c-suite

 


AI-first: The next operating model for enterprise AI strategy

Why the shift to AI-first is not a technology upgrade, it is the most consequential operating model transformation since the rise of the internet.





insight
April 23, 2026
9 min read

Author

Eugen-Rosenfeld


Eugen Rosenfeld

A CTO & a Solution Architect in Life Sciences at Nagarro. He has more than 20 years experience in different programming languages, technologies and business domains.

Executive summary

While AI adoption is ubiquitous, systemic value realization remains elusive. This gap exists because most enterprises continue to superimpose artificial intelligence onto legacy processes rather than re-engineering the fundamental nature of work.

An AI-first enterprise strategy necessitates a complete model shift, embedding intelligence into the operational core through automated workflows, adaptive systems, and AI-native architecture, to catalyze superior decision-making. Beyond technical infrastructure, this strategy redefines the workforce, transitioning human capital from task executors to strategic orchestrators of intelligent systems.

Market leaders are already decoupling from the competitors, capturing disproportionate returns while others struggle to achieve scale. Transitioning to an AI-First posture is not a mere "tech upgrade"; it is a definitive leadership mandate to redesign how the enterprise operates, competes, and wins in a cognitive economy.

The third inflection point

Every generation of digital transformation has been defined not by the technology it introduced, but by the operating model it demanded. The web-first era, beginning in the late 1990s, compelled enterprises to make their services available online. The mobile-first era, a decade later, forced a redesign around accessibility, immediacy, and placed the experience in the consumer's palm. Each shift was less about the underlying technology and more about a fundamental rethinking of how enterprises create, deliver, and capture value.

Today, the enterprise stands at a third inflection point, one that is more profound than either of its predecessors. The shift to AI-first is not about adopting a powerful new tool, it is the cornerstone of a modern enterprise AI strategy that embeds intelligence as a core design principle of the organization itself, where systems think, decide, and act with increasing autonomy. 

woman initiating digital transformation

 

The urgency is real; as of 2024, 88% of enterprises have adopted AI in at least one business function, a dramatic acceleration from roughly 50% in the preceding five years. C-suite conviction is hardening into capital commitment: AI investments have surged 2.5x since 2023, with over 90% of large-cap executives planning further budget increases. Yet a stark paradox prevails, as enterprises struggle to achieve and scale value from their AI initiatives. While technology is being adopted, value is not being realized. This gap is the central challenge of our era, and closing it requires something far more ambitious than better algorithms; it requires a new enterprise AI strategy and operating model.

Beyond adoption: The operating model imperative

The prevailing enterprise approach to AI, of bolting intelligent tools onto legacy processes, is proving to be a losing strategy. S&P Global reported that 42% of enterprises abandoned most of their AI initiatives in 2025, more than double the 17% abandonment rate of the prior year. These are not failures of technology. They are failures of organizational design. The tools worked; the operating model did not.

The evidence for this conclusion is compelling. BCG's research posits that AI success is determined, 70% by people and processes, 20% by technology and data, and only 10% by the algorithms themselves. In a similar manner, Accenture's analysis identifies workflow redesign as the single strongest predictor of AI return on investment, finding that high-performing enterprises are three times more likely to have fundamentally restructured their workflows around AI capabilities. Yet only 21% of all AI-using enterprises have done so.

The heaviest lift in an enterprise AI strategy and transformation is not technical. It is organizational and cultural, involving the very core of an operating model, the people who do the work, and the processes they follow.

This is what distinguishes AI-first from previous model shifts. Web-first asked: "How do we put this process online?" Mobile-first asked: "How do we make this accessible on a phone?" AI-first asks a fundamentally different question that sits at the heart of any enterprise AI strategy: "If intelligence were the default, how would we design this from scratch?" The answer, invariably, looks nothing like the current state.

The five pillars of an AI-first operating model

An AI-first operating model is not a single initiative, it is the manifestation of a comprehensive enterprise AI strategy and a coherent AI-native architecture built on five interdependent pillars, each representing a departure from traditional enterprise design.

1. Default automation over manual workflows

In enterprises that put AI at the center, the natural assumption is that most processes will run on their own. People only step in when there's something unusual or important that the system can't handle. It's not a minor upgrade; it completely flips the way we think about work. McKinsey estimates that by 2030, up to 30% of the hours people work today could be automated. BCG goes even further, predicting that AI agents could take on half of all knowledge work by 2027. Fully automated workflows run by default. Humans get involved only when things get complicated, uncertain, or when the stakes are high. Seeing it in that light, both the predictions make for a complete re-think of how “work” will look in a few years.

