How Data, AI & Human-focused Leadership Drive Real Business Value

AI has disrupted the business world. While every company is caught in the race to adopt AI, the devil lies in aligning all stakeholders. This is where visionary leadership come into play.

Robin Sutara believes that in order to make an AI project actually go big instead of just dying in a test phase, leaders must act as a bridge between business, data, technology, and execution.

Robin is a global data and AI leader with over 25 years of experience. She helps organizations modernize data foundations, strengthen governance, and drive measurable outcomes. With leadership roles spanning product engineering, sales, and operations, she champions people-first transformation, responsible AI, and inclusive, data-driven cultures worldwide.

In an impactful interaction with Global Woman Leader Magazine, Robin shares her views on how effective leadership shapes the value of data and AI by blending empathy, strategy, and execution—driving practical innovation, responsible governance, inclusive design, and people-centric transformation that turns technology investments into meaningful, measurable business impact.

To learn more about her insights on leadership, ethics, and AI-driven transformation, read the full interview below.

As data and AI redefine industries globally, how do you see leaders influencing the way organizations unlock value from these technologies?

When companies move past the AI hype and actually start looking for value, leaders are often the ones who keep things real – focusing on people, purpose, and actual results. They are good at connecting big technical goals with what’s practical day-to-day. They don’t just ask if a model is right, but if people can understand it, use it, and if it helps the company's bigger mission.

In my experience, individuals frequently act as the bridge between business, data, technology, and execution, which is how AI projects actually go big instead of just dying in a test phase. That mix of curiosity, practicality, and empathy changes what they choose to solve and how fast they see real impact.

In industries adopting AI at scale, what shifts have you observed in how leaders drive innovation and strategic decisions differently than traditional approaches?

When leaders are in charge of AI transformation, I see a definite move away from massive, sudden launches to a steady stream of experiments tied to solving actual business problems.

Instead of obsessing over the algorithm first, they start with the goal: what needs to be fixed, what risk can be cut, or what experience needs a reboot. This approach leads to more mixed-discipline teams, clearer ways to measure success, and a willingness to simply stop projects that aren’t working. It also helps bring in voices that have been overlooked, which is huge when you’re talking about fairness, access, and trust in AI systems that will affect millions.

How do you see leaders shaping the integration of people, process, and technology to turn data into actionable insights that transform businesses?

Every successful data and AI program I’ve seen shares one trait: someone is totally focused on the less glamorous work of culture, process, and getting people onboard. That person is often a critical champion for change. They are more likely to check if the teams on the front lines get why data matters, if leaders are actually using data to make decisions, and if the processes in place let people use insights exactly when they need them.

In my CDO roles, making sure governance, change management, and platform strategy all worked together was key to moving from a PowerPoint idea to real execution. When leaders champion this whole-system view, they become the designers of transformations that stick around, no matter what the next tech trend is.

With governance, privacy, and ethics becoming critical, how can leaders redefine the way organizations innovate responsibly with data?

Governance isn't something you tack on at the end; it's the foundation.

Leaders are perfectly positioned to change how we see governance, privacy, and ethics – turning them from annoying “compliance taxes” into a “license to innovate.”

In my experience, the most successful organizations are focused on building transparency, history, and explainability right into the data products.

That way, we can confidently face a regulator, a customer, or an employee and explain what we did and why. When leaders push for that clarity, they build the trust that lets organizations push the limits of AI while staying compliant with new rules and meeting public expectations.

From your experience, how are leaders influencing the creation of next-generation products, services, or business models through data-driven strategies?

Not all of us took a standard route into data. So, we're used to anticipating things and connecting ideas that seem unrelated. That’s how many approach new products and business models: they mix operations data, customer feedback, and industry smarts to challenge the whole idea of "this is how we’ve always done it."

At Databricks and before that at Microsoft, I’ve watched individuals lead projects that take years of forgotten data and turn it into cool new AI services or efficiency gains that actually free up money for other innovation. They also tend to ask who benefits and who might be left behind, resulting in offerings that are more inclusive and more robust over time.

LAST WORD: Advice for Leaders Aiming to Shape the Global Data & AI Landscape

First, you do not need a traditional route into data or AI; my own journey went from repairing Apache helicopters to the C-suite, and that difference is strength, not a weakness. Invest in your fluency across three dimensions: technology, business, and people. Understand the capabilities and limits of AI, learn how your organization actually makes money, and get very good at storytelling and stakeholder management.

Second, be unapologetically bold in your perspective. Sit at the tables that make decisions about data strategy, regulation, and ecosystem partnerships, and ask the hard questions others avoid. Finally, send the elevator back down – mentor, sponsor, and help redesign systems so the next generation does not have to fight the same battles you did.