Foreword

The question I hear most from clients right now isn’t “should we be using AI?” They’re already using it. The question is why it isn’t doing what they expected.

That question has a more specific answer than most people realize. It is not a technology problem. It is a design problem.

Most organizations adopted AI the same way they adopted every other enterprise tool: they licensed it, distributed it, and waited for productivity to follow. Some of it did. But something else happened too – more output with less clarity about whether any of it was moving the organization forward. More speed with less confidence that speed was the right direction. More AI involvement in the work, and quietly, less human judgment applied to the moments that actually mattered.

I’ve watched this pattern with enough clients to know it isn’t a coincidence. It is what happens when you deploy a powerful tool without first answering the harder question: what does good actually look like here?

Marc De Pape and John Jarosz – our Principal of Product Strategy and my co-founder – have been grappling with that question. Their answer is Work Intelligently. It is not a framework for adopting AI faster. It is a framework for deploying AI deliberately – in ways that create real capacity, protect human judgment, and produce outcomes worth having.

Working intelligently is not AI replacing human work. It’s not humans resisting AI. It’s a hybrid model where AI and human judgment working together with intention – each doing what the other cannot. That turns out to be a different design problem than the one most organizations are trying to solve. And it requires a different set of questions before any tool gets selected or any workflow gets redesigned.

Read on.

Matthew Tobiasz,
General Partner & Co-Founder, Sightglass  
sightglasspartners.com

Chapter 1:
The Broken Triangle

For years, the constraints on what any organization could accomplish were predictable: you could have something fast, cheap, or good – but not all three. Pick two. Every strategy, every resourcing decision, every tradeoff lived inside that triangle.

AI has broken it. Or more precisely, AI has collapsed two of its corners. Fast and cheap are now available simultaneously, to everyone, at what currently feels like negligible cost. That should be the best news organizations have received in decades.

It isn’t producing that result.

The reason is structural. When a constraint is removed from a competitive system, the default response is not to pause and ask what to do with the freed capacity. It is to move faster. And that is exactly what we see happening. Organizations are taking the speed and cost advantages AI delivers and redeploying them in the most obvious direction: more output, more quickly, at lower cost. AI makes fast and cheap the default – and in doing so, heavily skews the strategic choice. The triangle isn’t being rebalanced. It’s being pushed further in the same direction it was already going.

Reid Hoffman put it well: “Once everyone has the AI accelerant, it’ll be about how organizations intelligently deploy AI, and how they strategically reinvest the time savings that will separate the winners and the losers.” What this book proposes – working intelligently – is the deliberate choice to do exactly that. Not faster. Better.

The data is starting to confirm what many of us suspected. According to MIT’s NANDA initiative – in a report titled The GenAI Divide: State of AI in Business 2025, based on 150 executive interviews, a survey of 350 employees, and analysis of 300 public AI deployments – 95% of enterprise generative AI pilots fail to deliver measurable impact on the bottom line. Only 5% of AI initiatives create significant, scalable value.

In other words, the problem is not primarily the technology. The problem is the choice organizations are making about how to deploy it.

The data is starting to confirm what many of us suspected. According to MIT’s NANDA initiative – in a report titled The GenAI Divide: State of AI in Business 2025, based on 150 executive interviews, a survey of 350 employees, and analysis of 300 public AI deployments – 95% of enterprise generative AI pilots fail to deliver measurable impact on the bottom line. Only 5% of AI initiatives create significant, scalable value.

In other words, the problem is not primarily the technology. The problem is the choice organizations are making about how to deploy it.

When the Tool Is the Same, the Choice Is the Differentiator

Here is the strategic problem Hoffman’s observation creates, and the one we keep returning to. If AI promises fast and cheap simultaneously – and it does so for everyone – then fast and cheap are no longer differentiators. They are the floor. Every organization in your competitive landscape has access to the same accelerant. The ones choosing to harvest those gain – taking any productivity improvement directly to the bottom-line rather than redeploying it – and use that accelerant purely in speed and cost reduction are racing toward a floor that keeps dropping.

This is not a temporary condition. It is the structural reality of commodity technology. And the history of commodity technology is consistent: when the tool is the same, what separates outcomes is the quality of the judgment applied to it.

That judgment operates at two levels. The first is strategic: what do you do with the capacity AI creates? The second is operational: how do you deploy AI in ways that reliably produce the outcomes you’re after? Most organizations are getting both wrong – not because they lack capability, but because they haven’t stopped long enough to answer either question deliberately. The default is always to move. The discipline is to pause first and define where you’re going.

That discipline is what we mean by “working intelligently.” It is not a temperamental preference for slowness. It is a strategic orientation toward good – and a recognition that without defining good first, every efficiency AI creates will be consumed by the same undifferentiated race it was supposed to help you escape.

The answer does not have to be a choice between AI and human judgment. It is a hybrid model – one where AI handles the synthesis, the research, and the compression of information that would otherwise take hours, while human judgment handles the decisions that actually matter. The organizations that design their AI workflows around that distinction will not just be more efficient. They will be fundamentally better at the work that defines them.

The three principles that follow are our attempt to make that orientation actionable. They begin from a single premise: that the organizations separating themselves right now are not the ones moving fastest. They are the ones who stopped long enough to decide what good means – and then built toward it deliberately.

Chapter 2:
Why Speed Is Losing

“On every level, and from every angle, the prioritization of speed is emerging as the biggest contributor to those negative externalities, and the biggest strategic problem.

Speed of infrastructure build-out. Speed of change. The speed of output. And the speed of the requisite judgment to respond to it all.

