How do you choose an AI partner?

Your team has AI tools, a few people use them well, and someone built a promising assistant.

But people still copy orders between systems, hunt for information, and spend hours on reports. You want to fix that work, and the offers all sound alike. Do you need training, a pilot, an engineer, or a feature your software already has?

This guide is for leaders of European companies with roughly 50 to 500 people. It covers what works, what you can buy, who sells it, and how to choose.

What AI work is succeeding in European companies?

European companies report using AI for order intake, invoices, planning, and team coordination. Each example joins AI to a business system and a person who decides.

Work	Reported European example	What to ask
Order intake	A distributor reads incoming orders with AI and creates records in its business system. People handle the exceptions. [1]	How much work stays in the exceptions?
Invoices	A manufacturer extracts invoice details, matches purchase orders and passes them toward its ERP. [2]	Does it finish the handoff to finance?
Planning	A supermarket forecasts bakery demand. An airline predicts passenger numbers to plan meals. Both report less waste. [3–4]	Which decision will your forecast change?
Shared operations	An airport uses camera analysis and predictions so planners, handlers and airlines work from the same picture. [5]	Will teams act on the same information?

What does this look like for manufacturers and distributors?

Most of the European examples above come from companies that move goods. The work that pays off there is often in the office, not on the factory floor: orders, invoices, reports, and sales preparation.

We are building a sales assistant on a client's own data and cloud, with one rule: the AI explains the numbers, and approved analyses calculate them. Everyone gets the same checked figure, whoever asks.

What is not working yet?

The gap is between an impressive answer and finished work. A demo can look great while your team still feeds it context, fixes mistakes, and copies the result into another system.

Faster output also piles up in front of the people who check it. We wrote about this in why AI can create more work for a team.

What you hear	What to ask
"It saves time."	Did you count review, corrections and maintenance?
"It automates the process."	Which steps still need a person?
"It helped revenue grow."	How did you separate AI from other changes?
"Your team will own it."	Can they run it, investigate errors and make changes?

In the public cases we reviewed, the evidence was clearer for speed and waste than for revenue. A missing number does not mean a project failed. Ask for the baseline and the effort needed to keep it working.

What can you buy?

You can buy help to choose, learn, build or run AI workflows. These are different jobs. Start with the gap you need to close, then pick the offer.

Offer	Makes sense when…	You should end up with…
AI in existing software	A feature already covers your task	A configured feature and a clear way to use it
Training or workshop	People need practical skills	Practice on their own work and a way to check output
Discovery or scan	You can't choose where to start	A ranked shortlist with feasibility checks
Pilot	You need evidence before committing	A bounded test and a go, change or stop decision
Managed automation	You want a supplier to build and run it	Integration, monitoring and agreed support
Custom build	Your workflow needs tailored connections or rules	A tested system, documentation and an internal owner
Embedded engineer	Your team owns delivery but needs expertise	Engineering capacity inside your project

Check your existing software first. Microsoft Copilot Studio and similar platforms cover many simple assistants. Pay for custom work when the process needs connections or controls your tools can't provide.

Who are the AI partners in Northwest Europe?

In October 2026 we looked at the offers of more than ten providers across north-west Europe, reading each one's website, case studies and the facts they publish for AI assistants. We also looked at how firms in the UK and the US package the same work. These seven stood out because they deliver working systems or programs, instead of only publishing content. Most of them are based in the Netherlands and Belgium and work across Europe. We added ourselves at the bottom so you can compare us on the same terms. Read it as a snapshot of methods and services. We did not rank anyone or audit their delivery.

Provider	What they offer	How they work	A good fit if you…
Repetive	Office agents for companies that make, deliver, build or install	One agent per back-office job, such as quotes, orders or invoices, built on your ERP and then run for you every month	Want quote or order work automated inside Exact, AFAS or a similar ERP, and run for you
SPAIK	Implementation and adoption for organizations or departments of 50+ people	A four-week kickstart, then training and ongoing adoption support	Want a structured adoption program across the company
What's Next AI	Custom agents for mid-sized companies, such as tender intake, leads and support	First agent live within weeks, you own the code, then one extra process each month	Want a steady rhythm of new agents
DataNorth AI	AI implementation and training, from SMEs to large companies	Rollouts of ChatGPT Enterprise and Copilot, plus use cases like mailbox sorting and forecasting	Want a large tool rollout with training
ai.nl	Keynotes, tool training and agents	Inspiration sessions and training for many people at once, with agent work added	Want to inspire and train a large group
Rewire	Enterprise data and AI "engines" for pricing, B2B sales and supply chain	Large transformation programs	Need data foundations and a large transformation partner
ML6	AI engineering, agents and managed operations	Engineering teams that can also run systems long-term	Need deep engineering capacity and long-term operations
Aibl.to (us)	Agents on top of your ERP, assistants on your sales and product data, and machine learning or statistical models, plus workshops	Engineers build in your own cloud with your team, test on your real cases, then hand over	Want agents and models built on your own ERP and sales data, and your team able to run them

Four things we noticed:

• Speed no longer sets anyone apart. Almost everyone promises one process live in four to six weeks.
• Most providers pick one lane. Some build quote, order and invoice agents, others run training or large data programs. Few build agents, assistants and prediction models in one team, even though they often run on the same ERP data.
• Proof is thin. Most firms show one to four named cases, so ask to see a working system before the slides.
• Almost nobody shows how they test an agent. Ask how many real cases they tested it on, how many it got right, and what goes to a person. If a supplier can't answer, the testing probably didn't happen.

