Building the AI-first enterprise: Moving Beyond Pilots to True Transformation

Walk into any large enterprise today and you will hear familiar refrains:

“We have AI pilots in multiple functions.”

“We are experimenting with generative AI.”

“We are using various AI platforms.”

But ask the harder question “What has truly changed for customers, employees, or bottom line?” And the room often falls silent. Day-to-day operations still feel the same: decisions crawl through layers of approval, and core processes remain as cumbersome as ever.

The numbers tell the story. According to McKinsey’s 2025 global AI survey, 78% of organizations now use AI in at least one business function, but most leaders say AI has not yet made a meaningful difference to overall profits; only 39% report any bottom-line impact at all, and for most of them it is under 5%.

Boston Consulting Group puts it even more starkly. In a study of more than 1,200 organizations, only about 5% said AI was delivering real value at scale, while close to 60% admitted that, despite heavy investment, they had seen little or no meaningful impact.

In short, enterprises are busy with AI but not necessarily better because of it. The root issues? AI is still treated as a collection of tools and pilots, rather than a strategic design principle for how the business operates.

Building an AI-first enterprise demands more than another wave of experiments. It requires reimagining decision-making, operational workflows, and value creation with AI embedded into the fabric of the organization, not bolted on at the edges

The Pilot Trap: Why AI doesn’t automatically mean more value

Over the past two years many enterprises have built an impressive “AI museum” that features a row of pilots that look good on slides but barely touch daily work. There is a Chatbot in HR, a Generative AI experiment in Marketing, a forecasting model in Supply Chain, and a proof of concept to read invoices or contracts. Each function has its own innovation story, yet when you ask what has changed for customers, employees or the P&L, the answer is usually: not much.

Analysts have started to quantify this gap. A growing share of Generative AI projects never make it past proof of concept, and studies on the “AI value gap” show only a small minority of organizations scaling AI with confidence while the rest circle around similar experiments year after year.

Under the surface, the reasons are familiar: AI is bolted onto old processes instead of rethinking them; every function runs its own pilots with different vendors and data; once the project is “done”, no one clearly owns performance or risk; and employees experience AI as something that happens to them, not with them. The cycle is predictable: early excitement, friction at rollout, disappointment a few quarters later.

Breaking out of that cycle starts with a different question. Instead of “Where can we plug AI in?”, ask: “If we were designing this enterprise today, knowing what AI can do, what would we build from scratch?”

What an AI-first enterprise actually looks like

“AI-first” is easy to write into a strategy document. In practice, an AI-first enterprise shows up in how work is designed and how decisions are made. A few markers stand out:

• Workflows, not tools, sit at the center. Teams need to design the whole flow of work (from request to fulfilment) and AI is placed where it improves that flow, not a single step.
• Data is treated as shared infrastructure, curated and reusable across teams rather than locked in individual systems.
• Important decisions are made explicitly, with assumptions about risk, pricing or routing written down so both humans and AI systems can understand and challenge them.
• Human roles are redesigned on the assumption that some activities will be automated or AI-assisted, freeing people to focus on context, empathy, negotiation and creative problem-solving.
• Governance is built in from day one, so questions about bias, safety, security, and privacy shape how data is collected, how models are trained, and how workflows are monitored.

Put simply, an AI-first enterprise is not just about “using more AI”. It is structured so people and AI can work side by side safely, repeatably and at scale.

From concept to practice: Three moves for leaders

Turning that idea into reality is less about buying technology and more about changing how leaders frame problems and organize work. Three moves make a tangible difference over the next 18–24 months.

#1: Follow the value journey, not the use-case list

Most AI programs are organized around lists of use cases: predict demand, classify tickets, extract data. That is helpful for brainstorming but fragments effort.

AI-first enterprises focus on end-to-end journeys that matter to customers and to the business. Journeys such as policy application to policy issued, service request to resolution, order to cash, or patient admission through to discharge. Seeing the whole journey makes it easier to decide where AI, automation and human judgment belong, and where fixing one step will not help if everything around it is still slow or manual.

The practical way to start is to pick one or two “lighthouse” journeys where AI can make a visible difference and where leaders are ready to back real change. For example, claims in an insurer or citizen service requests in government. Strong lighthouse journeys cut across functions, have clear metrics for speed, cost, quality, and satisfaction, and are ambitious but not so political or complex that change is impossible. Success does more than improve a KPI; it gives the organization a pattern it can reuse.

#2: Build on shared foundations instead of scattered experiments

When every team runs its own AI initiative, organizations end up with a patchwork of tools, vendors and one-off models. Each project solves a narrow problem, but little is reusable, and risk is hard to govern.

A shared AI platform changes that conversation. Common data foundations, standard tools for building and monitoring models, and reusable components such as document-understanding pipelines or recommendation engines. This gives teams a starting kit instead of a blank page.

Around that platform you need a simple operating model. A small central group that sets standards and manages shared capabilities; business units that co-own outcomes instead of merely “requesting” AI; and funding that rewards reuse and discourages doing the same work five times.

As lighthouse journeys move into production, pay attention to patterns you see again and again – recurring data sets, model types and governance questions. Those patterns point to the “AI operating system” the enterprise really needs.

#3: Treat AI as real business change, not another IT project

Many AI initiatives still run like classic IT projects wherein requirements are gathered, a system is built, users are trained, and then everyone hopes adoption will follow.

AI cuts across this pattern. It changes who does the work, which decisions are automated, and how performance is measured. Studies consistently find that successful AI programs are driven far more by people and process than by algorithms.

A better approach is to treat significant AI deployments as business changes, co-owned from day one. Business leaders define the problem and the outcomes in everyday language. Process owners and frontline teams help design and test new ways of working. Risk, legal and compliance functions shape what “good” looks like before anything goes live. Technology teams bring options and constraints, but do not own the change alone.

Along the way, leaders need a clear baseline and simple, trusted metrics such as how many AI projects are live, where they sit in their lifecycle, which journeys they touch, and what has changed in cycle times, error rates, satisfaction, cost, and capacity. Sharing those results, including what did not work, is what builds organizational “muscle memory” for working with AI.

The real advantage: Rewired, not just augmented

AI tools will keep getting better and cheaper, and competitors will have access to many of the same models. The differentiator will not be owning a slightly more powerful algorithm but having an enterprise that is willing and able to redesign itself around what AI makes possible.

Most organizations will continue to add pilots and tools. A smaller group will tackle the harder work of redesigning workflows, building shared platforms and operating models, and investing seriously in culture, skills and trust. Those are the organizations that will move AI off the strategy slide and into the way work really feels.

An AI-first enterprise is not one where humans are pushed aside. It is one where people are freed from repetitive tasks, supported by better insight, and trusted with problems that demand judgment, empathy, and creativity. Leaders who embrace that vision and who are prepared to move from isolated projects to systemic change will not just “use AI”. They will teach their organizations to think with it, and that is a very different kind of advantage. 

About Author:

Joy Kundu is a Sales and growth leader at AgreeYa Solutions. He works with global enterprises to design and scale digital transformation, AI, and automation initiatives. A recognized strategist and “mega-deal” leader, he helps organizations modernize legacy landscapes, build AI-ready operating models, and turn technology investments into measurable business outcomes.