Driving Multi-Billion-Dollar Digital Transformations Across Asia
Nilesh Sharma
Chief Data & AI Officer (CDAO)
Sotheby’s International Realty, Dubai
Driving Multi-Billion-Dollar Digital Transformations Across Asia
Nilesh Sharma
Chief Data & AI Officer (CDAO)
Sotheby’s International Realty, Dubai
Nilesh Sharma: Driving Multi-Billion-Dollar Digital Transformations Across Asia
Digital transformation fails when ambition outpaces structure. Nilesh Sharma ensures structure comes first. A digital transformation specialist with over 15 years of experience across Asia and the Middle East, he works at the intersection of strategy, enterprise data platforms, and AI at scale, connecting vision with execution in complex, multi-country organisations.
As Vice President – Head of Data, AI & IT Ops at Central Retail Digital, Nilesh operates at the centre of enterprise complexity, leading transformation across diverse retail segments including grocery, fashion, and property. His experience spans retail, omni-channel commerce, proptech, supply chain, marketing, and customer intelligence, yet the constant throughout his journey has been building strong enterprise data and AI foundations.
His philosophy is disciplined and outcome-orientated: before scaling AI, build trust in data. Before accelerating innovation, define governance. Before investing in tools, align leadership around shared business metrics. For Nilesh, transformation is deploying technology while aligning strategy, architecture, governance, and people to create measurable value.
Blending technical depth with executive clarity, he ensures digital initiatives drive revenue growth, productivity gains, and faster decision-making. Speaking with TradeFlock, he shares insights from a journey dedicated to turning digital ambition into sustained competitive advantage.
What milestone or defining achievement stands out in your career journey?
Absolutely. Two major digital transformations in my career stand out, together delivering nearly $5 billion in profit impact. One, in particular, was complex. We were aligning over 25 business units across different segments and geographies under a single corporate umbrella.
Managing that scale was both challenging and rewarding. Technology was important, of course, but the real driver of success was collaboration—getting stakeholders, peers, and CXOs aligned. It was a powerful reminder that leadership often matters more than tools in driving transformation.
What structural or organisational issues most commonly slow down digital transformation efforts?
One of the biggest mistakes organisations make is assuming a single transformation roadmap fits all business units. In reality, each segment has unique needs. For example, food businesses rely on demand-supply optimisation and quick replenishment, while fashion focuses on trend cycles, and property emphasises footfall and rental yield.
A tailored roadmap for each segment is essential, but it must align with an enterprise-wide foundation. Without this balance, transformation loses relevance, momentum, and ultimately, impact.
What key priorities should a CDO set when joining a large enterprise?
There are three critical foundations a CDO must establish from day one. First, a single execution spine, aligning business priorities, data, architecture, and delivery squads while identifying gaps. Second, clear operating boundaries, defining what’s centralised, what’s decentralised, and how funding flows to eliminate ambiguity and speed up collaboration. Third, a strong value narrative, translating technical progress into outcomes that matter: faster go-to-market, lower costs, and higher customer lifetime value.
Why do many enterprises struggle to realise ROI as generative AI moves into production, and how can boards ensure digital transformation lasts?
Generative AI often falters because it’s a systems problem and not a model problem. Scaling effectively requires three pillars: governance with clear data policies and human-in-the-loop accountability, integrated architecture within enterprise data systems, and an operating model with KPIs tied to productivity, revenue, or risk reduction.
We built customer-facing assistants to shorten purchase loops and internal analytics assistants to give CXOs real-time insights. ROI emerges only when GenAI is treated as enterprise infrastructure.
For long-term transformation, boards must focus on changing how the organisation operates: treat data and AI as strategic assets, align incentives for cross-enterprise outcomes, measure real business impact, and embed transformation into daily operations.
What’s your approach to influencing stakeholders, particularly at the CXO level?
Influence depends on the audience. With my team, it’s straightforward, but persuading peers or CXOs requires a different approach. I rely on rational persuasion, presenting data, benchmarking, and case studies showing how leading global retailers solved similar challenges. External validation from firms like Bain, Accenture, or Deloitte also helps build confidence. At the CXO level, it’s above just innovation. It’s about credibility, clarity, and reducing risk. Evidence and structured reasoning are the keys to alignment and buy-in.
Can you share a particularly challenging moment from one of your digital transformations?
Yes, during a recent retail transformation, the CEO asked a critical question: “Where do we begin?” Each segment had different needs, but he wanted a unified starting point.
We realised that while businesses differ, all share core health metrics, including KPIs like average order value, customer churn, net revenue, daily orders, and NPS. These 10–12 KPIs became the backbone of our approach.
From there, we built unified data and AI foundations with strong governance, consistent KPI definitions, and enterprise-wide standards. Without this, AI cannot deliver reliable, trustworthy insights.
Do digital transformations fail more from communication gaps than technical issues?
Absolutely. Studies show 70–80% of digital transformations fail due to collaboration gaps, not technology. Many business units have independent data and AI teams, creating alignment challenges. We addressed this by forming a Digital Transformation Committee, holding weekly meetings, and creating a centralised technical blueprint repository. This enabled skill sharing, model reuse, faster execution, and cross-unit collaboration. True transformation succeeds only when knowledge flows horizontally, not just top-down.