It’s no longer a given that an increase in the speed of a technology leads to better outcomes. Speed as a stand-in for good is debatable.”

Speed has been the organizing principle of technological progress for the better part of three decades. Faster processors. Faster networks. Faster cycles of build, measure, and learn. The assumption underneath all of it has been consistent: if we can do more, faster, good outcomes will follow. Acceleration was not just a strategy. It was a proxy for progress itself.

AI has not broken that assumption so much as exposed it.

When speed is available to everyone at what today feels like negligible cost – though current AI pricing is heavily subsidized and the economics are still settling, it stops being a competitive advantage and starts being an environmental condition. Like electricity or internet access, it becomes infrastructure. Sam Altman has compared AI to a utility – intelligence on a meter. That future is not fully here yet, but the direction is clear: organizations still treating AI as a differentiator are optimizing for something that no longer creates meaningful distance between them and their competitors. What concerns me more, though, is not the strategic miscalculation. It is what the relentless prioritization of speed is doing to the people inside those organizations.

When speed becomes infrastructure, it stops being an advantage.

The Cognitive Cost

In June 2025, MIT Media Lab research scientist Nataliya Kos’myna and her colleagues published a study titled Your Brain on ChatGPT: Accumulation of Cognitive Debt When Using an AI Assistant for Essay Writing Task. The study divided 54 participants into three groups over four months – one writing with ChatGPT, one using a search engine, one using no tools at all – and used EEG neuroimaging to measure cognitive engagement throughout.

We want to be careful about how we characterize what the study found, because the researchers themselves are careful. This is a preprint, not yet peer-reviewed, with a relatively small and geographically concentrated sample. The authors explicitly ask that sensationalist language not be used to describe the results. The effect they are documenting is more specific than the headlines suggest, and more important for what we are arguing here.

Brain connectivity systematically scaled down with the amount of external support. The brain-only group exhibited the strongest, widest-ranging networks, while LLM assistance elicited the weakest overall coupling. When LLM users were later asked to complete the task without AI assistance, they remembered little of their own work and showed weaker alpha and theta brain waves, likely reflecting a bypassing of deep memory processes.

What the researchers are documenting is not damage. It is delegation – and its consequences. Wharton researchers Shaw and Nave have termed this tendency “cognitive surrender” – the adoption of AI outputs with minimal scrutiny, bypassing both intuition and deliberation. Across three experiments with 1,372 participants, the researchers found that people followed faulty AI recommendations roughly four out of five times when they chose to consult the model at all. Participants who used ChatGPT showed brain activity suggesting they were offloading tasks, following and using AI suggestions as opposed to generating and organizing ideas on their own. The task was completed. The output existed. But the thinking that would have made that output meaningful – and retrievable, and generative of future insight – didn’t happen.

This is what we mean when we argue that AI, deployed without deliberate design, does not reduce cognitive burden. The superpower of AI is its capacity to compress the time between raw information and the moment of judgment – to surface what would otherwise take hours of synthesis in seconds. But that compression only creates value when the person on the other side arrives at the judgment moment equipped to engage with it. It accelerates the volume of decisions requiring human oversight while simultaneously degrading the capacity to make them well. It is not an efficiency. It is a transfer of cost – from the visible effort of production to the invisible erosion of judgment. There is a cognitive burden on both ends: the surrender to what AI says, and the exhausting vigilance of not trusting it. Both deplete the same resource.

What This Looks Like at Scale

At the individual level, this is cognitive surrender – the gradual atrophying of skills that don’t get exercised. Everyone feels productive. The problem is that these are personal optimizations, not organizational ones – a collection of individual workarounds that never coalesce into shared capability. What spreads across the organization instead is the output: more of it, less trusted, passed from person to person until someone notices. It manifests as something most leaders we speak with recognize immediately when we describe it: teams stretched across more parallel initiatives than ever, each appearing close to delivering value but stuck in that difficult last ten percent. The constraint is no longer time and effort. It becomes impact, judgment, strategic direction, and accountability.

Speed produced that condition. More speed will not resolve it.

The organizations feeling this most acutely are not the laggards. They are the early movers – the ones that adopted fastest. They have more AI-generated output to review, more pilots running simultaneously, more decisions requiring human sign-off, and in many cases fewer people whose judgment they fully trust to make those calls. They traded a capacity problem for a quality problem and called it progress.

The MIT NANDA data from the previous chapter illuminates why. The core issue behind AI pilot failure is – for both tools and organizations – what MIT calls the learning gap. Generic tools excel for individuals because of their flexibility, but stall in enterprise use since they don’t learn from or adapt to workflows.

What looks like adoption is often just individual-level optimization – a collection of personal workarounds that never coalesce into organizational capability. The individual feels more productive – they produce more, faster. The organization does not get better. The failure mode is not dramatic. It is gradual. Organizations layer more AI-generated output on top of existing workflows without redesigning those workflows to handle it. The speed of output increases. The ability to act on that output meaningfully does not keep pace. And the gap between them becomes the new strategic problem.

Consider what happens when a task that used to take an hour takes five minutes. Does the organization ask that person to produce 20 documents when they used to produce one? Or does it ask them to spend 55 minutes ensuring that one document will have maximum impact and strategic alignment? The answer to that question is the whole game. Good decisions are the ultimate efficiency – they do not generate downstream costs to fix. Twenty times the volume of poorly considered output is exponentially more costly than a single well-executed one.

The Asymptote

here is a limit to how frequently any of us can exercise discernment and judgment before burning out. We believe we are approaching it. Not because people are weak or resistant to change, but because the pace of required decision-making has outrun the conditions that make good decisions possible.

