Construction
Alberici Companies
Finance, Investments & Banking
John Hancock, Bank of America, American Express
Logistics & Customer Experience
Sobeys, FreshCo and Safeways
Energy
Shell
Managers and teams experience AI very differently. In 15 days, we help managers individualize plans to drive agency, ROI, and adoption with their teams.
Trace a critical user journey from input to outcome. Identify repetitive effort, handoffs, decision delays, rework and the moments where expertise matters. Talk to the people doing the work, including those already using AI informally.
Working outputs: A current-state workflow, role and decision map, baseline measures, and a record of tool, data and policy constraints.
Define what the team will explore first, pilot, and scale. Make dependencies visible: approved access, usable data, subject-matter input, review capacity and time to learn. Align the team plan with the wider enterprise roadmap.
Working outputs: A 30/60/90-day action plan, accountable owners, milestones, dependencies, learning requirements and review checkpoints.
Give managers space to learn in their own work before asking them to coach others. Help them explain the purpose, demonstrate useful AI practices, agree on expectations, and discuss concerns about quality, workload, and changing roles. Build a reinforcement rhythm into existing team routines.
Working outputs: A manager playbook, coaching prompts, team agreements on human–AI responsibilities, escalation guidance and a practical cadence for reviewing experiments.
Separate individual productivity opportunities from shared-process changes. Assess each idea against the business outcome, feasibility, data access, impact on people and risk. Start with a small number of meaningful experiments, each with a named owner.
Working outputs: Prioritized use cases, a value hypothesis, success criteria and an explicit go, refine or stop decision for each pilot.
Establish a common foundation, then create role-specific practice. An analyst may need evidence checking and synthesis; a manager may need delegation and decision support; a champion may need reusable assistants and peer coaching. Practice uses approved tools and realistic tasks.
Working outputs: learning paths by role and starting capability, hands-on exercises, workflow guides, quality criteria and opportunities to demonstrate proficiency.
Compare pilot results with the baseline. Track time and capacity alongside quality, rework, decision speed, and the business measure that matters. Account for the effort required to review and correct AI output. Check whether released time goes toward useful work.
Working outputs: An outcome scorecard, lessons from the pilot, a decision to expand or retire it, and an updated roadmap. Useful practices become shared team routines rather than isolated personal shortcuts.
Six ways the roundtable creates deep value
1. Create Space for Every Voice. Bring managers together for open, honest conversations to share experiences, value perspectives, and build collective understanding.
2. Connect Experiences to Business Value. Hear where AI is making a real difference, where expectations aren't being met, and what meaningful progress looks like.
3. Discover Opportunities Together. Draw on managers' everyday experiences to uncover frustrations, repetitive work, bottlenecks, and opportunities to improve how teams work.
4. Learn From One Another. Build connections through shared experiences, practical insights, and fresh perspectives on leading people and AI-augmented teams.
5. Build Confidence Through Understanding. Create a safe space to surface concerns, explore uncertainties, and understand what managers need to succeed.
6. Turn Collective Insights Into Meaningful Action. Give leadership a clearer understanding of managers' experiences, helping shape targeted support and build stronger teams.
