Every week there’s a new AI tool promising to revolutionise the way your business handles data. Dashboards that build themselves. Natural language queries that replace your entire analytics team. Predictive models trained in minutes. The marketing is slick, the demos are impressive, and the price tag — for now, at least — often seems reasonable. But if you’re a BI practitioner or a business leader who relies on data to make decisions, buying the wrong tool doesn’t just waste budget. It quietly erodes trust in your entire data function.

Before you sign anything, ask these questions first.

1. What Problem Are You Actually Solving?

This sounds obvious, but it’s where most evaluations fall apart. Vendors are exceptionally good at showing you what their tool can do. Your job is to stay anchored to what your business needs done.

Start by documenting your current pain points in plain language. Are reports taking too long to produce? Is data scattered across systems that don’t talk to each other? Are analysts spending 80% of their time cleaning data instead of interpreting it? Once you have that list, you can measure every tool against it — not against a feature checklist the vendor hands you.

In a South African context, this discipline matters even more. Many local businesses are still consolidating data from legacy ERP systems, managing multi-currency reporting across African markets, or dealing with inconsistent data quality across regional branches. A tool that works beautifully for a US SaaS company may solve exactly zero of those problems.

2. How Does It Handle Your Data — Not Their Demo Data?

Vendor demos run on clean, curated datasets that behave perfectly. Your data does not. Before committing to any AI-powered BI tool, insist on a proof of concept using a representative sample of your own data. Then watch carefully.

• Does the tool handle missing values gracefully, or does it silently drop rows and skew your results?
• Can it connect to your actual data sources — whether that’s SQL Server, a cloud warehouse, flat files, or a combination?
• How does it perform when data volumes spike at month-end or during financial year close?
• What happens when a data feed breaks — does it alert you, or does it keep serving stale numbers with no warning?

Any tool that can’t answer these questions in a live environment isn’t ready for production use in your business.

3. Who Owns the Outputs — and Can You Explain Them?

AI-generated insights carry a hidden risk that purely rule-based reporting does not: explainability. When a machine learning model flags an anomaly or forecasts next quarter’s revenue, can you trace the reasoning? Can your team explain it to a finance director, an auditor, or a board member?

This is not a theoretical concern. In regulated industries — banking, insurance, healthcare — the ability to explain a decision or a recommendation is often a compliance requirement, not a nice-to-have. Even outside regulated sectors, a leadership team that can’t interrogate how a number was produced will eventually stop trusting it.

Evaluate every AI tool against the explainability standard your business actually operates under. And be wary of any vendor who dismisses this question as unnecessarily cautious. It isn’t.

4. What Does Total Cost of Ownership Look Like at Scale?

Introductory pricing is a feature, not a promise. Before you build workflows, train staff, and migrate reporting onto a new platform, model what the cost looks like at 2x and 5x your current data volume and user base.

• Does pricing scale per user, per query, per row, or per compute hour?
• What are the data egress costs if you need to move data out of the platform?
• Is there a local support structure, or are you logging tickets into a queue serviced in a different time zone?
• What’s the vendor’s track record? Have they raised prices significantly after locking in enterprise customers?

For South African businesses, rand-denominated budgets up against dollar-denominated SaaS pricing adds another layer of exposure. Model the currency risk too.

Make the Evaluation Process Work for You

The best AI tools genuinely can accelerate your BI capability — improving speed to insight, reducing manual effort, and surfacing patterns no human analyst would catch in time. But none of that value materialises if the tool doesn’t fit your data environment, your team’s skills, your compliance obligations, or your budget reality.

Approach every evaluation with the same rigour you’d apply to hiring a senior analyst: structured criteria, real-world testing, and a clear view of what success looks like before you start. The vendors who welcome that scrutiny are usually the ones worth talking to.

If you’d like an independent perspective on evaluating BI or AI tools for your specific environment — or help building an evaluation framework your team can use — reach out to us at [email protected]. We’ve seen enough implementations go wrong to help you avoid the avoidable ones.

oCode360 (t/a JVW Business Solutions (Pty) Ltd) — Making data make sense.