Optimisation efforts only succeed when their impact is visible and trusted. Without meaningful measurement, AI and automation initiatives risk being perceived as technical experiments rather than business investments – and the data bears this out: MIT research shows that 95% of AI pilots fail to deliver measurable ROI, with most stalling rather than accelerating value.

Effective measurement connects optimisation activity to outcomes.

Moving beyond cost metrics alone

While spend reduction is important, it tells only part of the story. AI-driven optimisation affects multiple dimensions, including:

• Efficiency of resource usage – how much value you extract from every pound spent
• Reliability and resilience – whether systems stay available when it matters
• Speed of delivery and change – how quickly improvements reach production
• Consistency of governance – whether controls operate predictably across teams and environments

Measuring these together provides a more accurate picture of value. Organisations that implement comprehensive measurement frameworks report dramatic improvements: 95% forecast accuracy, 80% reduction in reconciliation time, and 99% cost allocation accuracy. These metrics demonstrate real financial control rather than approximations.

AI-enabled measurement and insight

AI systems can correlate operational metrics with financial outcomes, revealing relationships that are difficult to observe manually. For example, they can show how changes in delivery speed affect cost, or how resilience improvements reduce incident-related spend.

This enables evidence-based decision-making rather than assumption-driven optimisation. McKinsey research shows that 80% of organisations set efficiency as an AI objective, but without measurement frameworks that connect technical activity to business outcomes, most cannot prove whether efficiency actually improved. The gap between intention and evidence is where initiatives fail.

Continuous improvement through feedback loops

Measurement is most powerful when it feeds back into action. AI enables continuous improvement loops where insights inform automated adjustments, and outcomes are measured in turn.

This creates a self-reinforcing system that improves efficiency over time rather than delivering one-off savings. Industry implementations we've analysed demonstrate this in practice: organisations with mature FinOps measurement achieve forecast accuracy rates above 90%, enabling proactive capacity planning and budget management that prevents the cost overruns and emergency spending that plague organisations relying on reactive monthly reviews.

Building trust in optimisation efforts

Clear, consistent measurement helps build trust across technical and non-technical stakeholders. When outcomes are visible and repeatable, optimisation becomes part of normal operations rather than a specific initiative.

This matters because the primary barrier to AI and automation adoption is not technical capability – it's stakeholder confidence. When 95% of initiatives fail to demonstrate measurable value, leadership becomes sceptical of new proposals. The 5% that succeed do so not because their technology is better, but because they measure outcomes explicitly and report them consistently. Trust is earned through transparency, not through promises.

Where Vertex Agility fits

Defining the right metrics and using them effectively requires alignment between technology, operations, and leadership.

Vertex Agility helps organisations establish measurement frameworks that demonstrate real value, ensuring AI and automation investments deliver sustained improvements rather than short-lived gains. We combine the technical expertise to implement AI-driven measurement systems with the strategic insight to define which metrics actually matter to your business – connecting operational activity to financial outcomes that stakeholders understand and trust.

Ready to prove the value of your optimisation efforts?

Our free AI readiness assessment evaluates your organisation's capability to implement AI-driven measurement and continuous improvement across Strategy & Vision, Data & Infrastructure, Talent & Capability, Use Cases & Implementation, and Governance & Risk. You'll receive a detailed report highlighting where measurement gaps are undermining stakeholder confidence and which frameworks will deliver the visibility you need.

For a comprehensive view of your operational maturity – including delivery effectiveness, governance, infrastructure efficiency, and business outcome alignment – our future readiness assessment identifies strengths, risks, and opportunities for acceleration across your entire technology estate.

Both assessments help pinpoint where measurement and visibility will transform optimisation from technical activity into proven business value.

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Sources: AI and measurement statistics from MIT State of AI in Business 2025 (Workato analysis), McKinsey State of AI Global Survey 2025, Hokstad Consulting FinOps implementation case studies 2025, FinOps Foundation State of FinOps 2025, Ternary FinOps KPI benchmarks 2025, and industry FinOps maturity benchmarks 2024–2025.