Tag Archives: Artificial Intelligence

What Happens If Your Healthcare AI Goes Out of Alignment?

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What Happens If Your Healthcare AI Goes Out of Alignment?

GUEST POST from Arlen Meyers

It’s pothole season so be careful when you are driving. If you hit a pothole, it might throw your front end out of alignment

When your front end is out of alignment, it means the angles of your wheels are no longer set to the manufacturer’s specifications. This affects how the wheels contact the road, which can change your vehicle’s handling, tire wear, and fuel efficiency.

Similarly, it’s AI season so be sure it’s aligned too.

Less than two years after a wave of administrative layoffs, Mass General Brigham began a new round of job cuts last week that affect clinical and operational programs, according to media reports.

“We must eliminate programs and activities that do not fully align with our strategy and priorities, even those we have had for many years. Administrative savings alone will not be enough,” said the email signed by Dr. Michael Brown, president of the academic medical center, CFO Niyum Gandhi and COO Dr. Ron Walls, the Boston Business Journal reported.

Artificial intelligence (AI) alignment is the process of encoding human values and goals into AI models to make them as helpful, safe and reliable as possible.

In sick care, the sextuple aims are quality, cost, equitable access, patient experience, sick care professional experience, and productivity. Most of it now has just improved shareholder value.

Society is increasingly reliant on AI technologies to help make decisions. But this growing reliance comes with risk: AI models can produce biased, harmful and inaccurate outputs that are not aligned with their creators’ goals and original intent for the system.

Alignment works to reduce these side effects, helping ensure AI systems behave as expected and in line with human values and goals. For example, if you ask a generative AI chatbot how to build a weapon, it can respond with instructions or it can refuse to disclose dangerous information. The model’s response depends on how its creators aligned it.

AI front end misalignment can cost you, patients and stakeholders.

Misalignment in medicine usually isn’t dramatic. It happens when a system optimizes for something that is not quite what clinicians and patients care about, and the gap shows up as quiet, systematic harm. Like bankruptcy, it happens gradually and all of a sudden.

The proxy problem. The best-known case is a widely used risk algorithm studied by Obermeyer and colleagues in 2019. It predicted future healthcare costs as a stand-in for health needs. Because Black patients historically had less money spent on them at the same level of illness, the model rated them as healthier than equally sick white patients and routed fewer of them into extra-care programs. The model did exactly what it was told. The objective was the problem.

Performing well in testing but not in practice. Models trained on one hospital’s data often degrade at another because of different patient populations, equipment, or documentation habits. An external evaluation of Epic’s sepsis prediction model found it missed many cases and generated heavy alert volume, which contributes to alert fatigue. Dermatology and imaging tools trained mostly on lighter skin or a narrow set of scanners can underperform for everyone else.

Human-AI interaction failures. Clinicians can over-trust a confident output (automation bias) or learn to ignore a noisy one. Over time, reliance on AI can also erode the skills needed to catch its errors. When something goes wrong, accountability among the developer, the hospital, and the clinician is often unclear.

Generative AI specific risks. Language models can fabricate details in a clinical summary, invent citations, agree with a patient’s or clinician’s mistaken premise (sycophancy), or give plausible but unsafe advice. Documentation tools can also drift toward whatever maximizes billing or throughput rather than accuracy.

Feedback loops. If a model shapes who gets screened or treated, future training data reflects the model’s own decisions, which can lock in and amplify existing disparities.

What helps. Choosing outcome measures that reflect actual patient benefit, validating locally before deployment, monitoring performance continuously by subgroup, keeping a meaningful human in the loop, being transparent about limitations, and having clear regulatory and liability pathways. For entrepreneurs, this is increasingly a commercial issue as well as an ethical one, since buyers and regulators are asking for this evidence.

What is the ROI on AI? When was the last time it reduced your sick care insurance premiums, the cost of the last prescription your filled or your out-of-pocket costs incurred when you went to the urgent care center with symptoms of the flu?

AI defensive medicine is making things worse. The care these systems recommended cost substantially more than physicians’ documented care, chiefly because they suggested testing at visits where the physician had ordered none. LLM outputs are suggestions, not plans of care; final decisions should remain with clinicians.

Artificial intelligence has produced “zero financial payoff” to U.S. companies outside of the tech sector, argues Apollo Chief Economist Torsten Slok. Slok claims that while revenue per worker and profit margins have risen for the “Magnificent Seven,” both have stagnated across the S&P 493 — with no clear indication of AI-driven productivity growth. “Mainstream corporate America is pulling back and starting to view token spending not as a source of growth but as a cost center that weighs on margins,” Slok said.

As noted in a recent WSJ article, perhaps we are experiencing a kind of Solow Paradox 2.0, with the digital age more around us than ever except in the productivity statistics. There are several reasons for this lag. First of all, we’re in the early deployment years of major recent innovations, including cloud computing, IoT, big data and analytics, robotics, and AI and machine learning. Notes McKinsey: “The challenge of adoption in the current digital wave may be even harder because of the broad range of uses of digital that not only help improve current processes but fundamentally transform business models and operations.”

How to make doctors more productive | LinkedIn

Dr Wachter reminds us: as if on cue, his post last week on AI alignment in healthcare just received some empirical meat to put on its bones. His argument, in brief, was this: the critical alignment question for healthcare AI won’t be how to prevent rogue AI from causing harm. It will be about striking the right balance between cost and quality in AI-enabled decision support and, increasingly, agentic systems.

In other words, How to Survive Being the Human in the Poop: A Field Guide.

The AI dissemination and implementation journey starts with the first step, but the last mile is the hardest to navigate.

Two common C-suite behaviors can stall AI initiatives: claiming sole control over decisions or disengaging from those seen as outside one’s expertise. Because AI crosses functional boundaries, executives must learn to share authority, resources, and accountability rather than rely on the domain-specific ownership that helped them advance. CEOs can foster this collaborative approach by: defining the business problem before assigning ownership, building cross-functional relationships before they’re needed, treating early AI plans as experiments, rewarding jointly owned outcomes, and establishing rules for resolving disagreements.

Compassionate capitalism should guide your way to stay in alignment.

Slow down, drive carefully and don’t ignore the dashboard warning lights.

Image Credit: Pixabay

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Does Hybrid Intelligence Solve Our Compute Shortage?

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Does Hybrid Intelligence Solve Our Compute Shortage?

by Braden Kelley and Art Inteligencia

Here’s a mental image I can’t shake. Somewhere right now, a very large, very expensive, very thirsty computer in a very large building is being asked to figure out whether a customer’s email is angry or merely brisk.

That’s the compute shortage in one sentence. We have built an astonishing capability, and then we’ve gotten into the habit of using it for everything, including the tasks a much smaller tool could handle without breaking a sweat.

So let’s ask the obvious question. If we stopped sending every request to the biggest brain in the cloud, could we ease the shortage? Does hybrid intelligence solve it? My honest answer is: it helps a great deal, it won’t solve it alone, and the most interesting part is what the fix might do to the software we use every day.

The shape of the shortage

First, the problem. The International Energy Agency reports that electricity demand from data centers rose 17% in 2025, far outpacing 3% growth in global power demand overall, and it expects data center consumption to roughly double by 2030, with AI-specific demand growing even faster. Gartner warned back in 2024 that power shortages could constrain 40% of existing AI data centers by 2027. And industry reports describe waits of two to six years or more in some regions to connect a new large facility to the grid. (Some of those figures come from industry sources and older forecasts, so treat the specifics as directional. The direction itself is not in dispute.)

Notice what that tells you. The bottleneck isn’t only chips. It’s electricity, transformers, cooling and the time it takes to build all of it. You can’t download a substation.

When supply can’t expand quickly, the remaining lever is demand. Which brings us to a very old idea wearing new clothes: don’t use more than you need.

Hybrid intelligence: the right brain for the job

Hybrid intelligence is a simple idea. Run small language models (SLMs) on the device in your hand or on your desk for most everyday intelligence needs, and call on large models in the cloud only when a request truly needs the extra horsepower.

Think of it the way a hospital works. You don’t send every patient to the specialist surgeon. Most needs are handled by a nurse, a general practitioner, or a pharmacist, and the surgeon gets the cases that require one. The system works because someone, or something, decides where each case goes.

The pieces for this are arriving. Neural processing units are becoming standard in new phones and laptops. Operating systems are beginning to expose local models to developers, so a small team can use on-device intelligence without paying a per-request bill. Small open models are getting better at doing more with less, and with modern compression techniques, a capable model of several billion parameters can fit in a few gigabytes of memory. And the routing pattern already exists in the wild: Apple’s Private Cloud Compute, for instance, is built around handling what it can locally and sending only what it must to a larger remote model.

