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How to Become an AI-First Enterprise — And Why It's a Strategic Imperative

25 March 2026 · 13 min read

.52 trillion in 2026 — a 44% year-over-year increase, according to Gartner. CEOs talk about AI on every earnings call. And yet, as a recent CIO Magazine article put it bluntly: AI isn't failing — enterprises are failing to operate it.

The data backs this up at an individual level too. Anthropic's landmark study — 81,000 interviews with AI users across 159 countries — reveals that 81% of people say AI has already delivered some value for them. But when you look closer, that value concentrates in personal productivity and cognitive partnership. The structural transformation that would make AI a true enterprise capability? That's still largely missing.

The problem is that most enterprises are using AI without becoming AI-first. And there's a fundamental difference between the two.

Using AI means adding capabilities to your existing operating model. Becoming AI-first means redesigning that operating model around AI as a core capability — changing how your organization creates value, makes decisions, structures work, and measures performance.

That's not a technology upgrade. It's a strategic transformation. And for enterprises that want to unlock a fundamentally different margin structure and competitive position, it's not optional — it's the journey that defines the next decade.

We've been here before

If this sounds familiar, it should. A decade ago, the enterprise world went through nearly identical growing pains with IoT and digital transformation.

Companies invested heavily in connectivity. They ran hundreds of pilots. They built dashboards and innovation labs. And most of them got stuck — not because the technology failed, but because the organization never changed. Processes stayed the same. Decision rights didn't shift. Governance was an afterthought. The result was "pilot purgatory" — impressive demos with minimal business impact.

The IoT industry only broke through when leaders stopped treating connectivity as a project and started embedding it into what I'd call the operating fabric — the processes, technologies, data, governance structures, decision rights, and workforce behaviors that determine how work actually gets done. The technology became invisible. The value became measurable.

AI is at that exact same inflection point. The enterprises that recognize this — and act on it — will define the next wave of value creation. Those that keep running pilots will wonder what happened.

What "AI-first" actually means

Let me be precise about the definition, because the term has become dangerously vague.

An AI-first enterprise designs its operating model around the assumption that AI is a foundational capability — not a tool, not a feature, not a department.

This means AI isn't something you add to how you work. It's something that shapes how you work in the first place. It influences which decisions humans make and which decisions are augmented or automated. It determines how workflows are structured. It changes what roles look like, what skills matter, and how performance is measured.

That's a fundamentally different starting point than "let's find use cases for AI."

The Anthropic study illustrates this gap vividly. The largest single group of respondents — nearly one in five — wanted AI to help them achieve "professional excellence": handling routine tasks so they could focus on higher-value strategic work, complex problem-solving, and professional mastery. That's not a desire for a better tool. That's a desire for a different way of working. And it's exactly what an AI-first operating model should deliver — but rarely does today.

The strategic case: Why this journey matters now

The reason to become AI-first isn't hype, fear, or competitive panic. It's economics.

The CIO Magazine article introduces a concept that deserves wider adoption: ROAI — Return on AI. Not a vanity metric, but the net realization of hard benefits — revenue growth, cost reduction, margin improvement, and risk mitigation — verified in actual financial results. Supported by soft benefits like productivity, decision quality, workforce capacity, and resilience that protect those returns over time.

This distinction matters. Most enterprises today measure AI in activity metrics: models deployed, employees trained, pilots launched. ROAI forces a different question — what showed up on the P&L?

AI-first enterprises operate on a structurally different model. They have lower marginal cost per decision. They scale operations without scaling headcount linearly. They compress cycle times — from product development to customer response to financial close. They turn data from a reporting asset into an operational one, creating compounding returns over time.

The Anthropic study provides a striking data point that illustrates this structural difference. When it comes to economic empowerment through AI, independent workers — entrepreneurs, small business owners, solopreneurs — report real economic gains at three times the rate of institutional employees (47% vs. 14%). People with side projects alongside institutional employment benefit the most, at 58%.

Why the gap? Because independents have embedded AI into how they actually operate. They've redesigned their workflows around AI as a core capability — not added it as a layer on top of unchanged processes. They are, in effect, AI-first micro-enterprises. The enterprise challenge is to replicate this at scale.

This is the same value-creation logic that separated digital-native companies from digitized incumbents over the past fifteen years. The companies that built their operating models around digital capabilities — not just adopted digital tools — created an entirely different margin structure that incumbents still struggle to match.

AI amplifies this dynamic. The gap between AI-first and AI-cosmetic enterprises won't close over time — it will widen. Because AI capabilities compound: better data leads to better models, which lead to better decisions, which generate better data. Organizations that embed this loop into their operating fabric create a flywheel that's very difficult to replicate.

That's the strategic imperative. Not "we need AI to stay relevant" — but "AI-first unlocks a value-creation engine that fundamentally changes our cost structure, decision quality, and speed."

