How Small Businesses Can Close the AI Adoption Gap Through AI Fluency

Looking at the Data: In the Southern Central region — Texas, Oklahoma, Arkansas, and Louisiana — 72% of employers use AI weekly. Only 46% of employees do, and about a quarter never touch it. Jasmin Weber, Client Strategy Owner at DTC SEO Agency in Austin, uses AI every single day. Her take on the gap: it isn't about access. It's that most employers frame AI as a way to multiply output — "now you can 10x your work" — when the real value is improving the quality of the work itself. Closing the gap takes fluency, not just licenses. 

 Watch the full interview with Jasmin Weber →

AI can multiply how much work you do. The better question is whether it improves the work you're already doing.

A 26-point adoption gap usually gets read as a tooling or trust problem. Weber's experience points somewhere more specific: employers and employees are using AI toward different goals, and most teams have never written down how the tool is actually supposed to be used. The fix isn't more access. It's fluency — and a shared standard for how the team works with AI. 

Quality, not quantity: the reframe most employers get wrong 

Weber uses AI constantly, but she's blunt about the most common misconception she sees between employers and employees. 

"The biggest misconception I've seen is an employer saying, 'You have access to AI now, you can 5x, now you can 10x your output.' I think that's kind of a slippery slope and a dangerous way to look at things. Because AI can absolutely help increase the amount of work you do, but I don't know that the focus of how you use AI should be, ‘how much work can I do?’ I think the focus should be on how much I can improve the work that I am doing."* — Jasmin Weber 

The distinction matters because it changes employee behavior. Frame AI as a 10x output machine, and the team feels pressure to produce more — not better. 

"An employee is going to feel pressured to produce as much work as possible, as opposed to producing as high-quality work as possible. For myself, and I think for our company as a whole, quality is non-negotiable."* — Jasmin Weber 

What that looks like in her own day isn't "more deliverables." It's compression and verification — shaving time off manual tasks, then spending the recovered time on deeper work. 

"Is there a way that I can shave 20 minutes off a task? Is something going to be terribly labor-intensive and manual, but I can put it into AI and get it done in 3 minutes? Amazing. Have I just done a bunch of research and I want someone to double-check my work? AI is a great way to double-check the work that you've done — see if you're missing some information... The more that I can expedite the work that I need to do, the more time that I have to spend on those really deep-dive research, highly technical tasks."* — Jasmin Weber 

The adoption gap is really a fluency gap 

If employers use AI and employees don't, the instinct is to buy more seats. Weber's shared that the gap can come from the absence of a shared, documented way to use the tool. 

"It really revolves around the ability to have SOPs in place. We have a lot of internal in-house prompts that we've created for very specific tasks. You need to do X, you use the prompt for X. You need to do Y, you use the prompt for Y. Making it very easy for anybody to jump into the role and to find the same kind of results that you're finding."* — Jasmin Weber 

Without that shared standard, AI use becomes a guessing game that produces inconsistent results across the team. AI is a skill to be built, not a switch to be flipped. Fluency and standard expectations are the differentiators, and they need to be taught. 

How DTC builds AI fluency: enforced research, shared learning 

The most transferable part of Weber's playbook is how her team keeps pace. It's not osmosis. It's scheduled, and it's shared. 

Three protected hours a week where the company does in-depth research and then brings it together to share their learning. For an SMB, that's a near-free skill-development program — and it directly addresses the Southern Central data showing employees see fewer growth opportunities (61%) than employers think they offer (74%). Structured AI learning is a growth opportunity, made visible for employees at little cost to the business. 

Her closing advice for hesitant owners reinforces that AI adoption is a learning posture, not an all-or-nothing bet. 

"Utilizing AI in your business doesn't have to mean relying on AI. You don't have to use AI for everything. It's there as a tool for you to utilize when it's best for you."* — Jasmin Weber 

Not just using AI, but showing up in AI search: build authority, not backlinks 

Weber's day job is getting DTC brands to rank — not just on Google, but inside ChatGPT, Perplexity, and Gemini. Her guidance for SMBs trying to be found in AI answers is concrete, and it doubles as a content-quality standard. 

"Build out your authority on your website. The more that you can show that you are an authority in your space, the more likely an LLM model is to trust you as a source. You can include firsthand testimonials. You can include as many markers as you possibly can about your credibility, your qualifications, certifications. These things are more important than ever."* — Jasmin Weber 

Her example for a trades business makes it practical: if you work in HVAC, show the certifications, build an About Us page with the person who does the installs, talk about their education and experience, write blog articles that demonstrate real depth on the tools and products you use, and document the location-specific issues you actually solve. Structure all of it to be easy for an LLM to read and rich with first-hand experience. 

And the fastest way to undo it: chasing cheap links. 

