The SMB Executive’s Guide to AI Adoption

Your team is most certainly using AI, anything from cleaning up email, summarizing a meeting, drafting a proposal outline, or fixing a spreadsheet formula. They may also be doing it in the downlow. In KPMG and the University of Melbourne’s Trust in AI research, based on more than 48,000 workers in 47 different countries, 57% of respondents said they had hidden their AI use at work. Some confessed to presenting AI-generated work as their own. It serves as a wake-up call for small and midsize business leaders: your AI adoption problem may be less about persuading employees to try AI and more about openly using it.

That changes your rollout entirely. Although a company wide AI announcement can sound exciting, an employee may see it as a vague warning about scary changes. People fill in the blanks quickly: “This will add work, expose what I do not know, or make my role easier to eliminate.”

We definitely felt this with our team last year. That first AI announcement at our All Hands meeting was welcomed with an awkward silence. We explained that we were absolutely not planning to replace anyone with agents. Our goal was (is) to move faster, do more, and deliver better results. Our benchmark was going to be apparent in CloudSee Drive development…

We’ll share our learnings – but it starts with creating momentum instead of anxiety. Treat trust as part of the implementation plan. It is essential in the AI operating system.

3 Questions Behind Every AI Rollout

When leaders announce an AI initiative, employees tend to run it through a few silent questions.

1. “Is this going to affect my job?”

Employees are already thinking about the personal impact. Pew Research found that 71% of Americans expected AI to lead to fewer jobs over the next 20 years, up from 64% two years ago. Your employees have seen the headlines, watched the demos, and heard the word “efficiency” used in enough budget meetings to know what it means.

Avoid making promises you can’t guarantee. “No one’s job will change” is not reassuring when everyone knows markets, budgets, and priorities can change. Use language you can stand behind:

“We are starting with work that drains time without improving results. Our goal is to reduce repetitive admin, speed up turnaround, and add room for customer work, problem-solving, and decisions that require judgment. If this leads to larger changes later, we’ll give you a heads-up early, with a clear plan and time to adapt.”

It’s direct and shows respect.

2. “Will I look incompetent if I don’t know how to use AI?”

Many employees have had uneven exposure to AI. Less than half of all workers in the KPMG study reported receiving formal AI training. The rest are learning through trial, error, and Youtube. Predictably, someone writes a rushed prompt, gets a bland answer, decides AI is useless, and never asks questions to avoid embarrassment.

This is especially true for experienced staff. A senior operations manager, account exec, estimator, or finance director may have built their reputation on knowing how the business works. Introducing a new tool without support can feel like you are rearranging furniture in the dark and asking them to map it out.

Start with practical, role-specific learning. Skip the 90-minute “future of AI” seminar. Show them how to use approved tools for their everyday work:

• Turn scattered meeting notes into a client-ready summary.
• Extract requirements from an RFP.
• Draft a project plan.
• Reformat information from an old template.
• Identify gaps in a proposal before it goes out.
• Prepare a plain-English explanation of a technical article.

Give employees permission to practice on low-risk work. Demonstrate that asking basic questions is normal. Reward useful lessons, especially the occasional “we tried this and it was a spectacular waste of 20 minutes.”

3. “Is this one more thing I have to manage?”

Employees have earned their skepticism. Every productivity platform arrives promising simplicity, then demands a password reset, mandatory training, a new dashboard, and a new section in the weekly status report. AI can easily become another performative tool for management.

The solution is easy: connect the workflow to a specific irritation employees already know. Start with a task that makes people groan when it arises. Repetitive reporting, document intake, manual data cleanup, expense categorization, proposal assembly, meeting follow-ups, and internal knowledge searches are ideal candidates.

A useful AI workflow should answer one question clearly: What annoying piece of work gets smaller? If nobody can answer that in one sentence, the workflow is still too abstract.

4 Moves That Build AI Adoption Buy-In

1. Start with the chore, not the craft.

Do not open with the most strategic, creative, or identity-defining work in the company. Asking AI to draft a top salesperson’s proposal or analyze a client relationship on the first day can make people feel like management is testing their replacement in public. Start with the repetitive front end of the process.

For example, an engineering firm might use AI to extract project requirements from incoming RFPs, flag missing information, and populate a proposal checklist. The project manager still decides what matters, guides the response, and owns the client relationship. The workflow removes two hours of copying, pasting, formatting, and swearing at PDFs.

Early wins matter because they are easy to verify. Employees can see that the tool helped, where it hallucinated, and where human review remains essential.

2. Recruit volunteers, including one skeptic.

Begin with three to five employees from different functions. Give them time during work hours to test approved workflows. Include at least one respected skeptic.
The enthusiast will find something useful. The skeptic will identify where the workflow creates risk, confusion, or extra cleanup. Both perspectives matter. A credible employee saying, “This saved me 90 minutes on Monday, but I would never use it for client-facing numbers without review,” carries far more weight than an executive claiming the tool will transform everything by Q3.

Ask pilot participants to share:

• The exact task they used AI to do.
• How long the task took before and after.
• What went right.
• What went wrong.
• What data they were allowed to use.
• Whether they would keep using the workflow.

That creates evidence people trust.

3. Publish a plain-English AI policy.

A policy vacuum does not stop AI use. It simply pushes it underground. Your first policy does not need to resemble a federal procurement document. It should fit on one page, use plain language, and answer practical questions:

• Which AI tools are approved for work use?
• What company, customer, employee, or financial data must never be entered?
• Which tasks require human review before output is shared?
• When should employees disclose AI assistance?
• Who can answer questions about unusual or unclear cases?
• What should someone do if they accidentally share sensitive information?

Be specific about client data, PII (personally identifiable information), financial details, source code, contracts, and confidential strategy documents. Employees need a safe route for asking, “Can I use AI for this?” before they guess.

4. Measure hours returned to the business.

Seat count is a subscription metric. Logins are a software metric. Neither tells you whether the work improved.Track the task. Measure how long proposal intake takes, how quickly service tickets get categorized, how much time an operations coordinator spends assembling weekly reports, or how long it takes to turn a client meeting into an actionable follow-up plan.

Then ask what happened to the recovered time. Did the team respond to prospects faster? Did account managers spend more time with customers? Did fewer tasks slip through the cracks? Did employees leave work with fewer tabs open and less resentment toward Tuesday?

Those are business outcomes. They also make the next investment decision much easier.

Eating Our Own Dog Food: AI in CloudSee Drive

We develop a SaaS product for AWS. CloudSee Drive started as an Amazon S3 browser. But our target market wanted more. We added single sign on (SSO), a project that took several months (planning took a few months and development did too).

Users had massive buckets with millions of files. We added Fast Buckets, which took a year on the calendar. That’s about the time we started development use of AI.

A solution for uploading large files, another common S3 issue, took us three months. Testing was the hardest part of both Fast Buckets and Large File Uploads, so we used AI to make us better there. The team saw the light.

The next feature took a month, so we calibrated that we could do one large feature a month. The next feature took two weeks. The next, one week. Development reached a faster velocity than our sprint planning. We laughed aloud but loved the outcomes.

Within a few months, our entire team has a repeatable workflow, a clearer policy, and evidence that AI didn’t replace them and they were actually rewarded for using it. That is AI adoption buy-in: useful, visible, governed, and grounded in the work people do.

Leading with AI

Your team’s readiness is shaped by whether employees believe leadership will use the technology responsibly, explain the tradeoffs honestly, and support people through the change. Technology will keep changing. Trust is the part you have to build on purpose.