The traditional ROI playbook is broken—and enterprises are investing anyway.

KPMG's Global AI Pulse Survey reveals that 65% of organizations say they would continue to invest in AI regardless of tangible ROI, and 75% of global leaders will prioritize AI investment despite economic uncertainty.

This isn't reckless spending. It's strategic pragmatism. The kinds of intellectual effort AI replaces have never been measured well—time reclaimed, decisions made faster, gaps plugged before they become problems. Traditional ROI frameworks ask for clean input-output metrics that AI simply doesn't produce yet.

But here's the critical finding: Only a small group of AI leaders are seeing clear returns. These leaders—82% of whom say AI delivers meaningful business value (vs. 62% of peers)—treat AI as enterprise-wide transformation, not incremental improvement.

For CFOs demanding ROI justification and CIOs struggling to quantify AI value, KPMG's research explains why the old playbook fails—and what separates winners (82% value capture) from laggards (62%).

The ROI Paradox: Investing Despite No Traditional Returns

KPMG AI Head Leanne Allen: "This shift in mindset by business leaders from viewing AI as something that must deliver an immediate return to one that sees AI as a long-term investment, recognizing it as a strategic enabler for enterprise-wide transformation, is an important milestone."

But she adds a critical caveat: "That shouldn't translate into investing in AI blindly, without a clear strategy."

The data reveals a paradox:

• Most organizations can't measure traditional ROI (clean input-output metrics don't exist)
• Yet AI leaders report 82% meaningful value delivery (20 points higher than laggards)
• The gap: transformation vs. incrementalism

The measurement problem: Traditional ROI wants "We spent $X, saved $Y headcount, achieved Z% margin improvement." AI delivers "Decisions made 3 days faster, risks identified before they materialize, employee productivity gains we can't attribute cleanly."

Lambton Capital Partners' Ben Grant: "Traditional ROI wants clean input-output. AI doesn't do that yet in most businesses. The value shows up in time reclaimed, decisions made faster and gaps being plugged before they become problems. Try putting that in a spreadsheet."

Why Traditional ROI Frameworks Fail for AI

The fundamental mismatch: AI replaces intellectual effort that was never measured in the first place.

What companies measured before AI:

• Headcount per function
• Cost per transaction
• Time to complete manual process
• Error rates in data entry

What AI actually improves:

• Quality of decisions made (better risk assessment, faster pattern recognition)
• Speed of decision-making (3 days → 3 hours for complex analysis)
• Preventative value (problems caught before they materialize)
• Knowledge retention (institutional memory doesn't walk out the door)

The attribution challenge:

• AI assistant helps employee complete analysis 40% faster → But employee also took training, workflow changed, data quality improved → How much of the 40% is AI vs. other factors?
• AI flags risk 2 weeks before traditional monitoring → Prevents $500K loss → But loss was probabilistic, not certain → How do you book prevented-loss ROI?

Gartner VP Analyst Nader Henein: "Some AI investments like AI assistants are becoming standard office tools, like the office suite. No one calculates ROI by counting the number of Word documents or presentations produced."

The Performance Gap: 82% vs. 62% Value Capture

KPMG's most important finding: The gap between AI leaders and laggards is widening.

What separates leaders from laggards:

Leaders:

• Treat AI as enterprise-wide transformation, not departmental tool
• Move beyond pilots to fully scaling AI agents
• Capture real business value outcomes, not just productivity metrics
• Build organizational capabilities for compounding returns

Laggards:

• Bolt AI onto existing models, achieving incremental gains
• Remain stuck in experimentation phase
• Measure individual productivity, miss structural value
• Lack talent, governance, and discipline for scaling

Info-Tech Research Group Principal Manish Jain: "It's not that companies don't care for returns. It's that they've learned that before focusing on ROI, they need to focus on maturing AI capabilities. When a new engine comes along, wise operators don't ask first what it earns. They ask what happens if they're the only ones without it."

The Scaling Barrier: Execution, Not Funding

65% of leaders cite scaling AI use cases as the top barrier to demonstrating ROI — not lack of funding, not technology limitations, but execution at enterprise scale.

The second barrier: 62% identify workforce skills gaps as blocking ROI.

KPMG: "Scaling, not spend, is the top barrier to ROI. This reinforces that execution, not funding, is the key to success."

Why scaling is harder than piloting:

Pilot success factors:

• Small team, high motivation
• Controlled data set
• Limited integration requirements
• Executive sponsorship and resources

Scaling blockers:

• Cross-functional coordination
• Legacy system integration
• Data quality/availability across enterprise
• Change management at scale
• Security and governance infrastructure
• Workforce skills gaps

Moor Insights VP Mike Leone: "Budget fell off the list of things killing AI programs a while ago. The money's there and the mandate's there. The real blockers now are security, privacy, and the fact that almost nobody has the people to run this at scale."

