Don’t Believe the Hype: Build AI capability

Hip-hop group, Public Enemy warned us nearly 40 years ago: Don’t Believe the Hype. And for today’s businesses, that message is alarmingly relevant according to a recent article published by Decision Marketing.

Don't believe the hype

A new McKinsey report reveals that 94% of CMOs in Europe have yet to advance their GenAI maturity, hindered by scattered initiatives, lack of capabilities, and cautious leadership. The hype is everywhere, but scalable value is not.

Yet those who have invested strategically in GenAI are already seeing results: 22% efficiency gains, with expectations to reach 28% within two years. These leaders understand something critical: AI is the outcome of strong data foundations, clear use-case alignment, and disciplined execution.

Meanwhile, the AI stakes are rising. As frontrunners move to harness marketing’s $463bn productivity potential, others risk falling into what McKinsey calls the “self-declared prioritisation paradox”:

• 50% of CMOs list GenAI-enabled marketing as one of the top three fastest growing investments

• Yet AI ranks 17th out of 20 in their actual 2026 strategic priorities.

  
  

Without focus, AI remains stuck in pilots burning budgets, not driving outcomes.

Why AI Isn’t Delivering: The Missing Operating Model

The problem isn’t a lack of ambition, it’s a lack of readiness. AI is a means to better decisions. At Beyond: Putting Data To Work, we believe those successfully harnessing the power of AI build organisational readiness at the intersection of humans, frameworks, and intelligent agents. These 'crews' prioritise business-aligned use cases, secure foundations, and responsible scaling.

Until businesses fix core data weaknesses: broken customer profiles, inconsistent definitions, unclear data ownership, AI initiatives will struggle to scale. Generative AI only amplifies existing weaknesses when data is wrong or untrusted.

Budgets Are Rising, But Proof Is Not

There’s no shortage of confidence. According to the McKinsey study 72% of CMOs plan to increase budgets relative to sales, despite broader cost-cutting. But only 3% can explain more than 50% of their marketing spend via MROI.

The path forward is clear: Stop chasing AI features. Start building

readiness. That means:

1. Anchor AI to specific business decisions and measurable outcomes

Avoid AI theatre. Every use case must link to revenue, retention, customer experience, or productivity.

2. Fix your data foundations before scaling AI

Create a Critical Data List, set clear data standards, and build confidence in the metrics that matter.

3. Build cross-functional AI Crews

Marketing, data science, architecture, and brand working together, not in silos.

4. Establish guardrails: value, safety, cost, and measurement

Before scaling agents and automation, you need visibility, accountability, and governance.

5. Adopt the 90-Day Foundation Sprint model

A practical, iterative way to turn pilots into production safely with momentum.

Now is the moment for businesses to move from hype to habit. From pilots to production. From data confusion to AI confidence.

Or, as Public Enemy told us: Don’t believe the hype—build the capability.