Why AI’s greatest payoff is growth and how leaders can build AI-native businesses to capture it

One of the greatest business rivalries of the past century has unfolded on the racetrack, with automotive giants competing head-to-head at speeds of 200 miles per hour. Winning teams don’t fine-tune last year’s engine; they build for an entirely new race.

They design lighter, more powerful systems delivering results without sacrificing agility. They move fast, invest deliberately and position every resource to unlock performance.

The same is true for business. As the engine sits at racing’s core, AI sits at the centre of growth. Yet despite leaders doubling down on their AI investments, fewer than one in five companies are realizing AI’s full potential. The reason is simple: most chase efficiency when AI’s real value lies in growing new products, channels and ways to create and capture value.

This gap is critical – and costly. In one decade, $27 trillion in enterprise value migrated across the world’s 3,000 largest firms, with a third of that shift occurring in the last two years. Our research reveals these companies lost nearly $5 trillion in revenue to disruption. This isn’t a cost story; it’s a story of new value pools forming faster than enterprises can rewire.

As the rules for growth, competitiveness and leadership are being rewritten, the imperative is to design AI-native business models in which AI shapes the economics of the enterprise, not merely its operations. In working with organizations across industries, we’ve identified what differentiates companies capturing real value from those still searching for it.

Many organizations treat data as an operational input rather than a growth engine. Lacking, inconsistent or disconnected data will produce low-quality outputs. One client paused a multi-million-dollar investment in a custom AI model after discovering it was trained on 37 different versions of the same operating procedures, leading to errors, rework and months of lost productivity.

High performers behave differently, using proprietary data to create new products, open new channels and build commercial models competitors can’t replicate. Encouragingly, a third (34%) of companies are scaling at least one industry-specific solution, which in turn makes them three times more likely to achieve enterprise-level returns.

Data as a differentiator isn’t optional; it’s existential. Growth models built on generic data are indistinguishable and replaceable. As racing teams constantly analyze telemetry to improve performance, leaders should ask: does our data help capture growth? What data are we missing to redefine our productivity frontier?

AI agents are reshaping the rules of competition. They can interpret context, act autonomously and orchestrate workflows in ways that change how businesses think about labour, assets and resource allocation. By 2030, leading companies will have deeply embedded agentic systems into their core operations – not as tools but as new factors of production.

Properly deployed, agentic AI delivers more than efficiency. It doesn’t just save time; it multiplies capacity and unlocks new revenue pathways by collapsing cycle times, eliminating bottlenecks and enabling autonomous decision-making. For example, we’re helping a major airline use agentic AI to handle over 40% of its digital queries and bookings, serving over 300,000 customers monthly.

The impact on human work is profound. One US healthcare insurer we worked with deployed AI agents that reduced document processing time by 90%, tripling daily document value. Human reviewers, once involved in nearly every document, now intervene in less than 3%, redirecting their time to higher-judgment, revenue-generating activities.

Agentic AI is not another pilot or tool. It demands a fundamentally different operating model that integrates AI into decision-making, workflows and business architecture. This is the essence of becoming an AI-native enterprise.

AI doesn’t operate in isolation. It depends on compute, cloud and data infrastructure that no company can build alone. Once leaders identify their agentic AI priorities, they can assess where to build proprietary advantage and where to leverage partners who are already best-in-class.

This means discerning when to co-create solutions with partners, when to rely on platforms and when to place bold bets on in-house capabilities. Leaders winning with AI treat their ecosystems, partners, customers, data and platforms as strategic assets, not suppliers.

The question is not whether to partner but how to architect an ecosystem advantage competitors can’t easily copy. Build where differentiation matters; partner where scale and speed matter.

The Formula 1 racing championship makes it clear: drivers are essential but mechanics, engineers and strategists make victory possible. AI, like racing, is a team sport.

Technology investments are only as strong as the talent activating them. Companies investing in tech and talent are four times more likely to achieve long-term profitable growth. Yet only 43% C-suite leaders plan to upskill employees for AI-enhanced work. Meanwhile, less than one in five (21%) of employees believe they have a voice in how AI is introduced and used in their workplace.

This is emerging as the defining constraint on AI-native reinvention. Closing the gap requires redesigning workflows, redefining roles and embedding continuous learning to empower employees to use AI effectively. Without this shift, no amount of technology investment will translate into business value.

Disruption is not a signal to retreat; it’s a jumping-off point for building resilience. Competing means redirecting investments when pilots fail, creating new business models and redefining how value is measured.

A US medical technology company provides a powerful example. Over the last decade, it rewired its business through targeted acquisitions and breakthrough research and development. In one year, it launched over 100 new products and expanded into new markets – a bold bet on reinvention that’s paying off.

Reinvention in the AI era is not episodic; it’s continuous. Organizations that thrive will pivot quickly, scale rapidly and abandon what no longer serves them.

Like racing champions, success with AI requires focus, orchestration and the courage to move boldly at speed. AI’s real value is not in cost savings but in unlocking entirely new forms of value with exponential, not incremental, growth.

Leading in this new era brings a new CEO mandate: build AI-native business models, architect differentiated data advantage, deploy agentic systems at scale, invest deeply in people and mobilize ecosystems to accelerate reinvention.

The race to make AI the engine of enterprise growth is underway. Are leaders ready to take the wheel?