SMBs Are Buying AI Tools and Getting Almost Nothing Back

Why are SMBs failing to get results from AI tools they already own?

The problem is not access. SMBs can buy a Copilot license or spin up ChatGPT Team in an afternoon. The problem is that buying the tool is the easy part. Using it in a way that actually changes how work gets done requires expertise most small business teams do not have and cannot afford to hire full-time.

Pax8 VP of Channel and Community Engagement Eric Torres put it plainly: partners need to move beyond selling AI to helping clients get results from it. That is not a minor distinction. It is the entire gap between AI spend and AI value.

"The bottleneck is not the software. It is the operational knowledge to make it do something useful."

What does the AI expertise gap actually look like inside a small business?

It shows up in a few predictable ways. A team gets a ChatGPT or Copilot subscription, a few people use it to write emails faster, and within 60 days usage drops off because nobody set up workflows, nobody trained the team on prompting, and nobody connected the tool to any real business process.

According to McKinsey's 2024 State of AI report, less than 30% of organizations that have deployed AI tools report capturing meaningful value from them. For SMBs operating without dedicated IT or data teams, that number is almost certainly worse.

The gap has three layers:

• Prompting knowledge. Most employees have never been taught how to write a prompt that gets a useful output. They try it once, get a mediocre result, and conclude the tool does not work.
• Workflow integration. AI tools running in isolation from actual business systems generate isolated results. A GPT that does not touch your CRM, your project management tool, or your internal documents is a novelty, not a productivity multiplier.
• Governance and judgment. Without guardrails, teams either over-trust AI outputs or under-use the tools out of fear. Neither produces results.

Is the solution to hire an AI expert or a data scientist?

For most SMBs, no. A full-time AI engineer or data scientist costs $130,000–$180,000 per year at minimum, and that role is not built for the operational, day-to-day deployment work most small businesses actually need. It is built for model development and data infrastructure at a scale most SMBs will never operate at.

What SMBs actually need is closer to an AI-literate operator: someone who understands which tools fit which problems, can build basic automations in platforms like Make or Zapier, can train a team on prompting standards, and can set up lightweight governance so the organization uses AI consistently and safely.

That is a skill set you can build internally over 60–90 days with the right curriculum, or bring in externally for a defined engagement. It does not require a PhD.

Which AI tools are SMBs buying versus which ones they should be using?

Here is what we see across the market right now:

| Tool | What SMBs are buying it for | What actually creates value | |---|---|---| | ChatGPT Team / GPT-4o | Writing, research, brainstorming | Custom GPTs built on internal docs and SOPs | | Microsoft Copilot | Email drafting | Meeting summaries, CRM data entry, document generation | | Notion AI / ClickUp AI | Note cleanup | Automated project briefs, status reports | | Make / Zapier AI features | Ad hoc automation | End-to-end process automation tied to real data | | Claude (Anthropic) | General Q&A | Long-document analysis, policy drafting, complex reasoning tasks |

The pattern: SMBs buy at the surface level and use at the surface level. The tools have significantly more capability than most teams ever reach because nobody took the time to build toward it.

How do you close the AI expertise gap without a big budget or a big team?

Three things that actually move the needle:

1. Pick one process and go deep. Do not try to "use AI across the business." Pick one workflow, such as responding to inbound leads, writing proposals, or handling customer service FAQs, and build a proper AI-assisted version of that process from end to end. Document it. Train your team on it. Get it to the point where it runs consistently before touching anything else.

2. Build internal prompting standards. Every team using AI tools should have a shared prompt library: 10–20 tested, approved prompts for the tasks they run most often. This alone closes a significant portion of the quality gap. It takes one afternoon to build the first version and it compounds over time.

3. Get external expertise for the setup, not the ongoing operation. The best use of outside help is to get the architecture right: tool selection, workflow design, governance basics, and initial team training. After that, an internal owner can manage it. You do not need a consultant on retainer forever. You need someone who has built this before to set it up correctly so your team can actually run it.

What we'd actually do

• Audit before you buy anything else. List every AI tool your business is currently paying for, who uses it, and what it is being used for. Most SMBs find they are paying for tools they barely use. Cancel the redundant ones and go deep on the one or two that fit your actual workflows.
• Run a 90-day internal AI training sprint. Pick an internal champion, not necessarily your most technical person but your most process-oriented one, and get them trained on prompting, workflow automation basics, and your specific toolset. The AI For Business community at skool.com/aiforbusiness is built specifically for this: SMB operators getting practical, implementation-level training without the enterprise overhead.
• Set a results benchmark before deployment. Before you roll out any AI tool to a team, define what success looks like in measurable terms: time saved per task, error rate reduction, volume handled per person. Without a baseline, you cannot know if it is working, and you will never be able to justify expanding it.