The journey: From AI-curious to AI-first

No enterprise wakes up AI-first. It's a deliberate journey — and being honest about where you are is the first step toward getting where you need to be.

As the CIO Magazine article observes, executives remain bullish on AI and continue to increase investment — but struggle to translate that ambition into verifiable financial outcomes. That tension defines the early stages of this journey for most organizations.

Stage 1: AI-Aware

This is where most enterprises are today. Leadership recognizes AI's importance. Budgets exist. Experimentation is happening — ChatGPT Enterprise licenses, a few generative AI pilots, maybe an internal hackathon or an AI task force. There's energy and enthusiasm, but no structural change.

The CIO Magazine article captures this dynamic precisely: deployment speed doesn't equal adoption speed. Enterprises can quickly implement advanced models, yet adoption stalls when AI isn't embedded in workflows. Employees revert to familiar processes, managers lack confidence in outputs, and productivity gains remain theoretical.

The Anthropic study reveals a telling nuance here. Half of all respondents cited time-saving as AI's primary benefit — but 19% simultaneously warned that those gains are illusory. The treadmill speeds up. Expectations rise. As one freelance software engineer put it: the ratio of work time to rest time hasn't changed at all.

This is what happens when AI delivers individual productivity without organizational redesign. People work faster, but the system absorbs the gains. Without changes to workflows, decision rights, and performance metrics, productivity improvements evaporate before they reach the P&L.

What's happening: AI is being explored as a technology. What's missing: Strategic intent, operating-model alignment, and measurable ROAI. Key risk: Pilot purgatory — the same trap that stalled IoT adoption for years.

Stage 2: AI-Enabled

The organization moves beyond experimentation and starts deploying AI in specific, high-value workflows. Individual functions — customer service, finance, supply chain — show measurable productivity gains. Data infrastructure is being improved. AI governance frameworks emerge. There's a shift from "what can AI do?" to "where does AI create business value?"

The sequencing matters here. The CIO Magazine article proposes a prioritization logic that resonates with what worked in IoT: start with workforce enablement (where adoption produces the highest economic return), then move to productivity and collaboration platforms (where time leverage delivers immediate efficiency gains), then to workflow orchestration (enabling scalability), and only then embed AI deeply into ERP environments where financial outcomes can be measured and governed.

This sequencing is critical because it builds organizational readiness progressively — each stage creates the foundation for the next.

What's happening: AI is delivering value in pockets. What's missing: Cross-functional integration, workforce transformation, and enterprise-wide accountability for ROAI. Key milestone: First AI initiatives that can be traced to a line on the P&L.

Stage 3: AI-Native

This is the stage where the operating fabric starts to change. AI is no longer a tool layered onto existing processes — it's embedded into how core workflows function. Decision-making processes are redesigned for human-AI augmentation. Roles and responsibilities shift.

The operating fabric — processes, technologies, data, governance structures, decision rights, and workforce behaviors — is being deliberately reengineered. AI is woven into the core enterprise platforms: human capital management, collaboration tools, workflow orchestration, ERP, and data analytics. When AI lives inside these systems, adoption stops being optional and starts being natural.

This is also the stage where the tension between AI-driven decision-making and reliability must be confronted head-on. The Anthropic study found that 22% of respondents valued AI as a decision-making aid — but 37% flagged unreliability as a concern. This was the only tension where the negative outweighed the positive. Critically, both sides were deeply rooted in experience: professionals in law, finance, and healthcare reported both the highest rates of decision-making benefits and the highest rates of being burned by unreliable outputs.

For enterprises at Stage 3, this means that governance, validation frameworks, and human-in-the-loop design aren't optional safeguards — they're structural requirements that determine whether AI-augmented decisions create value or destroy trust.

Critically, financial measurement changes. ROAI is tracked not in activity metrics but in business outcomes: margin improvement, cost-to-serve reduction, revenue per employee, decision velocity. The CFO becomes an active partner — not just approving budgets, but validating returns and connecting AI outcomes to P&L and balance-sheet performance.

What's happening: AI is reshaping how work gets done. What's missing: Full organizational alignment and the compounding effects that come from enterprise-wide AI integration. Key milestone: The CFO can validate AI-driven financial impact across multiple functions.

Stage 4: AI-First

The destination. AI is a foundational assumption in how the enterprise designs strategy, structures operations, and creates value. Every new process, product, and decision framework is built with AI as a native capability. Data flows are designed for AI consumption. Governance is built in, not bolted on. The organization doesn't "do AI" — it operates through AI.

At this stage, the value-creation flywheel is in motion: better operations produce better data, which improves AI capabilities, which drive better operations. The margin structure, speed, and scalability of the enterprise are fundamentally different from competitors still stuck in earlier stages.

ROAI is no longer a project metric — it's an enterprise performance discipline, continuously measured, governed, and optimized.

What's happening: AI is the operating model. What defines it: Compounding returns, structural cost advantage, and a self-reinforcing capability loop.

