The 3 Types of SMB AI Buyers — Which One Are You?

Every year, the research firm Techaisle surveys 5,500 small and mid-size businesses around the world. They ask what's keeping leaders up at night, where they're spending on technology, and what they expect from AI.

The 2026 report revealed something I haven't seen in 18 years of these surveys: AI isn't just a technology priority anymore. It's the #1 technology priority. For the first time, "GenAI and agentic automation" topped the list — ahead of cybersecurity, ahead of cloud modernization, ahead of everything else.

But here's what makes this year's data genuinely useful. The survey didn't just ask whether SMBs want AI. It uncovered how differently they want it depending on where they are as a business. And those differences aren't subtle — they're so distinct that Techaisle sorted SMBs into three categories that I think every business owner should know about.

I've started using this framework with my own clients because it immediately clarifies what kind of AI investment makes sense — and, just as importantly, what kind would be a waste of money.

Renters: "Just Make My Existing Tools Smarter"

Who they are: Small businesses with 1 to 99 employees. Typically no dedicated IT staff. The owner or a tech-comfortable employee handles technology decisions.

What they want from AI: Invisible upgrades. Renters don't want to evaluate AI vendors, build integrations, or train their team on new systems. They want the software they already pay for — their accounting platform, their CRM, their email marketing tool — to just get smarter on its own. When QuickBooks starts auto-categorizing expenses with 95% accuracy, or when their CRM starts flagging deals that are about to go cold, that's the AI experience Renters are looking for.

Their biggest risk: Getting sold standalone AI tools they don't need. Renters are the most heavily marketed-to segment right now. AI chatbot vendors, AI writing assistants, AI analytics dashboards — every vendor wants a piece of the small business market. But for a 30-person company, adding three separate AI subscriptions on top of their existing software stack creates more complexity, not less. The tool trap is real: 88% of companies are using AI in some form, but only 6% are seeing meaningful financial returns, according to McKinsey's latest global survey. For Renters, the path to that 6% isn't buying more tools — it's choosing platforms that have AI built in.

What Renters should do:

• Before buying any new AI product, audit what you already have. Microsoft 365 now includes Copilot features across Word, Excel, Outlook, and Teams. Google Workspace has Gemini woven into Docs, Sheets, and Gmail. Salesforce, HubSpot, Zoho, and most major CRM platforms have added AI-powered lead scoring and workflow automation. Your existing vendors are racing to add AI — and in most cases, you're already paying for it or can access it through a modest upgrade.
• If you find a genuine gap — say you need AI-powered customer service and your current tools don't offer it — pick one platform that integrates with your existing stack rather than a standalone point solution. Integration matters more than features.

Scalers: "Help Us Do More Without Hiring More"

Who they are: Core midmarket companies with 100 to 999 employees. They have some IT capability, maybe a small internal team or a managed service provider. They've likely already experimented with AI in one or two departments.

What they want from AI: Talent multiplication. Scalers are under intense pressure to grow revenue without proportionally growing headcount. They're using AI to let a marketing team of 5 do the work of 15, or to let a customer support team handle 3x the ticket volume without burning out. The Techaisle survey puts "driving profitable growth" as the #1 business priority in 2026, and for Scalers, AI is how they plan to get there.

Their biggest risk: Token Shock. This is the term from the Techaisle report that resonated most with me because I see it constantly with mid-size clients. When you pay for AI on a per-query basis — what the industry calls "token pricing" — costs are directly tied to usage. That sounds reasonable until your marketing team starts running 500 AI-generated content variations per campaign, or your data team starts querying an AI model against your entire customer database. Suddenly your AI bill for the month is 4x what you budgeted. Token Shock is the new version of the cloud cost surprise, and it catches Scalers off guard because they're the ones actually using AI at volume.

