From Hype to Use Case: What to Understand About AI

You’re responsible for people, performance, and risk. You’re hearing about AI daily in board meetings, media headlines, or hallway conversations with curious staff. Yet you’re cautious.

Why?

Because everything still feels abstract. You’ve trialled a few tools, joined the webinars, maybe even sat in a demo. But what’s the actual plan?

How do you separate the PR sparkle from the genuine, sustainable business use case? More to the point — how do you move from polite interest to aligned, confident action?

This blog is for decision-makers sitting in that exact tension:

People & Culture leaders unsure how to build capability without overwhelming staff

Operations and compliance heads watching pilots stall or run ad hoc

Public sector execs facing pressure to modernise without clear implementation pathways

Commercial leaders struggling to secure buy-in beyond “we should do something with AI”

Your hesitation isn’t a weakness. It’s a signal of responsibility. What you need now is not another showcase of tools — but a clearer way to assess what matters.

Understand the Market Shift: From Tech Demos to Business Value

In 2023, generative AI exploded. There were prototypes for everything — slide decks, meal plans, legal memos.

The pitch was often the same: “Look what this can do.”

But novelty wears thin fast.

As Sequoia Capital’s ‘Generative AI’s Act Two’ analysis points out, the market is shifting focus. Investors, teams, and buyers are no longer excited by surface-level functionality. They’re asking a tougher question:

Does it actually solve a real problem, end-to-end, better than what we had before?

This is the shift from tool testing to transformation and it’s where many organisations suck. You’ve trialled features. Now you need to link them to function.

Here’s what that pivot looks like in practice:

1. Stop Benchmarking Tools. Start Assessing Use Cases

Rather than comparing platforms, ask: Where in our workflow is there friction, inconsistency, or manual rework that AI could meaningfully improve? That’s where value lives, not in the features list, but in the work itself.

2. Understand Constraints Before You Commit

• What are your internal guardrails?
• Can staff access sensitive data?
• Do you need outputs to be explainable, not just fast?
• Are approvals or regulatory conditions slowing adoption?
• These constraints aren’t barriers. They’re your design criteria.

3. Build Internal Confidence

AI fluency isn’t about Python. It’s about shared language and strategic clarity. Until your leadership team understands what you’re doing, why it matters, and how it works in your business context, pilots will struggle to scale.

A Smarter Way to Decide What’s Next

The best AI leaders are asking this:

• Is this useful in our actual workflow?
• Does it reduce cost, risk, or time?
• Can we explain this to our CEO or frontline manager in one sentence?

If you’re unsure how to apply that thinking in your organisation, you’re not alone.

That’s why we created the AI Business Case Workshop.

It’s a working session with your team that gets three outcomes:

• Alignment across leadership on AI’s commercial value
• Agreement on the highest-potential use case
• A documented path forward, scoped to your reality

What This Looks Like in the Real World

A mid-sized government agency recently brought us in. They had dozens of staff experimenting with genAI tools, mostly for admin shortcuts like summarising PDFs, drafting email replies.

Useful, but scattered. And not tied to any strategic outcome.

In our workshop, we mapped out their internal workflow. One area stood out: FOI requests. Staff were spending hours redacting, summarising, and tracking document sets. We co-developed a business case for applying AI to automate classification and triage, while maintaining transparency and auditability.

Result?

1. Time spent per request dropped by over 60%
2. Human review was focused only on flagged or sensitive cases
3. Leadership finally had a case they could champion with clear ROI, risk controls, and internal alignment
4. That’s the difference between “look what AI can do” and “here’s how AI improves our work.”

Your job isn’t to follow the AI hype cycle. It’s to make confident, commercially sound decisions in a complex environment. The next chapter of AI isn’t about what’s new. It’s about what works.

If your team is ready to move from curiosity to clarity, the first step is scoping where AI fits and where it doesn’t.

That’s what the AI Business Case Workshop is designed to do.