AI in the workplace is no longer a future conversation. It’s already changing how businesses operate, how employees work, and how organizations think about productivity, data, security, and governance.

That’s exactly why we brought together seven technology leaders for our Executive AI Roundtable 2026: AI in the Workplace — What Every Business Leader Should Know in 2026. We wanted to move beyond the hype and have a candid conversation about what businesses are actually experiencing as AI adoption accelerates.

At Symmetric IT Group, we didn’t want to host another webinar focused only on the latest AI tools or predictions about where artificial intelligence might be headed. We wanted to talk about what happens when AI meets your people, your data, your security, your workflows, and your existing technology environment.

We wanted to have the kind of conversation business leaders are already having behind closed doors:

Where is AI creating real business value today?

How much AI is already being used inside our organizations?

What happens when adoption moves faster than governance?

How do we protect company and client data without slowing innovation?

And where must human judgment remain in control?

Our CEO, George Lakiotis, moderated the conversation and was joined by six technology leaders from across the United States and Canada:

• Scott Cook, President — Spire Technology
• Jesse Jervis, President — SureFire IT Solutions
• Zach Mesel, CEO — Wooden Spoon IT Services
• Ryan Huettner, CEO — Qoverage
• Louie Sadd, CEO — Data Stream IT
• Gregg Sales, CEO — BlueBolt Technology

What made this roundtable different was that these weren’t seven executives meeting for the first time on a webinar.

George and the other panelists have spent years meeting as peers, sharing what’s working inside their businesses, challenging one another, and discussing the mistakes and lessons that don’t always make it into public conversations.

For one hour, we opened that conversation to our audience.

And we started with an uncomfortable reality.

1. AI in the Workplace May Already Be Happening Without Leadership Knowing It

Early in our conversation, we addressed a scenario we’ve encountered before.

A business leader says:

“We don’t allow AI in our organization.”

But does that necessarily mean employees aren’t using it?

That question led us directly into one of the biggest themes of the roundtable: Shadow AI.

We discussed how employees may already be using personal AI accounts to draft emails, research topics, summarize documents, generate content, analyze information, or simply complete everyday work faster—even when the organization hasn’t formally adopted an AI strategy.

Louie Sadd compared what we’re experiencing today to the early days of the internet. Before organizations had formal strategies and policies around internet use, employees had already discovered its value and were finding ways to incorporate it into their work.

We’re seeing something similar with AI.

The problem isn’t necessarily that employees want to use AI. The problem is when the organization doesn’t know how they’re using it.

• Are employees entering company information into AI tools?

• Are they uploading documents?

• Are they using personal or business-managed accounts?

• What AI applications are connecting to business systems?

• And what company or customer information can those applications access?

This isn’t just a concept discussed during our roundtable. Microsoft describes Shadow AI as AI operating inside an enterprise without governance—including unsanctioned tools adopted by employees and unmanaged AI agents—and emphasizes that organizations need visibility before they can effectively govern this activity.

Before we can talk seriously about responsible AI adoption, we first need visibility.

You can’t govern what you can’t see.

One of our first takeaways was therefore simple: before building an ambitious AI strategy, understand how AI is already being used inside the business.

2. Where Is AI in the Workplace Creating Real Business Value?

Once we established that AI adoption may already be happening, we moved our conversation from usage to value.

And one idea came through clearly:

AI doesn’t have to begin with an enormous transformation project.

Sometimes the best starting point is much smaller.

We discussed examples ranging from document comparison and summarization to spreadsheets, research, internal knowledge bases, sales and marketing activities, and repetitive administrative processes.

Jesse Jervis brought the conversation back to simple productivity.

If AI can remove even a few minutes from a task employees perform repeatedly throughout the week, those savings can accumulate across an organization.

Ryan shared an example involving AI between accounting and payroll systems for reconciliation. Scott discussed how AI was helping save employees time and why sales and marketing can provide approachable areas for experimentation.

What we found interesting was that the conversation wasn’t really about the newest AI product.

It kept returning to the business problem.

Later in the roundtable, Zach brought that idea down to a practical level: choose one tedious problem or task you’ve been putting off and explore how AI might help address it.

That changes the starting question from:

“How can we use more AI?”

to:

“Where are we losing time, and could AI measurably improve that process?”

For us, that’s a much stronger starting point for AI in the workplace: begin with a real business problem, determine whether AI can improve it, and measure the outcome.

3. AI in the Workplace Can Reveal Risks That Already Exist

As we moved from productivity into governance and security, George introduced what became one of the central ideas of our discussion:

“AI doesn’t create risk. It reveals it.”