What would run locally? Summarizing a document, drafting a reply, sorting email, transcribing a call, classifying a support ticket, cleaning up an image, answering a question about the file you have open. What goes to the cloud? Long, complex reasoning, large-scale analysis, tasks that need broad world knowledge or the most capable model available.

If most requests are the first kind, and I suspect most are, the cloud’s workload shrinks by a lot. Every request that stays on your device is one fewer asking a data center for electricity.

Hybrid software: let each tool do what it’s good at

Here’s the companion idea, and I think it’s the underrated one. Hybrid intelligence is about where AI runs. Hybrid software is about when to use AI at all.

For decades, software has been written as explicit instructions: if this, then that. It’s predictable, fast, cheap and auditable. Language models are the opposite: flexible, adaptable and capable of handling the messy, ambiguous inputs that rigid code chokes on, but slower, costlier and occasionally creative in ways you’d rather they weren’t.

The wasteful approach is to use the flexible tool for everything. Asking a language model to add up a column of numbers is like hiring a poet to do your taxes: possible, expensive and slightly alarming. A better approach combines the two:

  • Traditional code handles what’s well-defined: calculations, rules, validations, database lookups, anything where the answer should be the same every time.
  • A small local model handles the fuzzy interpretation: understanding what the user meant, classifying, summarizing, extracting meaning from messy text.
  • A large cloud model is called only when the task is complex enough to justify the cost.

The result is software that’s smarter, more capable and more adaptable without being bigger. It’s smarter where intelligence helps, lean where it doesn’t, and cheap enough to run that you can afford to give it to everyone. That last part matters, and I’ll come back to it.

Adaptive interfaces: software that gets out of your way

Now for the part I find most exciting, because it changes how software feels to use.

Look at the menus of any mature application. They’re cathedrals of features, built up over decades. Hundreds of commands, nested menus, toolbars within toolbars. Most users touch a tiny fraction of them. Some features, in any given person’s hands, are never used once. And yet they sit there, every day, making the useful ones harder to find.

We’ve lived with this because the alternatives were bad. Hiding features meant frustrating people who needed them. Showing everything meant overwhelming everyone else.

AI changes that tradeoff. With a local model that understands how you work, an application can learn which features you actually use, surface those, and quietly tuck the rest out of sight. And, here’s the key, still make everything reachable. Ask for the hidden feature in plain language (“make this table a chart,” “remove the background,” “merge these two documents”) and the software finds it, or does the job for you. The capability never left. It just stopped getting in the way.

What this means in practice:

  • Simpler by default. A beginner sees an uncluttered tool. A power user sees their tools.
  • Nothing is locked away. A rarely used feature appears the moment you need it.
  • Interfaces that evolve. As your work changes, so does what the software puts in front of you.
  • Less to learn. You don’t need to memorize where things live. You describe what you want.

And there’s a compute angle too. An interface that adapts using a small, local model, with no round trip to a data center, is exactly the kind of lightweight intelligence that hybrid design makes affordable. The thing that makes software easier to use also happens to be easy on the grid.

There’s a caution worth saying out loud, though. Adaptive interfaces must be predictable enough that people don’t feel the ground shifting under them, and they should always let users see and control what’s been hidden. A menu that rearranges itself without warning is a terrible experience. The goal is to simplify, not to surprise.

Other technologies that can ease the squeeze

Hybrid intelligence isn’t the only tool in the box. Several others work alongside it.

  • Smaller, smarter models. Techniques like quantization (storing a model’s numbers with less precision) and distillation (teaching a small model to imitate a larger one) shrink the cost of running a model dramatically. Models that once needed a server rack increasingly fit on a laptop.
  • Architectures that don’t use everything at once. Some model designs activate only the portions of a network relevant to a given request, rather than firing every parameter every time. Less computing per answer.
  • Caching and reuse. If thousands of people ask nearly the same question, the system shouldn’t recompute the answer from scratch each time. Smart caching is a quiet and surprisingly large efficiency win.
  • Specialized chips. Hardware designed specifically for AI workloads can do more work per watt than general-purpose processors. The efficiency per unit of electricity keeps improving.
  • Better data center design. More efficient cooling, smarter power management and facilities built near abundant energy all reduce the electricity cost of each unit of computing.
  • Flexible scheduling. Not every AI job is urgent. Training runs and batch processing can be timed to match when power is cheap and plentiful, easing peak demand on the grid.
  • More power, built faster. Ultimately, supply matters too. New generation, grid upgrades and faster permitting are part of the answer, even if they take longer to arrive than software can.

None of these is a silver bullet. Together, they form something more like a portfolio.

The caveat I owe you: efficiency can backfire

Here’s the part that keeps me honest. When something gets dramatically cheaper to use, people tend to use a lot more of it. Economists have a name for it, the Jevons paradox, and it’s been observed with everything from coal to computing.

If hybrid intelligence makes AI cheap enough to put into every application, every device and every workflow, total demand could grow even as the cost of each task falls. I made a related argument recently about software itself: cheaper development means far more software gets built, not less. It’s the same pattern, and it cuts both ways. Efficiency is how we make wonderful things affordable, but it doesn’t automatically mean we use less in total.

So I wouldn’t promise that hybrid intelligence will shrink data center demand. I’d say it lets us do far more with whatever capacity we have, which is arguably the more useful outcome, and it buys time while supply catches up. Whether total consumption falls is a different question, and the honest answer is that we don’t know yet.

There are other limits too. Local models are less capable than the largest cloud models, so the routing has to be good. On-device AI uses battery, memory and heat. Different devices have different capabilities, which complicates life for developers. And keeping data on the device helps with privacy, but it doesn’t make an application automatically compliant.

What the future might hold

Let me make a few predictions, and mark them clearly as predictions.

  • Intelligence becomes a utility you don’t notice. Today, using AI feels like an event: you open a chat window, you type, you wait. I suspect that within a few years, most of it will disappear into the background. Your software will quietly handle small intelligent tasks locally, the way it quietly handles spell checking now, and you’ll hardly think of it as AI.
  • The cloud becomes the specialist. The big models won’t go away. They’ll become the surgeons, reserved for the hardest problems, which may make them more valuable, not less.
  • Software gets smaller and smarter at the same time. The old trend was that capability meant bloat. Hybrid design offers a different path: applications that are lean at rest, capable on demand and shaped around each user.
  • Compute becomes a design constraint, not an afterthought. Product teams will start asking, “where should this intelligence run?” alongside “what should it do?” That discipline, designing for efficiency from the start, may turn out to be one of the more important skills of the next decade.
  • The real shortage may be imagination. If we get better at doing more with less, the question shifts from “do we have enough compute?” to “what do we want to build with it?” I’d rather be wrestling with that one.

So, does it solve it?

Not alone. The shortage is a physical problem of power, hardware and time, and no software trick makes a transformer appear. But hybrid intelligence and hybrid software attack it from the other side, by making sure the biggest resources are used only when they’re needed, and by building software that’s capable without being bloated.

That’s the kind of solution I like best: not bigger, just smarter about where the work happens. And the next time a very large computer is asked whether an email is angry or merely brisk, I’d like a small, quiet model on a laptop somewhere to say, “I’ve got this one.”

Planning for the Future

If your team hasn’t yet pulled together the signals behind your current view of the future, the free FutureHacking Signal Picker helps you identify and prioritize the signals worth watching, which gives you the evidence to point to when the board asks where your view of the future comes from.

I’m also finishing the FutureCanvas Picker, which takes planners through the whole arc, from signals to trends to possible futures to your most probable future and the path toward the one you’d prefer. I’m opening early access to a select group first: Chief Strategy Officers, strategic planners and leaders preparing for planning cycles and board conversations right now, who can tell me honestly what works under real deadline pressure. If that’s you, reach out and let me know and I’ll follow up personally with the select few who get in first.

Frequently Asked Questions

What is hybrid intelligence?

Hybrid intelligence is an approach to AI in which small language models run locally on a device for most everyday tasks, and large cloud-based models are used only for complex requests that need more power. Because most requests stay on the device, less work lands on data centers.

Does hybrid intelligence solve the AI compute shortage?

It helps, but it can’t solve the shortage alone. Hybrid intelligence reduces how much demand reaches data centers, which buys time while power and hardware supply catch up. But the shortage is also a physical problem of electricity, equipment and construction time, and cheaper AI can lead people to use more of it, so total demand may not fall.

What is hybrid software?

Hybrid software combines traditional programming with AI. Standard code handles tasks that should give the same answer every time, such as calculations and rules. A small local model handles fuzzy interpretation, such as understanding what a user meant. A large cloud model is called only when a task is complex enough to justify it. The result is software that is smarter and more adaptable without being bigger.

Sources

Image Credits: m365 CoPilot

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.

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The Software Renaissance

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The Software Renaissance

by Braden Kelley and Art Inteligencia

For about two years now, I’ve been hearing the same prediction at conferences, in elevator pitches and at the occasional awkward dinner party: AI is going to write all the code, so the people who write code are finished. Usually the person saying it has the calm, slightly sad expression of someone delivering news about a distant relative.

