Why Only 1 in 5 SMBs Use AI for Business Intelligence - And How to Fix That in 2025

The AI boom has been wild to watch.

If you've been following from the sidelines with no skin in the game, it probably looked like entertainment.

But for most SMBs, it’s been a slow-burning panic.

Corporate giants are embedding AI for business intelligence into every corner of their operations, while smaller teams are left wondering if they’re going to be one of the ones who get left behind.

Between the endless hype, viral LinkedIn posts, and constant “revolutionary” product drops, just keeping up - let alone running experiments - can feel impossible.

Especially when you’ve got an actual business to run.

And yet, one of the biggest opportunities for SMBs right now is to adopt AI for data analysis  to make faster decisions, extract insights on demand, and finally put their data to work.

The tools are here. The costs have never been lower.
And still, less than 1 in 4 SMBs use AI to crunch numbers and find trends.

In this article, we’ll break down exactly why adoption is lagging, the most common misconceptions about AI-powered data analysis, and how to move toward a modern, flexible BI setup….no data warehouse required.

Why SMBs Struggle with AI for Business Intelligence Adoption

Barely one in four small businesses has moved beyond experimentation, but of those, 62% are using AI for data analysis. 

The use case is clear… so why is adoption stalling in SMBs? 

Trust & Explainability

This is a big one. 

According to a recent Pipedrive survey (1,100 SMB decision‑makers) 40 % have not adopted AI because they “don’t trust the outputs”. 

Another McKinsey study shows 40 % of companies rank “lack of explainability” as the main concern with AI tools. 

Add a year’s worth of headline‑grabbing hallucination scandals, GDPR disasters  and the looming EU AI Act transparency rules, and many SMBs decide it’s safer to stick with Excel than risk fines or bad data decisions.

Fair enough. 

Tech Barrier

48 % of SMBs say they simply “don’t know enough about AI” to start.

And with no in‑house data engineers, there’s a looming fear of mis‑configuring a tool and accidentally exposing customer data or breaching GDPR.

SMBs also often believe that you need an enterprise-grade data warehouse before being able to use AI for data analysis. 

In reality, modern AI tools for BI are made to be plugged directly into open-source databases like PostgreSQL or MySQL. And setup usually takes a couple of minutes.

Cost 

The second common misconception is that AI tools are expensive. This point requires a bit of nuance because it is very solution dependent.

For SMBs that have moved forward with cloud data warehouse storage, the fear of AI executing uncontrolled SQL queries and running up explosive bills is legitimate. Especially as cloud costs are increasing rapidly, at approximately 33% YoY. 

And that’s only accounting for data warehouse queries, not the costs relating directly to the AI tool itself. 

The good news is that 2025 has seen some considerable improvements in AI tools for data.

SMBs now have access to highly versatile open-source or SaaS style solutions that bill a flat monthly fee for unlimited queries AND can be plugged directly into open-source databases. 

This keeps costs controlled and at a minimum, whilst allowing extremely powerful (and accessible) AI-powered data analysis that large corporations are not yet capable of due to the complexity of their data structures.

“Dashboards Already Solve BI”

This one is simple. We probably don’t even need to give you some numbers because if you’ve ever worked in an office, you know exactly how the next part goes…

Nobody uses dashboards. But here’s a stat anyway :

Less than 30% of employees open dashboards weekly. 

That’s a painful stat considering how much time and effort is required to model data, create visuals and give permissions. 

Dashboards also only answer single questions, any follow-ups require a new tab, and report turnaround times take 4-5 days on average.

The problem is usability not infrastructure. And that’s precisely where AI tools can be extremely powerful provided you find the right solution. As a quick rule of thumb, here are some things to look for:

• Secure, transparent query generation

• Real-time data access (no warehouse needed)

• Flexible for both technical and non-technical users

• Flat pricing with unlimited queries

• EU-compliant, zero-retention design

How AI For Data Analysis Has Changed In 2025

Enter “conversational BI” or "NL2SQL" for the tech-savvy. 

