HRTech Interview with Jonathan Eighteen, Global Transformation Advisor at NIIT MTS

Welcome to HRTech Cube, Jonathan, we’re delighted to have you. Could you briefly share your professional journey and what led you to your role as Global Transformation Advisor at NIIT MTS?

My career has always sat at the intersection of learning, capability, and commercial performance — which means I’ve spent a long time working on problems that don’t have clean, off-the-shelf answers. I started in financial services, leading the learning and capability function at a time when FSI was being reshaped by regulation, technology, and the beginning of a serious reckoning with what professional competence actually means in a changing market. That experience grounded me in something I’ve never lost: the idea that learning has to be accountable to business outcomes, not just to itself.

From there, I spent fifteen years at Deloitte, leading Learning Advisory for the UK and EMEA. That gave me extraordinary exposure — working on some of the most progressive learning transformations and skills-based architecture programmes across financial services, healthcare and life sciences, consumer, and technology markets. Some of the world’s most recognised organisations were trying to figure out how to build capability at scale, with rigour, and I was part of those conversations at the most senior level. That shapes how you think.

Moving to NIIT and St. Charles Consulting brought a different kind of challenge. We work with more than 150 organisations globally, and the honest truth is that in the AI era, the playbook is being rewritten in real time. Leaders are asking questions that the consulting industry hasn’t fully answered yet. That’s exactly the kind of problem I find motivating.

You’ve said that as learning shifts into everyday work, AI is effectively “repricing” skills. What does that mean in practical terms for today’s workforce?

When I say AI is repricing skills, I mean that the market value of certain capabilities is moving — in some cases very quickly — and most organisations haven’t built the systems or the governance to track that movement in near-real time.

The practical effect is most visible in knowledge-intensive roles. Tasks that previously required years of accumulated expertise to perform reliably — synthesis, analysis, structured reasoning, documentation — are increasingly being augmented or replaced by AI at speed and scale. That doesn’t make expertise irrelevant, but it changes which layer of expertise commands a premium. The value shifts upward: toward judgment, toward the ability to evaluate AI-generated work critically, toward the professional accountability that can’t be delegated to a model. What gets repriced downward is the procedural and analytical competence that used to be the entry requirement for professional credibility.

For workforces, this creates an uncomfortable reality. People who built their identity and career progression around a specific skill set may find that skill set devaluing faster than their organisation’s capability frameworks are reflecting. And for enterprises, the risk is investing in building or maintaining capability that is being commoditised by AI — without realising it until the investment has already been made.

How is AI elevating judgment and accountability while compressing the value of routine expertise?

The compression of routine expertise is largely a function of speed and availability. AI can now perform many of the tasks that previously served as the developmental scaffolding of a professional career — the research, the drafting, the structured analysis. That work wasn’t just productive; it was how people built the experiential foundation that judgment eventually rests on. When AI takes over that work, the developmental pathway is disrupted in ways that most organisations haven’t yet fully reckoned with.

At the same time, accountability doesn’t compress — it concentrates. Someone still has to own the output. Someone still has to make the call when the AI-generated recommendation is wrong, or when the model is working from incomplete context, or when the situation requires an ethical judgment that can’t be automated. That responsibility is now sitting with people who, in many cases, haven’t done the underlying analytical work manually. They’re being asked to exercise judgment about outputs they may lack the experiential baseline to evaluate well.

That’s an underappreciated structural risk. The organisations handling it best are the ones that have been explicit about it — naming it as a capability question, designing for it deliberately, and building accountability frameworks that reflect the new reality rather than the old one.

Many organizations are moving faster with AI adoption. Why do you believe speed alone does not create sustainable advantage?

Because speed without structural readiness creates technical debt of a different kind — capability debt. You can automate workflows quickly. You can deploy AI tools broadly. You can move faster than your competitors on the surface. But if your people don’t have the judgment to work effectively alongside AI, if your governance doesn’t reflect the new accountability distribution, and if your capability infrastructure can’t tell you whether any of this is actually building organisational strength — you’ve moved quickly into a more fragile position, not a stronger one.

The organisations I’ve seen adopt AI most effectively are not necessarily the fastest. They’re the ones that have been clear-eyed about what needs to be true across their people, their processes, and their architecture before they move. They’ve treated AI adoption as a structural redesign question, not a deployment question. Speed is a byproduct of having the foundations right — it’s not a substitute for them.

The honest version of this conversation is that most organisations are under pressure to demonstrate AI progress to boards and shareholders, and that pressure creates incentives to prioritise visible deployment over invisible readiness. That’s where the risk accumulates.

What does it mean to structurally redefine how capability is built, governed, and evidenced inside enterprises?

It means breaking with three assumptions that have been baked into enterprise learning for a long time. The first is that capability is built primarily through formal interventions — programmes, courses, structured learning events. AI-augmented work makes the majority of real capability formation happen in the flow of work itself, which means organisations need infrastructure to capture and evidence that, not just to deliver content at it.

The second assumption to break is that governance of learning investment is primarily a function of HR or L&D. When learning is embedded in work, it becomes a line ownership question. The commercial leaders, the functional heads, the people responsible for execution — they have to be accountable for capability formation in their domain, not just consumers of programmes delivered by a support function.