Women pointing to text written on a clear surface while man looks on.

2. Context-aware, adaptive operations

Traditional systems follow rigid rules, but AI-first operations are different; they're constantly learning and adjusting in real time. Take customer service as an example. Gartner says that by 2026, almost three-quarters of customer interactions will be handled by AI. Instead of the old setup with strict tiers and handoffs, AI acts as a smart gatekeeper: It sorts out simple issues on its own and, when things get tricky, passes the case to a human expert who already has the complete background. The whole process becomes smoother and more integrated, with the system continually learning and its ability to solve problems.

People in discussion

3. AI-native architecture

Building for AI isn't just about plugging in new tools; it requires rethinking the entire foundation of your technology. The modern approach starts with a digital core built on the cloud, spanning everything from public to private and edge environments. But it doesn't stop there. True AI-native architecture weaves together things like AI agents, advanced knowledge stores that actually understand context, systems for managing models, and services designed to handle information instantly, not in slow, old-school batches. The catch is none of this works without high-quality, unified data. If your data is fragmented or poorly managed, AI projects stall out quickly.

Some enterprises are taking this even further, treating the very structure of their business, like code. That means every process and workflow is clearly defined, tested, and ready for enterprise AI to run or improve automatically. The result is a business that's programmable at its core, able to adapt and optimize itself, as conditions change.

Robotic hand on a laptop denoting AI agents operational efficiency

4. Embedded trust and governance

As enterprise AI systems become more independent, trust and oversight can't just be checked off after the fact; they need to be built in from the very beginning. Right now, a lot of enterprises are still scrambling to catch up. Many have governance gaps that leave them exposed to risks, and plenty have even discovered AI tools being used under the radar, only after they were already in use. The old ways of managing risk and compliance just don't cut it anymore, especially with enterprise AI making decisions at machine speed.

On top of that, rules and regulations around AI are becoming more complex and widespread, making the need for solid governance even more urgent. One of the biggest worries looming on the horizon is the possibility of losing control, as autonomous AI systems begin taking actions that don't align with the business goals. The answer isn't more paperwork or static checklists. Instead, it's about real-time monitoring and dynamic guardrails that keep things on track as AI systems learn and act. This shift means governance is no longer a box to check, but an ongoing part of how the organization runs.

background image with compliance symbol

5. Workforce evolution: From operators to supervisors

In an AI-first enterprise, the role of people is changing fast. We're already seeing big gains - enterprise AI is helping professionals win back a huge chunk of time by taking over routine work, and people using AI tools are getting a lot more done each day without sacrificing quality. Instead of just carrying out set tasks, employees are becoming supervisors of these intelligent systems. They set goals, handle exceptions, and step in when judgment or creativity is needed; things machines still can't do well.

But this new way of working calls for new skills. Critical thinking and complex problem-solving are quickly rising to the top of what's needed, yet most employees still don't feel fully at ease working alongside AI. That means upskilling is going to be a big part of the transition. As enterprise AI takes over more of the analytical and technical work, what sets people apart will be their ability to lead, navigate uncertainty, and solve problems creatively. In a truly AI-first environment, work is redesigned, so humans and AI can focus on what they do best.

Vision for workforce

The strategic adoption pathway

Shifting to an AI-first approach and implementing a robust enterprise AI strategy isn't something you can do overnight; it's a step-by-step process. Leaders who try to jump in all at once, without laying the right groundwork, often end up frustrated when things don't scale the way they hoped.

Phase 1
AI strategy and strategic direction



The journey starts with a clear sense of purpose. It’s about making AI a core part of how the business operates through a deliberate enterprise AI strategy, not just another tech upgrade. This means securing top-level buy-in, identifying two or three high-impact opportunities, and aligning leadership around a shared vision. Only a small fraction of enterprises see significant financial impact from AI today, so this phase is about building the foundation to become one of them.

Phase 2
Data foundation for enterprise AI



The next step is getting your data in order. A unified, well-governed data system is essential if AI is to deliver real value. This involves breaking down silos, ensuring data quality and traceability, and addressing challenges around labeling, bias, and privacy. Without this foundation, AI efforts remain fragmented and limited in impact.

