The corporate C-suite is undergoing a fundamental transformation as generative AI and evolving workplace dynamics reshape executive leadership roles and responsibilities. Business Insider’s third Workforce Innovation virtual roundtable brought together top executives from companies including Boston Consulting Group, IBM, Indeed, Mastercard, Infosys, and JLL to discuss how traditional leadership models are giving way to new approaches that embrace AI-enabled transformation across entire organizations.
Maggie Hulce, Chief Revenue Officer at Indeed, emphasized that AI transformation is no longer confined to specialized teams but has become a companywide expectation. “The reset message here is that no function is exempt,” Hulce stated. “Every function can change and should be changing and needs to take accountability and ownership for driving that, versus waiting for a central team to figure everything out.” This represents a significant shift from the old model where innovation was centralized within specific departments.
The roundtable participants predicted several new C-suite roles emerging to address transformation needs, including chief transformation officer, chief experience officer, chief automation officer, chief impact officer, chief sustainability officer, and chief ESG officer. These roles reflect the growing complexity of managing AI integration, employee experience, and corporate responsibility in the modern enterprise.
Kenon Chen from Clear Capital highlighted how generative AI tools like GitHub Copilot are eliminating traditional translation layers between business leadership and technical execution. This technological shift requires executives to communicate more precisely and clearly, as fewer intermediaries exist between strategic vision and implementation. Chen noted that leaders must now “be your own translator” to ensure stakeholders can take direct action on communicated objectives.
Justina Nixon-Saintil, IBM’s Chief Impact Officer, described how AI is democratizing innovation across organizations. Companies are now asking employees at all levels to identify AI use cases for their specific functions, rather than limiting innovation to software or CTO teams. Even corporate responsibility and ESG departments are deploying AI technology for learning pathways and data analytics.
The shift toward “leading from behind,” as described by Neil Murray of JLL, represents a fundamental change in leadership philosophy. Rather than top-down control of strategy and innovation, modern executives are creating environments where employees closest to problems can innovate and lead. This approach harnesses collective organizational genius while responding to employees’ expectations for meaning, purpose, and participation in shaping company culture.
Alicia Pittman from BCG noted that apprenticeship has become a “two-way street,” with senior leaders learning from younger generations about embracing AI technology, leading inclusive teams, and supporting employee wellness. Organizations with flatter, less formal structures that enable cross-connectivity are positioned to benefit most from this bidirectional learning model.
Key Quotes
The reset message here is that no function is exempt. Every function can change and should be changing and needs to take accountability and ownership for driving that, versus waiting for a central team to figure everything out.
Maggie Hulce, Chief Revenue Officer at Indeed, emphasized that AI-enabled transformation must become a companywide expectation rather than being confined to specialized teams. This represents a fundamental shift in how organizations approach innovation and change management.
When I first saw the GitHub Copilot demo before it was launched, I was struck by the idea of someone with a business use case and business knowledge being able to execute and create a technical solution without going through our typical translation layers.
Kenon Chen, Executive Vice President at Clear Capital, described how generative AI tools are eliminating intermediaries between business vision and technical execution. This requires leaders to communicate more precisely as the traditional layers of business analysts and project managers become less necessary.
When you think about AI, we’re asking employees, no matter where they are across the business, to come up with use cases and ways that AI can make a difference in their function. In the past, this would be the limit of just one entity, one function to come up with the right use cases.
Justina Nixon-Saintil, IBM’s Chief Impact Officer, explained how AI innovation is being democratized across organizations. Even non-technical departments like corporate responsibility are now deploying AI technology independently rather than waiting for IT departments to lead implementation.
One of the biggest shifts is around decision-making — the move from siloed decision-making, where each member of the C-suite has a defined set of decisions they’re accountable for, to a C-suite that acts more as a collective leadership body.
Lucrecia Borgonovo, Mastercard’s Chief Talent Officer, highlighted how AI transformation is breaking down traditional functional boundaries in executive leadership, requiring more cross-functional collaboration and shared accountability at the highest levels of organizations.
Our Take
This roundtable discussion captures a pivotal moment in corporate evolution where AI is the catalyst for reimagining leadership itself. The most striking insight is that successful AI adoption isn’t primarily a technology challenge—it’s an organizational design and cultural transformation challenge. The emergence of roles like chief automation officer and chief experience officer reflects recognition that AI integration touches every aspect of business operations and requires dedicated executive oversight.
The democratization of AI innovation described by these executives represents a significant departure from traditional IT implementation models. When IBM’s corporate responsibility team independently deploys AI for ESG analytics, or when Indeed expects every function to drive its own AI transformation, we’re seeing a fundamental power shift. This distributed innovation model could accelerate AI adoption but also creates new challenges around governance, consistency, and risk management.
The elimination of “translation layers” between business and technology, enabled by tools like GitHub Copilot, may be the most disruptive trend discussed. This suggests that mid-level technical project management roles face significant disruption, while executive communication skills become even more critical in a world with fewer intermediaries to interpret and refine strategic direction.
Why This Matters
This discussion reveals how AI is fundamentally restructuring corporate leadership beyond simply adding technology—it’s changing how decisions are made, how innovation happens, and what skills executives need. The emergence of new C-suite roles like chief automation officer and chief experience officer signals that AI transformation requires dedicated executive attention across multiple dimensions: technical implementation, employee experience, ethical considerations, and business process optimization.
The shift from centralized innovation to organization-wide AI adoption has profound implications for workforce development and competitive advantage. Companies that successfully democratize AI innovation—empowering employees at all levels to identify use cases and drive transformation—will likely outpace competitors stuck in traditional hierarchical models. This also addresses the critical challenge of AI adoption: it’s not just about technology deployment but about cultural transformation and change management at scale.
For workers, this evolution suggests that AI literacy and innovation thinking are becoming essential skills across all functions, not just technical roles. The elimination of traditional translation layers between business and technology means professionals must develop hybrid capabilities. The emphasis on cross-functional collaboration and collective decision-making in the C-suite also indicates that future leaders will need broader, more versatile skill sets rather than deep functional expertise alone.
Recommended Reading
For those interested in learning more about artificial intelligence, machine learning, and effective AI communication, here are some excellent resources:
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