Six common AI questions we get asked

Brilliant Noise 15 November 2024

Updated: May 2026. We refresh this page regularly to keep pace with fast-moving AI platforms and policies.

AI is everywhere. Clarity isn’t. In workshops, client calls and leadership briefings, we hear a lot of the same concerns repeated in slightly different ways.

This article collects the six most common questions we hear from business leaders and teams, along with the answers that have helped them move from hesitation to action.

1. How do I persuade my team to use AI if they’re sceptical?

Scepticism is a good sign. It means people are thinking critically. But it can also slow progress.

The key is to focus on useful, practical demonstrations rather than abstract hype. Show how AI can help with things people already find tedious: turning meeting notes into actions, summarising reports, speeding up admin.

Avoid defending “AI” as a concept – it’s too broad. Anchor it in tasks.

And remember: hands-on experience shifts perspectives faster than any slide deck. That’s why we run Power Hours and live demos as a first step.

2. In what ways could AI replace traditional software?

In many areas, AI is already doing it – particularly in knowledge work.

Think:

  • Automatic transcription and summarisation replacing manual note-taking
  • Smart assistants surfacing key insights from CRM tools
  • AI features embedded directly in email, project management and reporting systems

Rather than buying a new app, AI layers onto the systems you already use, automating routine work and freeing up bandwidth for strategic thinking.

3. How can I use AI to tackle a big, messy task?

Start by breaking the task into parts.

We recommend a three-step method:

  • Problem statement: What are we trying to solve?
  • Task breakdown: What are the individual steps?
  • Execution plan: What resources do we need? Where can AI help?

Rather than asking AI to “solve everything”, this approach helps you spot where it can help most – usually with analysis, formatting, summarising or reusing existing material.

4. Can AI help with information overload?

This is where most teams see the fastest, most visible gains. AI is well-suited to reducing cognitive load:

  • Summarising long documents or threads
  • Extracting action points from meetings
  • Reorganising and tagging content for future reference
  • Draft messages, responses or reports

The deeper benefit is freeing up the kind of attention that strategic work needs. When your brain isn’t doing all the sorting, scanning and structuring, you have more bandwidth left for judgement. That alone can ease much of the anxiety that comes with information-heavy roles.

AI won’t replace judgement. It clears the runway for it.

5. What does AI literacy really mean?

AI literacy is a way of thinking, as much as it is a set of skills. Working with AI is closer to learning a language than learning a piece of software – the same prompt won’t always produce the same answer, and progress comes from practice.

We define it as: the ability to understand, evaluate and use AI tools in a responsible, effective way.

That includes:

  • Knowing when not to use it
  • Understanding limitations
  • Exploring new applications over time

This skillset is becoming foundational, just as digital literacy was a decade ago. We’ve written about it in more depth here.

6. Should we use ChatGPT, Claude, or another model?

A version of this question comes up in nearly every leadership briefing. The honest answer is usually: probably more than one.

Different frontier models have different strengths. ChatGPT tends to lead on multimodal capabilities and the breadth of its plugin ecosystem. Claude is regularly cited for longer-form reasoning and writing quality. Gemini’s tight integration with Google Workspace makes it a natural fit for organisations already on that stack. Microsoft 365 Copilot makes the most sense where teams already live in Microsoft tools and want AI inside the apps they use every day.

Standardising on one model rarely turns out to be the right call. A more useful approach is to match models to use cases, give teams licence to experiment, and revisit the picture every few months as capabilities shift.

We’ll be unpacking this more deeply in a forthcoming piece on Claude versus ChatGPT. The short version: think in use cases, not single-vendor commitments.

Final thought

These six questions are signals as much as questions. The same ones are being asked across sectors, seniority levels and team types. If you’re wondering about them too, you’re not alone.

We’re in a period when AI use is still being discovered through trial and error. Mature design patterns will follow, but they’re not here yet. The focus right now should be fluency – applying AI across many contexts and learning what works for your team, your work and your goals.

If you’re ready to move from questions to clarity, book an AI Power Hour with us to see where it could take you.

Last updated: May 2026