How Tesco is Redefining Global Operations with AI and Analytics

Tesco's global analytics team is working tirelessly to solve complex business problems across multiple markets. By leveraging advanced AI and machine learning techniques, the team is delivering innovative solutions that drive growth and profitability. Raghavan provides insights into how the company is leveraging advanced analytics to drive innovation, improve operational efficiency, and enhance customer experiences across its global operations. Excerpts: 

How Tesco is Redefining Global Operations with AI and Analytics

What is your team’s role in Tesco’s analytics journey?

My team is the global capability house for analytics and AI for Tesco Group. Tesco is one of the world’s largest retailers; therefore, the playfield for my team is excitingly huge. Our work ranges from intelligent dashboards to cutting-edge AI solutions. We are a global team with presence in 6 countries, with the largest capability hub in Bangalore. We see our role as building awareness, demonstrating value, and scaling the culture of data-led decision-making across our group.

How has Tesco cultivated a data-driven mindset within its operations?

Tesco Business Solutions (TBS) is our global, purpose-driven solutions-focused division, enabling the business to thrive through strategic solutions in finance, customer experience, product development, property management, and advanced analytics and AI. Our analytics and AI solutions are designed to solve complex business problems within our market and strengthen the analytical expertise of TBS teams.

This helps us integrate into our operations across markets and functions. A large part of our analytics and AI teams are based out of Bengaluru, India. They build amazing solutions for our core retail business as well as the Global Business teams based out of India.

Over the last 3 years, we have picked up large problems across personalisation, AI-led pricing, online, and cost-price optimisation for all the countries in our group. Thereby, we impact our business. We deliver significant commercial impact on sales and profit through these programs of work. In addition, we use advanced AI to help simplify our business operations, such as cash and working capital management, product lifecycle management, and HR and payroll operations. A large part of these operations use GenAI for automation and insight generation.

What role does leadership play in driving data initiatives for Tesco?

Tesco has been a pioneer in analytics and AI for many years, and the executive leadership provides unwavering support to AI acceleration across the organisation.

This is an instrumental part of our exciting journey, as the single biggest driver for an organisation’s success with analytics and AI transformations is how the top leadership team treats it—with respect and seriousness or as a good-to-have agenda. In the former scenario, the fact that the executive leadership believes and invests their time in analytics transformations not only helps the program itself but also sends a clear signal to the rest of the organisation to take the topic with utmost seriousness, acting as an accelerator to the journey. 

How do you build organisational awareness for AI?

Analytics is a fast-evolving field, and for an observer, it might look like a maddening zone of new and shiny possibilities. The seasoned practitioners, however, know that the developments are unfolding within certain predefined boundaries, and many of them were defined decades ago, including the concept of neural networks, which is the brain behind the Gen AI revolution. In summary, the broader analytics includes the spectrum of problem solving, which is descriptive (what and why), predictive (what will), experimentative (what options), and generative.

Therefore, the key question that we keep asking ourselves is, “Does the larger organisation understand this spectrum, and can they think of possibilities without needing an expert in the room?”

How does your analytics strategy address the unique challenges of Tesco’s diverse retail market?

Our analytics strategy has evolved over the last many years to become closely integrated with the business priorities and plans. The key is to strike the right balance between solving specific problems in a business area or country and, at the same time, building strategic assets that can be applied as the standard solution across all the countries. 

Every year, we work with the CEOs and senior leaders of the countries to arrive at a set of priorities, each of them with a unique problem and a size of prize. Some of these priorities tend to be common for all countries (such as improved sales forecasting), and some tend to be unique to the business and the country of operation.

Our organisation is set up to create the long-term work plans to deliver both the horizontal priorities and the business-specific priorities. We have strong governance with the Execs to ensure we look at the priority programs on an ongoing basis and provide the necessary course corrections.

What is the one big watch-out for any organisation trying to scale their analytics & AI journey?

I believe the single largest mistake that organisations need to avoid is putting the solutions before the problems. The greatest forecasting solution, the most sophisticated pricing models, the most accurate customer personalisation models—we have seen them all fail because the coolness of the solution precedes or overtakes the real problem to be solved.

The real need is for the organisation to understand and believe in the power of analytics and AI and not to be floored by the glamour of the algorithms used. Therefore, it is a good habit to keep asking a simple question: are we focusing on solving business problems or are we excited by new and cool analytical possibilities? 

It is important for organisations to understand that it is easy to set up an analytics team and get the funding to run it, but the real victory is in driving a palpable commercial and cultural transformation. If this is underappreciated, what is lost is the most precious asset—time. Without the conviction, there is back and forth, there is belief and doubt, and there is clarity and indecision on the future of analytics in the organisation. All resulting in an organisational drag force.

Our analytics strategy has evolved over the last many years to become closely integrated with the business priorities and plans. The key is to strike the right balance between solving specific problems in a business area or country and, at the same time, building strategic assets that can be applied as the standard solution across all the countries. 

Every year, we work with the CEOs and senior leaders of the countries to arrive at a set of priorities, each of them with a unique problem and a size of prize. Some of these priorities tend to be common for all countries (such as improved sales forecasting), and some tend to be unique to the business and the country of operation.

Our organisation is set up to create the long-term work plans to deliver both the horizontal priorities and the business-specific priorities. We have strong governance with the Execs to ensure we look at the priority programs on an ongoing basis and provide the necessary course corrections.

How are you leveraging AI and data to innovate in India’s retail sector?

Indian retail provides unique opportunities and challenges. The Indian retail landscape is transforming at an exhilarating pace with changing consumption behaviours and the rapid adoption of digital channels. This presents an exciting opportunity for analytics and AI practitioners to understand, anticipate, and recommend actions across the business across customers, operations, and the supply chain. However, there are two key challenges that need to be overcome.

First is the lack of rich and structured data to analyse the business across customers, products, and operations. Second is the lack of awareness of possibilities with analytics and AI, and in many places these possibilities are limited to ‘lookback’ use cases such as reporting and dashboarding. Overcoming these challenges requires a strong partnership mindset, where we work closely with the decision-makers in our business to take them on a long-term AI maturity journey that includes problem solving as well as improving the data and AI skills of business teams.