The third is evidencing. Most organisations measure learning by activity — completions, hours, engagement scores. That’s not evidence of capability. Evidence of capability is demonstrated performance, assessed judgment, verifiable skill application in context. Building the infrastructure to capture that at scale, and connecting it to strategic workforce decisions, is genuinely hard. But it’s the work that separates organisations with real capability advantage from those with impressive learning metrics.

How should boards and executive teams rethink workforce strategy in response to this shift toward AI-augmented work?

The first shift is recognising that workforce strategy is now a board-level agenda item in a way it simply wasn’t five years ago. The pace at which AI is changing the capability requirements of organisations — and the speed at which skills can become obsolete or critical — makes workforce decisions as strategically consequential as capital allocation decisions. Boards that are still treating this as an HR topic are operating with a significant blind spot.

The second shift is in how workforce strategy is framed. The dominant model has been workforce planning — how many people, in what roles, with what skills. That’s necessary but insufficient. The question that executive teams need to be asking is: what is the architecture of capability we need to execute our strategy in an AI-first environment, and are we building it, buying it, or inadvertently degrading it? Those are different questions, and they require different governance, different data, and different accountability.

The organisations I’ve worked with that have made this shift — and it’s still a minority — tend to have one thing in common: a Chief People Officer or equivalent who can credibly sit at the commercial table and translate workforce capability into strategic risk and opportunity. That role, done well, is one of the most consequential in the enterprise right now.

What risks do organizations face if they fail to realign capability frameworks with the realities of AI-driven execution?

The most immediate risk is invisible degradation. Capability frameworks that were designed for a pre-AI environment will continue to signal competence in people who are, in practice, becoming increasingly dependent on AI for work they’re nominally credentialled to do independently. That creates a growing gap between assessed capability and actual capability — one that may not surface until it matters, typically in a high-stakes moment: a regulatory event, a market disruption, a client failure.

The second risk is misallocated investment. If your capability frameworks aren’t reflecting the AI-driven shift in what actually needs to be built, your learning and talent investment will continue to flow toward capabilities that are either being commoditised or are already adequately addressed by the tools your people have access to. You’ll be spending real money reinforcing the wrong things.

The third risk is talent. The professionals who understand what the AI era actually demands — who can operate at the judgment layer, who are building the skills that AI can’t easily replicate — will be drawn to organisations that reflect that reality in how they develop and reward people. Capability frameworks that haven’t moved signal to that talent that the organisation hasn’t either.

How can companies better integrate learning into the flow of work without losing rigor, governance, or measurable outcomes?

The rigour question is actually the most important one, because the instinct in a lot of organisations is to treat flow-of-work learning as informal and therefore unaccountable. That’s a mistake. The fact that learning is happening in context, in the execution of real work, doesn’t mean you can’t govern it or evidence it — it means you have to build the infrastructure to do so differently.

What that looks like in practice is connecting work outputs to capability evidence. It means building assessment into the review and feedback structures that already exist in the organisation, rather than creating parallel learning events to replicate real conditions. It means being explicit about what judgment is expected at each stage of work, and creating structures where that judgment is visible, evaluated, and recorded.

The governance question is partly about accountability and partly about data. Organisations that have done this well tend to have a clear ownership model — where capability formation in a given domain is someone’s job to track and improve — and a measurement architecture that can surface whether capability is actually forming, not just whether activity is being completed. Those two things together create the conditions for rigour in a flow-of-work model.

From your perspective, what is your personal strategy for staying effective and credible while advising leaders through complex transformation cycles?

The most important thing I’ve learned is to stay honest about what I don’t know. The AI transformation is genuinely novel in ways that reward intellectual humility over confident prescription. Leaders can tell the difference between someone who is pattern-matching to previous transformations and someone who is genuinely working through the problem with them — and the former erodes credibility faster than uncertainty ever would.

My approach to an engagement starts with trying to understand the specific structural reality the organisation is in — not the version they’ve presented publicly, or the version they’ve told themselves, but what the data and the operational evidence actually show about where capability is forming, where it’s degrading, and where the gap between strategy and execution is widest. Getting clear-eyed about that picture, together, is usually the most valuable thing I can offer.

From there it’s about sequencing — which problems have to be solved before others can move, and what’s the minimum viable structural change that creates real momentum rather than just activity. Most transformation programmes try to do too much at once. The ones that work tend to pick their ground carefully and build from demonstrable progress rather than comprehensive ambition.

What advice would you offer workforce and HR leaders navigating this transition, and what final thought would you like to leave with our readers about building durable capability in the age of AI?

For HR and workforce leaders specifically: resist the pressure to lead with tools. The AI technology landscape is moving fast enough that any specific platform or solution you anchor your strategy around today will look different in eighteen months. What endures is the clarity of your capability architecture — knowing what you’re trying to build, having the governance to direct investment toward it, and the measurement infrastructure to know whether it’s working. Those things transcend any particular technology cycle.

More broadly, I’d leave readers with this: durable capability in the AI era is not primarily about keeping up with AI. It’s about being clear on what humans bring that AI doesn’t — and building the structures, the culture, and the accountability frameworks that develop and protect that distinctively human contribution at scale. Judgment. Contextual wisdom. The ability to act with accountability under genuine uncertainty. Those aren’t soft capabilities. They are, increasingly, the strategic assets that separate organisations that will lead from those that will simply execute faster. Build for them deliberately, evidence them rigorously, and govern them like the strategic investment they are.