Is the AI skills gap actually a confidence crisis?

We’ve all seen the headlines. 

Millions of AI-related roles risk going unfilled. Businesses are scrambling to hire specialists. AI training bootcamps and certifications are springing up overnight (often with a hefty price tag attached). And in the channel specifically, partners are feeling the pressure to get ahead of the curve… or risk being seen as behind it. 

But what if the real barrier to AI adoption isn’t a lack of technical know-how? For many partners, the first hurdle isn’t infrastructure or machine learning expertise but simply knowing where to begin.

There’s no shortage of AI curiosity, and the partners I speak to want to explore AI, if they’re not using it already. They can see the opportunity, both internally and with their customers. But despite their appetite for AI, many don’t know how to approach it in a structured way. It’s not about a lack of intelligence or initiative – rather, the same thing keeps coming up again and again: the lack of a shared starting point. We’re talking about a framework, a common language, or even simply internal alignment on who owns the AI project and how to roll it out.

What we see, in parallel to this, is that individuals within the business might already be experimenting with AI tools. Whether it’s a marketing lead using ChatGPT to draft partner comms or a sales rep running quick competitor comparisons, this experimentation is happening, but often the efforts remain isolated.

And that informality introduces risk. When employees feed sensitive information into public tools – whether that’s customer data, internal documents, or access credentials – it’s often done without proper controls or visibility. As models become more integrated and capable, even seemingly simple actions, like granting access to an inbox or uploading a slide deck, can expose far more than intended. Without a clear understanding of how that data is stored, processed, or used in training, organisations may be expanding their risk surface without even realising it.

Rethinking ‘AI skills’

When we talk about the AI skills gap, the default image is a highly technical one: data scientists, machine learning engineers, prompt engineers. That is one interpretation, but it’s not necessarily the one most relevant to the channel.

What most partners actually need is broader fluency. The ability to spot where AI can drive value, the confidence to speak credibly to customers about emerging tools, the skill to connect new capabilities to real business challenges, and the awareness to navigate compliance and governance challenges.

These are not deep technical skills, but they are practical and highly valuable. Real-world AI adoption isn’t just about building things, it’s also about the strategic understanding that underpins this new technology. It’s seeing how leveraging AI can improve outcomes, simplify delivery, or reduce friction in everyday workflows. That’s the version of ‘AI skills’ that rarely makes the headlines, and yet it’s probably the one most partners need to get started.

Confidence vs. competence

So if the gap isn’t technical, what is it?

In many partner organisations, the biggest blocker is uncertainty. AI is happening, but not in a coordinated way. Few have formal internal policies. Even fewer have clear owners. Tools are often tested ad hoc, with no defined process or outcomes. In some cases, they’re blocked entirely over data concerns or regulatory ambiguity.

That caution isn’t unwarranted, especially with new AI regulations looming. But without internal leadership or shared direction, AI becomes harder to move forward – not because the tools aren’t ready, but because no one’s quite sure who should take the lead. 

Then there’s the emotional barrier. The sense that others are further ahead. That experimenting now only highlights how far behind you might be. That kind of self-imposed pressure leads to hesitation. Partners hold back. But it’s that mindset, more than any lack of technical skill, that stalls progress.

The most effective partners aren’t racing to deploy the flashiest tools. They’re the ones creating internal clarity, encouraging experimentation, and supporting non-technical teams as they get to grips with what AI could mean — for their customers, their services, and their own operations.

What real AI success looks like in the channel

AI doesn’t need to sit in a specialist unit or be limited to the dev team. In fact, some of the most successful use cases come from outside IT — from compliance, customer support, marketing, finance. The partners getting this right are taking a cross-functional approach. They’re identifying internal AI champions, creating shared working groups, and encouraging people to test, iterate, and learn.

They’re also looking outward. Across the channel, there’s huge value in learning from what others are trying, where they’re struggling, and how they’re adapting. No single vendor, distributor, or solution provider has all the answers. The partners making the most headway are the ones who remain open: to new ideas, to shared learnings, and to the understanding that AI adoption is a collective process, not a finish line to be crossed.

Some of the most valuable applications for partners won’t involve generative models at all. They’ll be found in streamlining onboarding, speeding up internal approvals, surfacing risk signals faster, or automating compliance checks. These meaningful improvements are what matters to customers, because they’ll be tested, tried, and working in real-world scenarios. But they only happen when people across the business feel empowered to act.

Here’s a final word to the partners who worry that they might not be ‘ready.’ Adoption does not need to be all-or-nothing. You don’t need to transform overnight. Some of the strongest strategies start small — with AI champions, achievable goals, and enough space to build confidence across the business, not just in IT.

This is the reality for many partners. You don’t need to lead the AI race. But you do need to take the first step in a way that feels intentional, sustainable, and right for your business. Progress doesn’t come from waiting until you feel ready. It comes from starting and being willing to build from there.