Metrics are like sensible shoes. Wear them anyway.

Metrics are the sensible shoes of business thinking. Practical, unglamorous, vaguely depressing. Nobody puts them on their mood board.

But we think metrics are anything but boring. They're where the real story of your business gets told. Metrics are where you find out what's really happening.

They're the language your AI needs to read that story.

The problem: AI can't see your business

If agentic AI is going to help you run your business, it needs something simple from you first: numbers it can trust. It needs metrics.

Your business runs on tiny decisions made a thousand times a day. A designer deploys a landing page. A product manager deprioritises a feature. A salesperson follows up with a lead. A support agent escalates a ticket.

Each of these is a small, deliberate act—shaped by context, constrained by resources, timed to circumstances you can't always articulate. Together, they form the operating logic of your organisation: the pattern of choices that turns strategy into revenue.

Here's the problem: without the right metrics in place, AI can't see any of it. To AI, your business looks like a locked room with the lights off and someone occasionally shouting numbers through the door.

Most organisations feed AI the executive summary: KPIs, dashboards, quarterly numbers. Polished figures designed for board decks. They describe outcomes. They say nothing about the work that produced them.

AI trained on this can tell you what happened. It can’t tell you why, and it certainly can't help you do it better next time. You've hired a commentator when you need an agent.

If you want agentic AI—systems that can act, not just observe—you need to give it something more granular, something it can actually work with.

What a metric actually is

A metric isn't a target. It's not a KPI. It's not a vibe, or a feeling about momentum based on someone’s expertise.

A metric is the digital artefact of work: a countable, structured record of something your organisation produced. An atomic action you can measure.

A block designed. A page published. An event triggered. A form submitted. A task completed. A comment left. A file uploaded.

These aren't abstractions. They're the atoms of organisational behaviour: small, discrete outputs that reveal how your business actually operates.

For a metric to be useful to an agentic system, it must be:

• Countable: A concrete output, not an impression
• Stored: As a digital record with attributes and relationships
• Queryable: Accessible to the system on demand
• Linked: Connected to goals, people, processes, outcomes

That structure gives AI a consistent grammar. It transforms your organisation from a black box into something legible—something a system can read, reason about and act within.

Making the invisible visible

Every organisation runs on tacit intelligence: the accumulated shortcuts, heuristics and local knowledge that people use to navigate ambiguity. This is the expertise that lives in someone's head—the pattern recognition that tells a product manager which feature to cut, or a finance lead when to escalate a budget request, or literally anyone when to stop replying to that email thread.

Humans draw on this constantly. AI has no access to it. (Which is why it keeps suggesting you circle back and touch base.)

Unless you externalise it.

Metrics are the visible residue of tacit intelligence. They turn ephemeral insight into something with coordinates: a timestamp, an owner, a context, a consequence.

Once outputs are expressed as structured entities, they become part of a navigable map. AI can spot patterns across teams. It can compare this quarter's behaviour to last quarter's. It can identify bottlenecks, flag gaps and coordinate responses.

This is where intelligence stops being personal and starts becoming part of your strategy.

The real unlock: triggers, not summaries

Most people assume the challenge with AI is cognitive: can it understand the problem?

That's not the challenge. The breakthrough for an agentic organisation isn't comprehension, it's knowing when to act.

Triggers depend on metrics. They translate your organisation's intentions into conditions the system can interpret and respond to.

• If registrations fall below threshold → adjust the flow
• If blocks aren't published by Wednesday → escalate to team lead
• If survey responses decline for three consecutive days → initiate outreach
• If a dataset has no connections → flag for integration

These aren't automations in the traditional sense. They're organisational behaviours encoded in logic. Metrics supply the raw material. Triggers supply the timing. Workflows supply the action.

This is how AI stops being a tool you use and starts being infrastructure that supports the work.

The cultural shift metrics create

When metrics are defined properly, two things happen.

First, your organisation develops a shared language for progress. Teams stop rewarding narratives of effort and start rewarding performance. The question shifts from "Are we working hard?" to "What did we produce, and does it connect to what we said mattered?"

Second, AI becomes a mirror. It reflects your business's actual behaviour, not its intentions. It shows not what people hoped to achieve, but what they built, shipped, decided, completed. (Turns out Tuesday's "quick sync" produced zero outputs. Wednesday's silent work produced twelve.)

That clarity can be uncomfortable. It's also liberating. It gives you a way to improve operations without relying on heroic individual effort or vague exhortations to "be more strategic."

Metrics become both your organisation's feedback loop and its forward momentum.

How to tell if your metrics are AI-ready

Most organisations think they're measuring enough. They're not. Here's how to audit whether your metrics can actually power agentic systems.

Test for granularity

Can you track individual outputs, not just aggregates? "Ten tasks completed this week" is a summary. "Task ID 47 completed by Sarah at 2:34pm on Tuesday, linked to Project Orion, blocked by dependency on Task 22" is a metric.

Check for structure

Are your metrics stored as entities with attributes, timestamps and relationships? Or are they just numbers in a dashboard? AI needs the former.

Look for connections

Can you trace a metric back to a person, a process, a goal? If a landing page was published, can you connect it to the campaign, the team, the expected outcome? Isolated metrics are noise. Connected metrics are signal.

Assess queryability

Can your system answer: "Show me all tasks unassigned for more than 48 hours across product teams"? If not, your metrics aren't queryable enough.

Find the gaps

What work happens daily that you don't count? Design iterations. Customer calls. Code reviews. Draft approvals. If it's not measured, it's invisible to AI.

Test for actionability

Can you write a trigger from this metric? "If X falls below Y, do Z." If you can't translate a metric into a condition, it won't power agentic behaviour.

Start small. Pick one workflow—onboarding, content production, customer support—and map every output it produces. Then ask: is each output countable, stored, connected and queryable? If not, that's where you begin.

The real competitive advantage

The future of organisational intelligence doesn't belong to companies with the most sophisticated models, the biggest data lakes or the fanciest dashboards.

It belongs to companies that make themselves readable.

Companies that express their work through clean entity types, structured data schemas and metrics that bring the organisation into focus for human and machine intelligence alike.

AI doesn't need your organisation to be perfect. It needs your organisation to be explicit.

Metrics create that explicitness. Build that legibility, and AI will do the rest. Ignore it, and AI will remain a very clever bystander, in fancy shoes.