With more than two decades at the intersection of technology, strategy, and large scale transformation, Deepak Chugh brings a rare blend of architectural rigour and pragmatic leadership to the AI conversation. As Principal Consultant for AI, Strategy and Architecture at Pivotpoint Partners, and formerly Director of IT Strategy at Transport for NSW, Deepak has led some of the most complex digital and data driven programmes across public sector, transport, banking, and public safety.
From shaping multi billion dollar technology investment roadmaps to delivering predictive analytics that directly improved road safety outcomes, his work consistently bridges strategy and execution. In this interview, Deepak shares how AI and machine learning have reshaped his approach to problem solving, the practical challenges organisations face when adopting AI at scale, and why trust, discipline, and alignment to long term strategy matter far more than algorithms alone.
How has AI/ML reshaped your approach to solving business problems?
AI has made me think differently about problem solving. Earlier in my career, I relied heavily on structured analysis, intuition, and experience. All valuable, but also limited. With AI, I can now test hypotheses quickly, uncover patterns I would never have spotted, and connect data points that once seemed unrelated.
At Transport for NSW, for example, we combined road, hospital, and emergency data into an AI driven crash analytics platform. Suddenly, we were not just analysing accidents. We were predicting and preventing them. In freight, AI showed us how congestion, scheduling, and even weather patterns were interconnected, helping us act before issues escalated. In public safety, advanced analytics revealed crime trends that were invisible when systems operated in silos.
Beyond large scale programmes, I have also seen AI deliver everyday productivity gains by automating analysis and freeing teams to focus on creative problem solving. Personally, it has made me more curious. Instead of asking what happened, I now ask what might happen and how we prepare for it. That shift from hindsight to foresight has completely reshaped my approach to strategy and leadership.
What key challenges do you face when implementing AI-driven solutions?
The toughest challenges are rarely about the algorithms. They are about integration, process readiness, and discipline.
First, integration. AI pilots often perform well in controlled environments, but embedding them into core systems and legacy platforms is where complexity emerges. Unless solutions are seamlessly integrated, they remain showcases rather than true business enablers.
Second, process maturity. I have seen organisations rush to adopt AI without fixing the fundamentals. If a process is broken, automating it only amplifies inefficiency. My mantra has always been to improve the process, automate it, and only then apply AI where it genuinely adds value.
Third, cost management. AI can be expensive, and not every use case justifies the investment. In some situations, simpler automation is sufficient, and I have often had to guide organisations away from unnecessary over engineering.
Finally, trust is critical. Executives and employees alike need confidence that AI driven insights are reliable, explainable, and actionable. For me, success comes from being pragmatic, balancing ambition with practicality, ensuring AI delivers outcomes without excess, and always keeping the focus on solving the real business problem.
How do you balance human intuition with machine learning insights in decision-making?
I see AI as a sparring partner for decision making. It pushes me to test assumptions, challenge biases, and look at problems from new angles. Machine learning is extremely effective at uncovering patterns across millions of data points, often linking elements I would never have considered related.
However, context, ethics, and judgement still come from people. For example, when I worked on fraud detection systems, AI flagged anomalies at scale, but it was human investigators who determined whether those anomalies represented genuine fraud or simply unusual behaviour. In freight, AI could forecast demand spikes, but human planners balanced those signals against investment priorities and customer impact. In public safety, analytics highlighted hotspots, but leadership decided how to act responsibly.
Personally, I have found that AI sharpens my thinking. It provides evidence I did not previously have, but it does not replace intuition. It also boosts productivity by handling the heavy analytical work, giving me more space to think strategically. The balance is straightforward. Machines provide clarity, but humans provide meaning.
Can you share a successful use case where AI significantly improved outcomes?
Several examples stand out, but one of the most rewarding was the predictive crash analytics programme at Transport for NSW. Traditionally, crash data was analysed months after incidents occurred, by which point it was too late to act. By combining hospital, emergency, and road condition data, AI enabled us to test new hypotheses, identify patterns we had never seen before, and intervene before accidents happened. That was a powerful moment, with technology directly contributing to saving lives.
In freight, AI uncovered connections between scheduling, congestion, and weather patterns, improving planning and reducing delays. In public safety, analytics brought together previously siloed datasets and revealed trends that allowed agencies to prevent issues rather than simply react. In banking, fraud detection systems became proactive instead of reactive, preventing millions in potential losses.
Across all these sectors, the common thread was productivity and impact. AI did not just make existing work faster. It unlocked capabilities that were previously impossible. For me, these outcomes reinforced the true promise of AI, not replacing people, but empowering them to deliver results that genuinely matter.
How do you ensure AI adoption aligns with your company’s long-term goals?
For me, AI only creates lasting value when it is tied directly to strategy. Too often, I have seen organisations run pilots that look impressive in isolation but fail to scale or align with long term objectives. At Transport for NSW, we embedded AI into the IT strategy itself, ensuring it aligned with state policy and governance frameworks. This meant initiatives supported goals such as safer roads and smarter mobility, rather than becoming side projects.
In freight, AI adoption was linked to supply chain resilience, not just operational dashboards. In public safety, analytics aligned with faster response times and stronger community trust. In my advisory work today, I use AI maturity assessments and decision trees to help leaders prioritise where AI adds genuine value and where simpler automation may be sufficient.
Cost management is essential. Not every problem requires AI, and avoiding unnecessary complexity helps build credibility. Personally, I have found executive sponsorship to be critical. When leaders champion AI as part of the organisation’s DNA, it gains the support, resources, and trust needed to scale responsibly and sustainably.
