5 Metrics Every Business Should Track to Maximise AI Investments
As the European AI landscape evolves, so too must the standards to measure success.
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The Office of National Statistics (ONS) updates its shopping basket every year to reflect how consumers spend, adding new items like VR headsets or yoga mats as habits evolve. Businesses need to do the same with performance metrics. Artificial intelligence is now a central force in driving growth, yet many companies still measure success using outdated KPIs.
With nearly half (42%) of European businesses now regularly using artificial intelligence (AI) — a 27% increase in just one year — the urgency is clear: if you don't measure what matters, you can't manage it. To truly maximise AI investments, C-suite leaders must update their own shopping baskets and rethink the benchmarks used to judge value.
Here are five metrics that every business should be tracking to ensure AI success.
1. Data quality
Even the most advanced AI models produce untrustworthy results if they're trained on inaccurate or irrelevant information. At best, this shortcoming is a temporary inconvenience that drains money and time. At worst, entrusting unsatisfactory data to AI systems leads to costly mistakes in user-level applications — all of which can damage an organisation's reputation and profit.
With the success of AI hinging on high-quality data, it's important to perform regular data audits focused on improving accuracy. Routine reviews like this are a way to patrol data pipelines, checking that they're free of inconsistencies that could otherwise undermine AI outputs.
2. Data coverage
Clean data is one priority; complete data is another. The AI models without access to every dataset are more vulnerable to blind spots, causing limitations in their ability to detect trends and identify key opportunities.
For instance, insurers that automate their risk assessment processes with AI typically ingest data from operational logs, market patterns and even independent sources like weather forecasts. Accidentally neglecting just one of these could result in the misinterpretation of costly payout claims.
To counter similar risks, conducting regular assessments of your data landscape to uncover overlooked data points. Eliminating visibility gaps allows businesses to paint a full picture of their digital environment, ensuring all data channels are readily available for AI usage.
3. Operational efficiency gains
The clearest way to measure the success of a new initiative is to see how much time or money it saves compared to the previous approach. Put simply: a factory that installs a faster conveyor belt should see an increase in productivity. AI is no exception to that logic.
From accelerating loan approvals to automating data entry, the long-term objective of AI in any industry is to reduce turnaround times and cut costs. Failure to gauge operational impact makes it difficult to justify ongoing investment.
As such, it's sensible to measure process durations before and after AI integration — a benchmarking method DHL deployed to recognise that its AI-powered robots had delivered a 40% increase in sorting capacity, quantifying their investment's active contribution to business KPIs.
4. Adoption rate across teams
Just because a solution successfully goes live, it doesn't mean adoption is fully guaranteed. Really, true value comes when AI is embedded into workflows across the whole company — not just the IT department.
Some teams will immediately embrace the AI tools presented to them, whereas others need more support. To assess where training or change management might be necessary, it's helpful to track departmental usage data and run regular employee feedback surveys.
This approach works for high-performing organisations, who are more likely to bring employees with them on their AI journey by providing extensive AI training. In this context, understanding digital behaviour is the starting point for extracting more engagement from AI.
5. Return on investment (ROI)
Naturally, businesses leaders need to understand the financial return they're getting back from investment. However, the ROI generated from AI initiatives is often complex, involving both tangible and intangible benefits.
Take the Berlin-based online retailer Zalando, which recently shared that it uses generative AI to produce digital imagery at a rapid rate. Not only has that directly reduced costs by 90%, but the faster turnaround in editorial campaigns also indirectly boosted the company's competitiveness in the fast fashion market.
Every possible performance metric must be considered when curating a digital strategy. That's why it's important to develop a well-rounded ROI framework for AI — factoring in both the direct and indirect consequences of any planned change.
Measure what matters, scale what works
AI is already demonstrating its ability to reshape organisations, but the reality is that many still struggle to prove its concrete value. Without establishing the right criteria for success, businesses will lack accountability and struggle to align tech performance with financial gains. To maximise ROI on AI, you must clarify the standards that you wish your digital growth to be founded on. This will unlock the insights needed to safely course-correct, scale success, and build long-term trust in your AI strategy.
As the AI landscape evolves, so too must the standards to measure success. Just like the ONS shopping basket reflects changing habits, businesses must ensure performance metrics reflect the realities of AI-driven operations. By focusing on data quality, coverage, efficiency, adoption, and ROI, leaders can ensure AI investments aren't just tracked but transformed into long-term value.
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