As enterprises rush to implement AI strategies, the pressure is on for AI leaders to start delivering results – and fast. CEOs, boards, business unit heads – everyone is clamoring for AI’s promised impacts on revenue growth, cost savings, efficiency gains and competitive advantages.
But in this hotbed of enthusiasm and impatience, randomly deploying fragmented AI projects is a recipe for failure. What’s needed is a cohesive, horizontal data and AI strategy aligned to measurable business KPIs. Designing and executing this company-wide vision requires deft coordination and buy-in across a complex web of stakeholders.
That’s why for modern Heads of AI, the most mission-critical skill right now is complex stakeholder management.
Why Stakeholder Wrangling is Job #1
In many organizations, the AI leadership role is elevated outside of the core technology organization. Heads of AI may report directly to the CEO, work under a Chief Innovation Officer, or partner with General Managers and business unit leaders. This matrix structure means complex stakeholder relationships from day one.
Each stakeholder inevitably comes to the table with their own AI priorities, use case ideas, data sources, existing investments, and more. The Chief Marketing Officer wants personalization and churn analytics. Supply chain leaders need demand forecasting. The product team craves intelligent search. And the CEO just read that one competitor is doing “machine learning for inventory optimization!”
With impatient pressure from the top to “do AI now,” it can be tempting to appease each request by rapidly deploying disconnected proofs-of-concept. But this spray-and-pray approach simply creates an ungoverned tangle of AI experiments unlikely to scale or move key business metrics.
The Head of AI’s real mandate? Corralling the zeal, surveying the landscape, and crafting a unified data and AI roadmap tied to enterprise-level goals. Then they must socialize that plan across the organization, securing commitments from stakeholders to prioritize, integrate, and sequence AI initiatives accordingly.
Tips for World-Class AI Stakeholder Management
Clearly, deft navigation of complex stakeholder dynamics is paramount for AI leadership success. Some tips I’ve gathered from discussions with leading AI executives:
- – Lead with a business strategy first, not AI. Understand true goals and KPIs for revenue, margin, cost savings, etc.
- – Map out existing data sources, capabilities and AI investments across teams. Conduct an objective assessment.
- – Craft an AI vision that aligns with and tangibly impacts broad business objectives. But be flexible to adjust based on feedback.
- – Communicate the strategy constantly at all levels. Use storytelling to bring the roadmap to life.
- – Give stakeholders a voice in priorities and sequencing to create shared ownership. But don’t let it derail the strategy.
- – Establish an AI program with clear governance to deliver against the plan. Appoint stakeholder champions.
- – Celebrate incremental wins through change management to build momentum and stakeholder confidence.
In today’s economy, AI is quickly shifting from experiment to strategic imperative. By bringing world-class stakeholder management skills, Heads of AI can orchestrate an enterprise approach to maximize impact and ROI.