If that is true – and so far the evidence is pointing that way – then the most valuable thing AI can do is not accelerate the pace further. It is to create the space for fewer, better decisions. To automate what happens between the moments of judgment, not the moments themselves. To be a release valve on cognitive load rather than an amplifier of it.

That is a different deployment philosophy than the one most organizations are pursuing. It is slower to implement, harder to measure in a six-month window, and more demanding of clarity about what good actually looks like. It is also, we would argue, the only one that creates durable value.

Chapter 3:
The Aviation Lesson

“The Aviation industry handled Automation-Induced Complacency by recognizing that the best outcomes required a hybrid approach. Pilots are now encouraged to manually fly to maintain their skills – take-off and landing in particular – while still taking advantage of the benefits of autopilot to help with the overall quality of the flight: safety, smoothness, punctuality, etc.

It is automation in service of quality.”

Aviation is the most instructive analogy available to us – and its lessons go further than we initially appreciated.

The problem aviation faced is not new – and in some ways, autopilot was one of its earliest documented examples of the delegation dynamic now playing out across knowledge-work domains. As autopilot adoption increased over several decades, a troubling pattern emerged. Pilots who had delegated the work of flying to automated systems were losing the ability to fly when those systems failed. NASA documented this formally in 2001. Lawrence Prinzel and his colleagues at NASA Langley Research Center published a technical memorandum – Examination of Automation-Induced Complacency and Individual Difference Variates – which established that automation-induced complacency had been documented as a cause or contributing factor in many airplane accidents, resulting when a crew works in highly reliable automated environments in which they serve as supervisory controllers monitoring system states for occasional automation failures.

The parallel to what we described in the previous chapter is exact. Pilots delegated the active work of flying to automation. In doing so, they shifted from participants to monitors. And monitors,  it turns out, are not as capable of intervening decisively when something goes wrong. The skill atrophies. The judgment degrades. The system that was supposed to make flying safer introduced a new category of risk.

Sound familiar?

How Aviation Responded

What interests me most about the aviation story is not the problem. It is what happened next.

The industry did not tell passengers they were being irrational for worrying about safety. It did not argue that automation was net positive and therefore any concern was obstructing progress. It did not wait for the models to improve and trust that the technology would solve its own failures. It looked at the evidence, acknowledged the risk, and institutionalized a response.

The FAA’s Safety Advisory SAFO 17007 formally required airlines to incorporate additional manual flight maneuvers into their training programs. Later, Advisory Circular AC 120/123 went further – recommending that pilots hand-fly during normal operations whenever conditions allow. Not just in training, but on actual flights with paying passengers. The logic was straightforward: manual flying skills are paramount for flight safety, automation requires more training not less, and it is not a binary choice between manual and automated flight – both are essential components with different but complementary skill sets needed.

This is the hybrid model. And it was not left to individual airlines to discover on their own. It was mandated, standardized, and regulated across the industry. The question of how to balance automation with human skill was treated as too important – and the consequences of getting it wrong too severe – to leave to organizational discretion.

What we find striking in contrast is how differently the AI industry has handled an analogous set of concerns. Automation reduces physical workload, but it can increase mental workload – and over-reliance on automation can lead to complacency and a decline in the skills it was meant to support. That observation applies as cleanly to knowledge work as it does to the cockpit. Yet the dominant response from those building and promoting AI tools has been to accelerate adoption rather than to interrogate the conditions under which the technology might maximize benefits and outcomes for all using and impacted by it.

There is no equivalent of the FAA here. No institution is setting the standard. No mandate to maintain the skills that make AI’s outputs impactful.  Aviation codified what it takes to preserve human capability alongside automation. Enterprise has not. No accountability for the succession planning and talent development that depends on those skills being passed on. The judgment that makes an organization valuable does not transfer automatically – it is built through practice, mentorship, and the kind of deliberate engagement with hard problems that cognitive surrender quietly removes from the equation.

That absence is not an argument for government regulation of AI, necessarily. It is an argument for organizations to set that standard themselves – deliberately, and before the skill erosion becomes an accident report.  It is also an argument for whoever holds AI leadership in an organization – whether that is a CIO, a CTO, or an emerging AI-focused executive role – to own not just the technology deployment, but the talent and judgment implications that flow from it. That is a meaningfully different mandate than most technology leaders currently carry. The organizations replacing people wholesale with AI are not just making a cost decision. They are fundamentally altering their relationship to talent and foreclosing the benefits of a workplace where AI augments human capability rather than replacing it.

That is a riskier bet than it appears. It prevents the compounding value of judgment developed over time – institutional knowledge that cannot be reconstructed from a prompt.

Safer Flights, Not Faster Flights

There is one more thing about aviation that we keep returning to, and it is the detail that feels most counterintuitive in the current moment.

The speed of commercial aircraft has not materially changed in fifty or sixty years. With the retirement of the Concorde in 2003, the average cruising speed of commercial aviation actually declined. This is an industry that has grown enormously – in passenger volume, in safety, in global reach – without speed as its primary growth lever. What aviation invested in, relentlessly, was safety. And safety, it turned out, was the thing that increased demand.

The Concorde jet – faster than everything that came after, and proof that speed wasn’t what made aviation grow

We are not suggesting AI should stop improving. But we do think the aviation model is worth sitting with. An industry that could have continued chasing speed chose instead to define good as safety – and built an entire framework of training, regulation, and operational practice around protecting that definition. The result was not stagnation. It was durable, expanding growth.

