How to prove the value of AI in your organization

Key concepts

The missing link between an enterprise’s AI initiative and a measurable return

What is the missing link between AI and measurable ROI?

The principal missing link between an enterprise’s AI initiative and a measurable return on investment is operational readiness. Businesses lack the data infrastructure to provide their AI solutions with the business context to keep generative or agentic outputs aligned with targeted business outcomes. Garbage in, garbage out could be making a comeback.

In our latest process optimization report, 2026: The year the agentic enterprise takes flight, 82% of business leaders say AI can only deliver if it understands how the business runs. But AI solutions can only gather this essential context if they’re trained on and fueled by comprehensive, up-to-date, and accurate business data. It’s the keystone that enables businesses to bridge the gap between AI investment and ROI.

Without it, generative or predictive models have neither the visibility nor understanding to produce enterprise-wide analyses or optimizations. Training AI platforms on incomplete information risks incomplete, and ill-informed decisions that fail to create business value. This then further erodes trust in AI, causing organizations to avoid deploying it where it matters most: in high-stakes, strategic workflows with the greatest potential to deliver significant ROI.

The trouble is many organizations struggle to provide AI solutions with this essential data foundation. Only 9% of businesses have made all their organizational data available and usable for AI systems, and just 38% say most of it is accessible. In today’s tech stacks, data is scattered across multiple public and private clouds, data centers, data lakes, and internal servers. Consolidating insights from these disparate systems (that don’t all play well together or speak the same language) into meaningful business context can stall Enterprise AI implementation and success.

• Discover how global polymer and petrochemical giant Vinmar uses Celonis to consolidate systems insights to enable its Enterprise AI solutions.

How to go from AI experimentation to execution?

Scaling an AI project beyond initial experimentation to full integration is another essential step towards measurable AI success. But it’s a step that’s tripping up many organizations. With the promise of game-changing business value, cost savings, operational efficiency, and competitive advantage, AI has become like commercial catnip.

AI is now a top-three strategic priority for 74% of businesses, an executive survey reveals. Pilot programs have proliferated the world over. Businesses are familiarising themselves with the technology, exploring viable use cases, and estimating their impact.

Unfortunately, many businesses have found themselves stuck in ‘pilot purgatory’, unable or even unwilling to scale their experimentation to fuller AI integration. In the executive survey mentioned above, even software development, which is the top AI use case, sees only 40% of pilots progress to production. This tracks with KPMG’s Global Tech report in which only 31% of businesses successfully scale AI to execution.

Closing the gap between AI experimentation and execution

Experimentation and pilot programs provide legitimate proof-of-concept for AI – but not the transformative benefits businesses crave. If deployment is limited, and access to the most impactful workflows or datasets is limited, AI success is understandably, well, limited. So what’s holding organizations back from fuller commitment? There are a number of challenges to overcome, including:

• Lack of a unified AI strategy: Many businesses miss a crucial step in their pursuit of meaningful ROI – developing an actual AI strategy. A report from Thomson Reuters indicates 43% of businesses make this mistake. AI deployment can’t exist on the periphery, owned by the IT or Finance teams, it has to be part of a clear business-wide project, with specific goals and cross-functional buy-in. That’s because ROI means different things to different people.
  To the CEO, the core value-add might lie in using AI to drive competitive differentiation or per-employee productivity. However, a CIO might target ROI via enhanced systems performance, integration, and reliability – and for the CFO, AI might be a lever for optimizing cash flow. These all need to be factored into a defined AI strategy.
• Trust and governance concerns: AI is still a new technology and many business leaders are wary of perceived risks around data security, black box decision making, regulatory uncertainty, ethics, and establishing robust governance.
• Bad data: As mentioned above, a lack of data quality and availability can hamstring even the most promising AI use case, denying it the necessary information to power enterprise-wide execution.
• Integration issues: AI pilots struggle to scale because they don’t integrate cleanly with legacy systems and processes.
• Skills shortage: A lack of in-house AI expertise can make it difficult to scale and manage enterprise-wide solutions.
• Change management and culture shock: AI deployment can be derailed when businesses underestimate the importance of change management, cultural buy-in, and building cross-functional teams.
• Cost / benefit conundrums: It’s tough to pin down either the full cost or ROI of AI integration. But large, front-loaded investment and longer-term and at least partially intangible benefits can dissuade leaders from wider deployment.

This last point is particularly important. The ‘go / no-go’ decision to fund enterprise-wide AI execution very often comes down to leaders’ ability to measure clear business value. So that’s what we’ll look at next.

• Discover how Celonis enabled BMW to scale business-wide AI-powered optimizations, driving significant business benefits.

How to prove and scale AI ROI?

AI isn’t like other tech investments. Its transformative potential makes proving ROI fundamentally harder than previous game-changing technologies. AI doesn’t just accelerate or enhance workflows, it changes them altogether. At the same time, it impacts multiple functions simultaneously, delivers both tangible and intangible benefits, and evolves continuously. And as we saw in the section on ROI challenges, benefits often materialize gradually and can blend with other initiatives.

Proving ROI means measuring ROI, and that’s where many businesses continue to struggle. A tiny 15% of business leaders have formal metrics in place for measuring AI returns, as shown by a KPMG poll. And 39% of executives cite measuring AI ROI and business impact as one of their primary challenges in a 2025 study.

There’s no single plug-and-play panacea to overcome these challenges. Just as AI is reframing how businesses work, so too businesses need to reframe how they measure ROI. Any such framework should include these additional considerations to (or variations on) standard cost/benefit ROI calculations:

• Set your baseline: To faithfully report AI ROI, it’s crucial to establish business performance baselines for all key workflows. These should capture existing cross-business KPIs, success metrics, cost levels, and processing times. They should extend beyond functions targeted for AI deployment to track broader business impact.
• Map use cases to targeted business outcomes: This anchors AI enhancements (like cost savings or risk mitigation) to strategic goals. Importantly it ensures you’re measuring real business impact, not just shifts in operational activity like time saved per task in newly automated processes.
• Adopt a holistic range of KPIs: AI success needs to be measured against both hard and soft ROI metrics; quantitative KPIs like productivity gains and qualitative factors like customer satisfaction. Different metrics will materialize over d
• ifferent timescales, from immediate efficiency gains to longer-term strategic benefits.
• Continuous recalibration: ROI calculations and metrics need to be re-examined regularly as your AI model learns, usage patterns evolve, and business context changes. Regular ‘check-ins’ enable you to update use case and ROI assumptions with actual performance data.
• Segmented ROI reporting: As mentioned previously, AI ROI distributes unevenly across functions and time periods. Consider reporting AI-generated value by department or stakeholder group, as well as aggregate bottom-line returns.

Taken together, these factors confirm that proving meaningful ROI from AI deployments relies on detailed, comprehensive, and accurate understanding of business systems and processes.