Again for having me at the Women in Tech Summit. As Anna said, I'm Robina Yeo, and I am the chief technology officer at Johnson and Johnson.And it's really great to be with you all here today on this topic around AI in the c suite and driving enterprise innovation. And this is the reality that we are all dealing with today. AI has crossed a threshold, and in many ways, the way leadership team talks about it has also changed. It is no longer a capability conversation, and often, it is a leadership conversation. And very often when I speak with CEOs and executive teams, the question is no longer whether to explore AI.

The question now is how to use AI to change outcomes at enterprise scale without compromising trust. And that question matters because access to technology, as you all can see, is no longer the constraint and what is still hard, however, is turning experimentation into real outcomes and scale. And with that, what I want to discuss today is why some organizations are able to scale AI while others struggle. And one of the things that, we want to talk about also is that across industries, the difference shows up in less in terms of ambition or intent, but much more in terms of the technology choices that each made along the way. And I see three leadership choices that consistently determine whether AI becomes a durable enterprise capability with trust intact. So moving to the next slide. Now, to understand these choices, I thought it's first very important to look at where leadership alignment enables scale or quietly prevents it.

And so across industries, the organization that scale are not the ones that found the most impressive pilot first. They are the ones that got really clear very early on about what they are trying to change and just as importantly, what they are willing to standardize so that success can be repeated. And this is where leadership alignment matters most. So at Johnson and Johnson, we anchor this in technology with purpose, And you can see that reflected in how our CEO, Joaquin Duarte, talks about our approach. And Joaquin Duarte makes it a very simple leadership point. We have the datasets, we have the algorithms and the people, and now what matters is how we deliberately apply technology with the goal of improving human health and strengthening the enterprise behind it.

And the purpose forces a very different conversation and question at the leadership table. Not what did we deploy, but what outcome are we accountable for. And on this slide, the Johnson Johnson framework is intentionally structured in terms of our vision, which is to accelerate business outcomes through data, digital, and technology. Our mission is to profoundly impact health for humanity, and our technology strategy translates into three areas leaders can actually act on. Innovation, reimagine with AI for impact. And that means really focusing on the highest impact processes, not ad hoc AI implementations. People, how do we foster a future ready AI fluent workforce? And this is not just about any tool rollout. It is about a capability shift in how leaders and teams work together, decide together, and partner together. And technology, that's how we strengthen our data and technology foundations.

And this is the the resilient digital core that makes scale repeatable and secure, moving from, very often, we see one offs built to really reusable platforms and patterns. And so for the c suite, these pillars translate into three alignment questions that must be answered clearly. Firstly, where will AI materially change productivity, decision quality, or the business model? Now if we cannot name the value, we will not be able to sustain the investment, and we'll keep relabeling pilots as transformation. Secondly, what governance and culture enable speed without regret? And if trust is treated as a break, organizations slow down. So trust must be designed as an enabler with clear accountability and risk tier approach.

Teams can move faster once they understand their gut rails and teams can move faster with confidence with that. And then thirdly, what data and platform investments are non negotiable? If the foundations are optional, every new use case become a custom built and then scale stays out of reach, and so misalignment in any one of these criteria really creates friction. Now to make this more concrete, let me show you a very short video which, the team will help me play. And what you should notice is not the technology itself, but how it is set up to scale, integrated into real work with clear ownership for results. Video, please.

At Johnson and Johnson, we're uniting science, technology, and determination to advance human health, accelerating drug discovery and development, using AI and machine learning, Advancing medical device prototyping with AI and three d computer simulation. Using tech innovation to create more effective and diverse clinical trials. Transforming operating room experiences through robotics and connected digital solutions. Empowering health professionals with data driven insights so they can focus on what matters most, patients.

Thank you team for playing the video on my behalf. And what you saw was not just about a single tool, It's about putting patient at the center in terms of what we do. And it's about what happens when leadership alignment turns intent into operating capability. And this brings us to where AI actually creates enterprise value and where leadership choices start to show up in real work. Now, on this slide, I ought to sort of make it tangible for the team. This slide shows five areas where we see AI creating enterprise value in J and J. Now, the first value pool is in accelerating drug discovery.

