Vishwa Kalmali is Global Head of Data and AI at Imperial Brands, where she leads the organisation’s enterprise-wide data and AI strategy, embedding trusted, responsible, data-driven decision-making at scale.
She brings more than two decades of experience building and scaling data and AI capabilities that deliver sustained commercial value and organisational transformation. Vishwa’s career has been defined by moving data and AI from experimentation into the core of business strategy, execution and everyday decision-making.
With a foundation in statistics and decision science, she combines strong analytical credibility with the ability to influence senior executives and boards. This enables organisations to invest in, govern and scale data-led change with confidence. She has led major data and AI transformation programmes across global consumer goods, retail, and regulated industries, consistently delivering measurable outcomes.
Prior to Imperial Brands, Vishwa spent time at Accenture, where she shaped and delivered large-scale analytics and AI transformations across Europe and globally. Her work generated significant value while establishing analytics operating models, building high-performing teams, and creating sustainable capability through talent development and analytics academies.
She later joined Unilever, where she led the creation of a real-time, cloud-native analytics and AI platform for a global B2B marketplace. Working closely with C-suite leaders, Vishwa scaled advanced machine learning and recommendation capabilities and played a key role in securing funding to spin out the platform as a growth engine.
At Imperial Brands, Vishwa is focused on turning data and AI into a durable competitive advantage, ensuring strong governance, responsible use, and lasting business impact.
As a data and AI leader, which traits and skills do you think matter most, and which of those have been most influential for you in your current position?
“Data and AI leaders must be close and conversant with business and with technology. They must have a great business acumen and have to be laser focused on value, this goes a long way and is beyond technical expertise.
“Also, having the ability to combine strategic influence, commercial judgement, and the ability to execute at scale is important. Data and AI needs leaders who can translate data and AI into enterprise outcomes, shaping executive decision-making, and building sustainable capability.
“The traits that matter most are outcome-led thinking to influence the organisation and maximise the impact from the capabilities we are building. Data and AI must be anchored to clear value cases, embedded into core business processes, and supported by the right platforms, operating models, governance, and talent.
“People leadership is critical, creating high-performing teams, developing future leaders, and fostering a culture where data is trusted and used to drive decisions.
“Perhaps the most influential capability in my leadership has been holding a sweet spot between technology and business, and enterprise influence value-driven and outcome-led thinking. By consistently reframing data and AI as a strategic growth and transformation capability, I have influenced senior leaders and boards to invest in, govern, and scale data-led change. This has enabled impact, delivered value at speed, and helped scale analytics and AI beyond pilots, thus the embedding of data-driven decision-making across organisations.
“My leadership continues to focus on marrying business and data & AI strategy to drive transformation and embed data culture, while building a resilient, future-ready data and AI organisation.”
Reflecting on your career, what is one non-traditional piece of advice (outside of technical skills) you would give to an aspiring data or AI leader aiming for the C-suite?
“Move away from centralisation to a robust governance model that allows democratisation and empowers the users and co-create and co-experiment with the business to drive the data culture. The power of a data and AI leader today is not in having the control but the extent of data and AI enablement, literacy, and value impact.”
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