AI Isn’t Your Problem. Your Starting Point Is.

Remember Clippy? Microsoft’s relentlessly cheerful assistant from the early 2000s.

Always popping up. Full of promise. Almost never useful.

A surprising amount of AI today feels eerily similar.

Not because the technology is weak but because of how organisations are trying to use it.

The Clippy Problem

Recently I saw a travel agency owner proudly explain he had “rebuilt his agency on Claude Code.”

The example he shared? An AI agent that checks his calendar and tells him when he’s free.

Many hours spent building it, thousands of tokens used and deeply underwhelming results.

Sadly, it’s not an isolated example.

Across industries, we see teams building impressive demos that solve problems nobody actually has.

A chatbot here, a summariser there, a few prompt-driven workflows.

Activity everywhere. Impact nowhere.

The Real Mistake 

Most AI initiatives start with the wrong question: “How does AI optimise our existing processes?”

That question assumes the process itself is worth accelerating.

Often it isn’t.

In marketing especially, many processes are designed for a world where:

• Data sits in silos

• Channels are analogue 

• Campaign cycles take weeks or months

AI doesn’t magically fix those systems and actually just exposes them as not fit for purpose. 

If the underlying system is inefficient you just accelerate the inefficiency rather than getting anything approaching transformation.

The Question That Actually Matters

The more valuable question is this: “What problems in our business are actually worth solving?”

• Where are we wasting budget?

• Where are teams drowning in manual work?

• Where are decisions happening too slowly?

For most enterprise marketing teams, the answers are surprisingly consistent:

• Campaigns take too long to launch

• Data sits trapped in disconnected systems

• Teams spend more time preparing reports than acting on them

• Personalisation never gets beyond the PowerPoint deck

AI becomes powerful when it targets structural problems, not novelty tasks.

Why Most AI Efforts Stall

When we look at organisations experimenting with AI in marketing, a clear pattern emerges.

Most companies are still in the early stages of AI maturity.

We typically see four stages.

1. Experimenting

Individuals are testing tools.

Teams are playing with prompts.

There are isolated AI trials across the organisation.

But there’s no shared data, no common framework and no clear outcomes.

2. Piloting

Now the organisation begins identifying real use cases.

Small teams run structured tests.

Early wins start appearing.

But the systems still require constant human supervision.

3. Integrating

This is where things start to shift.

AI becomes embedded in real workflows.

Data begins to connect across systems, campaign execution speeds up and waste starts to fall.

Decisions happen faster and results improve.

4. Optimising

At this stage, AI is no longer a project.

It becomes part of the marketing operating system: systems learn from performance data, campaigns self-adjust, teams focus on strategy rather than manual execution.

AI stops being a tool and becomes infrastructure.

A Better Place to Start

If you’re serious about using AI in marketing, the first step isn’t buying tools.

It’s understanding where you actually sit on the maturity curve.

Because the right move for a team in Stage 1 is very different from the right move for a team in Stage 3.

That’s exactly why we built the AI Growth Assessment.

In a focused 90-minute session we help teams:

• Identify where they sit on the maturity model

• Prioritise the highest-value AI opportunities

• Define practical next steps that actually move the needle

No hype. No endless pilots. Just clarity on where AI can genuinely improve your marketing.

Want to know where you really are on the AI maturity curve?

Take the AI Maturity Assessment.