I’d like to offer a different diagnosis. I don’t think we’re watching the end of the coder. I think we’re at the start of a software renaissance, and the coders are about to be more in demand than ever, just not for the reasons we expected.

The old math of software

Start with why software used to be so scarce. Building a custom application was expensive: months of work, a team, a project manager, a budget with a comma in it. That cost only made sense if you could spread it across a very large number of users or a very large number of dollars. So software got built for the big problems at big companies with big customer bases. Everyone else got a spreadsheet, a pile of sticky notes, and a heroic employee named Linda who “just knows how it works.”

Think about everything that never got built under that math. The scheduling tool for the 12-person plumbing company. The inventory app for the regional distributor. The intake workflow for the three-attorney law firm. The little internal dashboard that would have saved a department four hours a week. Not one of them was a bad idea. They were all just too small to justify the price tag. Linda kept the whole thing running on memory and caffeine.

What changes when building gets cheap

Now bring the cost of building software down by a large factor, which is what AI-assisted development is doing. The math flips. A project that was impossible to justify at $200,000 becomes an easy yes at a small fraction of that. All those never-built applications suddenly clear the bar.

Economists have a name for this pattern. When something gets dramatically cheaper, people don’t use the same amount of it and pocket the savings. They find a hundred new uses for it. We’ve seen it with electricity, with computing power, with bandwidth. Cheaper software won’t mean less software. It will mean vastly more software, built for far more people and far smaller problems.

And somebody has to build, direct and look after all of it.

The coder’s job is changing, not disappearing

Here’s the part the doom-sayers keep skipping. AI can generate code at a rate no person can match. It cannot, on its own, decide what the plumbing company actually needs, notice that the requirements contradict each other, judge whether the thing it built is safe, or keep it running when the world around it changes. That work takes someone who understands systems, who can ask the right questions, and who is willing to be accountable when it breaks at 7:00 a.m.

So the role shifts from typing every line to orchestrating: defining the problem clearly, directing the AI, reviewing what comes back, handling security and data, and maintaining the result. Call it conductor, not typist. And the conductor now has a whole orchestra to lead, for clients who never could have afforded one before.

Every company that couldn’t justify a development team last year has a reason to want at least one capable person this year. That’s a lot of newly interested employers.

What the data says (and doesn’t)

I like to check my optimism against the numbers, so I did. According to Indeed’s Hiring Lab, U.S. software developer job postings rose almost 15% between February 2025 and June 2026, while postings overall fell about 7% over the same stretch. That’s the opposite of what the “AI replaces coders” story predicted.

I’d be telling you only half the story if I stopped there. Those same postings are still about 27.5% below where they stood in February 2020, so this is a partial recovery from a low base, not a boom. And much of the recent growth is in senior roles and roles with AI in the title. Indeed also describes the link to AI tools as a correlation, not proof of cause.

I actually find that last point supports the renaissance thesis rather than undermining it. If the demand is for experienced people who can direct AI and own the result, that is exactly the orchestrator role I’m describing. The loss, at least for now, falls hardest on the entry-level, typing-focused end of the job. That’s a real problem, and it deserves its own conversation about how the next generation of developers gets trained. But it’s a shift in the work, not a vanishing of it.

The deflation angle

Now for the part I find most interesting, and I’ll flag that this is an argument, not a forecast.

The latest numbers show headline inflation at 3.7% in September, up from 3.4% in August. The Fed raised rates to a range of 3.75% to 4.00% last month, its first hike in three years, and has signaled more may follow. When the central bank’s main tool is making money more expensive, it works by slowing down demand. That’s a blunt instrument.

There’s another way to bring prices down: produce more with less. This is the oldest anti-inflation tool there is, and it has nothing to do with interest rates. Productivity.

Here’s how a software renaissance fits in. When a small distributor builds the inventory tool it always needed, it wastes less stock. When the law firm automates its intake, the same people handle more clients. When the plumbing company schedules its crews well, it makes more calls per day. Each of those is a small business doing more with the same or fewer resources. Multiply that by millions of small and mid-sized businesses, and you have a broad productivity gain that pushes the cost of delivering goods and services down, not up.

I don’t want to oversell it. Software alone won’t tame inflation, productivity gains take time to show up in the data, and the forces behind current prices are bigger than any one technology. But if cheap, custom software lets millions of organizations do more with less, that’s a quiet deflationary force working in the same direction as the Fed, and unlike a rate hike, it doesn’t require anyone to give up growth to get there.

What to do about it

If you run a business: make a list of the work in your organization that lives in a spreadsheet, someone’s head, or a stack of email. Each item is a candidate. Then find someone who can orchestrate the AI to build it and keep it healthy, whether that’s an employee, a contractor or a small firm.

If you’re a developer: lean into being the person who can define the problem, direct the tools, check the output and own the outcome. That skill set is in demand and is likely to stay that way.

If you’re worried about all this: I understand. Every technological shift feels like an ending to those standing in the middle of it. But I’ve spent my career studying what happens next, and the pattern is rarely “the work disappears.” It’s “the work changes, and there’s a lot more of it than anyone expected.”

Linda, for the record, will be fine. She’ll finally get to take a vacation.

Image Credits: 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.

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AI and the Enterprise

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Quo Vadis?

AI and the Enterprise

GUEST POST from Geoffrey Moore

Everybody gets that AI is going to change the world, but nobody is really clear as to how, which would be OK except that CIOs everywhere are under pressure to invest now, at a time when things are still forming, norming, and storming. We need to step back for a moment and survey the landscape so we can prioritize where we should engage first and why.

Let me suggest the following framework as a point of departure:

I submit that most of the value of enterprise IT today is delivered by the two systems highlighted in bold—the systems of record, which include finance, HR, supply chain, purchasing, and the like, and the systems of engagement, which include sales, service, marketing, commerce, and the like. These are the mission-critical stakeholder systems that define every enterprise’s relationship with its customers, partners, investors, regulators, and employees. They are quite simply indispensable, and as such, they are the anchor tenants of the Stack, with all the other elements ultimately justifying their existence by being in service to one or the other of the two.

Both systems rose to prominence in the 1990s as client-server applications running atop the Internet, and in this century, they have been advanced dramatically by two sets of adjacent sets of systems, the systems of infrastructure, which include cloud computing, mobile computing, data management, cybersecurity, and hyperscale outsourcing on demand, and the systems of collaboration, which include video conferencing, file sharing, messaging, threaded discussions, e-signatures, and the like. These have enabled companies to expand both the external reach of their systems of engagement and the internal productivity of their systems of record.

Now, we are seeing the emergence of a third set of systems, still nascent, consisting of systems of intelligence, which include predictive AI and generative AI, and systems of autonomy, which include agents. Both are based on advanced statistical software that can learn, and both are driven by machine learning feasting on all the data it can hoover up, including massive troves of log files that were never before examined except forensically.

The question before us is how will systems of intelligence and systems of autonomy impact the operation of the Stack as we have known it to date.

The first claim of this framework is that the AI-enabled systems will operate in the middle of the Stack, not at the top, and not at the bottom. That is, they will not displace any of the other systems but rather, will enhance them dramatically by operating behind the scenes. To get to more specific claims, let’s look at each layer of the stack one at a time.

Systems of Infrastructure

With respect to our systems of infrastructure, the most astounding impact of AI to date is the realization of just how much compute and storage it can consume. To generate a competitive Large Language Model (LLM) requires a hyperscale compute footprint that only a handful of companies have the resources to deploy. When we hear that Microsoft has invested $13 billion in OpenAI, we can rest assured that the bulk of that will come in the form of compute service. Ditto for Amazon, Meta, and Google.

Everyone else will need to license one or more of these LLMs and then adapt it for use in their own Stack. These adaptations, in turn, will rely heavily on first-party data from the enterprise’s own systems of record and engagement, supplemented by data from their systems of collaboration, as well as licensed second-party data, and publicly available third-party data. Extracting all that data, normalizing the metadata, filtering out the information that is protected by data sovereignty regulations, and staging the rest in a data lake for real-time use cases represents the most immediate challenge for CIOs today.

For many real-time applications in the physical world, latency issues will dictate that some processing needs to be done at the edge rather than in the core. This calls for a new kind of PC server armed with a GPU as well as a CPU, running a real-time operating system, connected under next-generation cyber-security protection. By contrast, digital-only applications for the virtual world, including most use cases for systems of record and systems of engagement, require modest infrastructure changes, if any. The current generation of co-pilots all run on the current end-user platforms, with the heavy lifting all being done by either in the core.

Systems of Record

Systems of Record, as we have already noted, are the foundation of enterprise operations. They are intentionally conservative by design. This ensures their integrity, but at the expense of ease of use and adaptability to circumstance. AI changes this game dramatically.

