What Explainable AI Means at Work

TL;DR: Many teams avoid AI because they can’t see how an answer was produced. This article explains explainability in plain language, why it matters for everyday work and how to bring it into your organisation without needing technical skills. If you want AI that your team can check, defend and rely on, this is the part to focus on.

Why explainability matters before anything else

When I meet people trying to introduce AI into their organisation, one issue appears more than any other. People don’t reject AI because of the technology. They reject it because they can’t tell whether an answer is reliable. And when a result affects a customer, a staff member or a decision that carries risk, that uncertainty becomes a big problem.

People want AI they can examine. They want to know what information shaped the output. They want to know why it reached that conclusion. They want to know whether they can justify it when someone asks questions.

Explainability gives them that visibility. It replaces guesswork with a simple way to understand the system’s behaviour. This is what actually makes adoption possible, because people will only really adopt AI when they trust the outcome.

What AI explainability means

Explainability is often spoken about in technical language, but the idea is straightforward.

It means being able to understand:

• what information the system used
  
• how it interpreted that information
  
• why it produced the answer you see
  

You’re not trying to inspect algorithms. You’re trying to make sure the path from input to output is visible. If your team can follow that path, they can make decisions around the results. If they can’t, they will avoid the tool or double-check everything manually, which defeats the purpose of using AI in the first place.

This matters because AI is now used to support judgments in hiring, customer service, financial commentary, compliance reviews, analysis and planning. These are areas where people routinely ask why a certain outcome was produced.

If your only answer is “the model generated it”, the system will lose credibility quickly. When people can’t explain a decision, they protect themselves by stepping out of the process. This is where adoption will stall.

• Explainability prevents that slide into uncertainty.
  
• It gives leaders a clear way to validate how decisions were formed.
  
• It supports staff who need to show their working.
  
• It avoids situations where the AI becomes a black box that sits outside oversight.

What explainability looks like 

Most organisations need a practical version of explainability, not a technical one.
In simple terms, an explainable system allows you to:

1. see the information that influenced the result
   
2. understand the steps the system took
   
3. confirm whether the reasoning makes sense
   
4. check that similar inputs receive similar treatment
   
5. recreate the decision when required
   

This level of clarity helps reduce errors and gives teams confidence to use the system in work that matters. It also supports leaders who need to defend decisions to boards, risk committees or external reviewers.  

The main issues I see have nothing to do with the model itself. They come from gaps in process and governance. Some teams introduce AI without defining what a “good” explanation looks like. Others don’t make clear which decisions require explainability. Some teams rely on outputs without linking them to evidence.
Others assume staff will review results, but never train them to do it properly.

These gaps create inconsistency. They also create an avoidable risk. Explainability fills them by giving you a single way to review, check and defend AI-supported decisions.

How to bring AI explainability into your organisation

Here is the practical path that works across non-technical teams.

• Start by identifying the decisions where explainability is essential. These are usually decisions involving people, money, customers or compliance.
  
• Once you know where explainability matters, define what “good” output looks like. This includes clarity, consistency and a visible link to evidence.
  
• Choose systems that show their working, not systems that produce answers without context.
  
• Keep human review around decisions that carry consequences.
  
• Record how decisions were made so they can be reviewed or audited later.
  
• Finally, train people to check outputs in a simple, consistent way. Most of the value comes from giving teams a repeatable structure for reviewing results.

This approach creates confidence without adding technical complexity. It also reduces the friction that often comes with introducing new tools. When organisations have this capability in place, adoption becomes smoother because people understand how the system behaves.

FAQ: 

Q: How do we decide which decisions need the strictest explainability?
Focus on decisions with human, financial or regulatory consequences.

Q: Who owns explainability inside the organisation?
Usually risk or governance, supported by the operational owner of the workflow.

Q: What does “good evidence” look like?
A clear link between inputs, the source documents and the output that was produced.

Q: How do we train teams to review AI outputs properly?
Give them a standard process: check evidence, check consistency, check alignment with policy and confirm the logic.

Q: What triggers a review or escalation?
Any output that looks inconsistent, unclear or out of pattern in a high-impact workflow.

How we support organisations that need this

We’re certified ISO 42001 Auditors. We can set up and assess, align and certify your AI governance practices under the global standard for responsible AI. Whether you're embedding LLMs, deploying AI products or automating internal workflows, we get you audit-ready and support you through the full certification process.

You receive:

• A full Assessment aligned to ISO/IEC 42001 requirements
  
• A Gap Analysis across all Annex A control categories
  
• A tailored Roadmap to reach audit readiness
  
• A Board Summary for risk committees, executive teams or investors
  
• Certification Guidance and audit preparation aligned to external certification bodies