AD.

Senior Product Designer Complex digital products

AD Writing

  • AI practice
  • Product design

AI savviness is a level — and it shows in how you design your work with it

Saying that you use AI is becoming as informative as saying that you use a computer. Almost everyone uses it now. My archaeologist friends use AI in their work.

The useful professional question is no longer whether you use AI. It is what level of AI savviness you have developed around your actual work.

For me, that level shows in the ability to recognise repetitive parts of my own workflow, make the methodology behind them explicit and turn them into reusable skills and processes. AI becomes an extension of how I already work — which is also why the workflows that are useful to me will not necessarily be the ones another Product Designer needs.

Recently, I have applied this approach to two different areas of my Product Design work.

From UX findings to Jira-ready work

In a client project, I needed a more efficient way to move UX issues identified in an implemented product into delivery.

I created a repeatable AI-assisted workflow in which every issue followed the same structure: context, steps to reproduce, expected and actual behaviour, user or business impact, proposed priority and supporting evidence such as recordings, screenshots or reference materials.

I used the workflow on real product issues and delivered the resulting Jira-ready packages to the Product Owner.

The automation did not replace identifying or evaluating the UX problem. It systematised the repetitive operational work around it.

The result:

  • faster preparation of delivery-ready documentation;
  • consistent structure across reported issues;
  • built-in quality control through required evidence and expected/actual behaviour;
  • more attention available for analysis and UX judgement.

From limited user access to structured audience research

A different problem appeared when I needed information about an audience but could not quickly speak to users.

I created an Audience Research Discovery skill that turns a product question, audience and research constraints into a structured desk-research process. It evaluates potential sources, separates user voice from documentation, creates an evidence table, identifies patterns and unknowns, assesses evidence quality and proposes what should be validated next.

I used it while exploring what information could matter to business users working with shipment-management products.

The purpose was not to make desk research impersonate user research. It was to use the evidence already available systematically while keeping its limitations explicit.

The result:

  • faster early discovery when direct user access is unavailable;
  • structured evidence instead of an unfiltered collection of sources;
  • explicit separation between evidence, assumptions and unknowns;
  • better preparation for subsequent user validation;
  • a methodology that can be reused for another audience or product question.

Building my own AI workflows

These examples changed how I think about AI as a Product Design competency.

Before I can automate part of my methodology, I have to make the methodology explicit.

That means deciding what information is required, what evidence is acceptable, which rules can be repeated, where uncertainty must remain visible and which decisions still depend on context and judgement.

I am applying the same approach to UX and design QA, structuring knowledge around accessibility, WCAG, usability, typography and interaction principles to support AI-assisted audits and verification.

For me, this is what developing AI savviness looks like: not collecting more prompts, but learning my own work well enough to decide which parts of it I should never have to do manually in the same way again.

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