Generative AI augments the click-based UI of the core system, which users need to be trained on, with a natural language interface that is much more forgiving and can be learned through trial and error. Advanced use cases can be created through prompt engineering, allowing a general-purpose LLM to be used in tandem with proprietary enterprise data to ensure privacy, integrity, and relevance. These prompts can be reused and are likely to become an important reservoir of trade secrets.

Predictive AI is an even bigger game-changer. We have been doing this long before the current AI wave but via software that is preprogrammed and does not learn. Machine learning allows for continuous discovery of next-best actions, be they for predictive maintenance, fraud detection, energy optimization, demand forecasting, or product sourcing. It is like having a six-sigma black belt on duty 24/7.

Agents are a bit more dicey, particularly for regulated applications or ones that pose liability issues. Here a human-in-the-loop co-pilot model is likely to prevail for a long time to come, even after it has been shown conclusively that agents can do the job as well as, or even better than, humans. Think X-ray diagnosis or self-driving cars.

Systems of Engagement

We are skipping up to the top because systems of record and systems of engagement have much in common. That said, they are less conservative because they must continuously adapt to unpredictable workflows based on prospect, customer, transaction type, market, and use-case.

Generative AI has a much bigger role to play in these market-facing applications because, in addition to all the UI benefits mentioned earlier, it can actually substitute for human beings in Level One interactions, including creating and running email marketing campaigns, fielding customer service requests through both email and chat, supplying field engineering professionals with on-demand technical support information, alerting sales professionals to next-best actions, and the like.

Predictive AI, on the other hand, is more of a stretch because human factors are less predictable than system behaviors. Nonetheless, data-driven decision-making trumps intuition in the long run, and advanced statistical software that learns outperforms even the best humans eventually, as our friends at DeepMind taught us with respect to the game of Go. Sales forecasting and market campaign attribution are two areas in particular where there is low-hanging fruit to pick. It just takes more patience and, given the reputational risks involved in market-facing interactions, a more prudent approach to rolling anything out at scale.

Agents fall somewhere in between in that, for highly routine interactions, they can shine and actually provide better customer service than humans, as we all learned many years ago from ATM machines who trumped in-bank tellers from day one. One thing to guard against here is to measure the success of such programs by cost savings instead of friction reduction, the latter creating a much bigger payoff in customer loyalty, active use, and churn reduction.

Finally, co-pilots for all customer-facing functions are really a no-brainer. They do not lose context, they do not lose track of time, and they do not get bored with routine. In addition, they are great at prompting and taking a first cut at any natural language task. The critical issue here is focus. The more tuned the co-pilot is to your specific business, the more impactful its contributions will be.

Systems of Collaboration

First of all, we need to appreciate the fact that systems of collaboration are a differentiating source of information for any enterprise. That is, more than any other source, they represent the quality and texture of all your relationships in flight, not just their progress toward achieving your targeted outcomes. They embody who you are and what you care about. By including such data in data lakes that feed your AI models, not only will you improve your systems of collaboration themselves but also make better recommendations to your systems of record and engagement.

Specific to improving collaboration itself, AI excels at summarizations that cut through the clutter of long communication threads, timely reminders that keep workflows from bogging down, and sentiment analyses that can detect concerns and improve tone. The more your enterprise depends on nuanced knowledge work, the higher such improvements need to be on your priority list.

Systems of Intelligence

As you can see from the foregoing, in my view, we should not think of systems of intelligence as a separate layer in the Stack but rather as a technology infusion that changes its very nature. That is, our core business systems are poised to become more intelligent. Indeed, a fruitful metric might be something like a “system IQ test,” which CIOs could use both to assess their current state and to target their future state, all to be done by overlaying and integrating a layer of advanced statistical software that learns. As the BASF slogan used to say, “We don’t make the product, we make it better.”

What we should not endorse is the notion that Systems of Intelligence, or indeed any form of AI, is going to “take over.” Yes, they could have unintended consequences, but no they could not have Game of Thrones ambitions.

With one possible exception.

Systems of Autonomy

Systems of autonomy are the natural extension of systems of intelligence wherever the task in question can be done better by a machine than a person. We use them to place digital advertising, to get astronauts to space stations, to orchestrate the Internet, to run the GPS applications that help us navigate the world, to detect military threats, to prevent spam. They are an indispensable part of the digital transformation we are still in the midst of, and their role will only increase going forward.

The question is when you take the human out of the loop, who or what is in charge? This is not an intractable problem, but it is also not one that will get solved theoretically. This one will require lived experience, a history of successes and blunders, some pleasant surprises, and some very unpleasant unintended consequences. We should not be shocked. Life never stops evolving, and natural selection never stops operating. We just need to step up.

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

— Image credit: Gemini, Geoffrey Moore

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The Actual Future of AI

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The Actual Future of AI

GUEST POST from Geoffrey Moore

AI is the number one topic in the boardroom this year, and everybody wants to know how it is going to play out and what that means for their enterprise. Well, it’s not as if we have never seen versions of this movie before. How did the Internet play out? The Worldwide Web? eCommerce? Cloud computing? Smartphones? Digital marketing? Ride-sharing? Streaming media?

This is the Technology Adoption Life Cycle at work, for sure, and has been the basis of my life’s work and that of many colleagues as well. That said, however, most of our time has been spent helping clients see how best to navigate the current state of that life cycle, either as a disruptor or a disruptee. That’s all well and good, but what the board really wants to know is what can we expect from the end state. Where will all this land, and what future do we need to prepare for?

To answer this question properly we need to lay out the landscape of work along two axes (shockingly, creating a 2X2 matrix, the gold standard for all consulting models). One axis discriminates between output that is differentiating, giving customers a reason to choose your offers over those of your competitors, in contrast to work that is industry standard, things every competitor is expected to deliver. The other axis discriminates on the basis of risk exposure, distinguishing between outcomes that are mission-critical where failure is not an option, in contrast to outcomes that can tolerate a certain amount of scrap or rework.

We call this the core/context framework, and when overlaid onto the future of AI, it can look something like this:

Core Context Framework Geoffrey Moore

In the context of this diagram here is how I predict the next ten years of AI will play out:

1. Stupid stuff first. One thing to which we will all testify is that there is no lack of stupid stuff encumbering the workflow of our daily lives. Wherever bureaucracy reigns, there will be an unending stream of compliance requirements, be that in the public or the private sector, and we suffer this burden because it does underpin the rule of law, albeit onerously.

Fulfilling these requirements does not take creative genius, it takes attention to detail and considerable patience. Humans are not strong on either front, but AI is, and this is where enterprises are already weighing in and will continue to do so for the rest of the decade and more.

That said, the ROI from such efforts is modest. Basically, we are automating an inefficient system, which does reduce cost, and does free up human talent to be used elsewhere. But what we find is that human talent is not as fungible as we would want, many displaced workers cannot find employment at a comparable salary, and the rest of the enterprise is not improved in any marked way. Things overall are better, but not a lot better.

2. Cool stuff in parallel. When it comes to new technology, wherever the risk barrier is low, technology enthusiasts will lean in regardless, whether that be on their own time or their employer’s. What they are doing is playing with an inefficient system, seeing what the bright new shiny object might bring to the table. This does take creative genius as well as some creative license, so once again, patience is required, but this time it is on the part of the investor or supervisor.

That said, in the domain of consumer products and services, there is the potential for blockbuster ROI here, as any number of freemium plays have demonstrated in the past. Many are short-lived, to be sure (remember ringtones?), but in their short life, they can capture an extraordinary amount of discretionary spend. And others can mature into abiding infrastructure as search, messaging, and file sharing have all demonstrated.

The key here is that these are not mission-critical and, therefore, do not attract regulatory regimes. If and when they should do so, as is happening with social media now, then their economics can turn upside down in a hurry.

3. Serious stuff takes time. This is where most of the big and abiding ROI will come from. When work is mission-critical but not differentiating, it requires considerable investment in table-stakes processes, which garner no premium in the marketplace. To be brutally honest, there is no prize for doing this work well but considerable penalties for any mistakes you make (think ransomware for a bone-chilling example).

In the 1990s, there was an Internet-enabled revolution in enterprise manufacturing that introduced global outsourcing as a high-ROI way to address mission-critical context workloads—move the work from your company, where it is context, to their company, where it is core. This was a hugely disruptive innovation, allowing the economies of China, India, and now Southeast Asia, to expand at exponential rates, creating increasingly vibrant middle classes where there were none before. However, these benefits have come with a price, both in terms of geopolitical risks, supply chain vulnerabilities, and societal downsides, all of which we are still trying to come to terms with.

