Dr. Sebastian Wernicke is a data scientist, strategist, and author of Data Inspired. With a background spanning bioinformatics, strategy consulting, and data science, he has spent much of his career helping organizations turn data into business value.

His work explores a fundamental question: why do some organizations succeed with data while others struggle despite investing heavily in technology? In Data Inspired, Sebastian argues that the biggest barriers to unlocking the potential of data are often not technological, but organizational and cultural.

In this episode, Sebastian and Subbu explore why being data-driven is no longer enough, how organizational culture determines whether data leads to better decisions, and what leaders must understand about the opportunities and limitations of AI.

We explore:

• Sebastian's journey from bioinformatics to strategy consulting and data science
• Why the hardest data problems are often organizational rather than technological
• The difference between data-frustrated, data-driven, and data-inspired organizations
• What a world-class violinist performing in a subway station teaches us about data and context
• Why data is not as objective as we think, and how human interpretation shapes decisions
• Why organizations risk becoming “data-decorated” rather than genuinely data-driven
• Whether evidence can challenge the opinion of the most powerful person in the room
• How data literacy, digital mindset, and culture work together to enable transformation
• What the world's “most wanted song” teaches us about the dangers of managing purely through metrics
• Why Google's famous experiment with 41 shades of blue reveals both the power and limitations of data-driven decision-making
• Why AI's greatest strength—filling in the gaps—can also produce mediocre, standardized results
• What AI can and cannot do when it comes to creativity, judgment, and original thinking
• Why the rise of agentic AI makes human intent, oversight, and judgment even more important

Biju Dominic is Chief Evangelist at Fractal Analytics and co-founder of FinalMile Consulting. For more than three decades, he has pursued a central question: what truly shapes human behaviour? After spending seventeen years in advertising, Biju began combining cognitive neuroscience, behavioural economics, and design to develop new approaches to behaviour change. His new book, MicroStimuli: The New Science of Persuasion, challenges the belief that awareness naturally leads to action—and explores how precisely designed cues, delivered at the right moment, can influence the choices people make.

In this episode, Biju and Subbu explore what really drives human behaviour, why rational thinking may play a smaller role than we imagine, and how microstimuli can reshape advertising, organizational culture, and the development of more human-centred AI.

We explore:

• Why awareness alone rarely changes behaviour
• The incident that led Biju to question rational decision-making
• How neuroscience offers a new lens for understanding human behaviour
• Why traditional advertising and consumer research may no longer be fit for purpose
• Why the brain prefers “exformation”—and what communicating in milliseconds requires
• How emotions and context shape decisions more than we realize
• What microstimuli are and why the 30-second advertisement may be outdated
• How workplace cues and everyday micro-decisions shape organizational culture
• Why culture change requires more than training programmes and PowerPoint presentations
• Why the future of AI must be human-led—and ultimately human-brain-led

Stephen Brozovich is an Executive in Residence—People and Culture—at Amazon Web Services. He joined Amazon in 1999, long before it became the organization we know today, and has spent more than two decades working across technology, product development, talent management, leadership development, and organizational culture. Today, as part of AWS’s Executives in Residence team, Stephen helps senior leaders navigate large-scale transformation—work that, as he points out, often has less to do with technology than with human and organizational patterns.

In this episode, Stephen and Subbu explore how Amazon translates leadership principles into everyday decisions—and what organizations must rethink as agentic AI transforms talent, team structures, governance, and the very nature of work.

We explore:

• Stephen’s unconventional journey from studying music to web development, technology leadership, talent management, and organizational culture
• Why learning and pattern recognition have been the connecting threads across his career
• How mental models, behaviours, and artifacts come together to shape organizational culture
• Why what leaders do matters far more than the values written on a page
• How Amazon’s leadership principles were developed—and how they enable decisions to be made across a rapidly growing organization
• How principles such as “Dive Deep” and “Bias for Action” create productive tension and a common language for constructive debate
• How customer obsession and Amazon’s Working Backwards process turn customer needs into new products and innovations
• Why Amazon reads written narratives during meetings rather than sending them as pre-reads—and how this creates shared context and equalizes voices in the room
• How ownership is reinforced through decision-making authority, compensation, and equity
• What “Strive to be Earth’s Best Employer” means—and why the best leaders are also teachers
• Why replacing junior employees with AI may create a serious judgment and leadership gap six to ten years from now
• How organizations can experiment with new workforce shapes while continuing to build their next generation of senior talent
• Why traditional, siloed operating models are giving way to multidisciplinary, outcome-based teams supported by specialized AI agents
• What Singapore’s AI governance framework teaches us about access, human accountability, technical controls, and transparency
• Why greater autonomy for AI agents requires clearer guardrails—and why “the AI made the decision” can never be an acceptable answer
• Why Stephen remains optimistic that AI will expand what domain experts can build while making distinctly human capabilities even more important