The lesson we take from aviation is not primarily technical. It is organizational. It is that the best outcomes from automation emerge when the humans working alongside it are actively protected from the degradation that unchecked delegation produces. When the judgment layer is treated as something to be preserved and strengthened, not optimized away.

Pilots fly manually to stay sharp. Not because autopilot doesn’t work. Because when it doesn’t, you need someone in the seat who can actually fly the plane.

The question for every organization deploying AI right now is a version of the same one aviation had to answer: who is flying this, and are they still capable of taking the controls?

The Work Intelligently framework is built around that question. Its three principles are, at their core, three ways of ensuring that AI amplifies human capability rather than quietly replacing it. The first of those principles is where every deployment should begin.

Chapter 4:
Define Good First

“Good can mean a lot of different things. And unless you interrogate what you mean by good, you will inevitably end up choosing fast and cheap, like everyone else, and race towards undifferentiated commodification. No one wants that.

I prefer my revolutionary technologies to deliver more than marginal efficiencies. I want net new modes of value creation and exchange.”

Every organization we have encountered that is struggling with AI has the same problem underneath the presenting symptoms. It is not a technology problem. It is not a budget problem. It is not even a talent problem, though all of those things may be present. The problem is that no one has answered the question that has to come before any of the others.

What does good look like here?

Not in general. Not for the industry. Not according to the vendor selling the deployment. For this organization, in this context, with these people, serving these customers. What is the outcome we are actually trying to produce?

The reason most organizations skip this question is that it feels obvious. Of course we know what good looks like. We want better results, lower costs, more efficiency. But those are not complete definitions of good. They are starting points – necessary but insufficient. Directions without destinations produce motion without progress, which is precisely the condition most AI deployments find themselves in six months after launch.

The MIT data from Chapter 1 makes this concrete. The core issue behind AI pilot failure is what MIT calls the learning gap – tools and organizations both lack what is needed for AI to actually deliver results. Generic tools excel for individuals because of their flexibility, but stall in enterprise use since they don’t learn from or adapt to workflows. A tool cannot adapt to a workflow that has not been deliberately designed. And a workflow cannot be deliberately designed without a clear definition of the outcome it is meant to produce. This is also why individual AI adoption does not automatically scale into organizational capability – one person’s optimization does not become the organization’s.

This is where Work Intelligently begins. Not with a technology decision. With a definition.

What “Good” Is Not

It is worth being precise about this, because the word is doing a lot of work, and we do not want it to collapse into sentiment.

Good is not the same as fast. We have argued that at length in the preceding chapters. But it is also not the opposite of fast. There are contexts where speed is genuinely part of the definition of good – emergency response, time-sensitive decisions, competitive windows that close quickly. The point is not that fast is always wrong. It is that fast without a defined purpose is aimless velocity.

Good is not the same as efficient, either. Efficiency, as we have noted, has two distinct meanings that rarely get separated in the AI conversation. There is efficiency as the reduction of waste – doing more with less, eliminating steps that add no value. And there is efficiency as the reduction of friction – accelerating the cycle time of capital, moving faster through transactions regardless of whether the transactions themselves are worth having. These are not the same thing. The first has a cleaner path to demonstrable value.  The second often just redistributes it, or destroys it more quickly.

And good is not the same as measurable. This is perhaps the most important distinction in the context of AI deployment, where the pressure to demonstrate ROI within a short window consistently produces a narrowing of what counts as success. The things that matter most in most organizations – the quality of judgment, the depth of client relationships, the confidence of a team executing at the edge of its capability – are real, but they are not easily quantified in a six-month pilot window. Defining good only as what can be measured in that window is how organizations end up optimizing for the wrong thing.

The Questions Worth Asking

At Sightglass, before we design any AI workflow for a client, we ask a set of questions that are less about the technology and more about the organization’s relationship to its own definition of value. They are not a checklist. They are a conversation – one that most organizations have never had explicitly, even though the answers are usually somewhere in the room.

The Work Intelligently Diagnostic

Most AI deployments fail not because the technology doesn’t work, but because the organization deploying it hasn’t answered the questions that determine whether it can. This diagnostic is designed to surface those questions – before a tool is selected, a workflow is redesigned, or a pilot is launched.

There are no right answers here. The value is in the clarity that emerges when you sit with them honestly.

Work through them alone or with your team. Notice where the answers come easily and where they don’t. The places where you hesitate are usually the places worth starting.

Part One: What Good Looks Like

These questions establish the destination before any deployment conversation begins. Without a clear answer to both, every efficiency gain AI creates will be absorbed by the same undifferentiated pressure it was supposed to relieve.

1. If you were describing your organization to a trusted peer two years from now and said “we’re in a really good place”  – what would you actually be describing? Not the metrics. What would be different about how the work feels, what you’re able to pursue, who you’re able to serve?

2. When your best people are doing their best work, what are they doing? How often do they actually get to do it?

Part Two: What’s Actually at Stake

These questions identify the failure modes worth designing against. Organizations that cannot describe what a bad outcome looks like are not equipped to design against one – and AI, deployed without that clarity, will find the gaps.

3. What does a bad outcome look like here – not a catastrophic one, but the kind of quiet failure that compounds over time? Who carries the cost of that outcome, and do they have visibility into it?

4. Which decision in your organization, if it consistently went poorly, would you most want to know about first? Who owns that decision today, and what does it depend on?

Part Three: Where Judgment Lives

These questions identify what should never be delegated – to a junior person, a contractor, or a system. You cannot protect the judgment layer without naming it first.

5. Which decisions in your organization should never be delegated? Have you ever named them explicitly, or is that just implicit and assumed?