The second risk is Shadow AI. At this company size, employees are already using ChatGPT, Claude, Midjourney, and a dozen other AI tools — often on personal accounts, often with company data. The Techaisle survey ranks Shadow AI governance as the #4 IT challenge for SMBs. It's not that employees are being reckless. They're being resourceful. But when sensitive customer data, financial projections, or proprietary strategies flow through AI tools the company doesn't control or even know about, that's a real liability.

What Scalers should do:

• First, get your AI costs predictable. This means working with your cloud or AI provider to set up usage caps, budget alerts, and committed-use pricing where available. Azure, AWS, and Google Cloud all offer ways to cap AI spending — but they require deliberate setup. Nobody's going to do this for you by default.
• Second, channel the Shadow AI energy into approved tools. Don't ban AI use — that never works and it drives the behavior further underground. Instead, provide sanctioned AI platforms with proper data boundaries, train your team on what data can and can't go through AI tools, and create a lightweight governance framework. This doesn't need to be a 50-page policy. It can be as simple as: here are the approved tools, here's what you can feed them, here's who to ask if you're unsure.
• Third — and this is the insight from McKinsey that I think matters most for Scalers — redesign your workflows before scaling AI across departments. Companies that restructure their processes around AI see 3x more impact than those that just add AI to existing processes. If your customer support workflow was designed for humans-only, bolting an AI chatbot onto the front of it won't deliver the results you're expecting. Redesign the workflow so AI and humans each handle what they're best at.

Builders: "We Want to Own Our AI Infrastructure"

Who they are: Upper midmarket companies with 1,000 to 5,000 employees. They have established IT departments, possibly a CTO or VP of Technology, and the budget to make significant technology investments.

What they want from AI: Control. Builders have moved past the experimentation phase. They've seen what AI can do, they believe in its value, and now they want to run it on infrastructure they own and govern. Some are "repatriating" AI workloads — moving them off third-party platforms and onto private infrastructure — because they need guaranteed data residency, predictable costs at scale, and the ability to fine-tune models on proprietary data.

Their biggest risk: Building in isolation. The temptation at this level is to treat AI as a technology project — hire AI engineers, build custom models, spin up a GPU cluster. But the McKinsey data is clear: the companies getting real ROI from AI aren't the ones with the fanciest models. They're the ones that redesigned their business processes around AI. A custom model that doesn't connect to your ERP, your CRM, and your supply chain data is an expensive science experiment.

The Redwood analysis calls this "agentic orchestration" — the idea that AI agents shouldn't operate as isolated point solutions. They need to coordinate across your entire operation. One agent handles the customer inquiry, another updates the order in your ERP, a third adjusts the demand forecast. Without orchestration, you end up with AI-powered silos instead of human-powered silos — different problem, same result.

What Builders should do:

• Invest in governance as an operating model, not a policy document. At this scale, AI governance means defined boundaries for what AI can decide autonomously, explicit escalation paths to human decision-makers, and a full audit trail for every AI action. This isn't bureaucracy — it's what makes AI trustworthy enough to actually give it real responsibilities.
• Build the data layer before the AI layer. Builders often have the most data but also the messiest data — decades of systems, acquisitions, migrations, and workarounds have left information fragmented across dozens of platforms. An AI model trained on bad data makes confident-sounding bad decisions. Clean, unified, accessible data is the prerequisite.
• And plan for orchestration from day one. When you build or buy AI capabilities, design them to talk to each other and to your core business systems. If your AI customer service agent can't access real-time inventory data, it's going to make promises your warehouse can't keep.

Which One Are You?

Most business owners recognize themselves immediately in one of these three profiles. The value of this framework isn't just self-identification — it's knowing what to do next.

If you're a Renter, your move is to unlock the AI already in your tools — not buy new ones. If you're a Scaler, it's to get your costs predictable, govern Shadow AI, and redesign workflows before expanding. If you're a Builder, it's to invest in data quality, governance, and orchestration — not just bigger models.

The common thread across all three? AI success has less to do with the technology you buy and more to do with the foundation you build: clean data, smart workflows, and practical governance. That's true whether you're a 20-person company or a 2,000-person one.