Why an AI Baseline Assessment Is Essential for SMBs
Applying artificial intelligence (AI) in small and medium-sized businesses (SMBs) can deliver real benefits, but without proper preparation it can also lead to costly mistakes. One of the most common mistakes is starting without an AI baseline assessment — a way of determining your starting position before you invest in AI technology. Let's look at five common mistakes that a baseline assessment helps you avoid.
1. Choosing Tools for Problems That Don't Exist
Many SMBs invest in AI tools that don't actually match their real problems. Without a clear baseline assessment to map out needs first, projects can stall quickly. The cheaper route: first verify that a problem exists that AI can actually solve.
2. Data That Isn't in Order
AI runs on data. Without clean, structured data, project results are likely to disappoint. According to MIT figures, only 5% of AI pilots deliver measurable results. Businesses need quality data in place before getting started with AI.
3. No Clear Owner
An AI project without a designated owner is doomed to fail. Someone needs to be responsible for progress and implementation — ideally someone with both technical and operational knowledge.
4. Forgetting Compliance
Under the GDPR and other regulations, every AI project must meet specific requirements. Focusing on compliance early prevents costly adjustments later. Make sure legal and ethical aspects are built into the project plan from the start.
5. Not Making Success Measurable
Without clear KPIs, success can't be measured — and the project as a whole risks not being taken seriously within the business. Set clear goals and benchmarks to monitor progress.
Avoiding the mistakes above starts with a thorough baseline assessment. AI Advies Bureau can help you with that. Visit our AI readiness page to learn more and get acquainted.
Frequently asked questions
What is an AI baseline assessment?
An AI baseline assessment is a concise review of your current processes, data, and goals before you invest in AI technology, so you know where you stand and what's realistically achievable.
Why do most AI pilots fail?
According to MIT research, only 5% of AI pilots deliver measurable results, often because tools are chosen for problems that don't actually exist or because the underlying data isn't in order.
Who should own an AI project at an SME?
Ideally someone with both technical and operational knowledge, responsible for progress, implementation, and making success measurable.