6. Where are your best people currently doing work that doesn’t require their judgment? And where are they making judgment calls with less cognitive bandwidth than those calls deserve?

Part Four: What Your Work Depends On

These questions examine the information layer underneath your most consequential decisions – and whether the people making those decisions can trust what they are working with.

7. Where in your work does the quality of a decision depend most directly on the quality of the information behind it? Where do your people spend time finding, verifying, or reconciling that information before they can do the work that actually matters – and how confident are they, when they finally act, that what they are working with is right?

8. Where does the last ten percent of a project or deliverable most consistently fail – and when you trace it back, what is usually the actual cause?

How to Use What You Find

There is no score at the end of this diagnostic. But there are signals worth paying attention to.

If the answers to Section One came easily – if you have a clear and shared definition of what good looks like  – you are better positioned than most organizations to deploy AI in ways that compound toward it. The remaining questions tell you where to focus.

If the answers to Section One were harder than expected, that is the most important finding. It means any AI deployment you make right now will be optimizing for a destination nobody has agreed on. The technology problem is not yet the problem.

The questions in Sections Three and Four are the ones most organizations have never asked explicitly. If they produced recognition  – a sense that something important has been operating on assumption rather than design  – that gap is solvable. But it requires the kind of deliberate workflow thinking that rarely happens inside the pace of normal operations.

That is usually where an outside perspective is worth the most.

What Happens When You Answer Them

The value of these questions is not in any single answer. It is in the clarity that emerges when an organization actually sits with them. What they reliably surface are two things.

The first is where the organization’s genuine definition of good resides – usually in the answers to questions four and five, where the cost of failure becomes concrete. Organizations that have never articulated what a bad outcome looks like are not equipped to design against one.

The second is the gap between where AI is currently being deployed and where it would create the most durable value. In my experience that gap is almost always significant. The deployments that get prioritized are rarely the ones that matter most. They are the ones that are easiest to build, fastest to demonstrate, and most legible to whoever is tracking the pilot program. That is fast and cheap making the decision again, even inside organizations that believe they have escaped it.

Defining good first is an act of organizational discipline. It requires someone with enough authority to slow the deployment conversation down and ask the questions that feel, in the moment, like they are holding things up. They are not. They are the thing that makes everything that follows actually work.

Aviation did not define good as flying faster. It defined good as arriving safely. Everything followed from that – the training standards, the hybrid model, the mandate to maintain manual skills. The definition came first, and it was clear enough to organize an entire industry around.

Your organization does not need to organize an industry. It needs to answer the question for itself. Clearly enough that the answer can travel into workflow design, into deployment decisions, into the evaluation of whether any of it is working.

That is the work of Define Good First – the first principle of working intelligently. And it is the work that makes the next two possible.

Chapter 5:
Deploy Vertically

“The semantic search capabilities of LLMs are truly remarkable – the ability to connect, stitch, condense, and make accessible disparate forms of information is an achievement – but only when executed against a known corpus of knowledge and data.

Investing in discrete deployments of LLMs against a known corpus is where quality emerges. Removing the requirement to question whether to trust the outputs should be an imperative. Prioritizing it is one way to choose ‘good’.”

There is a distinction that almost never gets made in the conversations we have about AI deployment, and its absence is responsible for more failed pilots than any other single factor. It is the distinction between deploying AI horizontally and deploying it vertically – and it is the difference between a tool that burdens the people using it and one that actually creates capacity.

A horizontal deployment is what most organizations reach for first because it is the most accessible. You license a general-purpose model – a copilot, a chatbot, an assistant – and you make it available to your team. It can do many things across many contexts. It is fast to implement and easy to demonstrate. It is also, in most enterprise settings, the source of the trust problem that quietly undermines the entire investment.

When a model is deployed horizontally, operating across domains without a defined and controlled knowledge base, the user cannot know whether the output is reliable without verifying it independently. That verification burden does not replace the work the model was supposed to eliminate. It sits on top of it. The tool that was meant to free up capacity instead adds a new layer of oversight – one that requires more judgment, not less, from the people it was supposed to help.

We have argued that the superpower of AI is its ability to foreground moments of discernment and judgment. But that superpower only operates in one direction. Deployed horizontally against an open-ended corpus, AI multiplies the moments requiring human verification. Deployed vertically against a known and trusted corpus, it compresses them – doing the synthesis work so that the human can move directly to the decision that actually matters.

That is not a subtle distinction. It is the difference between an AI deployment that creates capacity and one that consumes it.

What Vertical Deployment Actually Means

To be precise about this: vertical deployment is not about building a custom model. It is about controlling the scope of what the model operates against.

The most practical form this takes in most organizations is Retrieval-Augmented Generation, or RAG – a technical approach in which an AI system draws its responses from a defined, curated set of internal documents, databases, or knowledge sources rather than from its general training data. The model’s general reasoning capability is preserved. Its scope of reference is bounded. This is similar to consulting a trusted reference – enabling the model to access verified internal documents or databases during a response, rather than generating answers from general pre-training alone.

The practical effect is significant. When the corpus is known and trusted, the user does not have to interrogate the source. They can interrogate the output – which is the judgment task that actually belongs to them. The burden of discernment is focused where it should be, rather than distributed across the entire chain from source to conclusion.

The market is beginning to reflect this. According to Menlo Ventures’ 2025 State of Generative AI in the Enterprise report, vertical AI – targeting specific industries like healthcare or finance – reached $3.5 billion in enterprise spending in 2025, nearly triple the $1.2 billion invested in 2024. Organizations that moved earliest into general-purpose horizontal deployments are now building the vertical layer they skipped in their rush to implement. They are discovering, at scale, what the enterprise-trial failure data has been telling us all along: that specificity and workflow integration are not nice-to-haves. They are the conditions under which AI produces reliable value.

