We are on the cusp of the most profound transformation in business since the Internet revolution. But this time, it’s not just about adopting new technology, it’s about fundamentally rewiring how we think about our organizations and their capabilities.

Most businesses today approach AI as a tool to optimize existing processes, provide faster customer service, more efficient supply chains, and better demand forecasting. But what if the real opportunity isn’t in doing existing things better, but in doing entirely new things that create value?

The organizations that will dominate the next decade aren’t just implementing AI—they’re completely reimagining their operations around it. They’re asking not “How can AI improve what we do?” but “What new markets, products, and business models become possible when AI is the foundation of everything we do?”

What is an AI-First Mindset?

An AI-first mindset isn’t about using ChatGPT occasionally or running a few machine learning models. It’s about making AI your default way of thinking—your organizational operating system.

How many of you approach problems by first asking: “How could AI help us create entirely new value propositions?” rather than “Where can we plug in AI to improve our existing approach?”

The difference is profound. One represents incremental thinking—the familiar path of process optimization. The other represents exponential thinking, reimagining what’s possible when intelligence becomes the engine of innovation and growth.

The 5 Elements of an AI-First Mindset

How do we build this AI-first mindset? I see five foundational elements:

1. Lead From the Multiple, Not the Earnings

Most CEOs focus on how AI will drive next quarter’s earnings, leading to disconnected use cases that deliver incremental benefits. Leaders with an AI-first mindset focus on their company’s multiple—the indicator of long-term value creation.

McKinsey’s research confirms this by noting that leaders at AI- enabled companies “take a more systematic view, focusing on their company’s multiple, to add value to the organization.”

They understand that AI’s true value isn’t in cost-cutting but in fundamentally transforming how the business captures and creates value, opening new revenue streams, entering adjacent markets, and building entirely new business models.

2. Build Learning Loops, Not Knowledge Silos

In traditional organizations, knowledge gets trapped in one person’s mind, in team documentation, in departmental silos. AI-first organizations develop what McKinsey calls “global learning loops” that transform “individual knowledge and local insights into an ever-increasing flow of collective wisdom that everyone in the organization shares and contributes to.”

These learning loops don’t just improve operations—they accelerate innovation cycles. Better data leads to better AI, which leads to faster product development, more accurate market insights, and quicker identification of growth opportunities, which generates more valuable data. The organization becomes not just a learning organization but a growth-accelerating system.

3. Cultivate Technological Adaptability

AI technologies are evolving at a breathtaking pace. Organizations wedded to specific tools, vendors, or approaches will quickly fall behind. An AI-first mindset embraces what McKinsey describes as “technological adaptability,” creating infrastructures where “technologies can be easily integrated inside end-to-end processes to turn data into actionable insights and predictions and easily swapped out for newer ones without breaking the entire system.”

This isn’t just about flexible IT architecture. It’s about maintaining the agility to pursue emerging opportunities as AI capabilities evolve. Your people should expect and embrace regular shifts in tools and techniques, seeing them as pathways to new competitive advantages rather than disruptions.

4. Democratize AI While Maintaining Governance

Many organizations make one of two mistakes: either they restrict AI to specialized teams, creating bottlenecks and limiting innovation, or they allow uncoordinated AI proliferation, leading to redundancy and risk.

The AI-first organization democratizes access to AI capabilities while maintaining appropriate governance. According to recent research, about half of the companies surveyed have little to no restrictions on AI usage at work (51%), while larger organizations tend to implement more guardrails.

Finding the right balance is critical; too restrictive and you stifle entrepreneurial thinking; too loose and you risk fragmented efforts that don’t drive strategic growth.

5. Reorient Human Capital Around Uniquely Human Value

The ultimate question isn’t “What jobs will AI replace?” but “How do we reorient human contribution to focus on what’s uniquely human?”

The same research found that the majority of leaders believe AI will enhance employees’ skills in some tasks while also replacing skills in others.

The key insight is that by automating routine cognitive tasks, AI frees humans to focus on high-value activities: identifying unmet customer needs, designing breakthrough products, building strategic partnerships, and creating differentiated experiences that drive revenue growth.

Making the Shift: From Theory to Practice

How do we translate these principles into action? Here are four practical steps:

1. Invest in Deep Understanding, Not Just Implementation

Many organizations are still in early stages of putting best practices in place, with less than one in five saying they’re tracking KPIs for their AI solutions. True AI literacy goes beyond tool familiarity. It requires understanding the fundamental concepts, capabilities, and limitations of AI.

Every leader should be able to identify how AI can unlock new customer segments, create novel offerings, or enable business model innovation, distinguishing between genuine growth opportunities and mere operational improvements.

