Across industries, organizations are investing heavily in the potential of artificial intelligence to reshape how they operate and grow. Nearly 80% of executives expect AI to contribute significantly to revenue by 2030, but only 24% know where that revenue could come from.
This is not an awareness gap. It’s an architectural gap.
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Companies that are already capturing the value of AI are not waiting to discover it through pilots and proofs of concept. They are projecting this value through deliberate choices about how work is designed, how human and digital workers come together, and how productivity gains are reinvested.
From our work with companies across all major sectors, a clear divide is emerging.
Some organizations are only coupling AI into legacy workflows and seeing marginal productivity gains. Others are redesigning the way value is created and building growth trajectories that competitors cannot replicate.
By 2030, this will not just be a short-term positioning advantage. This will determine who remains in business. The difference comes down to three architectural choices that separate AI-first companies from all others.
Redesign your own work, don’t just complement it
Most AI adoption initiatives fail because organizations are automating fundamentally broken processes. They are making inefficient work more efficient — and wondering why the transformation isn’t happening.
AI-first companies start with a different question: If we were designing this work today, without legacy constraints, what outcome would we want? And what combination of human judgment and AI capability best achieves this result?
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Nestlé offers a powerful example of a global company that is more than a century old. The company isn’t just adding AI capabilities to existing systems. It is building an AI-driven enterprise architecture that understands its entire product ecosystem, supply chain, and consumer relationships in ways that generic models never could.
The goal isn’t incremental improvement — it’s the ability to deliver superior products faster while creating more personalized experiences for employees and customers.
Riyadh Air represents the opposite end of the entrepreneurial spectrum — a startup without legacy constraints. But the principle is identical. The airline is building an AI-native operation from day one, with a unified architecture that connects operations, employees and customers as a single intelligent system.
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The insight they both share is that the digital backbone is not just infrastructure. It is the intentional architecture that allows humans and AI to work as integrated capabilities, creating adaptability that multiplies over time.
Build proprietary intelligence, not just model access
By 2030, everyone will have access to powerful AI models. The winners will be those who have custom AI that knows their business better than any third-party AI ever could.
L’Oréal isn’t just using AI to speed up R&D. It is building a custom foundational AI model, trained with its proprietary formulation data, scientific research, and sustainability requirements.
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These models will give your scientists capabilities that no competitor could replicate, enabling new scientific possibilities that would not otherwise exist.
In our recent survey, more than half of executives expect their competitive advantage to come specifically from the sophistication of AI models.
Sophistication also comes from proprietary data, custom models tuned to specific challenges, and continuous learning cycles.
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Organizations need multi-model portfolios—some proprietary, others licensed—all integrated into architectures that evolve as quickly as their markets.
The most valuable companies will not be those with the most data. It will be those that transform data into AI-driven decisions at scale, with intelligence that competitors cannot imitate simply by licensing better models.
Design growth cycles, not just efficiency gains
Most AI strategies fail because they treat productivity as the ultimate destination.
Executives expect AI to increase productivity by 42% by 2030. But if you count these gains solely as cost savings, you have fundamentally misunderstood the opportunity.
AI-first companies treat productivity as fuel, reinvesting efficiency gains into new products, services and markets.
The pattern works like this: AI-driven efficiency frees up capital and talent. This freed up capacity finances innovation in new markets. New markets generate new data. New data trains better AI. Better AI creates more efficiency. The cycle accelerates.
L’Oréal scientists will not only create formulations faster — this speed will allow them to explore sustainable ingredients that were previously not economically viable.
Nestlé isn’t just optimizing supply chains — it’s using these gains to build direct consumer relationships that transform the way people interact with its products.
Riyadh Air isn’t just creating a new airline — it’s eliminating 50 years of legacy in one fell swoop, in a move that will define the next decade of aviation.
This creates an exponential divergence. While laggards optimize margins, leaders accelerate into new markets, building capabilities that accumulate. Until 2030, the difference will not be measurable in productivity percentages. It will be measurable in completely different business models.
The questions that determine who wins
The next era of growth will not be predicted. It will be designed. Leaders now need to answer three uncomfortable questions:
- If we redesigned our operations with AI-first principles, what would we completely stop doing? Not what would we do faster, but what would we eliminate? Most organizations find that 30% to 40% of their workflows exist solely to compensate for constraints that AI eliminates. But eliminating takes courage — something that optimization avoids.
2. What proprietary intelligence could we build that competitors cannot replicate? Not what AI can you license, but what AI could you design — built on your organization’s unique human expertise — so deeply tailored to your business that it would take competitors a decade to catch up?
3. Are we saving productivity gains or reinvesting them in growth cycles? Cost savings are finite, but growth cycles are exponential. Which one is your strategy building?
By 2030, companies that can answer these questions will not only be more productive. They will be operating in markets that competitors didn’t even know existed, with capabilities that competitors can’t build and business models that competitors can’t afford.
The real risk is not moving too fast with AI. It’s designing too slowly while competitors completely redesign the game.
The opinions expressed in Fortune.com commentary articles are solely those of the authors and do not necessarily reflect the opinions and beliefs of Fortune.
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