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  • Claire Williams, Vice President, Analytics and AI, Retail and Consumer Product, Capgemini Invent

Claire Williams, Vice President, Analytics and AI, Retail and Consumer Product, Capgemini Invent

Describe your career to date


From the age of 14 all I ever wanted was a career in computers and business, travelling the world with my laptop. A strange ambition for a 14-year-old, but one I am very proud to have made happen! Starting my career in a small software house (as the only woman) as an Analyst Programmer, quickly moving into client facing roles in the regeneration sector, it provided a grounding that gave me the appreciation and love of data, and a thirst to tell stories and change lives with data. Having worked the checkouts and customer service at my local Sainsbury’s in my teens – an early love for the dynamic world of retail – the opportunity arose to progress my career at Sainsbury’s, so I leapt at the chance to help with their MI Transformation. These elements gave me a strong grounding and thirst to work with more Retail and Consumer Product companies, and so I began my Management Consulting career first over a decade at IBM, and most recently at Capgemini Invent leading our Retail and CPG Analytics and artificial intelligence (AI) work: the dream role for me. Not forgetting that 14 year old’s dream and the experience of being the only woman in the room, I have the huge fortune to work with Women in Data, co-leading Girls in Data, a newly formed charity on a mission to inspire Girls to follow a career in data by enabling the 30,000+ Women in Data members to share their stories and be role models to Girls and arm them with the skills to follow their future career. Another dream role! 

What challenges do you see for data in the year ahead that will have an impact on your clients and on the industry as a whole?  


There is always a balance in data between getting carried away with the power of innovative technology and getting to value delivered. There is no doubt that generative AI is a truly generational transformative evolution, however, getting to value delivered at scale is the hard part. Focusing on value and the human impact must always be the starting point, from making sure that teams are diverse (to remove bias), to being driven by data validating the value in all design decisions. Sometimes the simplest of solutions are the right ones to get to long-lasting value. 

How are you developing the data literacy of a) your own organisation and b) your clients?


For our teams, we have the perfect combination of analytical entrepreneurship with a razor-sharp focus on getting to value delivered by data. I encourage all our teams to be curious, constantly ask themselves the so what questions and to work with others that are different from them in solving problems so that they can together challenge conclusions without bias. Our teams are hugely inclusive, our Women and Allies in the Analytics and AI team are a trailblazing example of that. Again, it is about challenging what the value is, and the so what aspect, being able to understand whose life will be made different by transformation underpinned by Analytics and AI rather than getting over excited about the technology and mechanisms to get there. If you cannot answer that question, and do not have a diverse team represented to get to grips with the challenges, then no amazing algorithm or machine-learning model is going to drive any impact. 

How are you preparing your organisation and your clients for AI adoption and change management? 


Far too many AI initiatives stall at POC, then it gets hard! There are a few common reasons for this; firstly, the idea was a bad one in the first place, listening to the loudest person in the room, rather than following a data driven approach to identifying value (and feasibility); secondly, teams get carried away with the technological possibilities and forget to get input and buy-in from the humans (employees, customers, and consumers) to find out if there is a problem to solve in the first place, and finally, teams focus on the wrong measures of success – time, quality, and cost – rather than the value and human impact delivered. By being pragmatic and investing time to really validate hypotheses and gut feel before jumping into lines of code, not being afraid to innovate, by bringing everyone together in a room with diverse, real life perspectives, and by not being averse to throw away more ideas than you take forward because value can be realised quickly – adoption is much more likely and value and trust in AI soon follows, and it is not so hard! 

Claire Williams
has been included in:
  • 100 Enablers 2024 (EMEA)