You may have heard of this type of technology before, it was all the rage for a while in 2021. And the early solutions flopped… hard.

But 2025 NL2SQL is a whole different story. All thanks to “agentic AI”.

Picture this: 

You ask a question in plain language - “Show me total MRR this quarter by product.”

The AI understands your business context and interprets your intent. It identifies metrics, filters, time frames, etc…

The agent is schema‑aware and is able to build SQL with the right joins and aggregations.

You get a transparent preview which you can use to view/edit the SQL if needed.

Your answer comes instantly – tables, charts, and a short narrative summary.

You can ask any follow up questions. You might ask it to “Break that down by region” and the AI will use previous context.

This is truly the big shift compared to early “one‑shot” NL2SQL attempts, which just translated questions into an SQL prompt. 

Modern agents validate results, retry on errors, and clarify ambiguities (“Q2 2025 or rolling quarter?”) to ensure they don’t misfire or hallucinate. 

The experience feels less like opening a fixed dashboard and more like chatting with a colleague that knows your data inside-out.

What Does This Mean for SMB Business Intelligence? 

• Skip dashboard set-up. Just ask your question in plain English.

• Get insights MUCH faster. Get answers in seconds, rather than stacking up long ticket queues.

• Democratise access to data for all your teams.

• No data warehouse required, just plug straight into your database.

What About Trust, Compliance, and Security?

As we mentioned earlier, concerns around regulatory compliance, AI transparency, and data protection are some of the biggest reasons SMBs hesitate to adopt AI - and rightly so.

That's exactly why tools like Myriade.ai exist.

It’s been intentionally designed for small and mid-sized teams who want the benefits of AI for data analysis and AI for business intelligence, but can’t afford to mess around with security or complexity.

Here's how Myriade handles those concerns: 

• Transparent by Design - The agent’s workflow and the SQL queries it generates are fully auditable and explainable.

• Zero Setup, Plug & Play - Plugs directly into databases (cloud, serverless or on-premise) with zero setup. Connect up to 3 databases simultaneously.

• Compliant & Secure Architecture - Uses read-only connections, stores nothing on AI servers, offers zero-knowledge protection, and is fully EU-hosted. It’s aligned with GDPR and ready for the strictest compliance standards.

• Use It Your Way - Prefer open-source? Or need a managed cloud solution? Myriade gives you options.

In short, Myriade is a lightweight, secure layer that gives you all the power of AI for data analysis without needing a massive IT team or advanced data stack.

If you want to try it free, click here. 

Not sure where to begin? We’ve laid out a quick 4-step implementation guide below.

BONUS Implementation Guide: 4 Steps to Trusted AI BI on a Budget

So how do you actually get started with AI for business intelligence - without blowing your budget or hiring a team of data engineers?

Here’s a simple 4‑step plan that gets you from zero to insight without the headaches:

1. Take Inventory of Your Data Sources
List out everywhere your business stores useful data. That means spreadsheets, databases, tools like Stripe, HubSpot, QuickBooks, or any other SaaS you rely on.
You can’t ask good questions if your data is scattered or invisible. Visibility comes first.

2. Install a Read‑Only NL2SQL Layer
Use a secure, read‑only interface that translates natural language into SQL - tools like Myriade let you plug straight into your existing databases with zero risk of data overwrite.

Bonus: Myriade installs in minutes and works with Postgres, MySQL, and others. No complex setup, no vendor lock-in.

3. Define Governance & Guardrails
Decide who gets access, what types of queries are allowed, and how results should be reviewed.
Assign user roles, set usage limits if needed, and document basic policies to stay compliant - especially if you’re dealing with regulated data.

4. Train Teams on the Ask/Verify/Iterate Workflow
AI is powerful, but it still needs human judgment.
Encourage your team to:

• Ask clear, focused questions

• Verify the query logic and results

• Iterate with follow-up prompts to refine insights

This mindset turns your analysts into decision accelerators without ever needing to write a line of SQL.