LoginDecision intelligence: The new currency of IT leadership
As chief digital and technology officer for GSK, Shobie Ramakrishnan is helping one of the worldâs most science-driven companies turn digital transformation into a force for human health and impact. Drawing on her deep experience in both biotech and high-tech companies, Ramakrishnan has led the transformation of GSKâs capabilities in digital, data, and analytics and is playing a pivotal role in establishing a more agile operating model by reimagining work.
In todayâs fast-paced and disruptive environment, expectations on CIOs have never been higher â and the margin for error has never been smaller. In a recent episode of the Tech Whisperers podcast, Ramakrishnan shared her insights on how to capitalize on ITâs rapid evolution and lead change that lasts.
With new tools, data, and capabilities spurring new opportunities to accelerate innovation, CIOs have entered what Ramakrishnan calls a high-friction, high-stakes leadership moment. She argues that the decisions IT leaders make today will determine whether they will be successful tomorrow. With so much hinging on the quality and speed of those decisions, she believes IT leaders must create the conditions for confident, high-velocity decision-making. After the show, we spent time focusing on what could be the new currency of leadership: decision intelligence. What follows is that conversation, edited for length and clarity.
Dan Roberts: In an era where AI is reshaping the fabric of decision-making, how will leaders navigate a world where choices are co-created with intelligent systems?
Shobie Ramakrishnan: Decision-making in the age of AI will be less about control and more about trust â trust in systems that donât just execute, but reason, learn, and challenge assumptions. For decades, decision-making in large organizations has been anchored in deterministic workflows and, largely, human judgment thatâs supported by a lot of analytics. Machines provide the data, and people make the decisions and typically control the process. That dynamic is changing, and as AI evolves from insight engines to reasoning partners, decisions will no longer be static endpoints. Theyâll become iterative, adaptive, and co-created. Human intuition and machine intelligence will operate in fast feedback loops, each learning from the other to refine outcomes.
This shift demands a new leadership mindset, moving from command-and-decide to orchestrate-and-collaborate. Itâs not about surrendering authority; itâs about designing systems where transparency, accountability, and ethical guardrails can enable trust at scale. The opportunity is really profound here to rewire decision-making so itâs not just faster, but fundamentally smarter and more resilient. Leaders who embrace this will unlock competitive advantage, and those who cling to control risk being left behind in a world where decisions are definitely no longer going to be made by humans alone.
In the past, decision-making was heavily analytical, filled with reports and retrospective data. How do you see the shift from analysis paralysis to decision intelligence, using new tools and capabilities to bring clarity and speed instead of friction and noise?
Decision-making has long been data-enabled and human-led. Whatâs emerging with the rise of reasoning models and multimodal AI is the ability to run thousands of forward simulations, in minutes or days sometimes, that can factor in demand shocks, price changes, regulatory shifts, and also using causal reasoning, not just correlation. This opens the door to decisions that are data-led with human experts guiding and shaping outcomes.
In situations I call high-stakes, high-analytics, high-friction use cases or decisions, like sales or supply chain forecasting, or in our industry, decisions around which medicines to progress through the pipeline, there is intrinsic value in making these decisions more precise and making them quicker. The hard part is operationalizing this shift, because it means moving the control point from a human-centered fulcrum to a fluid human-AI collaboration. Thatâs not going to be easy. If changing one personal habit is hard, you can imagine how rewiring decades of organizational muscle memory â especially for teams whose identity has been built around gathering data, developing insights, and mediating decisions â is going to be when multiple functions, complex, conflicting data sets, and enormous consequences collide. The shift will feel even more daunting.
But this is exactly where the opportunity lies. AI can act as an analyst, a researcher, an agent, a coworker who keeps on going. And it can enrich human insights while stripping away human bias. It can process conflicting data at scale, run scenario simulations, and surface patterns that human beings canât see, all without replacing judgment or control in the end. This isnât going to be about removing people; itâs about amplifying that ability to make better calls under pressure.
The final thing I would say is that, historically, in a world of haves and have nots, the advantage has always belonged to the haves, those with more resources and more talent. I think AI is going to disrupt that dynamic. The basis of competition will shift to those who master these human-AI decision ecosystems, and that will separate winners from losers in the next decade plus.
Many organizations still operate in a climate of hesitation, often due to fear of being wrong, unclear accountability, or endless consensus-building. How do you create a culture where people feel empowered and equipped to make decisions quickly and with confidence?
Confident decision-making starts with clarity. I can think of three practical shifts that would be valuable, and I still work hard at practicing them. The first one is to narrow the field so you can move faster, because big decisions often stall because we are juggling too many variables or options at once. Amid a lot of complexity, shrinking the scope and narrowing the focus to essential variables or factors that matter forces both clarity and momentum in decision-making. So focus on the few aspects of the decision that matter most and learn to let go of the rest. In the world we are going into where we will have 10x the volume of ideas at 10x the speed, precision has definite advantage over perfection.
The second tip is about treating risk as a dial and not as a switch. What I mean by that is to recognize that risk isnât binary; itâs a spectrum that leaders need to calibrate and take positions on, based on where you are in your journey, who you are as a company, and what problems youâre tackling at the moment. There are moments to lean into bold bets, and there are moments where restraint actually protects value. The skill is knowing which is which and then being intentional about it. I do truly believe that risk awareness is a leadership advantage, and I believe just as much that risk aversion can become a liability in the long run.
The third tip is around how we build a culture of confident decision-making and make decisions into a team sport. We do this by making ownership very clear but then inviting constructive friction into the process. Iâm a big believer that every decision needs a single, accountable owner, but I donât believe that ownership means isolation or individual empowerment to just go do something. The strongest outcomes come when that person draws on diverse perspectives from experts â and now I would include AI in the experts list thatâs available to people â without collapsing into consensus. Constructive friction sharpens judgment. The art is in making it productive and retaining absolute clarity on who is accountable for the impact of that decision.
Ramakrishanâs perspective reminds us that successful leadership in this era wonât be defined by the amount of data or technology we have access to. Instead, it will be about the quality and speed of the decisions we make, and the trust and purpose behind them. For more valuable insights from her leadership playbook, tune in to the Tech Whisperers.
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