Authenticity in the AI era: lessons from Charlotte's tech leaders

One of the best things about moving to Charlotte last year has been the diversity across our tech, cybersecurity and AI communities. It matters, and it goes a long way.

We need awareness of the people we serve, recognition of our blind spots, and the courage to have critical conversations — not despite the challenges of this moment, but because of them. Technology should increase our knowledge, not inherit our ignorance.

Co-hosted by friends at Zero Networks, the Women's Technology Alliance of Charlotte panel on Authenticity in the AI Era captured that exactly. Three insights stood out.

Explainability is not optional

Chris Boehm of Zero Networks described building a generative AI and machine learning feature at Microsoft with 99 per cent accuracy. The Fortune 100 client shut it down immediately, because it could not explain its reasoning. They did not care about the accuracy. They cared about trust.

Trust is the foundation

Charlitta Hatch of the City of Charlotte explained that the city's 311 contact center handles thousands of resident calls. Despite clear efficiency gains, the city rejected agentic AI for the use case — not because it would not work technically, but because losing public trust would be catastrophic.

When you are dealing with citizens and government services, transparency and human oversight are not optional. A single bad experience, or one news story about robots replacing people, could undermine years of community trust-building. So the city is leading with AI literacy internally and in the community before launching initiatives.

AI does not create bias, it reflects it

Wendy Zhang of Hayward Holdings made the point that AI does not hallucinate or invent bias on its own. It acts as a mirror for what already exists in our world, our data and our systems. The bias is already there in society; AI simply reveals it. The real question is whether we are willing to confront what the mirror is showing us about the systems we have built.

The pattern

The pattern across all three: enterprises need AI that explains itself, operates within guardrails, and keeps human oversight at the critical decision points.

Those are the gaps we are committed to addressing in everything we advise and build at Flowchestra — transparent orchestration, human-in-the-loop controls, complete auditability. Governance is too often overlooked in favour of the flashy.

Thanks to the moderator and the other panelists, Pamela Wise-Martinez of Novant Health and Kara Martin Schlageter, and to the Women's Technology Alliance for creating spaces where these conversations happen.