Only 20% of AI leaders worry about rising costs, but less than half have financial guardrails in place: Gartner

As artificial intelligence moves rapidly from experimentation to enterprise deployment, organisations are facing a new challenge: proving that the technology actually delivers measurable value.

New insights from Gartner suggest that while AI adoption is accelerating sharply, many organisations still lack the financial and operational discipline needed to extract real returns.

A survey of 353 data and analytics (D&A) and AI leaders conducted by the research firm between November and December 2025 reveals a striking paradox. Only one in five leaders say uncertain costs could limit AI value, yet just 44% of organisations have implemented financial guardrails or AI FinOps practices to manage those costs.

At the same time, AI deployment is surging. Adoption has doubled in a year, rising from two out of five organisations in 2024 to four out of five in 2025, highlighting the speed at which enterprises are integrating AI across operations.

The findings were presented at the Gartner Data & Analytics Summit, where analysts warned that organisations now face mounting pressure to convert AI enthusiasm into measurable outcomes.

From AI hype to measurable value

According to Adam Ronthal, VP Analyst at Gartner, the rapid pace of experimentation has created both opportunity and risk. Organisations are racing to deploy AI capabilities, but many are still unclear about the returns they expect. 

In an environment where discussions about a potential AI bubble are growing louder, data leaders are increasingly accountable for demonstrating tangible value.

This shift is forcing enterprises to rethink how they measure return on investment from AI. Traditional ROI metrics alone are no longer sufficient, analysts say. Instead, organisations must think about value across financial performance, data integrity, and workforce transformation.

Three pillars of AI value

Gartner analysts outlined three core pillars organisations must focus on to maximise AI ROI.

1. Set AI ambition - “Return on intelligence”

Organisations must define the scale and purpose of their AI investments rather than simply experimenting with new tools. Setting a clear AI ambition requires leaders to rethink the role of data and analytics, build a shared organisational vision, and anticipate hidden costs associated with large-scale deployment. Without this strategic clarity, AI initiatives risk remaining scattered experiments.

2. Strengthen AI foundations - “Return on integrity”

For many companies, weak data infrastructure remains the biggest obstacle to AI success. Years of technical debt, siloed data systems, and outdated platforms cannot be fixed by AI alone. 

Analysts stress that organisations must make data “AI-ready,” implement robust governance, and create a unified context layer to prevent errors, hallucinations, and misuse of sensitive information.

3. Empower people for AI transformation - “Return on individuals”

While AI capabilities are evolving rapidly, human adaptation is moving more slowly. Leaders must therefore shift their focus from job roles to skills. 

Investing in workforce training, change management, and cross-functional “fusion teams” that combine human expertise with AI systems will be critical to unlocking productivity gains and sustaining adoption.

The widening AI readiness gap

One of the most significant risks emerging from the survey is the growing gap between technological readiness and human readiness.

Organisations are adopting AI tools faster than employees can adapt to them. 

Without deliberate investment in skills development, mindset shifts, and behavioural change, analysts warn that AI initiatives could stall despite heavy spending on technology.