The rush to deploy enterprise AI has reached a fever pitch. According to global recruitment firm Harvey Nash, we’re now facing the largest and fastest-developing technology skills shortage in more than 15 years.
At the same time, headlines are dominated by salaries for so-called “AI-native” talent. We’re quickly creating new roles, such as “Forward Deployed Engineer” and “AI Researcher.” Meta reportedly offering a $1 billion pay package to lure an AI researcher is an extreme example, but it reflects a broader trend of inflated expectations in the market.
For most organisations, this raises an uncomfortable question. If you recognise the value of AI but can’t compete in a winner-takes-all talent market, where do you start? The answer is to tune out the noise. Enterprise AI success will not be determined by landing a single hire. It will be defined by how effectively you enable your existing workforce to rethink, redesign, and scale their processes with AI built in — safely, responsibly, and at speed.
The Risk of Missing the Point
For some organisations, particularly tech startups and software companies, there is a real need to hire highly technical talent to build custom LLMs, inference pipelines, and advanced AI systems. But that is not the problem most enterprises are trying to solve and focusing there is where many begin to miss the point.
For the typical enterprise, the goal of an AI rollout should not be to create a small group of AI specialists or hire and train a bunch of these new “forward deployed engineers.” It should be to give as many employees as possible, both technical and nontechnical, the ability to rethink how work gets done with AI.
Central IT has a critical role to play in this process, especially when it comes to data infrastructure, governance, and security. But it cannot lead the rollout alone. That would repeat the old analytics model, where teams submit tickets to IT, wait days for answers, and move far too slowly to deliver impact. AI adoption requires speed and iteration.
There is another reason IT-led rollouts struggle. Context matters. The people best positioned to integrate AI into sales workflows are not AI engineers. They are BDRs and account executives who understand how the work actually happens. No amount of expertise in LLMs or data platforms can replace that frontline knowledge.
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Upskilling Focus
The key requirement for enterprise AI success is giving employees a solid foundation in working with AI so they can identify the most valuable use cases. That’s where upskilling comes in — and it should be a central focus of any AI rollout.
One-off training sessions on LLMs or AI agents are not enough. Enterprises need to build a culture of working with data and AI. This means combining hands-on training in low- or no-code tools for building AI workflows with education that embeds critical behaviours, such as analytical thinking and data literacy. When employees understand data and AI, they can better craft inputs and prompts, judge where AI works and where it doesn’t, and make informed use of its outputs.
Practical steps matter. Establishing a library of learning resources that employees can access on demand and holding regular forums to share experiences and lessons learned from working with AI are simple but effective ways to nurture a culture that makes enterprise AI work.
AI Champions to Accelerate Progress
Driving enterprise AI forward works best when responsibility is clearly assigned. A designated leader, such as a Chief Data and Analytics Officer, can oversee initiatives and support internal teams in implementing AI effectively.
The CV of these individuals doesn’t need to include the highly sought-after AI-native background. Some of the most effective leaders come from IT consulting, with experience breaking down barriers between IT and the business, establishing processes for data sharing, and ensuring projects succeed. Those skills translate directly to AI rollout.
Success is about combining people management with enough technical familiarity to empower employees to create their own AI workflows. This is how enterprises scale AI in the real world.
Talent Wars are a Distraction
For AI to make a real impact in 2026, enterprises need to focus on the right priorities. Success doesn’t come from chasing scarce AI-native talent. It comes from empowering as many business users as possible to use AI effectively.
The result is simple: AI success is within reach for most enterprises. That’s the outcome that actually matters.
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