Why the companies winning with AI are not the ones with the best models, but the best people.
Over the last 18 months, conversation around AI has shifted from hype to execution. Most organisations now have access to powerful foundation models, cloud platforms, vendors, and a growing library of AI transformation frameworks.
Despite all this, most AI initiatives still fail to make it past the pilot stage.
- 30% of GenAI projects are abandoned after the proof-of-concept stage (Gartner, 2025).
- Fewer than one-third of AI pilots ever reach production, and 31% of enterprises say under 5% of their PoCs ship (Omdia, 2025).
- Less than 7% of companies have deployed even half of their GenAI pilots (William Blair, 2025).
- Across industries, estimates show 70–90% of AI pilots get stuck in “pilot purgatory” and never scale.
When you dig into why, the issue is never the technology. It is the talent.
AI adoption has quietly become a hiring challenge and the companies that realise this are already pulling away from those that do not.
1. The Myth That AI Adoption Is a Technical Problem
Executives often assume slow AI adoption stems from missing infrastructure, immature models, or tricky data challenges. And while those matter, they are rarely the real blockers.
Across companies where AI efforts stall, the same patterns show up:
- No clear ownership for AI initiatives.
- Understaffed or misaligned data and engineering teams.
- Product managers who do not understand AI well enough to shape viable use cases.
- Executives demanding outcomes without the talent to deliver them.
- Critical skills scattered across the org instead of aligned to outcomes.
These are not technical issues. They are organisational and hiring issues.
And the result is the most expensive failure mode in any AI transformation: confusion.
2. The New Organisation Chart of an AI-Ready Company
The companies deploying AI into production and seeing commercial ROI have one thing in common:
They are hiring differently.
AI Without Strong Data Infrastructure Is Theatre
Top performers invest early in:
- Data platform engineers.
- ML/LLM Ops engineers.
- Retrieval engineers.
- GPU aware platform engineers.
These roles are not “nice to have.” They are the foundation.
No matter how many innovative models a company buys, nothing sticks without the engineering talent to operationalise them.
AI Without Product Ownership Breaks
The most overlooked hire of 2025: the AI Product Manager.
Companies fail when they treat AI as a tech experiment instead of a product. High-performing teams put a PM in charge who can:
- Translate business problems into model capabilities.
- Prioritise realistic, high-value use cases.
- Define measurable success.
- Align expectations across stakeholders.
This role often shows up too late, usually when the project is already burning.
AI Without Delivery Capability Never Leaves the Lab
Seasoned engineering leadership (Heads of Engineering, Principal Engineers, Delivery Leads) is what turns prototypes into stable, secure, customer-ready products.
They own the last mile, the part most AI projects never reach.
3. The Three Hires That Predict AI Success
Across the PE/VC-backed companies, consultancies, and AI forward companies we support, the most consistently successful AI builders make these three hires first:
Senior Data/ML Infrastructure Engineer
If you hire nothing else, hire this.
They build the pipelines, retrieval layers, evaluation systems, and GPU-aware foundations that every AI capability depends on.
AI/ML Product Manager
A PM who can say no to bad ideas, prioritise real commercial value, and turn AI from a science experiment into a roadmap.
Engineering Lead with Delivery Accountability
The person who ensures AI features do not just work, they ship.
These three roles create leverage.
They make every future hire additive instead of corrective.
4. What the AI Winners Are Doing Differently
High-performing companies are separating themselves from the pack by doing four things most competitors do not:
Hiring ahead of AI demand
They know AI capability compounds; it is not a linear project.
Investing in platform and infrastructure roles early
Infrastructure is what de-risks scale.
Designing interviews to measure true AI readiness
Less emphasis on toy model-building.
More on:
- System design.
- Data maturity.
- Use-case evaluation.
- Cross-functional execution.
Building small, senior, outcome-driven AI teams
Not 20-person research groups.
More like 5 to 8 exceptional specialists who deliver value quickly and repeatedly.
5. What This Means for Leaders in 2025
If you are a CTO, CPO, or CEO, AI adoption is no longer about access to the technology.
It is about whether you can hire and retain the people who know how to use it.
The companies that win the next wave of AI will not be the ones with the biggest budgets or flashiest model demos.
They will be the companies with the clearest hiring strategy.
Because in AI: Technology amplifies talent. It does not replace it.