This session was designed for attendees of NACD Chicago’s recent workforce programming, including The New Workforce Equation: Generations + GenAI + Governance on April 23 and the Designing the Workforce of the Future discussion during NACD’s June Master Class in Chicago.

John Bremen, the Forbes columnist from WTW who was interviewed by NACD’s Linda Myers, closed the April 23 event. He generously hosted the group at Willis Tower, while Jeff Perry, chair of the NACD Chicago Board of Directors, facilitated the discussion.

Rather than a panel, the session was structured as a working session focused on developing practical, board-ready questions related to AI, workforce transformation, trust, culture, productivity, risk, and governance.

Below are the questions that came from the  discussion.

Strongest Board Questions on AI + Workforce

The most useful questions from the Workforce Workbench were not simply technology questions. They were strategy, workforce, culture, priority, risk, and evidence questions.

Five strongest questions overall

1. What does management know — not assume — about employee concerns related to AI, and how is that being measured by role, level, function, geography, age, or career stage?
Evidence: pulse surveys, focus groups, listening sessions, manager feedback, segmented engagement data.
Red Flag: management says employees are “excited” or “adjusting” without real evidence.

2. Which AI initiatives are being scaled, stopped, redesigned, or paused — and what evidence drove those decisions?
Evidence: pilot scorecards, baseline metrics, future-state targets, value created, lessons learned.
Red Flag: every AI pilot continues because “AI is strategic.”

3. Given how the company creates value, what are the most important AI guardrails, and how is management testing that those guardrails actually work?
Evidence: AI governance framework, data/security controls, human review standards, exception reporting.
Red Flag: guardrails exist as policy language but are not monitored or tested.

4. How is management aligning workforce capability investments — training, role redesign, critical thinking, and change leadership — with the company’s highest-priority AI strategy?
Evidence: role-specific training, workflow redesign, skill measures, adoption data, change-leader network.
Red Flag: AI training is generic and disconnected from the actual work people do.

5. How will the company look different in three years because of AI — in its business model, workforce, customer value proposition, operating model, and risk profile?
Evidence: three-year roadmap, business-model implications, workforce impact plan, operating model changes.
Red Flag: management can describe AI tools but not the future company.

Questions by Category

Trust & Culture

Are we addressing employee fear and preserving trust?

1. How is management validating that AI-related messages from senior leadership are actually being understood on the front line?
Evidence: communication testing, manager feedback, frontline listening sessions, Q&A themes.
Red Flag: leadership has a communication plan but no feedback loop.

2. What specific employee fears about AI has management identified, and what is being done to address them?
Evidence: segmented pulse data, focus groups, issue/remediation tracking, attrition trends.
Red Flag: employee fear is treated as a soft issue rather than a measurable workforce risk.

Upskilling & Productivity

Are we preparing people to work differently?

1. Which parts of the work should AI automate or augment, and how does that map to the highest-value work that drives results?
Evidence: workflow analysis, 80/20 prioritization, productivity baselines, quality measures.
Red Flag: AI is deployed broadly without clarity on which work matters most.

2. What role-specific AI capabilities are employees expected to build, and how is management measuring whether those capabilities are improving work?
Evidence: role-based training, skill assessments, quality metrics, customer outcomes.
Red Flag: training is measured by attendance or license usage rather than better performance.

ROI & Priorities

Are we measuring value, not just usage?

1. Is AI supporting the current strategy, or is AI changing the strategy the company should pursue?
Evidence: strategy review, market analysis, customer impact, competitive intelligence.
Red Flag: AI is treated only as a productivity project.

2. What AI use cases are materially tied to market share, new products or services, or efficiency — and how is value being measured?
Evidence: use-case portfolio, market-share metrics, innovation pipeline, cost-to-deliver, productivity gains.
Red Flag: management reports AI activity but not business impact.

Responsible Disruption

Are we moving boldly but not blindly?

1. How does management measure the true impact of AI before the board decides whether the company is moving boldly enough or safely enough?
Evidence: value-creation metrics, risk measures, productivity and quality data, board reporting framework.
Red Flag: management and the board are using different definitions of AI impact.

2. Given the way the company creates value, what guardrails will protect the company without stifling growth?
Evidence: risk appetite, governance model, data rules, cybersecurity controls, human review standards.
Red Flag: guardrails are either too vague to protect the company or too restrictive to allow innovation.