Here, AI can have a major impact, not by automating an efficient system, but by completely reengineering it. This will entail on-shoring a lot of manufacturing and displacing a lot of call centers as we introduce more and more AI into these mission-critical workflows. The gains will be in faster response to changing market demands, better returns from high-variability small-volume manufacturing runs, and reduced scrap and rework. At the outset, these changes will entail a considerable amount of human-in-the-loop processing, but eventually, because AI-enabled systems need never stop learning, the human will become the biggest source of error in the system, and we will transition to full autonomy (self-driving cars will be a case study of this transition).

4. Game-changing happens when it happens. There is a reason why so many billionaires have appeared in my lifetime (I personally forgot to become one). Their fortunes are the natural outcome whenever a disruptive innovation does not simply reengineer the existing inefficient system but instead completely replaces it. Amazon Prime, Uber, Netflix, Salesforce, NVIDIA, Apple — each company categorically changed the landscape, rendering the incumbents irrelevant, redirecting the cash flows of their entire industry to flow past their doors.

These outcomes are inevitable, but they are also unpredictable. They are what puts the venture in venture capital. The good news for incumbents is that they do not happen very often, and even when they appear to be succeeding, they can still stumble and fall. That said, sooner or later there will be a reckoning, and that is when the incumbents need to activate their Transformation Zone if they are going to survive the transition.

My altruistic wish is that for AI, this time around, the target will be the public sector. Higher education, social services, healthcare, and law enforcement are all staggering under increasingly untenable demands accompanied by shrinking workforces and restricted budgets. All of them are target-rich environments for applying AI both to stupid stuff and serious stuff. We have never disrupted these sanctums in the past, each having been ruled by regulatory regimes and professional guilds that have deeply conservative roots blocking the kinds of changes that are now needed. Displacing these systems will take decades, but the ROI, both financial and social, would be truly game-changing.

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

— Image credit: Gemini

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AI’s Absurd Response to an Absurd Question

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AI's Absurd Response to an Absurd Question

GUEST POST from Shep Hyken

This article answers the question: Can AI be fully trusted to handle customer service issues, and what are the limitations of relying on AI for customer support?

As I write this article, I’m at the CX Malaysia conference, where I was the opening speaker, watching Dr. Shreekant Vijaykar of COPC Inc., one of the other speakers, talk about AI and how you can’t always trust its responses.

Dr. Vijaykar said that he asked ChatGPT the following question:

“I just bought a pair of shoes from Amazon, and they messed up the order. The shoe for the right foot was for the left foot, and the shoe for the left foot was for my right foot. What should I do about this mistake?”

He then shared that the response was to contact Amazon to let them know about the mistake, and they will refund the purchase price or exchange the shoes. I didn’t believe him, so I asked ChatGPT the same question. To my surprise, here was the answer:

That’s definitely a strange mix-up – and something Amazon should fix quickly. Here’s what to do:

  • Start the Return/Replacement Process: Go to Your Orders on Amazon. Find the shoe order and click “Return or Replace Items.” Choose the reason: “Wrong item was sent” or “Item defective or not as described.” Amazon will usually offer a free return label and a replacement option.
  • Describe the Issue Clearly: In the notes, explain exactly what happened–something like: “The right shoe is shaped for the left foot and vice versa. The pair I received cannot be worn properly.” That level of clarity helps Amazon’s quality control team flag the issue for the seller or manufacturer.
  • Choose the Replacement (if you still want the shoes): If you like the shoes and they’re still available, request a replacement instead of a refund. Amazon will often ship it out before your return even arrives.

Shep Hyken ChatGPT Cartoon
I took this absurd response a step further and wrote: “Maybe there’s a better response. It’s not really a mistake. … Just put the shoes on the opposite feet and they will work just fine. Thoughts?”

ChatGPT’s response was:

“Ha! That’s a funny way to look at it, but unfortunately, that’s not quite how shoes work. Even if you can technically put them on opposite feet, they’ll be uncomfortable and could mess up your posture or cause pain after a while – especially if you wear them for long periods.”

Dr. Vijaykar’s experiment revealed an important truth about AI: It makes mistakes. ChatGPT treated two normal shoes as if they were defective, offering a detailed explanation of Amazon’s return process.

AI is powerful, but just like humans, it can make mistakes. This ridiculous exchange is why some customers fear using AI for customer support. If you use AI tools like ChatGPT, Claude, or others, you must have a way to check answers. Apparently, common sense isn’t something that can be programmed. At least, not yet!

Image Credits: Pixabay

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Is Your AI in the Zone to Win?

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Whether you are catching up, keeping up, getting ahead, and fending off an existential threat, get in the zone!

Is Your AI in the Zone to Win?

GUEST POST from Geoffrey Moore

We’re already almost three years into the modern AI era, and everyone wants to know — What are we doing with AI? The board wants to know, the sales team wants to know, the customers want to know, the analysts want to know. Heck, you want to know.

So, how do you decide?

Start with a clear-eyed assessment of where your company stands relative to its peers in your industry. Are you behind? Are you on par but need to keep up? Are you out ahead or have a chance to be so? Or, in a darker vein, is AI opening your entire industry up to disruption, putting you and your peers under existential threat? The good news is that there are playbooks for dealing with each of these situations. The caveat is that they are all different. You need to execute them, if not linearly, then at least separately. And that’s where zone management can be a big help.

Catching Up

Industries in catch-up mode with respect to implementing AI might include retail (supply chain management, contact center productivity), higher education (admissions, alumni relations), and public sector (regulatory compliance services). The wolf is not at the door, but with AI moving as fast as it is, time is not your friend, so you need to get cracking. This is a job for the Productivity Zone.

Every organization in this zone — finance, HR, marketing, purchasing, customer success, security, you name it—is a candidate for implementing some kind of AI on a low-risk, get-acquainted basis. None of these applications will be so dramatic as to disrupt normal services, but each will give your AI team hands-on experience with the latest available technology, and most should deliver enough ROI to pay for themselves. Even when they don’t, they will contribute to the “Win or Learn” kitty, and that is what catching up is all about.

The key here is to move fast and on every front. That means every organization in this zone has to participate every quarter. Conduct AI progress reviews to hold each org leader accountable for net new wins or learnings every quarter. The goal is to catch up within a year, and it is important enough to tie performance to discretionary compensation to ensure both prioritization and inspection.

Keeping Up

Industries in a keep-up model with respect to implementing AI might include insurance (underwriting, claims), management consulting (tax, audit), and public sector (tax collection). All these areas represent customer-facing processes that are currently served by humans loaded down with routine work that can be done better, faster, and cheaper by AI applications. This is a job for the Performance Zone.

The goal here is to use AI to increase the competitiveness of your established lines of business, either through materially differentiating your products or dramatically reengineering your processes to lower costs, speed response times, and improve quality. The current generation of AI technology, because it is so remarkably approachable, is ready-made to take on this work. Your job is to make sure you use it to target those opportunities where there is the most trapped value to release. These will likely be a bit more gnarly than the others, but when you are mining for gold, you have to go where the gold is.

Generating such higher returns does not come without taking risk. Good as it is, AI is still a work in progress, so you will likely be taking a human-in-the-loop approach for the foreseeable future. The good news is your workforce is expert in your business, so you have the guard rails you need already in place. The challenge is that we humans are comfortable in our established routines, and you need everyone to break out of the old ways to free your company’s future from the pull of the past. This is more of a change management problem than an AI issue, so you should have no qualms about holding the leaders of this zone accountable.

Getting Ahead

Industries with a lot of built-in trapped value represent opportunities for first-movers to get ahead of their peers by radically reengineering the way business gets done. Examples might include residential real estate (title insurance, buyer agent compensation), health care (value-based care, home care), and public sector (social services). In each case, traditional bureaucracies are at odds with where the industry needs to go next, and implementing AI applications can be highly disruptive. This is a job for the Incubation Zone.

Venture-backed start-ups are normally the fastest movers here, but they take a long time to scale. Established enterprises have the customers, the ecosystems, and the balance sheets to get to the finish line first if they can get out of their own way. That’s what the Incubation Zone is designed to do. As described at length in Zone to Win, it emulates the VC operating model without attempting to replicate its financial model. The goal is to win early market marquee customers and cross the chasm, all without any help (or hindrance) from the core business. You have all the resources you need to do this, but it requires muscles you haven’t used in a long time, so funneling one or more acquisitions into the Incubation Zone is often a good tactic.

The key challenge will come when you reach enough scale to bring the new line of business into the Performance Zone. In the best of circumstances, you can leverage the more forward-thinking elements in your partner ecosystem and customer base to create a soft landing, running both the old and new lines side by side, as Netflix did for some time with its DVD and streaming businesses. Sooner or later, however, you will have to rip off the Band-Aid and make the transition to the new path, again as Netflix did.

Fending Off an Existential Threat

At present, the existential threat posed by Generative AI and its successors is still hard to predict, but two industries that have already sensed it are media entertainment (content creation, acting) and publishing (copyright, fair use). What should their playbook be?

