AI Says It's Done. Are Your People? Care-Centered AI for Mission-Driven Leaders
Dr. Danny Glassmann
Former Dean + Human-Centered AI Advisor
AI can close the ticket. Who carries the unfinished work?
Your AI workflow can look like a success: faster responses, more cases closed, less time per task. Yet the person needing help may still be waiting, while your team spends hours correcting, explaining, or chasing what the system missed.
For leaders in higher education, nonprofits, and other mission-driven organizations, the risk isn't just an inaccurate AI answer. It's mistaking task completion for human resolution, and overlooking the work and trust lost in the gap.
In this live, 90-minute working session, we'll examine an AI-enabled (or AI-ready) process. Together we'll look beyond the dashboard to spot repeat contacts, broken handoffs, and hidden rework; identify who still needs help or carries the burden; and redesign one practical step.
You'll leave with a first-pass process improvement, clearer ownership for follow-through, and a way to test whether people actually got what they needed. Use a de-identified example from your work, one we provide, or one another participant voluntarily shares. No technical background required.
AI can make work faster. Let's make sure it also makes work better for people.
What you’ll learn
Learn to spot what AI leaves unfinished, uncover its human costs, and redesign work for better outcomes.
Compare what AI marks complete with what the person actually needed.
Look for repeat contacts, unresolved requests, corrections, and hidden rework.
Identify one success metric that may conceal an unfinished human outcome.
Follow a real or illustrative case through its AI interactions and human handoffs.
Identify who's waiting, repeating information, correcting errors, or compensating.
Recognize where clarity, access, dignity, trust, or follow-through may be missing.
Apply Protect–Assist–Automate–Review to clarify AI's role and necessary human checks.
Improve one handoff, safeguard, or escalation step with clear ownership.
Choose one measure and next action to test whether the human need was actually met.
Workshop agenda
See Beyond the Dashboard: What AI Says Is Done vs. What Actually Happened
Compare what an AI-assisted process reports as complete with what people actually experienced. Spot unresolved requests, repeat contacts, hidden rework, and gaps in success metrics.
Find the Hidden Human Costs: Who's Still Carrying the Work?
Trace an AI-enabled process through its human handoffs. Identify who's waiting, repeating information, correcting mistakes, or compensating for unmet needs.
Redesign for Real Resolution: Fix One Handoff, Safeguard, or Process Step
Apply Protect–Assist–Automate–Review to improve one process. Strengthen human touchpoints, clarify accountability, and build a better path to resolution.
Make the Improvement Measurable: Define Success and Next Steps
Choose one improvement to test, identify who owns the follow-through, and determine how you'll know whether people actually received what they needed.
Learn directly from Danny
Dr. Danny Glassmann
20+ yrs higher ed leadership · Former Dean Ohio/SF State · PhD · AI + belonging
Who this workshop is for
Leaders in higher education, nonprofits, and mission-driven organizations overseeing student, employee, client, or community services.
Managers using or exploring AI who want to reduce rework, improve handoffs, and ensure processes actually resolve people's needs.
HR, organizational development, and AI adoption professionals guiding change while protecting trust, accountability, and human judgment.
What's included
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Participant Workbook & Practice Map
Map an AI-enabled process, uncover hidden work and human impacts, and identify opportunities for improvement.
Guided Case Practice
Work through a realistic example to see where AI reports completion but people still need help.
Your Practical Process Improvement Plan
Leave with process improvement, clearer accountability, and a way to measure whether people's needs were met.
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