This idea matters because AI can work with information and permissions that already exist inside an organization.

Imagine an employee who technically has access to a SharePoint folder or a Microsoft 365 file that they don’t normally use.

Maybe those permissions were established years ago.

Maybe an employee changed roles.

Maybe someone was added to a group and never removed.

Maybe a sensitive document is accessible more broadly than leadership realizes.

Those problems weren’t necessarily created by AI.

They were already there.

What AI changes is how quickly information can potentially be found, analyzed, summarized, and surfaced.

Something that previously required someone to know exactly where to look can become much easier to discover.

That’s why we asked three questions during our roundtable that we believe every business leader should consider:

Who actually has access to your sensitive information?

When were those permissions last reviewed?

Could AI surface something tomorrow that shouldn’t be broadly visible?

At that point, our conversation about AI naturally became a conversation about cybersecurity, data access, identity, permissions, and governance.

And that’s an important connection.

AI strategy shouldn’t exist separately from your IT and cybersecurity strategy.

At Symmetric IT Group, we take a security-first approach to technology. Organizations evaluating AI should also consider how their broader cybersecurity strategy and managed IT environment support secure adoption.

4. AI Governance Starts With Three Questions

Once we started discussing data access, we needed to go deeper.

Gregg Sales helped us frame the next part of our conversation around three questions businesses should consider before connecting AI to company systems.

What can AI see?

What information does the system have access to?

Emails? Files? Chats? Calendars? Financial information? Customer information? Internal company documents?

What can AI do?

We discussed the difference between allowing AI to access information and allowing AI to take action. As AI moves from simply reading information to acting on it, the level of authority and the need for governance changes.

The more AI moves from reading information to taking action, the more important permissions, governance, and human oversight become.

Where does the data go?

We also discussed questions around data retention, model training, third parties, and customer information.

Businesses aren’t always working only with their own data. Many organizations have been entrusted with information belonging to customers, employees, vendors, or other stakeholders.

That means responsible AI governance needs to account for more than whether an AI tool is useful.

It needs to consider what the tool can access and what authority we’re giving it.

IBM defines AI governance as the processes, standards, and guardrails used to oversee AI systems while addressing areas including security, privacy, accountability, and human oversight.

One practical approach discussed during our roundtable was beginning with read-only access in appropriate scenarios and expanding capabilities as the organization better understands the use case and its risks.

That gave us another important takeaway:

AI access and AI authority are not the same thing.

Allowing AI to see information is one decision.

Allowing AI to act on that information is another.

5. Human Oversight Is Essential for Responsible AI Adoption

That naturally led us into one of the most important questions of the hour:

What decisions should AI assist with—but never make alone?

Ryan Huettner offered a framework that helped us think about human oversight according to the consequences of getting something wrong.

For lower-consequence activities—such as organizing information, initial research, categorizing information, scheduling, or preparing a draft—businesses may be comfortable allowing greater levels of AI assistance.

As the potential consequences increase, however, human oversight should increase too.

If an incorrect decision could result in losing a customer, spending significant money, creating legal exposure, affecting someone’s employment, or causing another serious outcome, a person needs to remain involved.

Ryan gave us a useful test:

If something goes badly, would you be comfortable telling an employee, customer, or board of directors, “AI made the decision, not me”?

If the answer is no, then a human needs to own that decision.

We also discussed why this is especially important for licensed professionals. An attorney, accountant, architect, engineer, physician, or other professional might use AI to assist with research or drafting.

But AI doesn’t inherit that professional’s accountability.

George summarized the responsibility another way during our conversation:

You are the editor in chief.

AI can help create the output.

We still need people to decide whether that output is accurate, appropriate, and ready to be used.

This need for meaningful human oversight is also being emphasized beyond our roundtable.

6. AI in the Workplace Is a Leadership Issue, Not Just an IT Issue

By this point in our conversation, something else had become clear.

AI governance isn’t exclusively an IT issue.

It’s a leadership issue.

Louie emphasized the importance of executives being involved in setting the direction for AI adoption.

And we agree.

AI in the workplace can affect how employees work, how company information is handled, how decisions are made, how customers are served, and eventually how certain roles evolve.

Those aren’t simply technology decisions.

They’re business decisions.

During the roundtable, we discussed the importance of leadership establishing why AI should be used, where it is appropriate, which guardrails are necessary, and how employees should approach the technology.

We also talked about the potential value of AI champions inside an organization—people who can help identify practical use cases and encourage responsible experimentation.

