What is missing is role-based clarity. It is not enough to say, “we are going to use AI agents”. It needs to be: “You’re a software engineer, a programme manager, a financial analyst, etc – and here is exactly how AI shows up in your role: X, Y and Z.”
There is also a cultural component, particularly around managers. The organisation can set the direction at the highest level, but it is the manager who has to translate that into day-to-day guidance for employees. If that does not happen, the strategy never really lands.
On top of that, there is still a lot of friction because AI is not embedded in the flow of work. If people have to go somewhere else – another tool, another portal – it feels like “one more thing I have to do”, so they forget about it or do not use it.
That is where the Cornerstone Workforce AI platform comes in. It operates where people are already working – in Slack, Teams, an employee digital hub, and so on. If you meet people in the channels they already use, you remove friction and increase adoption. The moment you force them into a separate environment, you lose traction.
What risks come from employees teaching themselves AI – and how can HR address those risks?
One of the most interesting findings from the same research is that 17% of employees are actually faking their use of AI. They are not using it at all, but they are putting in things like em dashes or even misspelling words on purpose to make it look as if AI has been involved.
That is problematic because it means organisations are not getting a realistic picture of how AI is really being used. Leaders might believe adoption is higher than it is, when in fact people are masking non-use.
We also found that 65% of employees are taking time outside of work to teach themselves AI, because they do not understand what they are supposed to be doing from their company’s perspective.
If I think about myself, I have got a lot of responsibilities outside work. If I then have to teach myself AI on my own time because my employer is not investing in me, I may start to wonder why I should stay. There is a clear retention risk there: employees may feel the company is not supporting their development.
The silver lining is that 52% of employees told us they would take time during work hours to learn AI – if the company told them what to do, again with that role-based clarity. So they are willing, but they need direction.
Besides AI, what other major challenges are you seeing for HR teams this year?
I know we are focused heavily on AI, but the other side of that coin is what I call human skills – what earlier in my career we would have labelled “soft skills”. These are more important than ever.
Not so long ago, tech itself was the differentiator. Today, the differentiator is the human element that sits around the technology.
Employees bring the critical thinking, creativity, judgement and communication skills. They are the ones who can design and execute change management plans. Those capabilities are essential to making AI work in a real organisational context.
In some ways, it is a case of “what’s old is new again”. The foundational components of being a good citizen in the workplace – communication, collaboration, judgement, creativity – need equal investment alongside AI tools, agents and bots. We cannot just invest in the technology; we have to invest in the human side at the same pace.
Are there any misconceptions about AI in the workplace that particularly concern you?
A big one is trust. Our research showed that many employees do not trust AI, especially because they have been told that AI will not take their jobs, yet the headlines and some corporate announcements around layoffs are attributing workforce reductions to AI.
I do not actually believe AI is taking jobs in the simplistic way it’s sometimes framed. What is often happening is a broader reinvestment or disinvestment by organisations – restructuring, reallocating resources, shifting priorities. AI then becomes the easy scapegoat or headline explanation.
Instead of using AI as the culprit, companies should be transparent and clear about what is really driving changes. If you are not honest about the underlying reasons, you deepen mistrust – both in AI and in leadership.
How is Cornerstone addressing the AI readiness gap you have described?
A lot of it comes back to being in the flow of work and making sure that we are capturing the right signals.
With Cornerstone Workforce AI, employees can input their skills, managers can validate them, and the system can surface where you have concentrations of certain skills and where you have gaps. We also bring in labour market intelligence through Cornerstone People Graph, so you can see where you might need, say, a centre of excellence or additional investment.
Crucially, from a data privacy standpoint, organisations can decide who sees what. You can define access by security level, role, and so on. But at a leadership level, you have the data you need at your fingertips.
In a world that is changing this quickly, a static strategy is only as good as the moment you articulate it. With this approach, you can pivot much more quickly. Work that used to take days or weeks – pulling data from different systems, coalescing it into dashboards – can now be done in minutes. That’s a big part of how we’re helping customers close that readiness gap.
Looking ahead, what trends do you foresee in the HR tech space over the next few years?
Right now, the pendulum has swung very hard towards AI agents, and there is a lot of doom and gloom in the narrative – the fear that AI is going to take everyone’s job.
At Cornerstone we talk about human to the power of AI. My view is that as we move past the current hype cycle, the pendulum will swing back more towards the centre.
We will still absolutely use AI, but we’ll also recognise the importance of the foundational human elements I mentioned earlier. The conversation will become less about fear and more about how we work better with AI.
I would expect a more balanced approach: strong AI capabilities, yes, but with serious investment in human skills and change readiness, which is what really enables organisations to unlock value from the technology.
Is there any relevant data or perspective you can share around youth unemployment and entry-level talent?
Organisations need to ensure they are investing in entry-level roles. At Cornerstone, we’re doing a lot of intern recruiting and college graduate recruiting because these are the people who will be leading our companies in the future.
Going back to my “what’s old is new again” point, when I entered the workforce there were leadership rotation programmes. I think those need to come back.
If you skip that layer, you have to ask: who is going to be ready to lead when people in my generation (Gen X) are no longer in the workforce? It would be short-sighted not to invest in entry-level and college grad talent, both for succession and for building the right culture.
We are working on our own leadership programmes for early-career talent. We will partner with someone in our product organisation to bring in new college graduates into a structured programme.
Do you have any advice for organisations that are struggling to build an AI-ready workforce?
I would start by reiterating that we have a strong solution in Cornerstone Workforce AI, which does not require you to buy an entirely new software stack. It can sit over what you already have via that headless MCP approach, pulling in signals from your existing systems and giving you clear, actionable insight. Cornerstone serves more than 7,000 organizations with its technology and we already have more than a dozen customers who are leveraging the latest functionality. .
More broadly, in HR you have to be laser-focused on business outcomes. You need to be able to talk in the language of your CEO, CFO or CIO: return on investment, time to productivity, engagement, culture, retention, time to hire, attrition and so on.
If you can clearly articulate how your AI and people strategies drive those outcomes, it becomes much easier to secure the budget and support you need for the interventions you want to put in place.
Do you have a closing message you’d like to share?
For HR leaders, I would say: always come back to the business outcomes you are trying to drive for your organisation, especially in the context of AI and HR tech.
Think carefully about how you frame your plans to your CEO, CFO, CIO or other buying partners. If you can connect your initiatives to outcomes like productivity, engagement, culture, retention and hiring effectiveness, you will be in a much stronger position to secure the investment you need to deliver meaningful change.