Phase 3
AI-native architecture design



With data in place, the focus shifts to building the technology backbone. This includes adopting cloud-native platforms, enabling AI agent frameworks, and deploying tools for managing models and services. The goal is to move from slow, batch-based systems to real-time, event-driven architecture, while also introducing smarter ways to monitor and control compute costs.

Phase 4
AI governance and trust framework



At this stage, the priority is building trust in AI systems. This requires real-time oversight mechanisms that operate at machine speed, along with tools for monitoring models, detecting bias, and ensuring explainability. As regulations evolve across regions, compliance frameworks must remain flexible and adaptive.

Phase 5
Workforce transformation for AI



The focus now shifts to people and how work gets done. This phase involves redesigning workflows to leverage AI effectively and equipping employees with new skills to collaborate with intelligent systems. The aim is to move away from repetitive tasks toward roles centered on supervision, judgment, and decision-making, opening up new career pathways.

BCG's framework of Deploy, Reshape, and Invent provides a useful lens for calibrating ambition at each phase of your enterprise AI strategy, moving from quick wins with off-the-shelf tools, to the transformation of core functions, and ultimately to the invention of entirely new AI-powered business models. McKinsey's "Rewired" framework reinforces this holistic approach, emphasizing that successful AI integration demands simultaneous transformation across strategy, talent, operating model, technology, data, and change management.
North America
[GEO: North America] In North America, the enterprise AI strategy imperative is particularly acute. According to recent surveys, 76% of North American enterprises have already initiated AI transformation projects, yet only 23% report achieving measurable ROI. The competitive pressure is intense, with leading organizations in the US and Canada investing heavily in AI-native architectures. Organizations in this region should prioritize rapid execution of Phases 1-2, while building governance frameworks that comply with emerging US and Canadian AI regulations. The talent market for AI expertise remains highly competitive, making workforce augmentation and reskilling initiatives for critical differentiators.
Europe
[GEO: Europe] European enterprises face a unique enterprise AI strategy challenge: Balancing innovation with regulatory compliance. The EU AI Act and GDPR create a more stringent governance environment than other regions. European organizations should emphasize Phase 4 (Governance and Trust) early in their transformation journey. Enterprises in Germany, France, and the UK are leading the way with AI-first initiatives, but the regulatory landscape requires careful attention to bias detection, explainability, and data privacy. European enterprises should view governance not as a constraint, but as a competitive advantage in building customer trust.
Asia-Pacific
[GEO: Asia-Pacific] The Asia-Pacific region is experiencing the fastest AI adoption rates globally, with enterprise AI strategy becoming a board-level priority across China, India, Japan, and Southeast Asia. Enterprises in this region often have advantages in data availability and cloud infrastructure investment. However, the challenge lies in workforce evolution where the region faces acute talent shortages in AI expertise. Enterprises should accelerate Phase 5 (Workforce Augmentation) while simultaneously building data foundations. The competitive window in APAC is particularly narrow; organizations that delay their enterprise AI strategy transition, risk being outpaced by regional leaders.

The cost of inaction

The potential economic impact of AI is estimated between $2.6 and $4.4 trillion annually. That value will not be distributed evenly. It will accrue disproportionately to organizations that have restructured themselves to capture it. Research consistently shows that a small elite, between 5% and 12% of companies, is achieving transformational, enterprise-level returns from AI, and these leaders distinguish themselves through superior enterprise AI strategy and organizational alignment. They have redesigned their workflows, invested in their talent, and embedded AI into their strategic decision-making.

For organizations that successfully make the transition, the returns are significant: On average, for every dollar invested in AI, the returns are three and a half times. The gap between these leaders and the rest is not narrowing; it is widening. Every quarter an enterprise spends layering AI onto an unreformed operating model is a quarter in which the leaders extend their advantage.

A call for architectural courage

The transition to an AI-first operating model and the execution of a comprehensive enterprise AI strategy is the defining strategic challenge for this generation of enterprise leaders. It is not a technology project to be delegated to the CIO. It is an organizational transformation that demands attention, conviction, and continued support of the entire C-suite.

Web-first and mobile-first each rewarded the companies that moved early and decisively. AI-first will be no different, except that the stakes are higher, and the window for action is shorter. The enterprises that will lead the next decade are not those with the most sophisticated models. They are those with the courage to redesign their operating models around a simple, radical premise: Intelligence is no longer a feature. It is the foundation.

Image with idea bulbs depicting change management

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