TL;DR: Three years after ChatGPT’s public launch, organisations prioritise demonstrable business value over AI hype. Wavestone’s survey of 500 technology and business leaders reveals 80% expect operational benefits within two years, yet 46% lack structured ROI measurement frameworks. Whilst 99% report time savings and 32% note revenue growth, the broader landscape shows 85% of AI projects failing entirely with fewer than 10% generating positive returns.

The Expectations Reality Check

Wavestone researchers surveyed 500 technology and business leaders across the US, UK, France, Germany, Singapore, and Hong Kong, revealing significant optimism about AI’s operational potential:

  • 80% expect at least one operational benefit within two years
  • 73% anticipate business-related benefits including competitive advantage or revenue growth
  • 39% expect AI to streamline operations
  • 37% believe AI could boost customer satisfaction or accelerate product development

This optimism represents a maturation from initial AI enthusiasm toward concrete expectations for business impact. However, the measurement infrastructure to validate these expectations lags substantially.

The Measurement Gap Crisis

Despite widespread AI adoption, critical deficiencies exist in performance tracking capabilities:

  • 46% of organisations lack structured ROI measurement frameworks
  • 32% of these firms track value “informally” through expert assessments
  • 10% evaluate AI on a case-by-case basis without systematic oversight
  • 4% acknowledge having no measurement process whatsoever

This measurement gap creates fundamental challenges for validating AI investments, optimising deployments, and justifying continued funding. Without structured frameworks, organisations cannot distinguish successful AI applications from failures, prevent resource misallocation, or build evidence-based business cases for expansion.

The informal tracking approaches employed by many organisations—relying on expert judgment rather than systematic metrics—introduce subjectivity and bias into investment decisions that should be data-driven.

Current Impact and Adoption Progress

On the positive side, near-universal benefits are reported in specific areas:

  • 99% of respondents confirm AI has saved time in their organisations
  • 32% report revenue growth and improved client satisfaction
  • Competitive positioning improved: only 46% now describe themselves as “behind most competitors” in AI adoption, down from 75% in 2024

This year-on-year improvement in relative competitive position suggests accelerating adoption across industries, reducing the competitive advantage of early movers whilst raising baseline expectations for AI capability.

The universal time-saving reports indicate AI delivers immediate productivity benefits even when strategic value remains unclear or unmeasured.

The Failure Rate Context

The broader AI investment landscape presents sobering challenges:

  • 85% of AI projects fail entirely
  • Fewer than 10% generate positive returns

These failure rates underscore the critical importance of structured ROI measurement. Without systematic tracking, organisations cannot identify which 10% of projects deliver returns, understand why 85% fail, or apply learnings to improve future success rates.

The measurement gap effectively prevents organisations from learning from both successes and failures, perpetuating high failure rates through inability to systematically identify and replicate effective approaches whilst avoiding known pitfalls.

The Strategic Imperative

As organisations shift from AI experimentation to scaled deployment, the measurement infrastructure gap becomes a strategic liability. Expectations for operational and business benefits within two years create accountability pressure that informal tracking cannot satisfy.

Organisations lacking structured ROI frameworks risk continuing high-cost AI initiatives without evidence of value creation, whilst those with systematic measurement can optimise resource allocation, demonstrate stakeholder value, and build competitive advantages through data-driven iteration.

The transition from AI hype to demonstrable business value requires measurement capabilities matching deployment ambitions—a gap that currently affects nearly half of organisations pursuing AI strategies.

Source: Consultancy.uk

Share this article