Research & Insights

Presented by Wright Technical Services
Written by Denise D’Angelo

Leaders have taken deliberate steps to prepare for an AI-enabled workforce. They have hired differently, redesigned roles for expanded reach and impact, and embedded AI into everyday workflows.

Yet performance can plateau, and in many cases, leaders step back at precisely the moment teams need ongoing support. Transformed role boundaries and autonomous systems that assist judgment & execute decisions require continuous navigation.

Harvard Business Review’s 2025 article “Leaders Assume Employees Are Excited About AI. They’re Wrong” documents this dynamic, noting that operating at a distance causes leaders to overestimate readiness and confidence just as AI enters day-to-day work.

Two forces drive this pattern. Leaders may treat AI like infrastructure, viewing it as a technical deployment that runs on its own once implemented rather than ongoing team development. Leaders sometimes see AI-equipped, capable teams and interpret stepping back as appropriate delegation. It feels responsible, not neglectful. The AI implementation problem isn’t technical. It’s that leaders mistake a technology deployment for team capability development.

Performance Through Leadership Presence

Across large-scale initiatives, I’ve found that transforming team performance requires staying engaged, forcing difficult conversations, and moving teams through trust, challenge, commitment, and accountability to deliver results.

When leaders step back too soon, they can create avoidable performance decline. A procurement team illustrates this pattern. One analyst heavily used an AI model to analyze contracts while teammates continued manual reviews. The model had been trained on standard language rather than the manufacturing-specific terms the team knew, which led the analyst to miss critical clauses. As contract rework and disputes increased over several months, leadership assumed deployment meant adoption and never created feedback loops for the team to refine the model. Had leaders regularly asked where AI helped and where it needed improvement, the team could have refined the model with their expertise.

For leaders who have disengaged assuming AI fully enables the team, re-engagement doesn’t require starting over. It starts with one conversation: convening the team to ask where AI is helping, where it’s creating friction, and what they need. This conversation surfaces gaps in understanding, decision rights, or model performance that have been invisible from a distance. Leaders then establish regular structures (retrospectives, check-ins, or reviews) to sustain engagement.

McKinsey’s research “Organizational health is (still) the key to long-term performance” found that leaders’ decisiveness, accountability, and judgment predict long-term value creation. These fundamentals become more critical, not less, as AI enters team operations.

Leading AI-Enabled Teams

1. Model the Leadership Teams Need Leaders who use AI in their own work and discuss their choices openly demonstrate what effective AI use looks like. They address scenarios where AI can’t explain its reasoning, outputs need verification, or human judgment must override automated recommendations. They make governance visible by demonstrating escalation processes and sharing both successes and failures.

2. Build Feedback Loops That Develop Judgment Leaders who create structures for teams to discuss how AI recommendations translate into real decisions enable ongoing learning. Through retrospectives, team reviews, or regular check-ins, they ask teams to articulate which AI contributions strengthened outcomes and which required human override. They coach teams on when to trust AI recommendations and when human judgment should prevail, and when to verify outputs as AI evolves. This learning feeds directly into model refinement.

3. Establish Clear Expectations and Decision Boundaries Clear expectations about what individuals own, what AI owns, and how success is evaluated improve team performance. Leaders facilitate conversations to establish decision rights: which choices require human judgment, which AI can handle autonomously, and what triggers the need to override AI recommendations. These boundaries need revisiting as AI capabilities expand and roles adapt.

The Continuous Nature of AI-Enabled Work

AI-enabled roles are evolving. As individuals leverage AI, they gain access to work and decisions previously beyond their reach. These roles continue to morph as AI becomes more pervasive across the organization.

Agentic AI makes decisions, takes actions, and executes key steps, creating new complexity. Leaders help teams understand embedded governance and navigate what they’re now authorized to do versus what AI decides, recognizing these boundaries shift as capabilities and organizational needs evolve.

Trust, accountability, and commitment remain the fundamentals of high-performing teams and require more deliberate leadership attention as AI capabilities grow.

Sustained performance depends on leaders recognizing that AI-enabled work is not a deployment milestone but an ongoing transformation requiring continuous support.

About Denise D’Angelo

Denise D’Angelo is a transformation executive specializing in AI workforce readiness, governance, and operating model evolution. Her experience spans Fortune 500 financial services, research institutions, and defense programs, where she has led enterprise transformation and AI-driven data strategy in regulated environments. A trusted executive facilitator and consensus builder, she brings disciplined clarity to workforce design, decision authority, and role strategy, aligning expanded AI capability with accountability and measurable performance, grounded in a Master’s degree from Johns Hopkins’ Whiting School of Engineering and graduate studies in Data Privacy Law at Seton Hall.