But we also acknowledged something important:

Governance can’t become a roadblock.

If an approval process becomes so difficult that employees have to wait endlessly to experiment with a legitimate AI use case, some may simply find another way.

And then we’re back where our conversation began:

Shadow AI.

Responsible AI governance shouldn’t mean creating a giant “NO” machine.

It should mean creating an approved path that is clear enough for employees to actually follow.

Leadership defines the purpose and accountability. IT and security help establish the technical guardrails.

We need both.

7. The Human Side of AI Adoption May Be the Most Important

Toward the end of our roundtable, the conversation shifted again.

This time, we weren’t talking primarily about technology.

We were talking about people.

What happens when employees see AI becoming capable of performing parts of their jobs?

How do leaders introduce AI to people who may already be resistant to change?

And how do we make AI adoption an opportunity to improve people’s work rather than simply a discussion about replacing it?

Zach talked about the cultural challenge surrounding AI and the importance of helping employees understand how AI can augment their performance and remove some of the repetitive work from their day.

Then Louie introduced a mental model that gave us another way to think about it:

The first mile. The middle mile. The last mile.

AI can help with repetitive work at the beginning of a process—collecting, organizing, consolidating, or preparing information.

It can also assist with work at the end—formatting, packaging, or preparing the final result.

But the middle mile is where human value can become especially important.

Judgment.

Context.

Experience.

Critical thinking.

Decision-making.

Understanding the customer.

Recognizing when an AI-generated answer simply doesn’t make sense.

That led us to a question we think is much more useful than simply asking which jobs AI might replace:

Which parts of a job should AI handle so our people can spend more time doing the work that requires human judgment?

That’s where training and upskilling become part of responsible AI adoption.

Because adopting AI isn’t just about giving employees access to a new tool.

It’s about teaching people how to work effectively—and critically—with that tool.

What We Learned About AI in the Workplace

At the end of our hour together, we asked each panelist to leave business leaders with one final thought.

Although the answers were different, together they formed something close to a practical roadmap.

We heard that AI conversations need to start with leadership and happen often.

We heard that employees need enough knowledge to challenge AI output instead of automatically trusting it.

We heard that businesses first need visibility into where AI is already being used.

We discussed putting the right human checkpoints around AI based on the consequences of its decisions.

We heard the recommendation to pick one tedious problem and take one step toward solving it.

And finally, Jesse brought the conversation back to a simple approach:

“Crawl, walk, run.”

We believe responsible AI in the workplace starts with understanding what’s already happening inside your organization before trying to scale it.

You don’t need to transform the entire organization tomorrow.

But you do need to understand what’s happening today.

Discover where AI is already being used.

Understand what information it can access.

Establish practical governance.

Choose a meaningful business problem.

Keep humans involved where the consequences demand it.

Measure whether AI actually improves the process.

Then scale what works.

Discover → Govern → Pilot → Measure → Scale

What Will AI Reveal About Your Organization?

When we organized the Executive AI Roundtable 2026, our goal at Symmetric IT Group wasn’t to convince business leaders to adopt every new AI product.

We wanted to open a more practical conversation about what happens when AI meets the reality of your organization:

AI may reveal a repetitive process that should have been automated years ago.

It may reveal employees who have already found innovative ways to improve their work.

It may reveal opportunities to deliver better service or make information easier to access.

But it can also reveal outdated permissions, uncontrolled applications, governance gaps, or sensitive information that has been accessible more broadly than leadership realized.

And that’s why, after an hour of conversation among seven technology leaders, we came back to the idea George introduced near the beginning:

“AI doesn’t create risk. It reveals it.”

We don’t see that as an argument against AI.

Quite the opposite.

It’s an argument for understanding your organization well enough to use AI with confidence.

So instead of asking only:

“Are we ready for AI?”

we think there’s a more important question business leaders should be asking:

“What will AI reveal about our organization?”

Because it’s better to discover the answer intentionally than accidentally.

Continue the Conversation: Watch Our Executive AI Roundtable On-Demand

Our Executive AI Roundtable covered much more than we could capture in a single article.

If you’re evaluating AI inside your organization—or you’re already discovering that your employees are moving faster than your AI strategy—we invite you to watch the complete conversation.

Hear our panel discuss AI in the workplace, Shadow AI, business value, AI governance, data security, human oversight, workforce readiness, leadership, and responsible AI adoption.

A candid conversation about what we’re seeing as AI becomes part of everyday business.

Watch the Full Executive AI Roundtable On-Demand →