AI Leadership: From Execution to Influence in a Multiagent World
AI isn’t just automating tasks. It’s reshaping how leaders provide direction amid accelerating uncertainty. In this episode of the podcast, AIXC Council Members Ananya Ghosh Chowdhury chats with Ling Zhang about evolving from managing AI projects to influencing organization-wide strategy.
- Evolving Leadership in AI Transformation
Ling: AI has exploded from a siloed technology where leaders sponsored projects and allocated budgets to reaching employees, customers, partners, and products in real time. “Leadership has to evolve as well, providing direction in uncertainty,” Ling explains. The pace and depth of change demand broader thinking and deliberate action, even when the path isn’t clear.
From her career leading data teams, Ling shares a pivotal lesson: early business impact lagged until they looped in data, product, and marketing teams to clarify AI’s role in decisions. “The role is moving away from manager or control toward our translation and sense-making, much like a conductor. Leaders don’t click every instrument button. They ensure harmony.”
Ananya’s Perspective: Senior leaders now own full AI visions, including ethics, data governance, and change management. With POCs scaling to production, ROI scrutiny intensifies. “End of 2025 was the year of agentic AI; 2026 is multiagent frameworks,” Ananya notes, where specialized agents collaborate to solve bigger problems.
Actionable Tip: Audit your AI initiatives. Do they align business decisions early, or stay tech-focused?
Tip: Map agent interactions across teams for emerging multiagent wins.
This evolution primes leaders for strategic shaping, not just project management.
- Shaping AI-Driven Strategy Across the Organization
Ling: Leaders shift from deliverables to enterprise-wide vision by adopting a systems-level perspective. AI blurs team boundaries, so the key question is: “What decisions will AI make, under what conditions, with what business impact?”
Three enablers stand out:
- Clarity on business outcomes, treating AI as strategic, not experimental.
- Confidence to influence cross-functions via shared understanding.
- Tech fluency for informed trade-offs.
“Leaders move from managing AI product to shaping AI-driven strategy when they stop optimizing delivery to start aligning decisions cross-interface.”
Ananya’s Perspective: The turning point is integrating high-impact use cases (e.g., customer service, supply chain) into core strategy. “Leaders need to see connections between use cases upfront,” tying them to an organization-level vision.
Actionable Tip: Prioritize 3 to 5 domains; prototype interconnections before scaling.
Tip: Use roadmaps to visualize use case synergies for buy-in.
With alignment foundational, influence follows naturally.
- Building Alignment and Influencing Stakeholders
Ling: Alignment isn’t forced agreement. It’s a shared framework providing clarity, transparency, and a common language. In interconnected AI work, ripples across teams demand early collaboration. “When leaders use such a shared framework, it becomes a shared language cross-organization.” This makes complexity navigable, turning influence into trust.
Ananya’s Perspective: Frame AI around outcomes like faster decisions or better experiences to engage business teams. Anchor the North Star vision in purpose/values for willingness to disrupt. “Storytelling, having a shared vision makes people way more willing to experiment.” Ling adds frameworks enable consistent, inspiring stories.
Actionable Tip: Cocreate a one-page framework mapping AI to revenue, risk, and ops.
Tip: Test stories on diverse stakeholders for resonance.
This sets up resilience when challenges hit.
- Guiding Teams Through Challenges and Strategy Resets
Ling: Challenges reveal unspoken assumptions in data, processes, or maturity. Start by revisiting the problem framing and expectations. Foster psychological safety: “What did we learn that we couldn’t have known before?” Treat setbacks as signals for seasonal progress: growth, tension, renewal.
A reset revisits goals, validates assumptions, and adjusts (e.g., narrowing scope or adding human loops). “Resetting strategy isn’t a retreat, it’s an act of resilience.”
Ananya’s Perspective: Use structured retrospectives to pivot problem statements or metrics honestly. “As long as leaders are open to pivoting, it’s learning.” With new tech, everyone experiments together.
Actionable Tip: Schedule biweekly “learnings reviews” post-experiment.
Tip: Document trade-offs in a shared Notion/Trello board.
Resilience bridges to balancing horizons.
- Balancing Execution Excellence with Strategic Thinking
Ling: These aren’t opposites. They’re intertwined. Strategy sets direction; execution delivers with discipline. “Execution is also about building organizational capability. Learning is part of execution.” Operate on dual horizons: near-term wins for momentum, long-term investments in skills and culture. Without both, you get fragmentation or frustration.
Ananya’s Perspective: Measure success by capability gains: skilled teams, usable data, mature governance. “Build reusable platforms for future projects,” balancing quick wins with foundations like data quality.
Actionable Tip: Track “capability ROI” alongside financials (e.g., skills uplifted per project).
Tip: Parallel track: 70% execution, 30% platform building.
This duality preps leaders for embedded AI.
- Capabilities and Mindsets for AI-Embedded Leadership
Ling: As AI advances, human mindsets amplify humility to learn what you don’t know, continuous learning as duty, and managing human-AI hybrids. “Leaders who stay effective will build AI fluency, learning-oriented cultures.” Redefine success by growing capabilities.
Ananya’s Perspective: Prioritize AI literacy, human oversight, and augmentation. “See AI as augmenting human potential; invest in reskilling.” Ensure ethical use and escalation paths. Ling closes: “AI can’t do what makes humans more human.”
Actionable Tip: Mandate quarterly AI literacy sessions for leaders.
Tip: Design hybrid workflows: AI for scale, humans for judgment.
Key Takeaways
- From Silos to Systems: Evolve to conductor-like leadership harmonizing teams.
- Shared Frameworks Drive Influence: Create clarity for natural alignment.
- Embrace Resets as Resilience: Learn from assumptions; pivot with safety.
- Dual Horizons: Execution builds credibility; strategy ensures maturity.
- Mindset Over Models: Cultivate humility, literacy, and human-AI synergy.
- ROI Beyond Projects: Measure capability growth for scalable impact.
- 2026 Focus: Leverage multiagent frameworks with ethical oversight.
AI leadership demands influence over execution: vision, alignment, and courage to learn publicly.
Podcast: Full episode (33 mins)