AI can help identify targets, optimize molecules, move faster from hypothesis to candidate, and the leadership point is not simply speed. It is the decision quality. Now when AI shortens the cycle from question to evidence, our best scientists spend more of the time on high value judgment and less time on manual search and iteration. Secondly, advancing our clinical development. We use AI to enhance clinical trials by improving site selection, speeding enrollment, and creating increasing diversity of our trial candidates. Now, this is one of the clearest places where data and trust have to come together. We are working with sensitive data in a highly regulated environment, but we still need momentum. And hence, to scale, we need to treat privacy, security, and governance as enablers of speed and not barriers to it. Third, improve access and patient outcomes.

And this is where AI enable analytics, personalized information, and also support better decisions for clinicians, care teams, and patients at scale. Now even outside healthcare, this pattern holds timely, relevant insight changes experiences and outcomes. And fourth, enhance training and analysis. Generative AI, particularly when paired with simulation technologies, accelerate learning, and this increase information retention and help people build confidence faster. Now in any industry, this is about compressing the time it takes to become proficient and making expertise more accessible across the workforce. And fifth, internal process enhancements. And this is where automation and gen AI can materially improve productivity, cycle time, and decision quality across functions from finance to supply chain to customer operations. And it's often the fastest path to measurable back value, but only if we look at adoption, workflow integration, and outcome measurement as a part of the design.

So here are the leadership takeaways that applies regardless of the industry. Number one, pick the few value pools that matter most. Now if everything is a priority, nothing is going to scale. Number two, build repeatable patterns and platforms. Scale comes from reuse, not reinvention, not duplication of efforts. And thirdly, measure outcomes responsibly. Enterprise leaders win when results show up in performance, in quality, in speed, and experience. And this, along with governance, security, and ethics designed from the start. And so if you're aligned on these three, the value pools stop being isolated projects but start to become an enterprise capability. So the question becomes, what consistently separates organizations that scale from those that stall? And this is where leadership discipline shows up over time.

Now, this slide is intentionally simple because scaling AI is not about a single breakthrough. It is about a disciplined leadership over time. An organization stalls when they fund too many ideas and avoid making trade offs. And hence, it is important to first prioritize value. At Johnson and Johnson, AI enhances decision making in areas that struck discovery, clinical trials, as I mentioned earlier on, and our goal is to ensure that we're getting patient centric therapies to people faster. And it's important to be really explicit about the value and also willing to stop work when that doesn't deliver it. Secondly, invest in shared foundations such as data and platforms. Now, increasing investments in data utilization and management are what make innovation repeatable. If data is fragmented, access is going to be inconsistent and platforms are not standardized, every use case becomes a custom built And that drives up cost, slows delivery down and really makes risk harder to manage.

And the organizations that scale treat data and platforms as shared enterprise infrastructure asset, not project by project decisions. And thirdly, establish governance that enables speed. And this is where many organizations hesitate because governance is often associated with delay. But the reality is good governance is not bureaucracy, it is clarity. It is who owns the outcomes, who controls match what controls matches the risk, and then how decisions get made quickly and transparently. Without it, you may move in too slowly because you are uncertain or too fast or create avoidable trust issues. Now, if you can do these three things well, you can dramatically increase the odds that AI become an enterprise capability and not a collection of disconnected plots. And then this brings me to responsible deployment because trust is what really makes scale sustainable.

So, as AI becomes embedded into our everyday work, responsible deployment is no longer an option. AI expands the digital footprint of the enterprise and accelerates decision making, and that amplifies both strengths and weaknesses. And that is why trust cannot be treated as just a compliance exercise or checkbox. On this slide, you see four anchors that determine whether AI can truly scale. A foundation of trust and integrity, a clear commitment to ethical standards, proactive risk management, and strategic investments in cybersecurity. Practically, that means four things leaders need to insist on. First, ethical data handling that aligns with your values and stakeholder expectation because trust is earned long before a model ever goes into production. Secondly, strong data security with continuous monitoring.

