Becoming AI-native

Connecting AI to core business metrics

AI adoption only matters if it moves the needle. For all the prototypes and pilots being launched, the real differentiator is whether or not AI can be translated into business performance.

The companies getting ahead aren’t reporting “number of models deployed.” They’re showing how AI shifts revenue, margin, market share, and speed of learning, the metrics that define enterprise value in the eyes of stakeholders.

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Why most AI metrics miss the point

The real problem is misalignment. AI projects are often launched without a clear link to a core business metric. As a result, progress looks impressive on dashboards but invisible to leadership.

The solution is better framing:

AI initiatives need to be anchored in strategic KPIs that already drive decisions: cost-to-serve, churn, margin, conversion, cycle time, working capital, sustainability impact.

If your model doesn’t move one of those, it may not be solving a real problem. Looking at model accuracy, API calls, or user adoption rates is useful to understand performance, but they’re not viable business outcomes on their own.

How leading enterprises link AI to measurable impact

Successful AI projects start with the business outcome, not the model. They start by mapping each AI use case to a core KPI and design metrics around both efficiency and effect:

Efficiency impact: time, cost, or resource savings created by AI systems

Effect impact: measurable improvements in decision quality, customer satisfaction, or revenue per interaction.

For non-technical stakeholders, this also links more “abstract” AI system metrics (like prediction accuracy or drift rate) directly to the business metric they influence. For example:

Better demand prediction → lower inventory holding → improved cash flow

Personalized pricing model → higher basket value → improved gross margin

Better risk model → faster credit decisions → higher loan throughput

By embedding these cause-effect chains into governance, AI becomes a driver of P&L, not a side experiment. Leaders can then prioritize investments, reallocate resources, and design incentives around measurable learning and performance improvements, not just convincing demos that will stay in the innovation labs.

In that sense, AI alignment is leadership alignment. The more tightly AI connects to business metrics, the faster it moves from pilot to production, and from potential to impact.

Avoiding the trap of measuring what’s easy

It’s easy to count pilots, dashboards, or data pipelines. But it’s harder to measure the valuable metrics, such as how fast AI changes the ways of working, decision speed, or financial outcomes.

Organizations looking to quantify their AI success have to avoid three common pitfalls:

Counting outputs instead of outcomes (e.g., “number of models in production” doesn’t equal impact).

Ignoring adoption metrics. That is, if teams don’t use it, it doesn’t matter what business impact it could have.

Focusing too much on short-term ROI. Early projects often create structural capabilities, not instant profit, but they set up a necessary foundation to avoid the systems stalling or breaking at scale. Instead, take a portfolio view of the value that’s being created: some AI initiatives generate immediate cost savings; others build foundations for exponential learning. Both matter if you’re transforming your ways of working.

Keynote