2. Create Cross-Functional “AI Innovation Labs”

Break down silos by establishing cross-functional teams dedicated to applying AI to strategic challenges. Recent research shows that 46% of companies have a single existing team responsible for AI strategy, while 45% rely on multiple teams.

These shouldn’t be traditional “centers of excellence” focused on optimization. Instead, they should be growth engines exploring questions like: What customer problems can we now solve that were previously impossible? What markets can we enter with AI-powered offerings? How can we create network effects and platform dynamics?

They should be catalysts that spread growth-oriented AI thinking throughout the organization—inch-wide and mile-deep experts embedded within business units.

3. Implement “Learning Cycles”, Not “Projects”

Traditional project management—with defined beginnings, middles, and ends—is fundamentally misaligned with AI’s continuous learning nature. Instead, structure AI initiatives as ongoing learning cycles with regular reflection points.

McKinsey describes how one pharmaceutical company developed a “clinical control tower” that “continually updates and shares findings derived from the diverse data gathered from hundreds of clinical trials across thousands of sites around the world.” This isn’t a one-time project; it’s a persistent learning system.

4. Redesign Performance Metrics Around Learning

If learning is the meta-skill of the AI era, our performance metrics should reflect this. Beyond traditional KPIs, measure:

Efficiency metrics:

• Velocity of learning (how quickly teams incorporate new information)
• Cost reduction impact (savings from AI-driven automation and optimization)
• Process cycle time improvements (acceleration in key workflows)
• Quality enhancement (error reduction, consistency gains)

Growth metrics:

• Revenue expansion velocity (how quickly AI initiatives open new revenue streams)
• Market opportunity identification (how effectively AI reveals untapped customer segments or needs)
• Innovation cycle time (how rapidly teams move from insight to market-ready offering)
• Customer lifetime value expansion (how AI enables deeper, more valuable relationships)
• New product/service launches enabled by AI capabilities

McKinsey suggests tracking metrics that connect directly to value creation, not just operational efficiency. Focus on how AI contributes to top-line growth, market share gains, and strategic positioning.

5. Reimagine Work Through Specialized Roles

The traditional organizational structure won’t survive in an AI-first world. Today’s typical job descriptions combine generalist and specialist work, with most employees spending 60% of their time on general tasks and only 40% on specialized activities. This model fundamentally limits AI integration.

An AI-first organization requires a radical redesign of roles, similar to how the “Future of Work” concept within the UnBPO™ tenets redefines Who (employees, gig workers, AI agents), How (task allocation), and What (skills).

This approach breaks processes into atomic-level tasks, then identifies who is best suited to perform them, how tasks will be performed (AI supporting humans, humans in the loop, or completely autonomous), and the skills required. The goal is to free human talent for high-impact growth activities: strategic planning, creative problem-solving, relationship development, and market innovation.

This transformation might mean evolving from having 50-100 general role categories to 500-1000 highly specialized positions that focus deeply on specific functions, allowing AI systems to coordinate work effectively, creating a networked organization that’s more adaptable, efficient, and innovative than today’s hierarchical models.

The Future Belongs to the AI-First

The organizations that thrive in the coming decade won’t be those with the best AI technologies. They’ll be those with the best AI mindsets, the ability to think differently about how intelligence can be orchestrated and augmented to drive sustainable, scalable growth.

According to recent surveys, while 78% of organizations now use AI in at least one business function (up from 55% a year earlier), only a small percentage describe their gen AI rollouts as “mature.” Most organizations have yet to see a significant top-line growth impact.

This isn’t just about staying competitive; it’s about unlocking entirely new avenues for value creation. Just as the Japanese manufacturer reimagined what was possible with quality control, AI-first organizations will reimagine fundamental assumptions about market expansion, product innovation, and customer value creation.

The question isn’t whether your organization will use AI; of course, it will. The question is whether you’ll develop the AI-first mindset that turns AI from an efficiency tool into a growth engine.

Adopting an AI mindset is not inherently good or bad; it depends on how thoughtfully and responsibly it’s applied. These views serve as important guardrails, reminding leaders to pair their optimism with humility, governance, and long-term thinking.

At its core, this transformation isn’t about technology alone. As one leader put it, “Artificial intelligence isn’t advancing work processes. It’s completely reimagining them.” The true change is happening not in our machines but in our minds, shifting from optimizing what exists to imagining what’s possible. The future belongs to those who can think differently, not just about AI, but with AI.

Will you be the organization that merely operates more efficiently, or the one that grows into entirely new territory?