IBM Study: By 2030, AI Drives Smarter Growth as Spend Shifts from Efficiency to Innovation

AI Is Moving From Efficiency to Growth: What Executives Need to Do Before 2030

New research signals a clear shift: AI is moving from cost control to growth creation. According to the IBM Institute for Business Value, 79% of surveyed executives expect AI to materially contribute to revenue by 2030, up from 40% today. Yet only 24% have a clear view of where that revenue will come from.

Investment is set to climb about 150% by 2030. The risk is real, though-68% worry their efforts will stall without tight integration into core business activities.

Efficiency to Innovation: The Budget Rebalance

Today, 47% of AI spend targets efficiency. By 2030, leaders expect 62% to fund innovation. That shift is backed by action: 70% plan to reinvest AI-driven productivity gains into growth initiatives.

The study projects a 42% productivity lift by 2030, with 67% expecting to capture most of those gains. The message is simple: use efficiency wins to fund new revenue plays.

Quote That Frames the Moment

"AI won't just support businesses, it will define them," said Mohamad Ali, Senior Vice President, IBM Consulting. "By 2030, the companies that win will weave AI into every decision and operation."

Where Advantage Will Come From

• Model strategy, not just model size. While 57% believe sophistication will drive edge, only 28% know which models they'll need by 2030.
• Portfolios over one-size-fits-all. 82% expect multi-model capabilities; 72% expect small language models to surpass large ones for many jobs.
• Scale across workflows. Organizations using smaller, custom, and foundation models across multiple workflows anticipate 24% higher productivity and 55% higher operating margins by 2030.
• Quantum: belief outpaces readiness. 59% say quantum-enabled AI will change their industry by 2030, but only 27% expect to be using quantum by then. Early movers gain options others won't have.

Leadership, Org Design, and Skills Are Changing Fast

• By 2030, 25% of boards are expected to have an AI advisor or co-decision maker.
• 74% say leadership roles will be redefined; two-thirds believe entirely new leadership roles will emerge.
• 67% say roles are getting shorter-lived; 57% expect most current skills to be obsolete by 2030; 67% say mindset will matter more than skills.
• 67% expect AI to remove resource and skills constraints that slow them down today.
• AI-first organizations are 48% more likely to create net-new roles and 46% more likely to redesign their org structure to extract more AI value.

The Executive Playbook: Turn AI Ambition into P&L Outcomes

• Define the revenue paths. Identify 3-5 high-conviction value pools where AI can create net-new revenue (new SKUs, pricing models, embedded AI services, premium tiers). Set target contribution by 2028 and 2030.
• Rebalance the budget. Shift from efficiency-first to an innovation mix that reaches ~60% by 2030. Ring-fence reinvestment from productivity gains.
• Integrate with the core. Tie AI use cases to customer journeys, pricing, supply, and service operations. Avoid isolated pilots that never touch the operating model.
• Adopt a portfolio of models. Use SLMs for speed, cost, and control; LLMs for complex generation; custom/foundation models where differentiation matters. Decide "build," "buy," or "partner" by domain.
• Own your data and integration layer. Standardize data contracts, instrumentation, and feedback loops. Latency and handoffs kill ROI more than the model choice.
• Ship faster, measure harder. Productize AI into workflows with release cadences. Track time-to-value and model performance against business KPIs, not just benchmarks.
• Upgrade governance. Treat AI risk like financial risk: usage policies, model registries, lineage, bias testing, and audit trails. Automate as much of this as you can.
• Build a talent flywheel. Stand up an AI enablement function. Upskill by job family, certify critical roles, and create internal marketplaces for AI projects.
• Modernize board operating rhythms. Add AI metrics to board packs, run quarterly model risk reviews, and pilot an AI advisor for strategy and scenario planning.
• Place early bets on quantum-aware workloads. Identify use cases that benefit from quantum-enabled techniques (optimization, materials, complex simulation) and start proofs of concept.

Benchmarks to Track Through 2030

• Share of AI spend on innovation (target ~60% by 2030)
• Productivity gain and capture rate (target: realize >60% of modeled gains)
• AI contribution to revenue (track by product line and segment)
• Operating margin uplift attributed to AI (baseline annually)
• Percent of workflows with embedded AI copilots or automation
• Average time-to-market for AI features (from idea to production)
• Model portfolio mix (SLM/LLM/custom) and per-use-case unit economics
• Quantum readiness milestones (skills, partnerships, PoCs)

Study Source and Method

The findings come from a global survey of 2,007 senior executives across 33 geographies and 20 industries during Q3-Q4 2025. The study examined AI-first operations, integration of advanced models into products and services, workforce transformation, and readiness for emerging tech like quantum computing.

Read the full analysis from the IBM Institute for Business Value: Enterprise 2030 report.

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