The Middle Layer Nobody Invested In

here is a structural reason that most AI deployments remained horizontal for as long as they did, and it is worth naming directly.

The investment in AI – at the infrastructure level, at the model level, at the application layer – has been overwhelming in its concentration on the model itself. The implicit belief has been that if the model is good enough, everything downstream will work. Build a better model and the ROI problem solves itself.

It has not. And the reason is that between any foundation model and any meaningful organizational outcome, there is a layer of work that is neither glamorous nor fundable at the scale of model development – but without which nothing else functions. It is the work of understanding the specific workflows in which AI will operate, designing the scoped knowledge base it will draw from, building the quality assurance processes that make outputs trustworthy, and creating the feedback loops that allow the deployment to improve over time.

This is the middle layer. And its chronic underinvestment is, in my view, the single most consequential blind spot in how organizations are approaching AI. The failure of most enterprise AI pilots stems from implementation approach rather than model quality or regulation – tools built by external vendors succeed twice as often as internal builds precisely because they bring the workflow integration and domain specificity that most organizations lack the capacity to develop themselves.

The organizations getting this right are not the ones with the most sophisticated models. They are the ones that invested in understanding their own workflows deeply enough to scope a deployment that could actually serve them.

The Staging Problem

If the first mistake was believing that value was intrinsic to the model’s horizontal training, the second is closely related: the assumption that what is true in software engineering will naturally flow upstream to everyone else.

The effort required to do this well is proving to be much larger than the early promise of AI suggested. And the challenge only grows when organizations try to extract capacity from LLMs outside of the software delivery lifecycle. The reason is that most domains do not have the same kind of rapid validation cycling that is baked into software: write the code, test and validate, fix bugs, repeat until done. In other domains – finance, marketing, industrial design, electronics – validation is a much slower, costlier, and riskier endeavor. Some domains do not have staging environments. Some domains only exist in production.

This is why figuring out where LLMs offer genuine advantages – and how to integrate them to create capacity rather than consume it – is not fundamentally a technology question. It is better given to people who understand how work actually gets done: subject matter experts paired with system and service designers who can map the workflow, locate the leverage points, and design the middle layer with the care those domains require.

The investment any business needs to make to deploy AI well – to create the kind of capacity that can be redeployed to the problems humans are best suited to solve – begins with understanding how work is done today. How value is created for the business and its customers. And from there, the identification of where genuine opportunities for AI-powered workflow improvement actually lie. Those opportunities become investments in new ways of working, where the middle layer between the model and the end user is built with intent and purpose.

Trust Is the Product

We want to return to something we said in the anchor passage of this chapter, because we think it is the most practically important thing we have written in this entire ebook: removing the requirement to question whether to trust the outputs should be an imperative.

Trust in AI outputs is not primarily a model quality problem. It is a deployment design problem. A general-purpose model deployed without a bounded corpus will produce variable outputs across an open-ended domain. No amount of model improvement changes that fundamental condition. The variability is structural, not incidental.

Vertical deployment is how you engineer trust into the system rather than hoping it emerges from the model. When your people know that the AI is drawing from a specific, curated, governed set of sources – sources they have agreed represent the organization’s knowledge – the nature of their engagement with the output changes. They are no longer auditors of an unknown process. They are participants in a known one.

That shift matters enormously for adoption. It matters for quality. And it matters for the organization’s ability to actually redeploy the capacity that AI creates – because capacity that flows from outputs no one trusts is not real capacity. It is latent anxiety, distributed across the organization in the form of verification tasks nobody wants to own.

What This Looks Like in Practice

The path to vertical deployment is not as technically complex as it is often made to appear. It begins with a question that is organizational rather than technical: where in our workflows does the quality of information determine the quality of outcomes?

It’s the same question the Work Intelligently Diagnostic in Chapter 4 is designed to help surface – approached through a series of smaller, more specific questions that together illuminate what organizations rarely arrive at by asking directly.

That question identifies the deployments worth investing in. Not the broadest ones. Not the fastest to demonstrate. The ones where a scoped, trusted AI deployment would change what your best people are able to do – by removing the work that sits between them and the decisions that require their judgment.

At Sightglass, the work of identifying those moments – what we think of as the specific points in a workflow where AI integration unlocks genuine capacity – is where every deployment conversation begins. The technology follows from that. It is never the other way around.

The case for vertical deployment is ultimately simple. You are not choosing between a more capable AI and a less capable one. You are choosing between an AI that your people can trust and act on, and one they cannot. That choice determines everything that follows – including whether the capacity AI creates ever gets redeployed into something that matters, which is the subject of the final principle.

Chapter 6:
Protect the Judgment Layer

“The superpower of AI is not automation through delegation, but rather its ability to foreground moments of discernment and judgment that would previously take exponentially longer to synthesize concretely.

But, asking people to execute more of those moments as the realization of its value is an acceleration of burden, fatigue, and burnout; not necessarily the creation of capacity through efficiencies.

AI can be a release valve on the cognitive load of all the judgment and discernment required today. It doesn’t have to be an amplifier of burnout.

It looks like deploying AI to create real human capacity. Capacity that can be redeployed to areas AI can’t service. Capacity to slow it all down.”

There is a version of AI deployment that looks, on paper, like everything its advocates promised. Faster outputs. More throughput. Lower cost per unit of work produced. Teams doing more with fewer people, or the same number of people doing demonstrably more. The metrics are improving. The case study writes itself.

