Many organizations are investing heavily in AI and getting wildly different results. The surprising divider isn’t the model, the platform, or the vendor roadmap, according to a Deloitte analysis. Outsized AI value shows up when technology investment decisions are intentionally shared among complementary leaders, most notably the CTO, CFO, and CSO.
Why it matters: When the lenses of the key C-suite leaders are combined, organizations are far more likely to report stronger profitability, broader KPI gains, and more mature automation progress.
Tech still dominates decision-making:
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In Deloitte’s survey, 60%–80% of respondents said CIOs/CTOs are the driving force behind investments, often co-owning areas like modernization and automation.
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That builds capability but it can also narrow the lens if workforce and operating-model impacts are not co-led by C-suite leaders, including the CHRO.
CTO authority correlates with automation maturity:
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When CTOs moved from no control to full control over tech investment decisions, organizations were more than twice as likely to reach “advanced” AI automation maturity.
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Yet the analysis also found a paradox: CTO-led environments often invest less aggressively in new data monetization initiatives—suggesting a tilt toward optimizing existing assets.
CFO involvement maps to profitability:
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Where the CFO had no decision authority, only 18% of organizations achieved above-average profitability. With full CFO authority, that jumped to 42%.
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CFOs may be cautious about three-year AI ROI forecasts, but hands-on involvement appears to increase confidence in hitting milestones.
Strategy can be the multiplier:
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When the CSO has substantial authority in major tech investment decisions, organizations could be up to 88 times more likely (per the model) to achieve high ROI across a broad KPI set—financial, customer, operational, workforce, and purpose-driven metrics.
What this means for CHROs: CHROs need ensure the “people system” is designed into AI governance from day one:
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Push for shared decision rights on major AI investments, especially where workflows, roles, or workforce risk are impacted.
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Tie AI funding to capability outcomes (skills, adoption, productivity, quality), not just technical delivery.
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Build “nexus skills” in the leadership bench: enough tech fluency, financial literacy, and market awareness to steer AI like a business portfolio.
