, native vision, and a 1-million-token context window, with full weights promised for public release.

Be precise about the scoreboard, because hype helps no one:

Moonshot itself says K3’s overall performance still trails Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol.
On multiple evaluations, though, K3 is competitive with — and on some coding, agent, long-horizon engineering, and frontend-building tasks ahead of — strong closed models sitting just behind the absolute tip of the spear.
Independent evaluators have placed it near GPT-5.5 / Claude Opus-class systems on several complex multi-step workloads, while still acknowledging Fable 5 as the tougher overall ceiling.

That combination is the real story: not “open models already own everything,” but “open models are close enough, open enough, and cheap enough to change the game.”

Constraint here is structural. Moonshot is not playing with the same geopolitical, capital, and closed-ecosystem advantages as the largest U.S. labs. So it optimized for:

Open weights (download, run, modify)
Architecture efficiency (MoE-style sparsity and novel attention choices)
Task-relevant dominance where developers actually feel pain (coding agents, long context, UI building)

That is constrained innovation: win where it matters for users, not where the press release wants a clean sweep.

Inkling: constraint as a product philosophy

Days earlier, Thinking Machines Lab — founded by former OpenAI CTO Mira Murati — released Inkling, its first open-weights model.

Inkling is a multimodal Mixture-of-Experts system (~975B total / ~41B active parameters), trained across text, images, audio, and video, with a large context window and Apache 2.0 weights on Hugging Face. Critically, the lab is not claiming Inkling is the strongest model available, open or closed.

Instead, Thinking Machines is making a different bet — one every human-centered innovator should recognize:

The winning model is not always the biggest generalist. It is the one an organization can shape.

Their framing is customization, efficient controllable “thinking effort,” and a base model designed to be adapted. Alongside Inkling they previewed Inkling-Small (lighter active-parameter footprint) for lower cost and latency.

This is constrained innovation as strategy:

Don’t outspend OpenAI/Anthropic on every frontier benchmark.
Out-enable customers on fit, control, and adaptation.
Treat “open weights + fine-tuning path” as the product, not a side quest.

In experience-design terms: they are optimizing for agency, not spectacle.

The pocket frontier: intelligence that fits in 6 GB

While the giants argue about trillion-parameter scoreboards, another constrained race is rewriting daily experience design: on-device AI.

Phones with ~6 GB of RAM are now practical homes for capable small language models — typically 1B–3B class models under aggressive 4-bit quantization, often with NPU acceleration (Apple Neural Engine, Qualcomm Hexagon, and peers). Families like Gemma’s efficient variants, Phi-class minis, Llama 3.2 small models, and Apple’s on-device foundation model path are not “tiny ChatGPT cosplay.” They are differently designed systems: distillation, quantization-aware training, sliding-window/grouped-query attention, and task specialization.

What becomes possible when intelligence must fit in a pocket?

Privacy by architecture (data never leaves the device)
Latency that feels like UI, not waiting for a cloud round trip
Offline resilience
Ambient assistance without a permanent surveillance subscription

This is FutureHacking in the literal sense: the future arriving first where constraint is non-negotiable — battery, thermal envelope, memory bandwidth, and user trust.

Unconstrained cloud models will still win the hardest reasoning contests for a while. Constrained on-device models will win moments — the thousands of tiny interactions that shape whether people feel helped or hunted by technology.

A simple framework: Three Arenas of Constrained AI Advantage

Leaders should stop asking only “Who has the best model?” and start asking which arena they are competing in:

Frontier Arena — Absolute peak reasoning. Still often favors well-funded closed labs (Fable 5 / GPT-5.6 Sol class). Use sparingly for the hardest 10–20% of work.
Open Adaptation Arena — Near-frontier capability + weights you can own, route, fine-tune, and host. Kimi K3 and Inkling are attacking this arena hard. Ideal for product teams, agents, and regulated environments.
Edge Experience Arena — Models compressed into phone-scale memory. Wins on privacy, speed, cost-at-scale, and human experience continuity. This is where unconstrained cloud thinking often fails customers.

Constrained innovation beats unconstrained innovation when the arena rewards focus.

Implications for organizations (not just AI labs)

If you are charting change inside a company, the lesson is operational:

Budget is a design tool. Cap tokens, latency, and model size early. Force product clarity.
Route by job-to-be-done. Don’t send every prompt to the most expensive frontier model. Reserve it for true hard cases.
Prefer adaptable over mythical “best.” An open model you can fine-tune to your workflow may outperform a slightly smarter generalist you can’t shape.
Design for the edge. Anything frequent, personal, or privacy-sensitive should be a candidate for on-device or hybrid architectures.
Measure outcomes, not vibes. Benchmarks matter; customer task completion, cost per successful outcome, and trust matter more.

This is human-centered change applied to AI portfolios: start from experience, not ego.

We’ve seen this movie — and the sequel is here

Constrained innovation beat unconstrained innovation in Japanese postwar manufacturing quality, Israeli “startup nation” necessity engineering, mobile-first product design in bandwidth-poor markets, and every great design brief that began with “You only get X.”

Now it is beating — or at least pressuring — unconstrained AI again.

Kimi K3 shows that open, resource-conscious frontier building can meet or beat closed leaders on key developer battlegrounds even while still trailing at the absolute peak. Inkling shows that refusing the one-size-fits-all arms race can itself be a strategy. Phone-scale models show that the most human future may be the one small enough to live beside us without phoning home.

The organizations that win the next decade will not be those with the least constraint.
They will be those who treat constraint as a creative operating system.

Because in innovation, as in life:

Limits don’t stop the future. They decide who gets there first — and who arrives with something people can actually use.

Image credits: Meta.AI

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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How to Calculate the ROI of Customer Experience

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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:

Experience Metric — the number you already track: NPS, CES, or CSAT.
Behavioral Outcome — the specific customer behavior that metric predicts: renewing, referring, buying again, or churning.
Financial Outcome — that behavior’s dollar value: retained revenue, reduced cost-to-serve, lower acquisition cost.
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.

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.

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

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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

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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

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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

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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?

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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

The 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

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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

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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.

Contact Braden Kelley

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

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