Clare Kitching on linkedin

tgroenwals shared this post · Apr 27

AI isn’t a single technology you adopt.
It’s a set of building blocks you choose from.

AI feels sudden.
But what looks like an overnight breakthrough is really decades of layers quietly stacking up.

Thanks to my friend Luís Rodrigues for the image inspiration, it captures that story better than most explanations I’ve seen.

Here’s how we got here…

⏭️ At the bottom: Rules and logic...sometimes rebranded as Classical AI
Rigid rules. No learning.
Think eligibility checks in insurance or compliance rules in banking.
Useful, but fragile.

⏭️ Machine Learning
Data replaces rules.
Patterns replace assumptions.
We see better sales forecasting, fraud detection or customer churn prediction.
Better decisions, still narrow.

⏭️ Neural Networks
We stop telling machines what matters.
We let them figure it out.
Messy inputs suddenly become usable.
This has given us image recognition for quality control or speech to text for call centres.

⏭️ Deep Learning
Scale hits hard.
Transformers unlock language.
Vision finally works. Computers can spot defects humans miss.
This is where performance jumps, not inches.

⏭️ Generative AI
The public moment for AI.
Suddenly AI writes, draws, builds.
Not perfect. But good enough to turn heads and change behaviour.

That’s why everything feels different now.

⏭️ Agentic AI
Is this the shift people underestimate or overhype?
Generative AI gives answers.
Agentic AI takes action.
Memory. Planning. Tools. Execution.
Software that behaves less like a model and more like a junior employee.

Here’s the part worth remembering...
Every layer in this stack still matters.

Not every business needs agentic AI straight away.
Not every problem needs generative AI.

→ Rule based systems still power compliance and control.
→ Machine learning quietly drives forecasting and optimisation.
→ Neural networks and deep learning handle complexity at scale.
→ Generative AI accelerates thinking and creation.
→ Agentic AI orchestrates work when the foundations are strong.

The opportunity is in knowing & choosing which layer creates value for your problem.

AI isn’t a single bet.
It’s a portfolio.

Are you missing a layer of value in your business today?

♻️ Repost to help someone understand the layers of AI.
🔔 Follow Clare Kitching for insights on unlocking value with data & AI.

Dr. Monika Mkhitaryan, MHBA The portfolio framing is the right starting point, Clare, and the part most leaders haven't priced sits one layer beneath the diagram.

Each layer in your stack implies a different infrastructure choice. Rules and classical ML run on commodity hardware; the top of the stack does not. Generative AI runs at per-interaction cost, and agentic AI multiplies those calls by an order of magnitude per task, which is how a useful pilot becomes unviable at production scale.

In the enterprise AI projects we are brought into, the silicon beneath the software is the layer that decides which parts of the portfolio actually scale. A purpose-built inference path commonly cuts cost-per-query by more than half against the general-purpose default, and that ratio is what separates strategic capability from pilots that quietly stall. Apr 25 1 like

Batchu V Sarath Chandra Brilliant breakdown. The mistake most orgs make is trying to skip to Layer 5 (GenAI) before they’ve mastered Layer 2 (Machine Learning). If you can't predict churn with ML, you shouldn't be asking GenAI to 'solve' customer retention. AI is a portfolio, but it requires a Single Source of Truth to be effective. In engineering, we call this the Medallion Architecture—build the floor before you buy the furniture. Apr 25 1 like