Ashley Gross Speaking Topics

• AI Agents at Work: Designing the Next Layer of Business Operations

  The interesting question about an enterprise agent is not what it can do. It is what it is allowed to do when nobody is watching, and who answers for it afterwards.

  AI agents change how work gets done, but only when they are designed around real workflows, clear guardrails, and accountable owners. This session moves past chatbot experimentation into operational design: which processes suit agent-assisted work, how to define roles and boundaries, where approval gates belong, and how to improve speed without giving up trust or accountability.

  Best for AI, automation, operations, IT, business process, innovation and technology audiences.

  Three things attendees will learn:

  1. How to identify which business processes are genuinely suitable for agent-assisted work, and which only look suitable.
  2. How to define agent roles, boundaries, approval gates and measurement before deployment rather than after an incident.
  3. The over-automation mistakes that force a rollback six months in, and how they get scoped in on day one.

• Build It With the Team: Turning AI Use Cases Into Working Prototypes

  This is not a slide-deck session. Participants start from a real operational problem in their own organization and leave with something tangible they can build on.

  Ashley guides teams through identifying a high-impact workflow, mapping the current state, defining the desired business outcome, and designing an AI-enabled future state with appropriate human review. Groups leave with a prototype, use-case brief or implementation plan rather than inspiration.

  Best for workshop environments, leadership offsites, innovation days, Microsoft 365 and Power Platform communities, and enterprise teams.

  Three things attendees will learn:

  1. How to identify a workflow genuinely worth improving, and how to size the opportunity before committing budget to it.
  2. How to map current state and define a desired business outcome that someone will actually own.
  3. How to design an AI-enabled future state with the right human review points, and leave with a working artifact.

• The Agent Worked Until It Didn't: Debugging, Rolling Back, and Knowing When to Stop

  Your agent works in the demo. Then a call site never gets its third argument, output silently corrupts in production, and you have to decide whether to keep patching forward or roll back. Real debugging stories from production agent pipelines.

  We will walk through a real document-processing pipeline: an agent chain that transformed structured documents, passed review, shipped, and then silently produced wrong output because one call site was never updated when a function signature changed. No exception, no alert, just quietly incorrect results downstream. We will look at why the tests did not catch it, why the failure was invisible rather than loud, and the moment where the right engineering decision was to revert to the last known-good state rather than keep patching forward under pressure.

  Then the generalizable part: where to place approval gates so failures surface loudly instead of silently, what to instrument when the output is text rather than a status code, and how to decide in advance which agent actions require a human signature. Bring your own war stories.

• The AI-Ready Workforce: Building Skills, Confidence, and Adoption Beyond the Demo

  Giving employees access to AI tools is easy. Helping them use those tools well, responsibly, and in ways that genuinely improve the work is the real challenge. Access produces logins. It does not produce adoption.

  Built on training more than 2,500 professionals at Fortune 500 companies, this session replaces vague AI literacy with a concrete adoption plan: role-based use cases, practical guardrails, hands-on practice, and measurement that holds up in a budget conversation. It also addresses the uncomfortable finding that building enablement around your most enthusiastic people is usually the wrong move.

  Best for HR, L&D, people leadership, workforce transformation, employee experience and cross-functional audiences.

  Three things attendees will learn:

  1. Why access does not equal adoption, and the specific skill and confidence gaps that prevent meaningful AI use.
  2. How to sequence enablement by workflow frequency and business ownership, so effort is not spread evenly and thinly across the organization.
  3. How to measure AI adoption in terms a CFO accepts, rather than counting seats and logins.

• The Practical AI Operating System: Governance That Enables Innovation

  AI governance cannot be a static policy that appears after employees have already started using the tools. Done properly it is an operating system for responsible experimentation and scale, and it makes teams faster rather than slower.

  This session gives leaders a practical way to connect governance to real work: where it belongs in AI workflows and decision-making, how to balance experimentation with risk, and how to build shared language between technical teams, legal and compliance, and the business. Based on deploying AI inside regulated enterprise environments where the governance question was not optional.

  Best for CIO, security, compliance, IT governance, digital workplace and enterprise leadership audiences.

  Three things attendees will learn:

  1. What practical AI governance looks like outside of policy documents, and the minimum set of controls that lets people experiment safely.
  2. Where governance belongs inside AI workflows and decision-making, so it catches real failures without strangling throughput.
  3. How to build shared language between technical teams, legal and compliance, and business leaders, so governance conversations stop stalling projects.

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