In a world racing toward AI-driven transformation, the balance between innovation and responsible governance has never been more critical. This tension between progress and control sits at the heart of every data leader’s mission — shaping how organizations innovate, scale, and build trust in emerging technologies.

The first part of this two-part series explored the professional journeys and leadership philosophies of Michelle Boston, CIO, Head of Data Management Technology & Enterprise Architecture at Bank of America, and Dr. Adita Karkera, Chief Data Officer, Government and Public Services at Deloitte.

In this concluding installment, moderated by Informatica’s Amy Horowitz, the two leaders delve deeper into how to sustain that balance — from fostering enterprise-wide AI literacy to preparing for the next wave of data-driven opportunity.

Guardrails for responsible AI innovation

As AI capabilities evolve, maintaining trust, transparency, and compliance remains paramount — especially in regulated industries. “We are a financial services institution, so the bar is high,” says Boston. “We build and deliver innovative capabilities like AI with responsible growth in mind. Our mature risk management framework includes considerations like intellectual property, information security, and GenAI-specific risks such as hallucinations. We understand those risks and build strong controls to mitigate them.”

Boston adds that the framework enables the organization to “innovate responsibly” while embedding safeguards such as monitoring tools and, crucially, a human-in-the-loop process. “We might take the toil away from somebody, but there’s ultimately a human involved in making the final decisions on what AI delivers.”

Karkera agrees that responsible innovation begins with intentional collaboration. “Innovation roles come with a lot of power, but with every role that comes with power comes a great deal of responsibility and risk,” she notes. “Leaders need to find that delicate balance between power, risk, and deployment.”

She cautions against developing AI use cases in isolation: “Don’t go out on your own tangent and forget to collaborate between the innovation and governance teams. The sooner you can get your risk management frameworks in place, the sooner you’ll have a more robust intake process. Early cross-sector collaboration is essential.”

Building data and AI literacy across the enterprise

Both leaders emphasize that AI readiness begins with data literacy. “I’m a firm believer in constant learning forever,” says Karkera. “Technology is evolving so quickly that we as leaders must stay abreast of changes and deploy the right programs for our teams.”

At Deloitte, she has implemented data and AI fluency programs structured around bite-sized knowledge pieces or “nano learning.” “It’s getting harder for executives to step away for a five-day program, so we’ve embedded shorter modules that can be taken online. We’ve also introduced a badging program to incentivize participation,” she explains.

Karkera stresses that literacy initiatives must be customized. “Executives don’t need the same training as technical staff. Understanding the right personas and building competency-based programs is key.”

At Bank of America, Boston highlights a similar approach through a dedicated internal organization known as the Academy. “It’s solely dedicated to continuous training and reskilling. We have on-demand learning tracks — think 101, 201, 301 — where employees can earn badges and incentives to continuously improve,” she says.

These programs go beyond technology skills. “It’s also about sound data management and data quality controls, because AI and data are two sides of the same coin,” Boston emphasizes.

The Academy complements digital learning with “technology focus groups” and “innovation days” that provide hands-on experience. “We bring people together to apply their learning in sandboxes, experiment safely, and learn from peers. It’s about creating a culture of continuous education and practical application.”

Empowering the next generation of women data leaders

When asked what advice they’d offer to aspiring women in data, both leaders stress confidence and curiosity. “We need more of you,” Karkera says. “If you’re hesitating to apply for a data or AI leadership role, don’t. It’s okay not to check every box — focus on learning and go for it.”

Boston underscores the value of technical depth and storytelling. “Women in data are excellent storytellers — and data is all about telling stories with information. Stay technically deep; it’s not optional. I listen to AI-focused podcasts daily, read the Wall Street Journal, and think globally. Continuous learning and curiosity will differentiate you.”

The AI opportunity

Both leaders see the coming year as a pivotal moment for AI’s evolution — from experimentation to practical impact. Karkera identifies agentic AI as the next frontier. “We’re only seeing the tip of the iceberg. As organizations move from prototypes to production, agentic solutions will unlock new efficiencies.”

Boston envisions the rise of composite AI systems — where multiple specialized agents work together with human oversight. “We’re moving past the hype that AI will replace jobs to a model of human-assisted capability,” she says. “Composite AI will deliver real value when agents collaborate to achieve business outcomes. But that will require curated, high-quality data — and the experts who know how to manage it.”

CDO Magazine appreciates Michelle Boston and Dr. Adita Karkera for sharing their insights with our global community.