How AI-first operating models unlock scalable, responsible value

Artificial intelligence (AI) is now embedded across nearly every industry. More than $250 billion was invested globally in AI in 2024 and most large organizations are running pilots in multiple functions, as revealed by the AI-First Leadership Roundtable in August and October 2025.

Yet for many leaders, a familiar frustration remains: despite growing investment and experimentation, AI has not fundamentally changed how their organizations operate or create value.

The problem is not a lack of ambition or technology. It is structural. Most enterprises are attempting to layer AI onto operating models designed for a pre-AI world, models built around linear workflows, static roles and incremental optimization. As a result, value creation remains uneven, adoption is slow and pilots struggle to scale into production.

A different pattern is beginning to emerge with AI-first enterprises, which are redesigning how work is done by embedding intelligence directly into workflows, decision-making and delivery.

Rather than treating AI as a tool, they treat it as a core collaborator. Their operating models offer a practical window into what works when intelligence is placed at the centre of the organization.

But how are AI-first organizations rethinking capabilities, processes and outcomes, and what does this shift mean for leaders navigating the next phase of enterprise transformation?

The World Economic Forum’s AI-First Enterprises workstream and the Annual Meeting 2026 stakeholder dialogue, Enterprises with a Neural Spine, provide some insights.

AI-first organizations are designed – or redesigned – around AI-native operating and business models as the primary mechanism for creating, delivering and scaling value. Rather than applied as a supporting layer, intelligence is embedded end-to-end across workflows and decisions to drive sustainable performance at massive, enterprise scale.

In practical terms, this means AI is integrated into how work is structured, how decisions are made and how performance improves over time rather than added on top of existing processes.

Across sectors, three defining shifts are becoming clear.

AI-first companies build intelligent capabilities that dramatically increase operational leverage. As Yutong Zhang, president of Moonshot AI, observes: “Startup companies are lean and small; they have less than 10 people but they have hundreds of agents helping them to run all of those things on an operational level. So right now, AI is giving all the companies a high operational leverage.”

This leverage changes what skills matter most. AI literacy and managerial capability, particularly the ability to delegate work to both humans and machines, become essential. Richard Socher, CEO of You.com, notes:

“Most individual contributors aren't managers and the adoption would be very low until we said, ‘here's a training programme and a certification programme’. When people had to do it, adoption in older organizations really picked up. But that managing and delegation mindset doesn't come naturally to most people.”

Team structures and norms evolve accordingly. Early AI-first leaders report human-to-AI ratios exceeding 10:1, with subject-matter experts, engineers and users working alongside AI systems. Work increasingly begins with clearly defined outcomes, followed by intelligent workflow and process design.

At the same time, the human-AI interaction model is still forming. As Ioannis Antonoglou, co-founder, president, and chief technology officer of Reflection AI, observed:

“Both humans and many agents interact with each other and what the dynamics of these systems are is not well understood.”

As a result, rapid prototyping, tight pilot-to-production loops and close collaboration between experts and AI systems are becoming standard practice.

AI-first operating models replace sequential workflows with continuously learning, adaptive systems. Leaders often begin with a simple but powerful question, according to Ravi Mhatre, partner and co-founder of Lightspeed Venture Partners: “If we had access to unlimited intelligence, what would we build?”

Five characteristics consistently emerge:

On where to start, Bipul Sinha, CEO of Rubrik, offers a pragmatic approach: “We are going line-of-business by line-of-business and asking everybody to identify three to five workflows that are done manually, or with a combination of SaaS applications and humans. Then we ask whether we can define end-to-end AI-driven outcomes.”

Rather than optimizing individual steps, AI-first organizations redesign workflows around outcomes from the outset.

3. What leaders measure: From process optimization to dynamic value creation

Traditional performance metrics still matter but AI changes how they are achieved and sustained. In the current phase, reliability, accuracy and governance are especially important because they build trust and accelerate adoption.

Across AI-first enterprises, seven outcome categories stand out:

The shift is not simply about efficiency. Value creation becomes continuous, with products and services improving through embedded feedback loops rather than periodic redesigns.

Many enterprises attempt to modernize operating models without adapting business models or vice versa. AI-first startups demonstrate that the two must evolve together.

Competitive advantage increasingly comes from orchestration rather than experimentation. Successful leaders treat capabilities, processes, outcomes and business-model redesign as a single, integrated evolution.

In an AI-first operating model, teams work with intelligence as a core collaborator, enabling faster decisions, greater adaptability and sustained value creation at scale.

AI-First Enterprises is part of the AI Global Alliance, which convenes more than 50 AI innovators. The group shares practical insights, tests emerging operating models, assesses economic and operational impact and develops tools to help organizations scale AI responsibly and effectively.