This is a job for the Transformation Zone. The playbook requires all four zones to fly in formation to get through a very rough patch. The Productivity Zone goes into action first, launching legal actions against the invaders and pursuing lobbying efforts to get protective legislation. This is not a long-term solution, but it does buy some much needed time.

Meanwhile, the Incubation Zone is charged with catching up to the new wave as fast as possible. The goal here is not to out-innovate the innovators. That is what Yahoo tried to do to fend off Google, and Nokia to fend off Apple. The attackers are too good at what they do, and you are playing their game. Instead, take a lesson from how Microsoft has played catch-up throughout its storied history, beginning catching WordPerfect with Word, Lotus 123 with Excel, Aldus Persuasion with PowerPoint, and moving on to the Mac GUI with Windows, Novell with Windows NT, and Netscape Navigator with Internet Explorer. Most recently, they executed the catch-up-fast playbook to head off Amazon Web Services with Azure. The key to their success is to get to “good enough, fast enough,” not to out-perform the disruptor but to keep their own existing customer base on their side, again buying time to innovate further once it is clear they are in the game to stay.

One thing you do not want to do as an established enterprise is to merge with a successful disruptor. The Time Warner AOL merger provides a cautionary lesson here. The cultures are too different, and the necessary level of mutual trust just isn’t there, so instead of running in parallel, they work at cross purposes, and the result is a tangled mess.

On the Performance Zone side, you have to keep pedaling (and peddling) the legacy line of businesses. Absent private equity, they are your only source of capital, and they are still providing value. But you have to realize that their profit margins are under direct attack and can only be defended through rear-guard actions. That is, your legacy profit pool is the current site of trapped value, and draining it is what is funding the next wave of innovation. You don’t like it, and your investors hate it, but that’s the hand you have to play.

And that brings us to the Transformation Zone proper. You are going through a transition, the intermediate stages of which are ugly, making everyone cranky, and causing rampant second-guessing of every move you make. This is where the CEO must lead with clarity, transparency, and conviction, rallying the troops, reaching out to the customer base, providing an investable narrative to the stakeholders, and reassuring the partner ecosystem. Moreover, every senior executive must unequivocally support the chosen path or else be asked to leave. When you are under existential threat, there can be no fooling around.

Summing Up

The AI tsunami is upon us, and we can expect wave after wave of disruption for the rest of this decade and the next one as well. Clearly, it offers a wealth of opportunity, but as with all waves, catching it depends on getting your timing right and finding the right angle of attack. The four playbooks outlined above have been tested over many decades within the high sector as it dealt with the disruptive impact of the microprocessor, the Internet, cloud computing, SaaS applications, smartphones, and social media. You may not be as familiar with them as you would like, but the risk of waiting on the sidelines exceeds the risk of taking the plunge, so I can only encourage you to grab your nose plugs and jump in.

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

— Image credit: Gemini

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Unleashing Your Innovation Potential

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Unleashing Your Innovation Potential

GUEST POST from Janet Sernack

It is not unusual, especially in the corporate world, to label someone who disrupts and challenges the status quo, or what others say, as argumentative and “oppositional”. According to Human Synergistics, the Oppositional Style, in their circumplex, when measured as a dominant leadership style, is summarized in their 360-degree Leadership Styles Inventory (LSI) by two items: “usually against things” and “opposes new ideas.” It labels the person, or leader as being aggressively defensive and causes them to disengage by avoiding engaging in any kind of conflict, dissent and disagreement. This inhibits and prevents them from unleashing their creative thinking and their innovation potential.

In my many years as a corporate trainer presenting high-performance cultures and transformational leadership programs, I learned to apply my imagination, curiosity and inquiry skills to be attentively present, listen deeply and ask generative questions. This helped me develop strategies to unleash a person’s innovation potential by encouraging and enabling thoughtful dissent and disagreement.  I learned to uncover and recognize a leader’s energy and positive intent behind many of the scary, argumentative, hostile, contrary, critical, and aggressive behaviors directed at me, especially in my years upfront as a female corporate trainer in many toxic organizational cultures.

I often found that they were compromised in some way by their personal and family needs and values, their assumptions and beliefs about themselves and their performance at work, poor physical and mental health, and/or a lack of emotional well-being. Any one of these factors made them self-protective, leading to either overt aggression toward others, passive avoidance of conflict, or an unconscious refusal to engage in thoughtful dissent and disagreement because they did not want to be perceived as hostile and argumentative.

All of these unconscious reactive responses inhibited them from unleashing their creative thinking and innovative potential. 

This blog explores the power of unleashing creative thinking and innovation potential through supporting people to thoughtfully dissent and disagree – by using conflict as a catalyst for better decision-making, problem-solving, and strengthened collaboration and innovation – not as a barrier.

Unconscious reactive responses

This manifests as a neurologically challenging reactive response, as people’s brains are wired to defend and protect them to ensure their survival. These defensive responses are composed of fight/flight/freeze/fawn reactions and arise when people face what their neurology (the limbic system) perceives as a threat to their security, safety, or survival.

These reactions stem from past experiences and traumas that are often stored in people’s somatic memories. They become neurologically wired and embedded in people’s habitual ways of being unless they are noticed, labelled, accepted, acknowledged and owned. Until there is a safe holding space for this to happen, it will be impossible to unleash their creative thinking and innovation potential.

Safe holding spaces

Once a safe holding space is established, an oppositional person can be carefully and empathetically challenged, disputed, deviated from, and redirected towards more self-regulated, resourceful, and emotionally healthy states and new approaches. This involves bravely engaging them in thoughtful dissent and disagreement, despite being encased in a toxic organizational culture. Its about building permission, safety and trust to encourage creative thinking that unleashes a persons innovation potential.

Fight-flight-freeze and fawn responses

The amygdala is designed to detect threats and trigger the fight-flight-freeze response to help people survive

  •  A fight response involves standing our ground when we feel threatened, driven by anger, fear, and anxiety.
  •  A flight response entails moving away from or avoiding a situation to reduce painful feelings.
  •  A freeze response involves shutting down, feeling numb, or experiencing paralysis to reduce painful feelings, resulting in immobility.
  • A fawn response involves appeasing and placating others or seeking approval while neglecting the need to feel safe.  

Role of smart conflict, thoughtful dissent and disagreement

This is particularly evident in political and organizational contexts, where people are suffering from change fatigue and overwork. As a result, they unconsciously disengage from their work, families, and daily lives through a flight-or-freeze response, driven by burnout, the pursuit of external validation (a fawn response), or frustration and anger (usually a fight response). A combination of any or all of these largely unconscious but fully present reactive responses creates toxic organizational cultures, in which leaders’ behaviors are either passively or aggressively defensive in their attempts to meet people’s core security, safety, and survival needs. Leaders avoid encouraging or engaging in thoughtful dissent and disagreement that mobilizes people’s creative and critical thinking and unleashes their innovation potential.

The constraints and challenges of the pandemic, coupled with threats to people’s stability and the desire for certainty, also led many people in organizations to fear that their needs for security, safety, and survival would not, and could not, be met. These fears unconsciously intensified people’s defensiveness and reactivity, leading to immobilization through passivity, helplessness, hopelessness, powerlessness, disengagement, and detachment. This indicates a lack of emotional intelligence, largely due to the absence of initial self-regulation and self-leadership strategies.  It is easier and sometimes safer to be a victim and blame others for any negative, pessimistic, or painful feelings of shame, guilt, and embarrassment, resulting from competing commitments, value violations, thought distortions, and emotional dissociation.

These emotionally overwhelming and cognitively overloading factors are also accepted inhibitors to unleashing a person’s innovative potential, especially when they are unable to engage in thoughtful dissent and disagreement under the influence of a toxic organizational culture.

Making the shift

To support people to effectively shift this way of being and safely unleash their creative thinking and innovation potential:

It is crucial to develop and apply the generative discovery skillset to welcome dissent and thoughtful disagreement:

  • Notice: by being aware and attentively present to what is happening to, and within the person, neurologically and physiologically, emotionally, cognitively and physically.
  • Disrupt: by being curious and safely asking open, explorative discovery questions, and by listening carefully to hear and notice what is really going on for them.
  • Dispute: by safely summarizing and challenging their assumptions, perceptions and perspectives, asking evocative and provocative questions that encourage contrary thinking, diversity of thought, differences and constructive disagreement.
  • Deviate: by safely summarizing and eliciting a mindset flip or an agility shift in the oppositional person, directing their creative energy towards constructive behaviors that deliver a positive outcome.

This enables people to redirect their emotional energy and move away from unresourceful internal thought patterns and distortions. It also encourages them to open their minds to thoughtful dissent and disagreement, allowing them to think differently and enter creative and innovative realms. 

