How Midmarket CIOs Can Lead Through AI Anxiety Without Stalling Progress

From boardroom pressure to employee fear, AI anxiety isn’t a technology problem. One midmarket CIO explains why it’s a leadership issue—and how to manage it without rushing AI into failure.

(Herman Brown, CIO, Office of the District Attorney, City and County of San Francisco, speaks at an Executive Session at the MES Spring Summit in Houston.)

Workforce anxiety around AI is real, and everyone’s feeling it.

C-suite executives, investors, and other stakeholders are pushing AI use in a frenetic state of FOMO — eager to reap the ROI and streamlined business processes that AI promises. Workers are urged to adopt AI quickly, while having to quietly manage their existential fears of becoming obsolete at their jobs.

Turning AI pilots into successful production outcomes relies on all parties within an organization sharing the same realistic expectations about what AI can and cannot do.

Failed AI initiatives often begin when CIOs misdiagnose AI anxiety as a technology or HR issue rather than a leadership challenge. It is not. AI anxiety is a leadership issue, and IT leaders must proactively address mistrust and other concerns about AI, advises one midmarket CIO.

During an executive session at the MES Midsize Enterprise Summit Spring 2026 in Houston, Herman Brown, chief information officer at the district attorney’s office in San Francisco, offered advice to midmarket CIOs on taking a leadership role in quelling AI anxiety in their organizations.

[RELATED: IT Leaders Buying AI Need An RFP, Not A Pitch Deck]

First and foremost, CIOs must remain steadfast against rushed AI plans.

Brown recalled an incident where an email was sent to 32,000 San Francisco employees urging them to “go out and start using AI.”

“I had to go to my leadership team and [I] said, ‘Absolutely not.’ I called the city CIO and said you can’t [send] my staff … these types of emails. You want to send something it needs to come through me,” he said.

Organizations are working through establishing the internal company pack order for AI accountability. Brown took the reins and positioned himself as an AI leader, not waiting for delegation.

He said his next move was to set up a solid AI policy.

He also shared other actions for IT leaders to take ownership and address concerns about AI:

Demonstrate How AI Replaces Labor, Not Laborers

AI’s real impact is on task-level work, not entire roles. Historical examples—from ATMs to mobile banking—show that automation removes routine work but creates demand for higher-skill roles, new functions, and more oversight.

Expect higher demand for mid-level and senior talent who can supervise, integrate, and validate AI-driven output.

Cutting “junior” roles too aggressively can backfire when AI output outpaces your team’s ability to manage it.

Actively reset the narrative: AI is here to augment productivity, not eliminate motivated workers.

“It’s natural for people to have that anxiety about AI, because there are mindsets out there that AI is going to replace people,” Brown said.

Clarity beats reassurance, he said. Saying “AI won’t replace you” isn’t enough; leaders must show how roles evolve.

Prevent ‘Just Try’ AI Without Guardrails

Letting employees “just try AI” without guardrails is dangerous, especially for organizations handling regulated or sensitive data (PII, HIPAA, CJIS, legal data, etc.).

“Just because we can do something, does that mean we should do it?” Brown said.

“We have an ethical responsibility – to the community, to our organization, to ourselves. … Governance is about putting guardrails in place, not stopping innovation,” he added.

AI policies must be local, contextual, and enforceable, not broad corporate directives.

[RELATED: 5 Rules To Getting Started With AI Governance]

Ethical responsibility matters even where regulations lag (e.g., sensitive or human-impact data).

Governance is not about blocking innovation, rather, it’s about controlling risk and accountability.

Create An AI Strategy That Starts With Task Mapping, Not Tools

Brown emphasized the need to establish a practical framework. Start by listing your weekly tasks and sorting them into three buckets:

  1. AI can fully automate
  2. AI assists, but a human stays in the loop
  3. AI cannot do this (human-only work)

“Bucket one: AI can do it for me. Bucket two: AI can help, but a human stays in the middle. Bucket three: things AI can’t do,” is how Brown outlined the framework.

ROI comes fastest from bucket-one tasks (basic reporting, summaries, repetitive workflows). Competitive advantage comes from investing in bucket-three skills.

[RELATED: Analysis: How The Midmarket Can Deliver ROI With AI]

Don’t Over-Automate Human Interaction

Using AI to over-automate traditional human communication: email, mentoring, leadership advice) can erode trust and credibility in AI.

As Brown put it: “When people use AI for everything, it cheapens the interaction.”

Mandate Reskilling, But Realize That Formal AI Education Is Fragmented

There is no single “AI school,” and the tool landscape is exploding (10,000-plus solutions cited).

Focus reskilling on AI literacy and use cases, not tool mastery.

Encourage experimentation across multiple platforms—but within governance boundaries.

Expect ongoing learning, not one-time training.

Make IT The ‘Yes’ Organization, With Constraints

Every technology problem is solvable if the business accepts the tradeoffs of time, money, and people (the “triple constraint”).

First diagnose: Is this a business problem or a technology problem?

AI often exposes business process debt, not technical gaps.

IT leadership credibility grows when you frame AI decisions in business terms.

Bottom Line for Midmarket IT Leaders

AI is already here. It will change tools, workflows, and org structures, but it will not replace leadership. The leaders who succeed will be those who: