Gartner’s survey of 353 data and analytics (D&A) and AI leaders, conducted from November to December 2025, reveals that AI deployment rates have surged from 40% of organisations in 2024 to 80% today, yet only 44% have implemented financial guardrails or AI FinOps practices. Only one in five leaders express concern over uncertain costs limiting AI value, signalling a risky complacency as enterprises scale experimental deployments into production environments without robust cost controls or ROI frameworks.
For enterprise leaders, this rapid expansion underscores a strategic inflection point: while AI hype drives broad adoption, the absence of disciplined financial oversight risks turning widespread experimentation into an unsustainable drain on resources, particularly as hidden costs like compute scaling and model retraining emerge at enterprise volumes. Gartner warns that D&A leaders must now prioritise delivering tangible business value to navigate fears of an AI bubble, shifting from tactical pilots to accountable, outcome-focused programmes.
Pillar One: Setting Clear AI Ambition for Return on Intelligence
The first pillar emphasises establishing a shared AI ambition that aligns technology deployment with enterprise strategy, redefining ROI as “return on intelligence” by integrating data insights, human intuition and AI outputs. Leaders must radically reassess AI’s impact on D&A functions, articulate a unified vision, assume accountability for outcomes and proactively manage unpredictable scaling costs to avoid fragmented experimentation that leaves organisations trailing competitors.
In practice, this requires senior executives to determine their organisation’s AI maturity level—whether exploratory, scaling or transformative—and assign clear leadership roles, ensuring AI initiatives directly support business priorities like revenue growth or operational resilience rather than isolated proofs-of-concept. Without this ambition-setting discipline, even high-adoption organisations risk diluting resources across low-impact use cases, undermining long-term competitive positioning.
Pillars Two and Three: Foundations and Workforce Enablement
Strengthening AI foundations forms the second pillar, focusing on AI-ready data, governance as a value accelerator and a unified context layer to deliver “return on integrity” by mitigating risks like data inaccuracies, hallucinations and unauthorised access. Gartner stresses that legacy issues such as siloed data, technical debt and delayed upgrades cannot be offset by AI alone; instead, foundational investments must precede scaling to prevent AI from remaining an expensive experiment.
The third pillar, empowering people for AI transformation, addresses the human readiness gap by prioritising skills, mindset shifts and change management over tools, yielding “return on individuals” through higher engagement and adaptability. Enterprises must budget substantially for upskilling, deploy fusion teams blending human and AI capabilities, and foster behavioural changes to counter finite workforce capacity against rapid technological evolution.
Implications for Enterprise Strategy and Risk Management
Collectively, these pillars provide a roadmap for D&A leaders to steer through AI turbulence, balancing acceleration with trust, control and measurable outcomes. For risk-averse organisations, particularly in regulated sectors, the low FinOps adoption rate signals an urgent need to integrate financial accountability early, potentially averting cost overruns as AI moves from 40% to 80% penetration. Gartner’s framework positions disciplined execution—ambition, foundations, people—not as optional enhancements but as prerequisites for transforming AI from hype-driven spend into a sustainable driver of enterprise value.
