Bhagyesh has made an impact shortly after being promoted to this role with direction, ambition, and a highly precise understanding of data and AI objectives, deftly highlighting why he has been selected as a prominent data and AI leader. Starbucks has been a member of DataIQ since 2024, with the EMEA branch of the organization being a member since 2022. Starbucks are regular attendees to DataIQ events and discussion opportunities to connect with other data and AI leaders and take a prominent role in the most pressing industry conversations.
With over 18 years of experience in data and analytics, I focus on driving consumer engagement, revenue growth, and profitability. Through research, statistics, experimentation, artificial intelligence, and machine learning model development, my teams have transformed the way in which marketing and operations are seen globally through end-to-end processes. My experience and expertise touch on supply chain management, consumer and marketing, pricing and revenue management, personalization, and omnichannel retail strategy and operations.
I have a proven track record of creating value by leading organizations through meaningful data-driven and digital transformation, as well as extensive experience and cross domain knowledge. Throughout my career, I have demonstrated success in building high-performing teams ground up of data scientists, data engineers, data stewards, and analysts.
I consider myself an initiative-taking achiever with solid people skills and an aptitude for developing sincere collaborative relationships with a customer-service mindset.
There is a strong foundation for analytics and AI adoption that has been established at Starbucks. Our initiatives go beyond strengthening our technological prowess; they emphasize tangible value creation and foster enduring relationships with consumers, empowering informed decision-making, enhancing communication, and enriching experiences.
Since our AI-driven strategies showcase the potential of innovation, it creates a natural desire and interest from the organization that helps with adoption. We then layer a test and learn approach to mitigate risk and deploy innovation that can help accelerate adoption of data & AI capabilities.
I believe that while there will be nuances, in the future there will be a higher emphasis on value delivery measured through ROI and efficiency improvements. Changing operating models will need more flexible and extremely agile data management and development processes to be enabled by data leaders. Overall, the evolution will be in data leaders being able to tactfully balance a portfolio of projects that deliver in-year value while building for the future.