Every week a client asks us some version of the same question: "We know AI is important — but where do we actually start?" It's a fair question. The hype is deafening, the use cases feel either trivially small ("AI writes your emails!") or impossibly large ("AI runs your entire operation!"). Reality sits somewhere in the middle.
Here's a practical framework we use when helping businesses take their first steps.
Start With a Process Audit, Not a Technology Decision
The biggest mistake we see is starting with "we want to use AI" instead of "here's a process that's costing us time and money." AI is a solution — you need to identify the problem first.
Ask your team:
- What tasks take the most time but require the least judgment?
- Where do we make mistakes due to volume or fatigue?
- What information do we have that we're not acting on?
The answers point directly to your highest-ROI AI opportunities.
The Three Entry Points That Actually Work
After working with dozens of businesses, we've found three categories of AI integration that consistently deliver value without requiring a complete operational overhaul.
1. Document Processing and Extraction
If your team spends hours extracting data from invoices, contracts, or forms — this is the fastest win. Modern AI (GPT-4, Claude, Gemini) can read unstructured documents and output structured data with >95% accuracy on clean inputs.
A logistics company we worked with was manually entering data from supplier invoices into their ERP. We built a pipeline that processes PDF invoices, extracts line items, validates against their product catalog, and drafts the ERP entry for human review. What took 3 hours a day now takes 15 minutes.
2. Customer-Facing Assistants
An AI assistant trained on your product documentation, pricing, and FAQ can handle 60–80% of repetitive customer inquiries — at any hour, in any language. The key word is trained: a generic chatbot is useless; one grounded in your specific knowledge base is genuinely helpful.
This isn't about replacing your support team. It's about letting them focus on the 20–40% of complex queries that actually need a human.
3. Internal Knowledge Search
Large organizations sit on years of internal documents, meeting notes, and project files that nobody can actually find. Retrieval-augmented generation (RAG) lets you ask plain-language questions and get answers sourced from your own documents.
"What was the outcome of the Q3 2023 supplier negotiation?" becomes answerable in seconds instead of requiring someone to dig through SharePoint folders.
What to Expect From a Pilot
A realistic AI pilot takes 4–8 weeks, costs €5,000–€20,000 depending on scope, and should target a measurable outcome: hours saved per week, error rate reduction, query deflection rate.
If a vendor can't tell you what metric the project will move — walk away.
The Honest Caveat
AI integrations require maintenance. Models update, your data changes, edge cases emerge. Budget for ongoing tuning — typically 15–20% of the initial build cost per year. The businesses that get the most value from AI treat it like software infrastructure, not a one-time project.
Curious what AI could do for your specific workflow? Let's have a conversation — no commitment, just honest advice.