This is impossible when toxic organizational cultures force people to hide behind conventional, rigid, and closed minds that won’t allow them to rock the boat and be, think and act differently by intentionally using conflict constructively by embracing dissent and thoughtful disagreement 

At the same time, it’s crucial to safely, empathically and compassionately evoke and provoke an opening of people’s hearts by purposefully and meaningfully aligning their needs and values, so that the change motivates them to change meaningfully and purposefully. This creates the safe holding space that allows them to let go of the need to be in control and be open-willed, unleashing their creative thinking and innovation potential to make the shift to the creative and innovative realms.

Empowering people and teams to redirect their emotional energy towards opening their minds to thoughtful dissent and disagreement allows them to handle hard conversations with constructive and creative intent.

Welcoming dissent and thoughtful disagreement involves daring yourself and others to be and think differently. Being able to think creatively maximize a person or a teams innovation potential, unleashes their human ingenuity in the age of AI, and mobilizes their collective intelligence to lead, manage, or implement constructive and sustainable change.

Leveraging constructive conflict, dissent and thoughtful disagreement unleashes people’s creative thinking and innovation potential and is a necessary part of enabling AI, digital transformation and innovation initiatives to succeed in uncertain and disruptive times.

Find out more about our work at ImagineNation™ and The Start-Up Game.™ Discover our collective learning products and tools that can be customized as playful bespoke corporate learning programs. Our blended and transformational change and learning programs provide a deep understanding of the language, principles, and applications of an ecosystem-focused, human-centric approach and emergent structure (Theory U) to innovation and entrepreneurship. It will also upskill people and teams, developing their future fitness within your unique digital transformation and innovation initiatives. Please find out more about our products and tools.

Image Credit: Gemini

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Dead Actors Society

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How AI Synthetic Likenesses, Estate Licensing, and the Experience Economy Are Disrupting the Talent Ecosystem

Dead Actors Society

GUEST POST from Art Inteligencia

I. Executive Summary & Thesis

The Paradigm Shift: Generative AI and real-time neural rendering are fundamentally decoupling an actor’s craft and visual identity from their physical body, availability, and natural lifespan. Cinema is transitioning from a discipline constrained by human logistics to an unconstrained digital canvas.

The Core Thesis: In the emerging era of AI-generated full-length feature films, the estates of deceased cultural icons—unencumbered by living human limitations, scheduling conflicts, or creative resistance—are uniquely positioned to lead the charge in licensing synthetic likenesses for entirely new cinematic roles.

The Macro Impact: This transition extends far beyond Hollywood production budgets. It represents a fundamental restructuring of the creative talent ecosystem:

  • Talent Ecosystem Disruption: Living performers will no longer compete solely against current peers, but against a century of cinematic legends performing at their peak aesthetic and charismatic influence.
  • Strategic Career Pressures: Mid-tier and emerging actors face extreme wage deflation as synthetic legacy assets provide predictable, risk-mitigated alternatives for studios.
  • Likeness Securitization: A high-yield financial marketplace—directly mirroring the multi-billion-dollar music catalog acquisition booms—will emerge to monetize, package, and trade post-mortem digital likeness rights as long-term yield assets.

II. Introduction: The Arrival of the Synthetic Cinema Era

The Friction of Change: For decades, visual effects relied on heavy post-production labor to achieve incremental milestones—de-aging an aging star for a brief flashback or rendering a digital double for high-risk stunt work. Today, generative neural rendering and real-time motion synthesis have crossed a critical threshold. We are shifting rapidly from post-production touch-ups to full-synthesis cinematic creation, where generative models can power entire lead performances across full-length feature films with hyper-realistic emotional fidelity.

The “Dead Actors Society” Phenomenon: As production costs drop and synthetic rendering capability matures, a new content category is taking shape: the deliberate, high-budget revival of iconic performers in entirely original narratives. This is not about re-editing archival footage or stitching together outtakes. This is the era of the Dead Actors Society—a dynamic marketplace where legendary figures from film history return to headline original screenplays, cross-genre experiments, and modern franchises decades after their passing.

The Human-Centered Lens: From an experience design perspective, human beings do not connect merely to high-resolution pixels; we connect to narrative resonance, archetypal familiarity, and shared cultural memory. In an increasingly fragmented media landscape, iconic stars carry immediate emotional context and built-in trust. For studios navigating rising development risks, leveraging synthetic legacy talent offers a powerful mechanism to derisk major film slates while tapping directly into deeply ingrained audience nostalgia.

III. Why Estates Will Lead the Licensing Charge

Incentive Alignment (Frictionless Talent): Unlike living performers who manage complex personal brands, physical constraints, and evolving artistic ambitions, estate management operates primarily as an intellectual property enterprise. For estate trustees, licensing a digital likeness eliminates traditional production friction: there are no onset delays, travel requirements, physical exhaustion, or behavioral liabilities. The actor becomes a predictable, high-performing digital asset capable of infinite deployment.

Algorithmic Consistency & Archetypal Clarity: Iconic stars of cinema’s Golden Age—such as Humphrey Bogart, Marilyn Monroe, or James Dean—possess clearly defined, universally understood cultural archetypes. Because their screen legacies are static, generative models can synthesize their specific charismatic signatures, vocal cadence, and emotional range with remarkable precision. Studios gain access to instant brand recognition and established storytelling shorthand that requires zero audience warm-up.

Economic Incentive for Heirs: For heirs and asset managers, passive ownership of legacy rights often faces diminishing returns over time as catalog titles recede from active streaming discovery. Transitioning static IP into active, synthetic licensing models transforms dormant archives into dynamic, high-margin revenue streams. Through royalty-per-frame or box-office participation models, estates can capture continuous commercial value across future generations of media.

IV. The Squeeze on Living Talent: Pressures and Disruption

The “Infinite Competition” Problem: Throughout cinema history, living actors competed primarily against their contemporary peers for coveted roles. In the synthetic cinema era, that competitive arena expands infinitely backward across time. Emerging and established talent will find themselves auditioning not just against current box-office leads, but against a century of screen legends preserved at their peak aesthetic, physical, and charismatic influence—available to perform on demand without fatigue or scheduling conflicts.

Bifurcation of the Acting Profession: The economic pressures of synthetic competition will restructure the performer labor market into two distinct tiers:

  • The Ultra-Elite Tier: A small upper crust of living megastars whose commercial value relies on genuine human presence, active cultural commentary, live press tours, and authentic real-world fan connections.
  • The Squeezed Middle and Entry Level: Character actors, supporting talent, and working professionals who face severe wage compression and diminishing opportunities as studios opt for cost-effective, risk-mitigated synthetic legacy models for mid-tier roles.

The Experience Value Proposition: As synthetic performances achieve technical parity with human delivery, experience design forces a critical question for creators and audiences alike: What is the intrinsic value of human vulnerability in art? While mass-market entertainment may readily accept polished synthetic performances, a premium live-action market may emerge, marketing the deliberate imperfection, unpredictability, and lived experience of authentic human performers.

V. The Financialization of Likeness: Wall Street Meets Hollywood Catalog Sales

The Music Industry Blueprint: Over the past decade, financial institutions and private equity firms created a multi-billion-dollar asset class by purchasing the publishing rights and master recordings of legendary musicians—from Bob Dylan to Bruce Springsteen. The core thesis was simple: predictable, long-term cash flows from enduring cultural IP. Synthetic cinema opens the exact same financial playbook for screen performance, transforming an actor’s visual and vocal identity into an yield-bearing financial asset.

Likeness Securitization & Valuation Models: As generative models require clean, high-density training data, an actor’s digital archive becomes quantifiable. Wall Street valuation models will price an actor’s “Synthetic Future Cash Flow” based on three core variables:

  • Training Data Quality: The depth, resolution, and emotional range captured in their historic filmography.
  • Archetypal Demand: How universally their persona maps to high-converting narrative genres.
  • Cross-Generational Longevity: The projected retention of their cultural relevance across global markets.

Pre-Mortem Rights Offloading & Likeness Royalties: Living actors will not wait for death to monetize their synthetic value. We will see performers offload their post-mortem rights—or even license mid-career synthetic clones—early in life to private equity funds for immediate lump-sum liquidity. This will give rise to complex likeness royalty structures, fractionalized ownership of synthetic talent libraries, and secondary derivative markets trading on the future performance of digital personas.

VI. Strategic Foresight: Governance, Ethics, and Experience Design Challenges

Human-Centered Change Management for Hollywood: Navigating the synthetic era requires robust governance frameworks that balance creative freedom with ethical stewardship. Labor unions like SAG-AFTRA, estate trustees, and legislative bodies will be forced to continually redefine right-of-publicity laws, digital consent boundaries, and posthumous labor rights to prevent non-consensual exploitation while enabling legitimate commercial innovation.

