My journey has never followed a straight line, and I think that's exactly what shaped me. I've worked across architecture, delivery, customer implementation, and go-to-market — and each of those vantage points taught me something the others couldn't. Delivery teaches you humility, because plans meet reality and reality usually wins. Architecture teaches you to think in systems rather than tickets. And go-to-market teaches you that the best technology in the world is worthless if you can't explain its value to the person signing the cheque.

Fifteen-plus years inside one organization, across multiple geographies and business models, gave me something else too: pattern recognition. I've seen the same transformation challenges resurface in different disguises — organizational silos, underinvestment in change management, technology deployed ahead of governance — and that repetition is where real judgment comes from. I didn't set out to become a "technology leader" in the abstract; I set out to solve the problem in front of me as well as I possibly could, again and again, in bigger and more complex forms. Leadership, for me, has been a byproduct of that discipline, not the goal itself.

I believe the digital workplace is moving from a support function to a strategic differentiator — and to earn that status, it has to stop being defined by the service desk and start being owned as one connected system. For years, "digital workplace" meant devices, service desks, and uptime — necessary, but largely invisible unless something broke. My vision is bigger than that: a workplace organization that owns the full employee technology experience end-to-end — the hardware someone is issued, the operating image and software stack it's built on, the SaaS and enterprise platforms they work in every day, and the digital profile and identity that follows them as they change roles, devices, or locations. Today those are too often run as separate programs with separate owners. My view is that they should be designed, governed, and measured as a single experience.

Concretely, that means three things I'm driving hard: consolidating fragmented technology estates — across hardware, software, and identity — into a smaller number of governed, AI-ready platforms; embedding intelligence and automation into every layer of that stack, not bolting it on as a chatbot at the ticket layer; and treating the digital workplace as a product with a roadmap and a P&L mindset, not a cost centre to be minimized. Done right, this becomes one of the clearest, most measurable levers a global enterprise has for productivity — and that's the story I want Digital Workplace Solutions to tell over the next few years.

xIQ Workplace came from a simple frustration: most workplace technology treats every employee the same, even though no two employees actually work the same way. A field sales engineer, a finance analyst, and a software developer have completely different friction points, tools, and definitions of "productive," yet they were often handed identical policies, identical devices, and identical support experiences. That's expensive and it doesn't work.

The idea behind xIQ was to build persona-based intelligence into the workplace itself — using AI to understand usage patterns, sentiment, and need by role, and then tailoring hardware, software, automation, and support accordingly. Instead of a generic help desk, employees get a system that increasingly resolves issues before they're even reported, and support that's calibrated to how critical their downtime actually is to the business.

The transformation isn't just about faster resolution times, though we've seen strong gains there. It's a mindset shift — from IT as a uniform utility to IT as a personalized service. Employees feel it in small ways that add up: fewer repetitive tickets, tools that fit how they actually work, and a sense that technology is finally working for them rather than the other way around.

Generative AI is doing something automation alone never could: it's collapsing the distance between a problem and its resolution. Traditional automation was good at executing pre-defined workflows quickly. GenAI adds judgment — it can interpret an ambiguous request, draft a first response, summarize a mountain of historical tickets into a root cause, or triage an incident with context that used to require a human expert to assemble manually.

In enterprise service management specifically, we're seeing the center of gravity shift from "how fast can we close a ticket" to "how do we prevent the ticket from ever needing to exist." Predictive detection of failing devices, AI-drafted knowledge articles generated from resolved incidents, and conversational interfaces that resolve requests in natural language rather than forcing employees through rigid forms — these are no longer proof-of-concept ideas, they're becoming table stakes.

The bigger shift, though, is cultural. Teams that used to measure themselves purely on SLA compliance are having to learn to measure themselves on business outcomes — time saved, experience improved, risk reduced. That's a harder metric to own, but it's the right one, and it's where I'm pushing my own organization to go.

If I had to pick one, it would be leading the consolidation of a sprawling, decades-old technology estate — dozens of overlapping platforms accumulated through years of organic growth and acquisitions — into a small number of governed, interoperable backbone systems. On paper that sounds like an infrastructure project. In practice, it was a change-management and stakeholder-alignment project disguised as an infrastructure project.

Every one of those legacy systems had an owner who believed their tool was indispensable, and every consolidation decision touched someone's workflow, budget, or turf. Getting from a landscape of 60-plus disparate systems down to a handful of true backbone platforms required building a fact-based case that could survive scrutiny from finance, from the business units losing "their" tool, and from technical teams worried about migration risk — and then actually delivering it without breaking the business along the way.

It was defining because it taught me that the hardest part of transformation is rarely the technology. It's the discipline of building a case so clear and so well-evidenced that resistance becomes a conversation about facts rather than a conversation about fear. That skill has served me in every transformation I've led since.

Three stand out consistently. First, sequencing: organizations want the AI headline before they've built the foundation underneath it — clean data, governed platforms, clear ownership. Skipping that step doesn't just slow AI adoption down, it can waste a meaningful share of the investment on tools that never get properly embedded.

Second, governance that keeps pace with ambition. Everyone wants to move fast on AI, but very few organizations have stood up the cross-functional governance — spanning IT, security, HR, and finance — needed to scale it responsibly. Without that, you either move too cautiously and lose the advantage, or you move too fast and create risk you can't see until it's a headline.

Third, and most underrated: treating AI adoption as a technology rollout rather than a change-management exercise. The tools are the easy part now. The hard part is helping thousands of employees, across different roles, trust and actually change how they work because of AI. Organizations that invest as much in that human transition as they do in the platform itself are the ones I see winning.

Three principles, above all. The first is fact-anchored communication — in every difficult conversation, whether it's a governance dispute between business units or a budget negotiation, I've found that leading with well-evidenced facts, not opinions, is what earns you the right to be heard and, eventually, to be trusted.

The second is direct boundary-setting. Global, matrixed organizations create endless ambiguity about ownership and accountability. I've learned that being clear — sometimes uncomfortably clear — about what I will and won't own, and what I expect from others, prevents far more damage than the discomfort of the conversation itself.

The third is treating people development as non-negotiable, not aspirational. Managing teams across APAC, EMEA, North America, and Latin America means you're constantly working across cultures, time zones, and career expectations. I've made a point of restructuring teams, sponsoring people into new roles, and staying involved in mentoring — including through my teaching at GLA University — because the leaders I respect most are the ones who leave people and organizations stronger than they found them, not just the P&L in better shape.

I see three waves converging over the next few years: AI moving from tools people use to agentic systems that act on their behalf; the workplace becoming genuinely persona-aware rather than policy-uniform; and a "control plane" mindset taking hold, where enterprises need one coherent layer to govern the growing sprawl of AI agents, data, and automation working across their business. Enterprises that get ahead of that third shift — governance and control, not just capability — will be the ones who scale AI safely and profitably.

For aspiring technology leaders, my message is simple: don't chase titles, chase breadth. The leaders who thrive in this next decade won't be the deepest specialists in one tool or platform — they'll be the ones who can move between architecture, delivery, go-to-market strategy, and people leadership, and connect the dots across all of them. Stay curious, get comfortable with ambiguity, and never let a job title define the size of the problem you're willing to take on.