5 key lessons for IT leaders
Often the problem lies in implementation, McKeon-White said. Tech teams make their best guess and don’t engage subject matter experts to find out how the processes really work.
• Start with one workflow: Rothbaum advises CIOs to resist the urge to “start with enterprise transformation.” Instead, choose one workflow with clear value, map the sources of truth, and tag data early. “If no one knows where the truth is, AI will amplify the chaos,” she warned. • Build AI literacy across the organization: Patria’s rollout worked because Microsoft Copilot wasn’t introduced in isolation. She trained executives, faculty, staff, and students systematically. The institution developed prompt literacy, low-code familiarity, and disciplined testing practices — skills that enabled rapid scaling. • Treat AI agents as systems of engagement: CRM systems and other legacy platforms continue to serve as the system of record, while specialized AI tools such as domain-specific bots supply the deeper insights. Low-code platforms provide the underlying automation, and the agent sits on top as the human-facing orchestration layer that ties everything together. This layered approach makes it possible to build coherent, end-to-end workflows. • Use g****overnance to protect innovation: Both leaders emphasize that guardrails are essential — not to restrict creativity but to ensure workflow automation remains safe, compliant, and explainable. Structured review processes, classification frameworks, evaluation committees, and transparent communication help maintain trust in AI-supported decisions. • Let humans anchor the process: Whether it’s attorneys reviewing case summaries or faculty providing feedback through voice agents, people remain central. AI accelerates the workflow, but human judgment shapes the outcome.