His estimate: "Perhaps one in ten enterprises has the talent, governance, and operating discipline to actually get compounding returns from AI spend. Everyone else is spending and hoping."

The Competitive Imperative: Fear of Falling Behind

75% prioritize AI investment despite economic uncertainty. Why?

The competitive calculation:

• Cost of AI investment: Measurable (budgets, headcount, infrastructure)
• Cost of falling behind competitors: Unmeasurable but existential (market share loss, talent flight, strategic irrelevance)

Independent analyst Carmi Levy: "The need to remain competitive in AI, or at least stay within sight of the competition while everyone struggles to figure AI out, means decisions may not be based on the same depth of fiscal rigor that might have been used in years past."

The strategic bet: Better to overspend on AI and course-correct than underspend and lose market position permanently.

What "falling behind" looks like:

• Competitors deploy AI agents, cut response time from days to hours
• Your manual processes can't compete on speed
• Top talent leaves for companies with better AI infrastructure
• Customers expect AI-powered experiences you can't deliver
• By the time you catch up, leaders have 3-year compounding advantage

Leone: "Leaders are right to keep funding through it. They've done the math on what falling behind costs, and they don't like the answer."

What to Measure When Traditional ROI Fails

If traditional ROI frameworks don't capture AI value, what should CFOs and CIOs measure?

Shift from input-output to capability maturity:

Instead of: "AI saved X hours, reduced Y headcount" Measure: "AI maturity level: experimentation → pilots → scaling → transformation"

Instead of: "AI delivered Z% margin improvement" Measure: "Decisions made per day (with AI) vs. without, decision quality improvement, preventative value"

Instead of: "AI ROI = (savings - cost) / cost" Measure: "Compounding returns trajectory: Are we accelerating or plateauing?"

Leading indicators of AI value (not traditional ROI):

1. Velocity: How fast can we deploy new AI use cases? (Time from idea to production)
2. Adoption: What % of employees actively use AI tools? (Not just have access)
3. Scaling rate: How many pilots reach production? (Not just how many pilots launched)
4. Capability maturity: Can we build custom AI agents? (Or just use vendor tools)
5. Compounding: Are returns accelerating quarter-over-quarter? (Not linear growth)

Grant (Lambton Capital): "I believe the problem is how we measure it. The value shows up in time reclaimed, decisions made faster and gaps being plugged before they become problems. Try putting that in a spreadsheet."

What This Means for Decision-Makers

For CFOs:

• ✅ 65% invest regardless of ROI — You're not alone in struggling to justify AI with traditional metrics
• ✅ Shift from ROI to maturity — Measure capability-building, not just cost savings
• ✅ Leaders capture 20-point value gap — Focus on what separates 82% success from 62%
• ⚠️ Scaling is the barrier — Execution, not funding, determines ROI

For CIOs:

• ✅ 75% prioritize despite uncertainty — Economic headwinds won't kill AI budgets
• ✅ Security and skills are top blockers — Not lack of technology or funding
• ✅ Transformation beats incrementalism — Enterprise-wide approach captures 20% more value
• ⚠️ Only 1 in 10 have scaling discipline — Talent, governance, operating model matter more than budget

For CEOs:

• ✅ Strategic enabler, not cost center — AI is infrastructure, not project
• ✅ Fear of falling behind justified — Competitive cost of inaction exceeds investment cost
• ✅ Informed bet, not blind spending — Leaders with strategy + execution capture 82% value
• ⚠️ Widening performance gap — Leaders compound advantage, laggards fall further behind

The Bottom Line

KPMG's finding that 65% invest in AI regardless of traditional ROI isn't evidence of reckless spending. It's evidence that the old ROI playbook broke.

The measurement problem: AI replaces intellectual effort that was never measured (decision quality, preventative value, time reclaimed). Traditional frameworks can't capture what they never tracked.

The performance gap: AI leaders report 82% meaningful value vs. 62% for laggards—a 20-point gap driven by enterprise-wide transformation vs. incremental bolting-on.

The scaling barrier: 65% cite scaling as top ROI blocker—not funding, not technology, but execution at enterprise scale.

The competitive imperative: 75% prioritize AI despite economic uncertainty because the cost of falling behind exceeds the cost of investment.

For decision-makers, the path forward is clear:

1. Stop demanding traditional ROI — Build capability maturity frameworks instead
2. Focus on scaling, not pilots — Execution determines value, not experimentation
3. Enterprise transformation, not departmental tools — Leaders capture 20% more value
4. Build talent and governance — Only 1 in 10 have discipline for compounding returns

The companies achieving 82% value capture aren't smarter or luckier. They're treating AI as enterprise-wide transformation, not incremental improvement—and building the talent, governance, and operating discipline to scale.

Sources

• CIO: KPMG report finds enterprise disconnect between AI and its ROI (April 12, 2026)
• KPMG Global AI Pulse Survey (March 2026)
• KPMG UK AI Investment Report (April 2026)

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