Over the past couple of years, watching AI adoption unfold across enterprises, SaaS, and native AI companies, I’ve noticed something interesting.

The digital divide is widening inside organisations. And it’s got a profound impact.

Engineering teams are building increasingly sophisticated AI-powered solutions. Meanwhile, business stakeholders and leadership teams are being asked to make investment decisions, define operating models, and manage risk — often without understanding what’s happening under the hood or why.

Unless you’re deeply curious (and humble enough to admit what you don’t know), or you have strong technical advisors at the table, this is where organisations quietly leave lots of value and $$s on the table.

AI observability sits right in the middle of that divide.

It got me thinking: non-technical leaders may have a superpower they don’t realise they have — the ability to help technical teams ask better questions.

How many non-technical leaders would volunteer to lead an observability program?

This may sound controversial, but if I had to choose, I’d give that mandate to the most curious non-technical leader in the business. The one who isn’t afraid to admit they don’t know everything. The one who surrounds themselves with technical expertise. The one who stays relentlessly focused on business value and ROI — not just technology.

There is enormous value in non-technical leaders getting curious about the AI solution, the observability strategy, and the way performance, risk, and cost are actually measured. Because observability, at its core, is more of a business discipline than a technical one.

What Is AI Observability — Really?

In the race to adopt AI, most organisations focus on building.

We launch the solution.
We integrate the model.
We customise it for our use case.

But then:

• Is it actually performing well?
• Are users completing workflows successfully?
• Is quality degrading slowly without us noticing?
• Is performance consistent across regions?

AI observability is about understanding the behaviour of the entire solution — across infrastructure, model, and application layers — and mapping outcomes back to their root cause.

When performance drops, is it infrastructure strain? Model drift?
A code issue?

Observability helps you find out.

Ultimately, it exists to answer one question: “Is this doing what it’s supposed to do?”

---

The Tools (Briefly)

Yes, there are tools.

Cloud-native platforms (native monitoring such as Azure Foundry , CloudWatch in AWS). Cross-platform tools like Splunk, Datadog, and Dynatrace.

Some production environments use a mix.

But for business leaders, the tooling matters far less than the questions being asked of the telemetry.

---

The Business Stakeholder’s Superpower

This is the part I want to highlight: You don’t need to understand every dashboard. You don’t need to know the difference between CloudWatch and App Insights or decide on the tooling. Leave that part to the tech team.

But – you need to help them define: What must this system prove, protect, and optimise?

Here are five ways to do that.

---

1. Connect Observability to User Experience

Ask:

• If a customer has a bad AI experience, how do we know?
• Is feedback linked to model versions and telemetry?
• Can we trace issues to root cause quickly?

Observability becomes powerful when:

Customer feedback → links to telemetry → links to versioning → drives action.

---

2. Make It a Funding Requirement

Bolting on observability once the solution has been built and the budget has been spent is challenging.

Ask:

• What percentage of build effort is dedicated to observability and continuous improvement?
• Do we have capability to drive useful insights from observability in-house, or do we need to partner?
• Is monitoring in the design from day one? If not, why not?
• Is there budget for evaluation and quality review?

When leaders require it, it gets prioritised.

---

3. Define What “Good” Looks Like

AI is probabilistic. That makes quality harder to measure.

Ask:

• What does a “good output” look like?
• How are we scoring quality for each type of user/persona?
• Who reviews outputs and how often?

Without clear definitions of “good,” teams only detect obvious errors, not gradual quality decline, or the quality may degrade for one type of user/persona while not for others.

---

4. Align to Risk Appetite

You define acceptable risk.

Ask:

• What level of inaccuracy is tolerable?
• What errors are unacceptable?
• Where must humans stay in the loop? Why?
• What’s the blast radius if this fails? What does it mean for our business?

This shapes alert thresholds, guardrails, and monitoring depth.

---

5. Embed a Learning Mindset

Observability isn’t just about governance. It should fuel improvement.

Ask:

• What are we learning weekly from production data?
• What experiments are we running?
• How are we improving models or prompts based on telemetry?

If observability doesn’t drive iteration, it’s just reporting.