Many companies layer AI onto old processes without redesigning workflows or updating governance, which limits returns and puts customer trust at risk.
Paramita Bhattacharya, Limited Partner at Stage 2 Capital, says companies that see real value rebuild workflows, embed accountability, and lead cultural change from the top.
She calls for AI-native operating models that pair continuous governance with hands-on workforce learning to scale AI responsibly and sustainably.
At the World Economic Forum, AI dominated the conversation in ways that had little to do with product roadmaps and capabilities. Instead, the focus centered on trust: how to scale AI responsibly when systems touch unprecedented levels of personal data, automate high-stakes decisions, and demand new governance models. Companies capturing real value are not just deploying tools but redesigning workflows with accountability built in, transforming workforce development, and establishing guardrails that evolve as risks emerge. The constraint is not technological readiness but organizational commitment to rebuild systems with trust and governance as foundations.
Paramita Bhattacharya is a three-time Chief Marketing Officer and transformational go-to-market leader with deep experience at the intersection of technological disruption and business evolution. Her career spans Fortune 500 giants like Adobe, Hitachi, and Nokia, as well as high-growth startups. Recognized as a Top 50 Women Leader in SaaS, she is a member of both the Forbes Communications Council and Harvard Business Review Advisory Council. Bhattacharya currently serves as a Limited Partner at Stage 2 Capital, where she advises B2B and AI-native companies on achieving sustainable growth. For her, the key to unlocking AI's potential lies in operational transformation, not technological capability.
The dividing line between companies capturing returns and those still experimenting is stark. "The companies that are seeing the benefit of AI are the ones who have redesigned their workflows. They're not putting AI on top of an existing workflow, they're rethinking the entire workflow," says Bhattacharya. Companies realizing a true return on investment have made a commitment to fundamental change and redesigned their operations around its capabilities, creating a new AI-native operating model.
Nowhere is this more apparent than in the world of personalized marketing. While marketers have used data for years to tailor experiences, AI unlocks a new level of intimacy. As Bhattacharya notes, AI creates a feedback loop that can deliver experiences at a "one-on-one capacity." That power, however, demands a new level of ethical oversight. The mandate for leaders is clear: anticipate risks, establish governance, and continuously monitor those guardrails. Misjudge that balance, and you risk eroding the very customer trust you’re trying to build. Yet not all organizations can move at the same pace.
The long game: Such an operational overhaul poses unique challenges, contributing to slower adoption in heavily regulated industries. For sectors like healthcare and finance, the path is longer, but the payoff is substantial. "Every company, depending on the size and industry, has a different propensity for change," Bhattacharya explains. "Some have changed faster and been able to redesign. Others will probably take a little time because they have friction elements that don't allow them to do this as quickly." The companies realizing tangible benefits are often those that commit to completely rethinking their systems and architecture, especially as the industry moves toward a more sophisticated agentic AI layer.
But this transformation isn't just about technology, it's about people. Success hinges on leaders who empower teams to become builders rather than passive users of AI tools. This means moving beyond consuming AI outputs to actively experimenting with prompts, integrating tools into workflows, and sharing discoveries across teams. The shift requires leaders to model behavior and orchestrate continuous learning rather than enforce rigid processes.
Show your work: "This responsibility ultimately sits with the leader. If you complete a vibe coding project, show your team. If you discover a method for writing prompts that delivers results, share it," says Bhattacharya. "Teams should be building shared knowledge bases to capture these insights." Bhattacharya points to hands-on, practical methods like internal "prompt-a-thons" designed to elevate writing skills and lunch-and-learns where employees demonstrate their work on emerging concepts like vibe coding.
Orchestrate the organic: A new culture of discovery is needed, which is redefining the role of the modern leader. "The old models of structured learning are no longer sufficient. AI is moving so fast that you must foster organic learning within the company. The leader's role is to bring people together, create waves of this learning, and orchestrate the entire process," explains Bhattacharya.
Unlocking AI's value is less about the tools organizations buy and more about their willingness to fundamentally rethink how work gets done. As AI capabilities expand and new use cases emerge, governance frameworks must evolve in parallel. "We also have to relook at governance of AI differently as we see more outcomes, more use cases," says Bhattacharya. "You have to continue to monitor that. You have to continue to build on that trust with your customers."
