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Hi AI Futurists,
IBM just published new research on how companies plan to use AI through the end of the decade. The headline takeaway is simple: AI is moving into the core of the business, shaping decisions and workflows rather than living on the sidelines as a tool. What does that mean? Let’s take a look.
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AI Is Becoming the Enterprise Operating Layer
According to IBM’s latest four-year outlook, enterprise spending and attention will shift away from isolated AI tools and toward systems that sit at the center of how businesses operate. By the end of the decade, AI is expected to shape how decisions are made, how work moves through organizations, and how quickly companies adapt. IBM Consulting SVP Mohamad Ali summarizes the direction clearly: “AI won’t just support businesses, it will define them.” The focus is on building AI directly into the structure of the business.
IBM’s research outlines a two-stage transition. In the near term, companies continue to use AI for productivity, with executives projecting a 42% improvement by 2030. Over time, those gains are expected to be reinvested into growth, shifting AI spend toward new products, services, and business models. By 2030, most enterprises expect AI to contribute directly to revenue, even if many leaders are still working out the exact pathways. The common thread is deeper integration into everyday workflows.
The advantage IBM points to comes from specialization and context. Enterprises expect to run multi-model environments, with smaller, purpose-built models becoming more common than large general systems. These models are trained on proprietary data and tuned to specific decisions. As AI embeds itself, roles and responsibilities evolve. Oversight, governance, and decision framing become more important, while routine analysis and coordination fade into the background.
Takeaways at a Glance:
AI investment is moving toward systems embedded in core workflows
Productivity gains are being treated as fuel for growth, not the end goal
Enterprises are standardizing on multiple, specialized models
Revenue impact from AI is expected by the end of the decade
Human roles are shifting toward judgment, review, and escalation
What We Think About It:
Most teams already have access to AI, but very few have decided where it actually belongs in their workflow. IBM’s research suggests the winners won’t be the most aggressive adopters, but the most disciplined ones.
People who understand how work moves through their organization, and where decisions slow down, are the ones best positioned to benefit as AI becomes more embedded.
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Map one decision you’re involved in from start to finish, then use AI to identify where delays or handoffs happen
Build one “default” AI prompt you trust for reviewing work quality, assumptions, or risks
Decide in advance which parts of your work should always stay human, and enforce that boundary
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This 158-page research report provides the first comprehensive taxonomy of public companies, private ventures, and tokenized protocols building the infrastructure for autonomous AI systems. Compiled by Lex Sokolin, former Chief Economist at ConsenSys, fintech strategist at Autonomous Research, and current Managing Partner at Generative Ventures, this report delivers institutional-grade analysis of 100+ companies across 14 critical infrastructure layers. Learn more here.
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