And then, six months later, the people inside that organization – and what it quietly did to the nature of work in the process.

We want to name this dynamic precisely, because it is the thing we see most consistently misdiagnosed in organizations that come to us frustrated with their AI investments. The problem is not the technology. The problem is a fundamental misunderstanding about what the technology is actually doing to human judgment – and what happens to an organization when that judgment quietly degrades.

AI Brain Fry

In early 2026, researchers from Boston Consulting Group published findings, reported in Harvard Business Review, from a survey of nearly 1,500 full-time U.S. workers on what happens to people who work intensively alongside AI systems. They named what they found “AI brain fry” – and defined it as mental fatigue resulting from excessive use, interaction with, and oversight of AI tools beyond cognitive capacity.

The symptoms workers described are worth sitting with. A persistent buzzing sensation mental fog, difficulty focusing, slower decision-making, and headaches after extended AI oversight. These are not the symptoms of people doing less work. They are the symptoms of people doing a different kind of work – one that demands sustained vigilance without the cognitive rewards that make concerted effort sustainable.

Person holding papers with a tangled scribble obscuring their head, representing mental fatigue.
Brain fry: not less work, just a more exhausting kind

The organizational consequences are significant. Workers who reported AI brain fry also reported higher levels of decision fatigue, meaning fewer mental resources available for high quality decisions. Error frequency increased – both minor mistakes and major ones with consequences for safety, outcomes, or significant decisions. Workers reporting AI brain fry were also more likely to express intent to leave their jobs.

The researchers found something else worth noting, because it maps precisely onto the distinction we drew in the previous chapter between horizontal and vertical deployment. AI can both reduce burnout and create it. When workers had to constantly supervise multiple AI systems or juggle several tools at once, mental strain increased sharply. By contrast, when workers used AI to actually offload repetitive tasks, their stress levels dropped.

In other words, the direction of the cognitive burden matters more than the presence of AI. The question is not whether your people are using AI. It is whether the AI they are using is taking work off their plates or adding a new category of oversight work on top of everything they were already doing.

The Judgment Layer

Every organization has decisions that are genuinely consequential – where the quality of judgment applied to them determines outcomes that matter. These decisions exist at every level, from the strategic choices a leadership team makes about where to compete, to the moment a client-facing person reads a situation and decides how to respond. They are not all high-profile. Many of them are quiet. But they are the decisions that, when made well, compound into competitive advantage – and when made poorly, compound into the kind of organizational fragility that is hard to diagnose from the outside.

Around those decisions sits everything else: the research, the synthesis, the administration, the coordination, the communication. The work that supports the judgment without being the judgment itself.

The deployment error most organizations make is to treat AI as a replacement for the surrounding work – the research, synthesis, and coordination – and then watch it quietly erode the judgment decisions that surrounding work was always meant to serve. They automate the research and synthesis – which is where AI genuinely excels – but they do it without redesigning the workflow around the judgment decisions that research and synthesis were always meant to serve. The result is that people are receiving more synthesized information faster, with less cognitive capacity to engage with it and often an implicit expectation to act on it without question. It is one thing to question the origins of an AI output. It is another to resist surrendering to what it is suggesting, even when the underlying data is sound.

This is the aviation model applied to knowledge work. Autopilot handles the long, stable stretches of flight with precision and reliability. It was designed to reduce fatigue over long hauls – to preserve the pilot’s cognitive resources for the moments that actually require them. The cockpit interfaces and crew procedures were redesigned around that intention. The pilot’s role was clarified. The judgment moments were identified and protected.

Most AI deployments do the opposite. They introduce the automation without clarifying the judgment moments. Without protecting them. Without designing the workflow so that the human arrives at those moments with the cognitive capacity to actually engage with them.

That is not automation in service of quality. That is automation in service of throughput . Accumulating a collection of rapidly produced, low-value outputs does not equal the value of a single, well-executed one, and it has a ceiling that most organizations are already approaching.

What Protecting the Judgment Layer Looks Like

The third principle of Work Intelligently is not about doing less with AI. It is about being deliberate about what AI is and is not asked to do – and designing workflows around that distinction rather than leaving it to individual workers to navigate on their own.

The leader responsible for this work is almost certainly not the one currently assigned to the AI initiative. Most organizations are treating AI deployment as a technology decision, led by engineering or IT. The talent and judgment implications described below require someone closer to how work actually gets done – and accountable for the people doing it. CTOs who think of AI as a service to provision rather than a capability to integrate will introduce more problems than they solve.

In practice, this means three things.

Identify the decisions that should never be delegated.  Every organization has them, but very few have named them explicitly. These are the decisions where the cost of a bad outcome – to the client, to the organization, to the relationship – is high enough that no efficiency argument justifies removing human judgment from the loop. Naming them is not a constraint on AI adoption. It is the foundation of trustworthy AI adoption. You cannot protect what you have not identified.

Design workflows that deliver people to those decisions well-resourced.  This is the positive version of the previous point. If AI is doing the research, the synthesis, the drafting, and the scheduling – all the work that surrounds a judgment decision – then the person arriving at that decision should be arriving with more cognitive capacity than they had before, not less. If they are arriving depleted, the workflow is not designed correctly. The AI is in the wrong place.

Measure cognitive load, not just output volume. This is the organizational signal that most leadership teams are not yet tracking – and the one the BCG research makes most urgent. Organizations should evolve their people analytics to monitor overall cognitive load and safeguard against mental fatigue linked to AI use as a novel job-related risk. Output volume can increase while judgment quality degrades. The organizations that will catch that divergence early are the ones measuring both.