What makes or breaks the journey

Having seen similar transformations in IoT and digital, a few patterns consistently separate the organizations that progress from those that stall:

The Strategic Quad: Shared ownership, not delegation. AI-first can't be delegated to a technology team. The CIO Magazine article proposes a model I find compelling: the Strategic Quad — Board, CFO, CHRO, and CIO as joint owners of ROAI. The CIO ensures technology enablement and reliability. The CHRO drives workforce adoption, skills, and behavioral change. The CFO measures, validates, and realizes economic outcomes. The Board governs and sets clear economic intent. Without this shared accountability, AI investments remain fragmented and returns impossible to validate.

Workflow redesign, not tool deployment. The biggest trap is adding AI to broken processes. AI-first enterprises have the courage to redesign workflows from scratch — which means challenging existing roles, authority structures, and ways of working. That's where the value is, and that's why it's hard.

Data as an operational discipline. Every IoT veteran learned this the painful way: you can have the best models in the world — if your data is siloed, inconsistent, or ungoverned, you get noise, not insight. AI-first enterprises treat data readiness as a continuous operating discipline, not a one-time project.

Workforce transformation as the critical path — done right. The CIO Magazine article rightly emphasizes the CIO-CHRO partnership. The CHRO is central to realizing ROAI by converting AI investments into sustained productivity and cost gains through workforce readiness, adoption, and behavior change. Without this, technology deployment is an investment that never yields returns.

The Anthropic study adds an important nuance here: how you transform the workforce matters enormously. The study found that cognitive atrophy — the fear of losing the ability to think independently — was a real concern, reported by 16% of respondents. But the pattern of who experienced it was revealing. Educators reported witnessing cognitive atrophy at 2.5 to 3 times the average rate — predominantly in institutional settings where AI was used as a shortcut. Meanwhile, tradespeople and self-directed learners who used AI volitionally reported almost no atrophy at all, while being among the most enthusiastic about AI-enabled learning.

The lesson for enterprises is clear: workforce transformation that empowers people to use AI as augmentation — expanding what they can do, not replacing what they should learn — produces durable capability. Transformation that simply automates tasks without redesigning roles and developing new skills creates dependency and erodes the human judgment that AI-augmented decisions still require.

Governance from day one. AI governance isn't a compliance exercise you add later. It's the foundation that makes scaling possible — and the thing that gives the Board confidence to keep investing. The Anthropic study underscores this: unreliability was the single most cited concern across 81,000 respondents. Enterprises that don't build trust frameworks around AI outputs will find that adoption collapses at exactly the moment they try to scale.

Honest self-assessment. Most enterprises are AI-aware or, at best, AI-enabled. That's not a failure — it's a starting point. But labeling yourself "AI-first" when you're running a handful of pilots creates a dangerous illusion of progress. The journey begins with honesty about where you actually are.

The global dimension

One final perspective worth noting. The Anthropic study reveals a striking global divide in how people see AI's potential. In developing regions — Sub-Saharan Africa, South and Central Asia, Latin America — AI is predominantly seen as an opportunity ladder: a path to entrepreneurship, education, and economic mobility. In wealthier regions — North America, Western Europe, Oceania — the dominant desire is for AI to manage the complexity of already-demanding professional lives.

For global enterprises navigating the AI-first journey, this isn't just a cultural insight — it's a strategic one. The value AI creates and the transformation it requires will look different across markets, functions, and workforces. An AI-first operating model needs to account for this diversity, not impose a one-size-fits-all approach from headquarters.

The bottom line

"AI-first" is not a label you claim. It's an operating model you build — deliberately, stage by stage, through strategic redesign of how your organization decides, works, measures, and creates value.

The enterprises that understood this with IoT and digital transformation eventually won. Those that treated it as a technology project eventually stalled. The same pattern is playing out with AI right now — faster and with higher stakes.

The strategic case is clear: AI-first enterprises don't just operate more efficiently. They operate on a fundamentally different economic model — one where ROAI compounds over time and creates structural advantages that are very difficult to replicate. The data supports this — from the macro-level spending trends to the individual-level evidence of 81,000 AI users who show us what works, what doesn't, and what's at stake.

The question for every leadership team isn't "are we using AI?" — it's "are we on a deliberate path to becoming AI-first?" And if so, what stage are we in today, and what needs to change to reach the next one?

That's the journey. And for enterprises that take it seriously, it's the most consequential strategic transformation of the next decade.

I'd love to hear from leaders who are navigating this journey: What stage is your organization in? Where are you seeing real progress — and where are you stuck?

This article builds on insights from

Toni Harrison-Hogan, MBA, AI, ITIL, POPM, CSM, SA

gan's "Why enterprises aren't seeing AI ROI — and what CIOs can do about it" in
CIO Magazine®
and
Anthropic
's "What 81,000 people want from AI" — both of which I'd recommend reading for deeper context.