HRTech Interview with Maruf Ahmed, Chief Executive Officer of Dexian

Welcome to HRTech Cube, Maruf. We’re delighted to have you. Can you share your professional journey and what drove you to your current role leading Dexian?

Thank you — it’s a pleasure to connect. My background is in engineering and IT consulting, which shaped how I think about organizations and how change actually shows up day to day. Early on, I saw how often technology initiatives struggled not because the tools were wrong, but because the surrounding systems weren’t ready to support them. When I partnered with my brother to create Dexian, over 30 years ago, the company started with a simple idea: build something sustainable from the ground up by balancing execution, trust, and long-term thinking. Over time, that approach supported steady growth, strategic acquisitions, and expansion into new capabilities as client needs evolved. Watching the organization grow from the earliest days to where it is today has reinforced the importance of scaling with intention, not just speed. Leading Dexian now means carrying that same mindset forward as workforce models, technology, and expectations continue to change.

You’ve noted that the biggest shift in AI adoption is trust, not technology. Why has trust overtaken technical capability as the primary hurdle for organizations?

AI technology has advanced faster than most organizations expected. Dexian’s 2026 Work Futures research shows that more than half of employers now feel very prepared to adapt to AI, and a significant portion are already realizing productivity gains at scale. The conversation has moved beyond “can we implement AI?” to “can we deploy it fairly, transparently, and responsibly?” Trust has overtaken technical capability because AI increasingly touches decisions that affect people’s livelihoods: hiring, performance, advancement, and workforce planning. Employees are optimistic about innovation, but they are also clear that trust is not assumed. When people don’t understand how decisions are made, or don’t believe safeguards are in place, resistance grows regardless of how advanced the technology is.

With AI entering sensitive areas like hiring, how can organizations ensure governance and explainability are active safeguards rather than just buzzwords?

Governance and explainability only matter if they are embedded into how decisions are made, not layered on afterward. That means being clear about where AI is used, who owns outcomes, and how accountability is maintained throughout the process. This is also where strong partnerships matter. Organizations benefit from working with partners who understand how to design governance into workflows from the start, rather than treating it as a compliance exercise. When governance is operationalized — not abstract — explainability becomes practical, and trust becomes easier to sustain.

Efficiency is often the main selling point of AI, yet you prioritize “human-in-the-loop” decision-making. Why is the human element critical for long-term success?

Efficiency is valuable, but efficiency without judgment creates risk. Human-in-the-loop models ensure that AI delivers time savings while preserving accountability. AI can accelerate insight, reduce friction, and surface patterns at scale, but humans are still responsible for interpreting those outputs and applying context, ethics, and experience. The goal is not choosing between humans or AI, but preparing people to work step by step with AI. When individuals are equipped to intervene, validate, and explain outcomes, organizations gain both speed and trust — which is essential for long-term adoption.

AI adoption doesn’t fail because the technology moves too fast. It fails when accountability isn’t clear. When people know who owns decisions and how oversight works, trust follows.

How do responsible AI frameworks help companies balance the speed of automation with the necessity of fairness and compliance?

Responsible AI frameworks help organizations move faster with confidence. When expectations around data use, oversight, and accountability are clear, teams spend less time questioning decisions and more time applying technology effectively. At Dexian, this thinking is reinforced through our internal AI governance structure, including an internal AI committee and dedicated teams focused on responsible adoption. That internal discipline helps ensure fairness and compliance are addressed proactively, not reactively, as automation scales.

Dexian approaches AI implementation across “people + process + technology.” Why is this holistic view necessary, rather than treating AI as strictly a tech upgrade?

The importance of people, process, and technology hasn’t changed but the way they interact has. AI has elevated the need for tighter alignment across all three. Technology is advancing faster, processes must adapt more dynamically, and people need clearer roles in how decisions are made and reviewed. Treating AI as a standalone upgrade ignores the operational shifts it creates. A holistic approach ensures AI strengthens how work gets done, rather than introducing uncertainty or fragmentation as it scales.

Regarding accountability in workforce AI, what specific demands should clients make of their partners to ensure ongoing compliance?

Organizations should expect more than reassurance from their partners. Accountability has to be proactive, not something that only shows up after an issue surfaces. That means partners are clear about how AI is governed, how bias is monitored over time, and who is responsible for stepping in when something isn’t working as intended. Just as important, the responsibility shouldn’t sit entirely with the client to identify risk after deployment. Strong partners stay engaged, anticipate regulatory and ethical shifts, and evolve solutions alongside the organization as needs change. That ongoing ownership is what turns AI from a one-time implementation into a trusted capability.

On a personal level, what specific leadership strategy do you rely on to navigate decisions during times of rapid industry disruption?

I lead with steadiness and systems thinking. In moments of disruption, there’s always pressure to move quickly, but I’ve learned that speed without structure often creates more risk, not less. My role is to slow the moment just enough to establish clear decision frameworks, align leaders around shared outcomes, and communicate openly, even when all the answers aren’t fully formed. That approach is especially important as AI becomes more capable. The real question for me isn’t how fast technology advances, but who stands behind the decisions it influences — and whether people trust that there’s a real human accountable when it matters. I take that responsibility seriously, but I also know it can’t sit with one leader alone. At Dexian, I intentionally rely on teams of experts who bring technical, operational, and ethical perspectives together to guide how AI is governed and applied in real-world conditions. Leading this way allows us to move forward with confidence rather than urgency. It ensures progress doesn’t come at the expense of oversight, and it reinforces trust by making accountability visible, shared, and continuous. That consistency is what creates stability, even as the industry continues to change.

What is one piece of advice for leaders who are hesitant to adopt AI due to ethical concerns?

Ethical hesitation around AI is understandable. In my experience, it’s most productive when it leads to deeper questions about oversight, decision rights, and how humans remain accountable as technology scales. The organizations that opt out entirely risk falling behind without actually protecting their people. Instead, leaders should engage early, establish governance, involve diverse perspectives, and ensure humans remain accountable for outcomes.

Finally, what are your closing thoughts on the future of responsible AI and workforce governance?

The next phase of AI adoption will separate vendors from partners. Tools are easy to buy. Accountability is not. As AI becomes embedded in workforce decisions, responsibility cannot end at deployment. Thoughtful AI means governance is designed into the work itself, with clear ownership, ongoing oversight, and a commitment to stay involved as systems evolve. It also means being able to explain decisions in plain language and step in when outcomes do not align with intent. Clients should expect more from their partners: proactive risk identification, continuous bias monitoring, and transparency around how models change over time. Workforce AI is not a one-time implementation. It is an operating responsibility that requires long-term engagement. Organizations that choose partners willing to stand behind outcomes will move faster because trust is built in. Those that do not will spend more time managing skepticism, corrections, and downstream risk. That distinction will define who succeeds as AI becomes part of everyday work.