Audience Fatigue & Experiential Saturation: From an experience design perspective, over-relying on familiar digital ghosts carries significant narrative risk. When iconic faces become ubiquitous across cheap spin-offs, interactive media, and localized ad campaigns, “nostalgia overload” sets in. This erosion of scarcity dilutes the actor’s original cinematic legacy and risks numbing audience emotional engagement through synthetic repetition.

Authenticity vs. Convenience: As synthetic content generation accelerates, experience designers and filmmakers must intentionally craft the boundary between efficiency and artistry. The challenge will not be technical feasibility, but human resonance—ensuring that synthetic revival serves a genuine artistic purpose rather than functioning merely as a frictionless, algorithmically optimized cash grab.

VII. Conclusion: Framing the Future of Talent

Summary of the New Landscape: The arrival of synthetic feature films does not spell the end of human performance, but it marks the definitive end of its monopoly. Cinema is entering a hybrid era where living performers, purely synthetic AI-generated entities, and licensed digital revivals of historic legends co-exist within the same creative ecosystem. Success in this environment will require a fundamental shift in how studios, managers, and audiences conceptualize talent, IP, and performance art.

Call to Action for Leaders and Creators: As leaders in media, technology, and human-centered innovation, our responsibility is to guide this transition with intentionality. We must build business models and governance frameworks that honor human legacy without stifling artistic evolution. By prioritizing authenticity, ethical consent, and meaningful experience design over mere algorithmic convenience, we can ensure that synthetic cinema expands the horizons of human storytelling rather than cheapening it.

Frequently Asked Questions

Why are the estates of dead actors more likely to license AI likenesses than living actors?

Estates operate primarily as intellectual property enterprises focused on asset maximization without the physical, emotional, or ego-driven constraints of living performers. Unlike living actors, deceased legends face zero physical friction—there are no set scheduling limits, press junket obligations, physical aging, or behavioral liabilities, making them predictable, high-performing digital assets for studios seeking to derisk major film investments.

How will the rise of synthetic legacy actors impact living performers?

Living actors will no longer compete solely against current peers, but against a century of film history preserved at peak aesthetic and charismatic performance. This will likely bifurcate the talent market: an ultra-elite tier of living megastars whose value lies in authentic human presence and live connection, and a severely squeezed middle tier of character and entry-level actors facing wage compression as studios adopt cost-effective, risk-mitigated synthetic models.

Will AI actor likenesses generate a financial market similar to music catalog sales?

Yes. Just as financial institutions transformed musician song catalogs into multi-billion-dollar yield-bearing assets, Wall Street will monetize actor likenesses based on training data quality, archetypal demand, and historic box office impact. Living actors and estates will offload post-mortem rights to private equity funds for immediate liquidity, creating a robust secondary market for likeness royalties and fractionalized talent libraries.


Image Credits: Gemini

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AI Will Create a More Human Future, Not a Less Human One

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An AI Soft Landing Scenario

AI Soft Landing Scenario
by Braden Kelley and Art Inteligencia

What If the Future Gets More Human?

We spend a remarkable amount of time rehearsing the wrong ending.

In one popular story, artificial intelligence hollows out work, flattens craft, and leaves people performing the emotional leftovers of automation. That is a hard landing: humans demoted by systems that do more of everything, including the parts of work that used to make us feel useful.

There is another future available to us, what I call an AI soft landing. In that future, organizations and societies deliberately design AI to absorb fragmentation, acceleration, and low-judgment transaction. What returns to humans is not emptiness. What returns is depth: larger blocks of time for insight, empathy, decision making, direction setting, problem definition, creativity, and collaboration. The future becomes more human, not less, because human attention is finally reserved for human work.

This is not a naive techno-optimism. Soft landings are designed. Hard landings arrive when efficiency is the only value on the dashboard.

The Hidden Enemy Was Never “Work.” It Was Fragmentation.

Most knowledge work did not become less meaningful because people stopped caring. It became less meaningful because attention was diced into tickets, pings, updates, status rituals, and micro-approvals. We mistook motion for progress and responsiveness for value.

Task switching is expensive. Every context shift asks the brain to unload one problem and reload another. Multiply that by a day of chats, forms, triage, and administrative glue work, and you get a workforce that is always “on” and rarely deep. Strategic thinking does not fail only for lack of talent. It fails for lack of contiguous time.

AI’s first gift, if we use it well, is not genius on demand. It is fewer interrupted minutes. When drafting, scheduling, summarizing, searching, classifying, routing, and first-pass analysis get accelerated or handled, the calendar can stop looking like confetti. Bigger time blocks reappear. And bigger blocks are the raw material of original insight.

AI Future of Work

What Humans Should Own in a Soft Landing

A soft landing is not humans “using AI better.” It is a clear division of cognitive labor, one that protects the uniquely human contribution instead of competing with the machine on volume.

In a more human future, people spend more of their capacity on:

  • Insight development — connecting weak signals into meaning, not merely producing more output
  • Empathy — understanding stakes, dignity, and lived context that no dashboard fully captures
  • Decision making — choosing under uncertainty with values, tradeoffs, and accountability
  • Direction setting — naming where we are going and why it is worth the journey
  • Problem definition — asking better questions before rushing to automated answers
  • Creativity — combining perspectives in ways that are novel, useful, and humanly resonant
  • Collaboration — building trust, resolving conflict, and making progress together

Notice what is missing from that list: being the fastest typist in the room. Soft landing excellence is not measured in tokens per minute. It is measured in clarity per hour — and in whether people leave interactions more capable, more trusted, and more oriented than before.

From Transactional Lives to Strategic Ones

When small tasks expand to fill the day, even senior roles become transactional. Leaders spend their best hours approving instead of directing, reacting instead of sensing, facilitating meetings about work rather than doing the work of judgment.

AI can reverse that inversion, but only if organizations stop using every efficiency gain to stuff more micro-tasks into the same damaged attention budget. Saving ten minutes and immediately filling them with ten more interruptions is not transformation. It is denser exhaustion.

The soft landing asks a different operating question: What human capability do we want more of, now that machines can carry more of the glue?

If the answer is “more throughput at any cost,” you will automate people into thinner slices of busyness. If the answer is “more strategic quality, better problem framing, deeper customer and employee understanding,” AI becomes a scaffold for human depth. Less task switching. More deliberate thinking. Fewer performative updates. More real collaboration around decisions that matter.

AI Human Endeavors

How Leaders Design a Soft Landing (Instead of Hoping for One)

Human-centered change makes soft landings practical. A few design moves matter more than tool catalogs:

  1. Automate the glue, not the judgment. Route AI toward fragmentation: search, draft, summarize, schedule, classify, prepare. Keep humans responsible for choices with ethical, relational, or strategic consequence.
  2. Protect deep-work blocks as policy, not privilege. If AI creates capacity, calendar culture must not immediately reclaim it for more meetings.
  3. Redefine roles around human endeavors. Job descriptions should emphasize insight, empathy, problem definition, and direction — not inbox velocity as a proxy for value.
  4. Measure success in human outcomes. Track decision quality, customer trust, employee agency, and innovation usefulness — not only cost per interaction.
  5. Teach the craft of better questions. In an AI-rich world, problem definition becomes a core leadership skill. Bad prompts and bad frames still produce confident nonsense.
  6. Build collaboration for synthesis, not status. Use reclaimed time for cross-functional sense-making, not another dashboard review theater.

This is experience design for the future of work: design the system so people can be fully human on purpose.

The Choice Ahead

Futurology is not prediction cosplay. It is responsibility with a longer horizon.

We can use AI to compress people into ever-faster transaction machines. Or we can use it to return something modern work has been quietly stealing: the ability to think, feel, decide, and create with integrity.

The soft landing is the second path, a future where machines handle more of the small so humans can do more of the meaningful. Where strategy is less of a slide ritual and more of a practiced habit. Where customer and employee experience improve not only because algorithms personalize, but because people finally have the attention required for empathy and judgment.

A more human future will not arrive by accident. It will be designed by leaders who refuse to confuse automation with progress, and who insist that the best use of artificial intelligence is the expansion of human capacity where it still matters most.

Frequently Asked Questions

What is an AI soft landing?

An AI soft landing is a future in which artificial intelligence absorbs fragmented, transactional tasks so humans can spend more time on deeper endeavors — insight, empathy, decision making, direction setting, problem definition, creativity, and collaboration — making work more human rather than less.

How does AI reduce task switching at work?

AI can handle or accelerate small tasks such as drafting, summarizing, searching, scheduling, classifying, and routing. When organizations protect the time this frees, instead of immediately filling it with more interruptions, people gain larger blocks for strategic thinking and higher-quality collaboration.

What should leaders do to make the future more human with AI?

Leaders should automate glue work rather than human judgment, protect deep-work capacity as policy, redesign roles around human endeavors, measure human outcomes as well as efficiency, invest in better problem definition, and use reclaimed time for real collaboration and decision quality — not denser busyness.

Image Credits: Cursor

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

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