Why Consultants Can’t Deliver Your AI Strategy

TLDR: In this blog we admit to what you arleady suspect. Traditional consulting models don’t work for AI strategy. They deliver polished roadmaps and pilots, but not the embedded skills needed for real adoption. AI isn’t a fixed digital transformation project, yes, we have done many of those. It evolves monthly and affects every workflow. According to Altrata’s 2024 Executive Insight report, Chief AI Officer roles grew 70% year-on-year, mostly filled by internal leaders. This blog explains why consulting-led AI initiatives stall, what an AI officer actually does, and why building embedded AI skills is cheaper, faster, and more sustainable. If we lose some business because of it, that is on us.

The AI consulting problem

Big consulting firms have perfected transformation strategy theatre. I know, because I have been there. They benchmark, produce an AI roadmap, launch a pilot, then leave. The client is left with the burden of culture change and implementation - a total and ever more costly mess at most times.

I know this model because I’ve done it myself as a consultant. It works when installing software with a defined endpoint after the fancy slide deck and 'engagment' model is done. But in AI adoption, it doesnt just stall...it actually totally collapses. For real.

Why? Because AI shifts monthly. We literally cannot keep up ourselves. New model capabilities, new regulatory requirements, and new competitor moves make static strategies obsolete. By the time a deck is delivered, honestly, it’s already outdated.

Why AI makes this worse

AI is not just another strategy and swithc and bait IT system. It changes how decisions are made, how workflows are designed, and how staff interact with technology - consultants make this more complex and call it an 'operating model'. But, it requires experimentation, iteration, and cultural adoption, not one-off project management.

Episodic consulting creates a stop–start rhythm. But AI transformation needs pace: testing, measuring, scaling. Without internal capability, companies lurch from pilot to pilot, paying more for re-engagements while skills and confidence stay on the outside.

The rise of in-house AI leadership

This is why companies are appointing Chief AI Officers (CAIOs), Directors of Artificial Intelligence or Heads of AI.

Altrata’s 2024 Executive Insight report shows:

• 70% year-on-year growth in CAIO positions.
  
• Most are internal promotions with an average six years in the company before appointment. Yes, your brand manager is your next AI guru.
  
• 63% are in private firms, 33% in public companies, and a small share in government.
  
• The median CAIO is 51, with two years in-role,  an emerging but fast-growing trend.
  

The message is pretty clear: businesses now see AI governance and leadership as roles to be embedded, not outsourced.

What an AI officer really does

A Chief AI Officer,  or whoever leads AI internally, is not just a technologist. Their mandate is to take a business from status quo to 100 use cases of AI in three years. That includes:

• Assessing AI readiness across departments.
  
• Identifying and prioritising high-ROI AI projects.
  
• Greenlighting pilots, tracking ROI, and scaling what works.
  
• Establishing KPIs and reporting to the board.
  
• Communicating the AI vision across staff, overcoming resistance.
  
• Embedding AI adoption into daily operations and culture.
  

These are practical deliverables (and TBF we have seen very little to support this coming to fruition, or even impacting the business in any marginally positive way). And because they are led inside the business, they cost far less than outsourcing every iteration to consultants.

Why embedded skills beat consulting

Traditional AI consulting produces strategies. Embedded leadership produces outcomes.

1. Flexibility: embedded skills adapt month by month to new models and regulations.
   
2. Continuity: capability stays inside, not with external vendors.
   
3. Cost: investing in people is cheaper than constant re-engagement fees.
   
4. Accountability: leaders inside own results, not just deliverables.
   

This should avoid the traps that sank many digital transformation projects: dependency, cost escalation, and diluted impact.

 
We get it - we are AI Strategy Consultants

The irony is real: yes, we are consultants telling you not to rely on consultants. But AI is different from past transformations. It moves faster, touches more, and punishes organisations that wait for someone else to lead it.

That’s why we work differently - legit. small, embedded, non-traditional. We focus on building AI capability inside your company with clarity and care, so your strategy doesn’t live in a deck but in how your people work every day.

FAQ

Q: Why do consulting-led AI strategies fail?
A: Because AI evolves too fast. By the time a static strategy is implemented, the technology and market context have already shifted.

Q: What is a Chief AI Officer?
A: An executive who leads AI adoption across the company. According to Altrata, CAIO appointments grew 70% year-on-year, showing demand for embedded AI leadership.

Q: What are the key skills for AI leadership?
A: Being older...just kidding! Strategic thinking, cross-functional influence, pilot management, ROI tracking, AI credibility beyond building a custom GPT and strong communication.

Q: Can’t the CTO handle AI?
A: Hell no! CTOs are focused on software delivery and hardware capex maintenance. AI requires cultural change and continuous testing, a broader mandate than technology alone.

Q: Why invest in embedded AI skills?
A: Because embedded leadership adapts at speed, builds trust inside the company, and avoids the spiralling costs of repeat consulting cycles.