Case Study 29 Jan 2026 8 min read

Moving Beyond Performative AI: How to Unlock Genuine Business Value in 2026

Let's be honest - the last couple of years have been a whirlwind of AI pilots, proofs of concept, and bold headlines. But as we step into 2026, the question on every leader's mind is simple: where's the real value? Gartner's latest figures are telling: by the end of this year, only 35 percent of enterprise AI projects will actually deliver measurable business outcomes.

What Is Performative AI - and Why Does It Cost More Than You Think?

Performative AI is all about appearances. It's when organisations prioritise looking innovative over actually solving business problems. In many mid-sized and large enterprises, this shows up as what I like to call 'pilot purgatory' - endless trials of AI tools that never quite make it into the heart of daily operations.

The real cost? It's not just the software licences or the development hours. Performative AI chips away at morale, as teams are asked to use tools that don't actually make their jobs easier.

Audit your AI portfolio for projects that look good but lack real business value.
Break down organisational silos so AI can access the data it needs.
Consider the opportunity cost of investing in surface-level AI tools.

Measuring What Matters: How to Prove Real Business Value

The difference between AI that delivers and AI that just ticks a box comes down to how you measure success. Too often, leaders focus on vanity metrics - how many people have a login, or how many queries were processed. True business impact is found in three areas: boosting productivity, cutting costs, and improving customer experience.

The key is to set clear KPIs before you start. Treat your AI agents like digital colleagues - give them proper job descriptions and hold them to account.

Align every AI project with at least three business-focused KPIs.
Ditch vanity metrics and focus on outcomes like resolution rates and cost savings.
Use AI to measure improvements in lead reactivation and operational efficiency.

Best Practice: Making AI Work Across Your Organisation

Scaling AI isn't just a technical exercise - it's an organisational one. For AI to really stick, you need a bridge between your tech teams and the business units they support. Don't try to automate everything at once. Start with one high-friction workflow - maybe employee onboarding or lead qualification - and automate it fully.

Set up a cross-functional AI task force with IT, HR, and Operations.
Start small with one high-impact workflow, then scale up.
Prioritise training and communication so staff feel supported and empowered.

Proof in Practice: Real-World AI Success Stories

Take DPD, for example. By integrating Olivia AI, they unlocked over 300,000 dollars in monthly recurring revenue, thanks to smarter automation and lead reactivation. MyClaim Group is another standout, managing over 100,000 emails every month with Olivia's communications layer.

Study how leaders like DPD and MyClaim Group use AI for revenue and efficiency gains.
Spot manual processes in your own business that could benefit from similar automation.
Focus on scalable, reliable AI solutions rather than one-off experiments.

Conclusion