The Investor Argument

For investors and PE partners, the argument for preserving the judgment layer has a valuation dimension that we do not think is adequately priced in most assessments of portfolio companies’ AI-enabled strategies.

No one can say with certainty what will be visible over a three-to-five year horizon. But we can identify the bet worth making. The organizations whose people are making better decisions – consistently, at the moments that matter, without the cognitive erosion that unchecked AI delegation produces – are building a kind of advantage that compounds differently than throughput metrics. The bet is that quality of judgment, depth of institutional knowledge, and the ability to execute at the edge of organizational capability will matter more over time, not less. That is the horizon worth planning toward – even if it cannot be measured in a six-month pilot window. How you define good will provide you better leading indicators of long term success than speed metrics

Conversely, the organizations that are optimizing for throughput and treating AI adoption as primarily a headcount reduction story are building a different kind of riskier asset . One where the judgment capacity that made the organization valuable in the first place is gradually being hollowed out – quietly, in the aggregate, in ways that will not show up in the metrics until the damage is significant.

Speed of AI adoption is not the right signal for organizational health.  The right signal is whether the people inside the organization are getting better at the decisions that matter – or whether they are getting faster at the decisions that don’t.

Safer flights, not faster flights.

That is the standard aviation set for itself, and it is the standard worth holding for the organizations you back.

Chapter 7:
The Sightglass Approach

“I believe in helping people achieve what they cannot do on their own. That fundamental asymmetry of needs and skills, supply and demand, is the foundation of commerce.

Good is a future where AI multiplies the ways in which we support each other.”

The argument we have made across these chapters is not that AI is overrated ; it still has much to prove but the latent potential for radical transformation is very real. It is that the way most organizations are deploying it is producing less than it could, and costing more than it appears to. The costs are not always financial. They are cognitive. Organizational. Strategic. They show up in the quality of decisions, the depth of trust in outputs, and the gradual erosion of the judgment capacity that made the organization worth building in the first place.

The three principles of Work Intelligently – define good first, deploy vertically, protect the judgment layer – are not a formula. They are orientations. Ways of approaching AI deployment that keep the human purpose of the work visible, rather than letting the technology determine the destination by default.

The value of an outside perspective here is structural, not just supplementary. An external partner does not carry the internal assumptions that prevent an organization from seeing its own workflows clearly. They are not invested in the way things have always been done. They do not need the organization’s domain expertise to ask the questions that domain expertise has made invisible. That distance is not a disadvantage. In this work, it is often the thing that makes the most important questions possible to ask at all – and the answers possible to act on.

What This Work Requires

All of this points toward a kind of organizational discipline that is genuinely difficult to sustain from the inside.

Not because the principles are complicated. They are not. Define what good looks like before you deploy. Scope your deployments to what the technology can actually do well. Protect the decisions that require human judgment rather than routing them around it. None of that is technically demanding.

What makes it hard is that it requires someone to slow the conversation down at the moment when every institutional incentive is pointing toward speed. To ask the questions that feel, in the moment, like they are holding things up. To insist on a definition of good when the default – fast and cheap – is so much easier to agree on and so much faster to demonstrate.

That is not a technology problem. It is a leadership problem. And it is one that most organizations are not structured to solve on their own – not because they lack the intelligence, but because the people who understand the technology and the people who understand the organization are almost never working on the same problem at the same time.

The capacity that AI creates is real. What you do with it is a choice. And that choice – more than the model you select, more than the budget you allocate, more than the speed at which you move – is what will determine whether the investment compounds into something durable, or dissipates into the same undifferentiated race it was supposed to help you escape.

This Moment Matters

We are aware that we have spent a significant portion of this ebook being skeptical of the dominant AI narrative. So let’s be clear about what that skepticism is and is not.

It is not pessimism about AI. The semantic capabilities of large language models – the ability to connect, synthesize, and surface information from a known corpus with a speed and comprehensiveness no individual human could match – are genuinely remarkable. We use these tools. We see what they can do when they are deployed well. We believe they will have a transformational impact on how organizations create value.

Our critique is about the deployment philosophy that has dominated the first wave of enterprise AI adoption. The assumption that horizontal, general-purpose deployment is the path to value. The belief that speed of adoption is equivalent to quality of outcome. The failure to invest in the middle layer – the workflow design, the corpus curation, the quality assurance – that separates a tool that burdens people from one that genuinely serves them.

That first wave is producing the failure data we reviewed in Chapter 1. The second wave – the one we believe we are entering now – will be defined by the organizations that learned from it. That means scoped deployments against trusted knowledge bases. That means workflows designed around the judgment moments they are meant to serve, not the throughput metrics that are easiest to measure. That means a clear answer to the question that has to come before all the others: what does good look like here?

The organizations that answer that question well – and build toward it deliberately – will not just be more efficient. They will be more trusted by their clients and by the people inside them. They will make better decisions at the moments that matter. And they will have the human capacity to pursue growth in the places where AI cannot go – the relationships, the judgment calls, the creative leaps that no amount of training data can produce.

Taken in aggregate, that is a definition of good worth pursuing and building toward.

If this raised questions worth answering, we should talk.

The work of defining good, scoping deployments that earn trust, and protecting the judgment your organization depends on – none of it happens in a single conversation. But the right conversation is usually where it starts.

Sightglass works with enterprise teams and their leadership to design AI workflows that create real capacity – and to identify where that capacity is most strategically redeployed.

If you are ready to have that conversation, let’s talk.

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