High acceptance level. Low transformation level.

Most organisations experiment. Few organisations undergo transformation.

• Seven of the top nine sectors show little structural change despite heavy investment in AI.
• Companies conduct pilots, present demos and discuss AI strategies, but conversion from pilot to workflow integration remains exceptionally low.

The problem is not enthusiasm, but design.

AI is often deployed as software, not as a tool for operational transformation. Generic tools increase individual productivity. For example, they help to write emails or summarise documents faster. But productivity gains at the individual level do not automatically translate into higher profit margins, faster revenue growth or lower costs at the corporate level. And that is where most AI strategies get bogged down.

Interestingly, 50-70% of the GenAI budgets go to commerce.

Why?

Because results are visible. Opening rates of e-mails, demo bookings and the speed of campaigns are easy to measure and clear for senior management.

But the highest return on investment (ROI) is often found elsewhere. Organisations that have successfully implemented AI report the following:

• 40% faster lead qualification
• 10% improvement in customer retention (retention)
• Annual savings of $2-10 million by shifting outsourcing from external agencies to internal, automated processes
• a 30% reduction in spending on external agencies.

Note the pattern: the biggest impact does not come from replacing employees. It comes from replacing external dependencies. AI creates economies of scale by reducing the cost of outsourcing before reducing the workforce.

Why some pilot projects fail and others succeed.

The study points to three recurring reasons why pilot projects stall:

• AI tools are not deeply integrated into processes.
• Systems do not learn or adapt over time.
• Ownership lies with centralised innovation teams rather than line management.

Successful organisations approach AI differently. They:

• Buy AI processes from specialists instead of developing it yourself (external partnerships have about twice the success rate of internal projects).
• Asking for customisation tailored to actual work processes.
• Work repeatably and scalably: start small, quickly demonstrate value and then scale up.
• Hold suppliers accountable for operational results, not technical performance indicators.

In other words, they see AI as a business transformation initiative, not an IT experiment.

The CMO's perspective: AI is a challenge for commercial leadership.

The success of AI is not primarily a technology issue. It is a matter of commercial leadership.

It requires someone who can do the following:

• Translate AI capabilities into a strategy for increasing profitable growth.
• Link AI initiatives to measurable objectives.
• Prioritise budgets towards work processes with the highest potential returns.
• Align marketing, sales, operations and finance around shared measurable results.
• Plan transition from pilot to implementation within 90 days

This is exactly where many organisations lack senior management engagement.

Why part-time commercial leadership makes sense in AI transformation.

Implementing AI requires clarity at board level. It does not always require an increase in the number of full-time board members.

A part-time CMO brings tangible benefits:

• High-level decision-making without long-term overheads
• Commercial discipline when investing in AI
• Cross-functional steering
• Responsibility for measurable growth

Not by building AI models, but by ensuring that AI provides a sustainable competitive advantage.

If your organisation is running AI pilots but struggling to show measurable results, the problem may not lie with the technology. It may have to do with the leadership structure.

As a part-time CMO, I work with management teams to turn the potential of AI into profitable revenue growth, without adding unnecessary complexity.