 Why this matters for SMB operators in Texas, Oklahoma, Arkansas, and Louisiana 

Weber's playbook and the State of Work data converge on a specific operator reality across the Southern Central region: 

•      The 26-point AI gap is a fluency gap, not an access gap. Buying more AI seats won't close it. Documented prompts, shared standards, and protected learning time will. 

•      How you frame AI shapes how your team uses it. Sell it as "10x your output," and you get pressure and burnout. Frame it as "improve the work," and you get quality. 

•      Structured AI learning is a growth opportunity employees can see. Southern Central employees report fewer growth opportunities (61%) than employers believe they offer (74%). Enforced research-and-share hours close that perception gap directly — and double as low-cost skill development. 

•      Authority-building is the new SEO, and it rewards real expertise. SMBs that document credentials, experience, and first-hand knowledge on their own site are the ones LLMs cite. This is the same standard that makes good content for customers. 

•      Lean teams still have to manage the change. Performance conversations around AI adoption, documented SOPs, and a learning rhythm are real management work. For owner-operators without an HR function, HR outsourcing or a PEO can support the people-ops load — performance frameworks, learning and development structure, and the Employee Benefits access that make continued education a real perk, not a line item. 

How to put this into practice 

For Southern Central operators trying to close the AI gap and build durable AI fluency: 

1.     Reframe the goal from output to quality. Tell your team AI is for improving the work efficiency, not just producing more of it. Say it explicitly — the framing changes behavior. 

2.     Write down your prompts. Build a shared library of in-house prompts for specific, repeated tasks. "Do X, use the prompt for X." Consistency comes from documentation, not talent. 

3.     Block protected AI research time — and enforce the sharing. A few hours a week per person, with a standing meeting where everyone brings back what they learned. The sharing requirement is what makes it real. 

4.     Treat AI fluency as skill development. Position continued AI education as a growth opportunity. It closes the perception gap on growth and builds capability at the same time. 

5.     Build authority on your own site. Credentials, certifications, first-hand testimonials, and genuinely deep content. This is how you show up in AI search — and it's the same work that earns customer trust. 

6.     Don't chase cheap backlinks or all-or-nothing AI mandates. Both backfire. Quality over quantity applies to links and to adoption. 

 What's next for Southern Central SMBs 

The State of Work data shows a region where employers are well ahead of their teams on AI — and where the gap is usually misdiagnosed as an access problem. Weber's experience says it's a fluency problem, and fluency is teachable. The operators who reframe AI around quality, document how their team should use it, and protect time to keep learning are the ones who'll turn a 26-point gap into a durable advantage. 

Read the full Southern Central regional report from TriNet's 2025 State of Work →

See how TriNet supports Texas, Oklahoma, Arkansas, and Louisiana SMBs with HR outsourcing, learning and development tools, and benefits access →

FAQ 

Why is there an AI adoption gap between employers and employees in the Southern Central region? 

TriNet's 2025 State of Work report found 72% of Southern Central employers use AI weekly versus 46% of employees — a 26-point gap. According to Jasmin Weber of DTC SEO Agency, the gap is less about access and more about fluency: most teams have no documented, shared standard for how AI should be used, so adoption stays inconsistent. 

Should small businesses use AI to increase output? 

Not as the primary goal. Weber's argument is that framing AI as a way to "10x your output" pressures employees to produce more rather than better. The higher-value use is improving work quality — compressing manual tasks, double-checking research, and freeing time for deeper work. 

How do small businesses close the AI adoption gap? 

Document a shared library of prompts for specific tasks, set a clear standard that the whole team uses the same prompts for the same work, and set aside dedicated time for AI learning with an expectation that findings are shared across the team. Access alone doesn't close the gap; fluency does. 

How can a small business show up in ChatGPT, Perplexity, or Gemini? 

Build authority on your own website. Publish credentials, certifications, first-hand testimonials, and genuinely in-depth content about your field. LLMs cite sources they trust, so the more you demonstrate real expertise and first-hand experience, the more likely you are to appear in AI-generated answers. 

What hurts a small business's visibility in AI search? 

Poor-quality backlinks. Per Weber, LLMs recognize manipulative link-building and will treat the site as untrustworthy — and untrustworthy sites have almost no chance of appearing in LLM results. Link quality matters more than quantity. 

How can an SMB build AI skills across the team? 

Treat it as continued education. DTC SEO Agency blocks several hours a week per person for AI research, where everyone shares what they find in a group session. The sharing element is what keeps the whole team growing at the same pace. 

Is AI adoption a performance-management issue? 

It can be. Some employees resist new tools regardless of efficiency gains. Documented SOPs, clear expectations, and a learning rhythm make adoption measurable. For lean teams without an HR function, HR outsourcing or a PEO can provide the performance management and learning and development tools to support your employees. 

Does using AI mean relying on AI? 

No. Weber is explicit that using AI in a business doesn't mean depending on it for everything. It's a tool to deploy where it's most beneficial. Starting small — and starting now — beats waiting for an all-or-nothing rollout.