What Is AI Leadership?
It’s helpful to imagine AI leadership as building blocks: vision, expertise, cross‑functional teams, strategy and agile execution, says Heena Juneja, industry principal for Frost & Sullivan.
“In practice, effective AI leadership means marrying all of these elements into an enterprisewide strategy,” she says.
AI success usually involves a dedicated leadership team; for example, an AI steering committee or a chief AI officer who reports to an agency’s CEO. These leaders should articulate a clear AI vision tied to mission objectives and ensure that resources — including budgets, data and technology — are committed accordingly.
They also set priorities on where AI should drive value, such as in customer experience or operations, and how it aligns with the overall strategy. “In well-led organizations, everyone from the CEO to the CIO is on the same page,” Juneja says.
Get Wide-Range Buy-In
Securing buy-in from internal stakeholders for AI initiatives starts with trust, according to Forrester Research Principal Analyst Carlos Casanova: “The tech leader has to design for transparency and make sure the systems explain their reasoning, show their data sources and have strong governance in place.”
Governance should not be seen as a purely technical function, but rather a strategic one that involves the entire organization, Casanova adds. That includes setting clear policies for data ownership, quality and privacy to ensure reliability and compliance with relevant regulations.
Just as critical is building adaptability into AI systems so agency leaders can act quickly. “When you see a new opportunity, you don’t want to hear it will take six months to rearchitect the system,” Casanova says. “AI-driven leaders must be agile and design solutions that scale with those asking for them, without violating compliance or trust.”
Failing to establish that confidence, he warns, can slow decision-making or push stakeholders to seek alternatives. “You have to instill trust so leaders can move freely,” he says.
Casanova says that AI-augmented decision-making combines domain knowledge with organizational context to produce meaningful, trustworthy insights.
“Generic models aren’t enough,” he says. “You need to layer in organizational data, history and context so the recommendations align with business value.”
Transparency and explainability are critical so leaders can understand why an AI model recommended a particular action. “AI can’t be a black box,” Casanova says. “If the system suggests a change in code or infrastructure, it has to explain why, just like a human decision-maker would.”
READ MORE: Here is a guide to AI governance for state and local agencies.
Build Cross-Functional Teams
AI projects flourish when handled by diverse, cross-disciplinary teams — not isolated data scientists in a lab, says Jabez Mendelson, research manager with Frost & Sullivan. “Success requires blending technical expertise with domain know-how and operational skills,” he says. “The most impact comes when AI teams include data scientists and engineers working side by side with department managers and IT leaders.”
AI leaders or managers set the strategy and roadmap, acting as the bridge between executives and technical teams, while AI builders — including machine learning engineers and data scientists — focus on developing models and solving complex technical challenges. Department leaders and domain experts provide essential context on customer needs, compliance requirements and operational realities, ensuring that solutions address real-world use cases.
Finally, IT and data architects should make sure the infrastructure, data pipelines and cloud resources are in place, enabling models to scale securely and efficiently into production.
Casanova says building AI-first operational models requires more than simply layering AI onto existing workflows; it demands a fundamental rethinking of how the agency operates. This includes cross-functional teams that bring together technical experts, leadership and operational staff to redesign processes around AI from the ground up.
“AI can’t just be a technology capability,” he adds “It has to become an enterprise capability.”
Manage AI Risk
Senior leadership must treat AI risk with the same gravity as financial or cybersecurity risk, Juneja says. AI must be integrated into enterprise risk management capabilities, with formal assessments addressing privacy, fairness, security and compliance, supported by human review for sensitive use cases. “There should be regular review of major AI initiatives, approval of high-risk projects and ensuring alignment with corporate values,” she says.
Organizations should define clear, ethical AI policies, including transparency and nondiscrimination standards and involve cross-functional teams.
