IBM: Can Executives Turn AI Ambition Into Revenue By 2030?
Most leaders see the money in AI, but few can point to the exact source. IBM's Enterprise 2030 Study shows 77% of UK and Ireland C-suite executives expect AI to contribute significantly to revenue by 2030, yet only 27% know where that revenue will come from.
That gap isn't a tech issue. It's a strategy issue. The next four years will reward clarity on value, not just activity.
Investment is surging - and shifting
Executives expect AI investment to rise about 149% by 2030. The spend is tilting away from pure efficiency (47% today) toward product, service and business model innovation (64% by 2030).
The risk: 73% fear their AI efforts will stall without tight integration into core business operations. Translation - pilots won't pay. Embedded systems will.
"AI is no longer just a tool for efficiency - it's becoming a growth engine for the enterprise... Success will hinge on integrating AI into core business strategies and reskilling the workforce." - Rahul Kalia, Managing Partner, UK & Ireland, IBM Consulting
Workforce, models, and governance will separate leaders from laggards
More than half of global executives expect workforce skills to be fundamentally reshaped by 2030. Reskilling becomes a board-level agenda, not a side project.
In the UK, 48% believe their edge will come from model sophistication. Yet only 29% know which models they'll need by 2030. Most (81%) expect a mix of large models and smaller, specialised systems tuned to their business logic.
Governance isn't red tape - it's how you move fast without breaking trust. The study shows the leaders moving ahead treat governance, data quality, and model controls as part of the product, not an afterthought.
Quantum is on the horizon - security can't wait
Six in ten executives think quantum-enabled AI will reshape their industries. Only 37% are preparing to make their organisations quantum-safe.
Practical move: start a quantum risk review now. Prioritise crypto agility and vendor requirements so your 2026 roadmaps don't create 2030 liabilities.
Macro outlook: upside is big, and timing matters
The UK Government's AI Opportunities Action Plan estimates up to £400bn in economic expansion by 2030 through productivity and innovation. IBM's data suggests AI-driven productivity in the UK and Ireland could rise by 44%, with most gains arriving before the decade ends.
Across 33 countries, mature markets are pushing AI into products, workflows, and decisions. Emerging economies may move faster by skipping legacy overhead and going AI-native with cloud and data upgrades in one sweep.
The executive playbook: turn expectation into revenue
- Write the revenue thesis. Identify 3-5 specific value pools: cross-sell uplift, dynamic pricing, new AI-enabled SKUs, premium support tiers, reduced churn, or usage-based models.
- Back-cast from 2030. Define which AI capabilities must be live by 2027 to hit your 2030 P&L. Tie each to an owner, budget, and milestone.
- Shift the portfolio. Balance 40% efficiency, 60% growth. Sunset low-yield pilots. Double down on use cases with line-of-business pull and clear unit economics.
- Treat data as a product. Name data product owners. Set SLAs for freshness, lineage, and access. Your models are only as good as the tables they read.
- Adopt a multi-model strategy. Combine foundation models with small, specialised models aligned to your processes and risk profile.
- Build-in governance. Standard policies for model registration, testing, bias checks, and monitoring. Automate compliance in the CI/CD path.
- Integrate into the P&L. Route AI features into pricing, packaging, and sales motions. Incentivise go-to-market teams for AI-led wins.
- Reskill at scale. Stand up role-based learning paths for product, ops, risk, finance, and frontline leaders. Make certification part of performance.
- Prep for quantum-safe. Inventory cryptography dependencies. Set a migration plan with your vendors now.
90-day sprint to de-risk and accelerate
- Weeks 1-3: Draft the revenue thesis, pick five use cases, appoint product and data owners.
- Weeks 4-6: Build thin-slice versions that flow through live data into a real workflow. Add guardrails and human review.
- Weeks 7-9: Instrument measurement: incremental revenue, margin impact, cycle-time reduction, quality uplift.
- Weeks 10-12: Kill two weak bets, scale two strong ones, brief the board on P&L impact and next capital asks.
Metrics that matter
- New revenue from AI-enabled features per quarter
- Gross margin delta on AI-augmented products
- Time-to-decision and cycle-time reduction in key workflows
- Model quality: precision/recall tied to business outcomes, not lab scores
- Governance health: % models with full lineage, testing, and monitoring
- Workforce readiness: % of priority roles certified on AI skills
Talent: make reskilling a system, not an event
Tie learning to roles, objectives, and incentives. Equip leaders to read AI metrics, challenge assumptions, and make capital calls with confidence.
If you need a structured path by role, explore curated AI learning tracks here: AI courses by job.
Sources and further reading
Bottom line
The winners will do three things well: pick the right value pools, integrate AI into the P&L, and build skills and governance that scale. Confidence without clarity won't fund itself. Clarity with execution will.
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