The cost of a trust event is far higher than the cost of prevention, especially as AI becomes more embedded into our critical workflows. And thirdly, governance that sets clear accountability for outcomes, applies controls that match the risk tier, and evaluates value and risk together so that leaders can move quickly without guessing. And fourth, sustained investment in cybersecurity infrastructure that protects data integrity and keeps the enterprise resilient as AI scales across the environment. Responsible AI is not a break on innovation. It is what earns the right to scale. And when leaders are clear about this ownership, risk posture, and acceptable trade offs, uncertainty is reduced for the organization. And this becomes even more important as AI systems grow more autonomous and more deeply embedded into our workflows, and, hence, leadership has to shift from approving individual use cases to really shaping the conditions under which AI can operate responsibly. And that brings us to this next slide. Because scaling responsibly does not come from policies alone, it requires a specific set of behavior.

And the leadership behaviors that really enable scale are simple but not easy, especially when organization is moving fast and technology is moving even faster. First, creating the conditions for responsible speed. Clear decision rights matter more than just detailed rules. Secondly, lead with curiosity. The quality of questions leadership asks often matters more than the answers. Third, build an AI fluent culture. Not everyone in the company is going to be an expert, but one where AI becomes a normal governed part of how work gets done. Then set an ambition that's outcome driven. And if the ambition is vague, then the portfolio becomes chaotic and you cannot measure progress honestly. And next, aligned on strategic scalable bets and be willing to say no.

Drive value by prioritizing use case that enhances outcomes and stop funding scattered experimentation that cannot be repeated. And in addition, make trade dots openly because risk, ethics, security, and value are not separate conversations anymore. The best leaders surface the trade offs early, decide on them transparently, and also adjust as we learn. And finally, pair bold experimentation with disciplined measurement. Learn fast, but do not confuse activity with progress. And so if you want a practical way to measure and to pressure tasks where an AI effort is ready to scale, consider these three questions. Number one, where exactly will AI create enterprise value and how will we measure it? Number two, what data, platform, and talent prerequisites must be true for this to scale?

And thirdly, what is our risk posture and who is accountable for the outcomes? So with that, I just want to thank you all for spending this time with me and let me close by bringing it back to the one point that underpins everything we discussed. AI at scale is a leadership choice. It requires clarity on value, investment in shared foundations, data and platforms, and governance that enable speed without regret. And for me, the reason that this matters is purpose. Purpose is not separate from governance or outcomes. It is the reason we take trust seriously and the reason we insist on measurable impact. When your work ultimately touches people's lives, you build differently. You make different trade offs. You invest in foundations in people development, and you insist on responsible deployment. And the lesson translates beyond health care.

In every industry, AI will amplify what is already true about your enterprise, your values, your culture, and your discipline. And leaders set the tone. So my encouragement is simple. Choose the few outcomes that matter, build the foundations to scale, and develop your teams along the way and earn trust as you go. And that is how AI becomes sustained enterprise value. Thank you. And thank you again for having me, and you have information sessions ahead over the next few days. And I hope you'll leave with practical ideas, new connections, and the confidence to scale what works. Thank you. And Anna, back to you.

Thank you so much, Rowena. It was a fantastic, super insightful. I'm just going to share a few comments. People are thanking you, asking if this presentation going to be recorded. They absolutely loved it. And I really loved so much the last thing that you shared that AI amplifies what you already have in your enterprise and that we all need to be driven by purpose that, especially in times of AI. I really love that, and a lot of people are thanking you. We have two minutes for questions. So here is one question. What is one lesson you learned the hard way while implementing AI at scale?

Yeah. It's a very good question, and thanks, Anna. And thank you all for, the, vote of confidence, and it's really great to be able to be here. Now what lesson I think when we first started, from our company perspective, having clear governance and responsible AI usage is nonnegotiable. So we started from there. But I think when we all started, I think a big part of us get so excited with the technology and we started to sprout, obviously, a thousand flowers. And that's where we started to realize that, hey. We've got to really focus on what really matters, what problems are we trying to solve for the patients, and that's why the big lesson for us is focus, focus, focus on the value, focus and focus on the what the reasons why and the outcome that really matters. So, and it's been, like, a few years into this journey, and it's something that we constantly learn.