Key Takeaways:
- 80% of executives expect AI to significantly contribute to revenue by 2030, but only 24% know where that revenue might come from
- The companies capturing AI’s value are engineering it through deliberate choices about how work gets designed, how human and digital workers come together, and how productivity savings are reinvested
- Three architectural choices separate AI-first enterprises from others: redesigning work itself, building proprietary intelligence, and engineering growth loops
- By 2030, the gap between leaders and laggards will be measurable in entirely different business models, not just productivity percentages
- Leaders must answer three uncomfortable questions: what to stop doing entirely, what proprietary intelligence to build, and whether to bank productivity gains or reinvest them into growth loops
Introduction to the AI-First Enterprise
Across every industry, organizations are investing heavily in the potential of artificial intelligence to reshape how they operate and grow. However, as noted in the article, "This isn’t an awareness gap. It’s an architecture gap." The companies already capturing AI’s value aren’t waiting to discover it through pilots and proofs-of-concept, but are instead "engineering it through deliberate choices about how work gets designed, how human and digital workers come together, and how productivity savings are reinvested." This approach is what sets them apart from others, and it’s what will determine who remains in business by 2030.
Redesigning Work Itself
Most AI adoption fails because organizations are automating fundamentally broken processes, making inefficient work more efficient, but not transforming it. AI-first enterprises, on the other hand, start with a different question: "If we were designing this work today with no legacy constraints, what outcome do we want? And what combination of human judgment and AI capability achieves that outcome best?" As the article notes, "Nestlé provides a powerful example of a more than a century-old global enterprise" that is building an AI-powered enterprise architecture that understands their entire product ecosystem, supply chain, and consumer relationships in ways generic models never could. Similarly, Riyadh Air, a startup with no legacy constraints, is building an AI-native operation from day one, with a unified architecture connecting operations, employees, and customers as a single intelligent system.
Building Proprietary Intelligence
By 2030, everyone will have access to powerful AI models, but the winners will have customized AI that knows their business better than any third-party AI possibly could. As the article states, "L’Oréal isn’t just using AI to accelerate R&D. They’re building a custom AI foundation model trained on their proprietary formulation data, scientific research, and sustainability requirements." This approach allows companies to create proprietary intelligence that competitors can’t replicate, enabling new scientific possibilities that wouldn’t otherwise exist. The article notes that "more than half of executives expect their competitive edge to come from AI model sophistication specifically," and that sophistication comes from proprietary data, custom models tuned to specific challenges, and continuous learning loops.
Engineering Growth Loops
Most AI strategies fail because they treat productivity as the destination, but AI-first enterprises treat productivity as fuel by reinvesting efficiency gains into new products, services, and markets. The article explains that "AI-driven efficiency frees capital and talent. That freed capacity funds innovation in new markets. New markets generate new data. New data trains better AI. Better AI creates more efficiency. The loop accelerates." This creates exponential divergence, where leaders accelerate into new markets, building capabilities that compound, while laggards optimize margins. As the article notes, "L’Oréal scientists won’t just make formulations faster—this speed will allow them to explore sustainable ingredients that were not economically feasible before."
The Questions That Determine Who Wins
The next era of growth won’t be predicted, it will be engineered, and leaders must answer three uncomfortable questions now: "If we redesigned our operations with AI-first principles, what would we stop doing entirely? Not what would we do faster, rather, what would we eliminate?" "What proprietary intelligence could we build that competitors can’t replicate?" and "Are we banking productivity gains or reinvesting them into growth loops?" As the article warns, "The real risk isn’t moving too fast on AI. It’s engineering too slowly while competitors redesign the game entirely." By answering these questions, companies can position themselves for success in the AI-first era and create a future that is engineered, not predicted.
https://fortune.com/2026/01/19/i-lead-ibm-consulting-heres-how-ai-first-companies-must-redesign-work-for-growth/