From Data to Decisions — and Now, Automation: FYT's Updated Approach to Informed Decision-Making

For over a decade, FYT's curriculum has been built around three words: Data, Insights, Decisions. As we wrote in Bridging the Gap in the Analytics Value Chain, good analytics doesn't start with a spreadsheet — it starts with a clear question, a set of hypotheses, and a disciplined path from data to insight to action. That framework has shaped how we teach, and how our students learn to think.

It has worked. But as AI becomes part of everyday work, we think it's time to make explicit something we've always taught implicitly — and to extend the framework to reflect what AI now makes possible.

The Missing Word: Critical Thinking

Data, Insights, and Decisions describe the stages of good analytics. They don't fully capture the thinking that carries someone through each stage. We define critical thinking as the ability to pause before concluding, ask better questions, consider alternative explanations, test assumptions with evidence, and judge what the evidence does — and does not — support.

This isn't a "soft skill" bolted onto the technical curriculum. It's the anchor that sits at the centre of Data, Insights, and Decisions, connecting them and keeping each stage honest. Good tools and clean data still produce poor outcomes if the thinking behind them is weak.

Automation: A Natural Extension of the Chain

Critical thinking doesn't just anchor the three original stages — it's also what gives us the confidence to extend the chain further. As GenAI matures, we see Automation as a natural continuation of the same value chain, not a separate discipline bolted onto the end of it.

Many professionals use GenAI through simple prompts. We take a different view — we teach people to build with GenAI, using the same rigour that has always underpinned good analytics: define the problem the tool is meant to solve, gather the data needed to train or guide it, test and evaluate its outputs, and only then weigh the risks before deploying it. It's the same discipline we've always taught, applied to a new kind of tool.

This matters because AI doesn't just speed up decision-making — it scales it. Good judgment, applied through AI, gets amplified. So do flawed data and weak thinking. And when that happens, it doesn't fail quietly. It fails at scale. Critical thinking is what keeps automation pointed in the right direction.

Our Updated Curriculum

This thinking now shows up directly in FYT's 2026 Curriculum, organised along four stages — Data, Insights, Decisions, Automation — for both leaders and practitioners, with Critical Thinking at the centre and Leadership & Culture as the context that surrounds and enables it.

Alongside our existing workshops on data management, mining, visualisation, and storytelling, we're introducing three new sessions that reflect this update:

1. Critical Thinking in the Age of Data and AI — building the deliberate thinking habits that keep automation grounded in sound judgment.

2. GenAI for Work — a practical, hands-on introduction to building and applying GenAI tools in everyday work, not just prompting them.

3. Strategic Workforce Planning in the Age of AI — applying the same data-to-decision discipline to one of the highest-stakes decisions any organisation makes: its people.

Dates and registration details for these workshops will be added to our FYT Academy page as they're confirmed.

Why This Matters Now

None of this changes what we've always believed: that lasting capability comes from thinking clearly, not just using tools well. AI accelerates tasks. Humans still have to interpret and decide. Our job is to make sure that as AI becomes part of how our clients work, the thinking behind it keeps pace.

If you'd like to explore how this applies to your organisation, get in touch — or subscribe to our blog for more on data, AI, and decision-making.