Does company size change what AI support you need?

Size changes how many people you coordinate more than it changes the project. Let the workload and your team's current skills set the scope.

Your company	Sensible first scope	Include from the start
Under 50 people	One recurring task with existing tools	Someone who can run and maintain it
50 to 250 people	One department workflow and its handoffs	Users, the process owner and IT
250 to 1,000 people	One workflow built for reuse	Shared connections, access rules and owners across teams
Over 1,000 people	A project inside existing platform agreements	Domain teams and agreed operating standards

One company can need several kinds of support at once. At one client, some people needed the basics while the strongest users were already building their own tools. One course could not serve both groups. We wrote about it in getting your team to use AI.

How do you find work worth automating?

Start with work your team knows well. We use four questions, and they catch the copying, checking and chasing between bigger tasks.

What repeats?

List work that comes back daily or weekly. Repeated work gives you examples to test, and you feel the benefit often. A monthly task can still matter if it holds up a big decision.

Can you describe the steps?

Explain how you would hand the task to a new colleague. Name the inputs, decisions, output and the exceptions experienced people handle without thinking.

Where do people need to decide?

Mark where someone must approve or check. AI can prepare the work while a person keeps the decision.

At an airport operator we worked with, teams tested their agents against instructions hidden inside emails. From that test, the teams saw why each agent needed clear limits and a named approver.

What does it connect to?

List the files, inboxes, and systems involved, and where the result needs to go. A quote draft that still leaves someone copying details into the CRM has not fixed the workflow.

Before a pilot, add two more checks: the improvement you will measure, and the person who will own the workflow. Our free AI workflow self-audit walks you through all of it.

Where do you stand right now?

Pick the row closest to your team. Different workflows can point to different next steps.

Your situation	Your next step
"We have ideas, but can't choose."	Map the work and rank candidates
"People use AI, but results vary."	Practice one workflow together and agree how to check it
"We know the task, but information is scattered."	Find trusted sources and sort out access
"We know the task and can reach the data."	Test a pilot against the current process
"The demo works, but people avoid it."	Watch real use and find the gap
"It works, but only the supplier understands it."	Arrange a practical handover
"Several workflows work well."	Reuse connections and tests before expanding

How do you know it helped?

Compare the full workflow before and after, including the time spent checking AI output. Our guide to measuring AI productivity at work shows how.

Then ask your internal owner to run the workflow, explain an uncertain output and make one small change. You'll find gaps a supplier's demo can hide.

Why we think Aibl.to is the right choice, and when it isn't

We are two founders, a software engineer and an AI engineer, and we both teach and build.

We build on your own data, in your own cloud, and we do three kinds of work:

• Agents on top of your ERP. They take an input, like an order email, a supplier invoice or a quote request, and produce a checked output in your system, like an order line, a matched invoice or a draft quote.
• Assistants on your sales and product data. A sales director asks which customers are slipping, or a webshop turns catalogue data into product pages.
• Machine learning and statistical models, such as demand forecasts or customer risk scores, deployed so your team uses the result in daily work.

Most providers do one of these. We do all three, often for the same client, because they draw on the same ERP and sales data. You don't need a second supplier when the order agent works and you want a forecast next.

Before handover, we show you how we tested it: the real cases we used, how many it handled correctly, and which cases go to a person. For a model, you see how its predictions compared with what happened.

We offer three things, and every engagement starts with a free 30-minute AI audit:

Service	What you get	Time
AI Workshops	Your whole team practices AI on its own work and tools, with materials and follow-up	1–2 weeks
AI Workflows	One process your team repeats, redesigned and running as an AI workflow, with a trained owner	4 weeks
AI Projects	Larger builds on your own data and cloud, such as ERP agents, prediction models or the sales assistant above	2–3 months

At the end of a workflow, you have:

• One AI-assisted process in daily use, tested on your data, with people checking the results.
• A team that can run and improve it without us.
• Instructions and a blueprint to build the next one yourselves.
• A before-and-after comparison of time, quality and adoption.

Workshops have no participant limit, so the whole team can join.

We only call a workflow done when your own person can run it, check an output against its sources and make a small change without us.

We build alongside your people so they learn during the build. On the sales assistant, the client asked us to slow down so their team could understand each decision and test every piece in their own environment. We did. You can read how in building internal AI capability.

We changed our workshops for the same reason. A participant told us to show the working system first and explain the parts after. We now teach that way, and buyers can use the same trick: ask any supplier to show the finished workflow before the slides.

Where we are not the best fit:

• You want a supplier to run the system long-term with 24/7 support. A larger firm such as ML6 is better placed.
• Your existing software already covers the task. Then you don't need us, and we will tell you so.

Sources

The European project examples summarize public accounts. Client and supplier reports are their own claims, not independent audits. Provider details come from each provider's own website in October 2026.

1. Order-intake case. Supplier-hosted account with a named client.
2. Invoice-processing case. Supplier account without quantified gains.
3. Albert Heijn bakery deployment, May 2026. Client-reported.
4. KLM catering deployment, February 2024. Client-reported, annual benefit extrapolated.
5. Schiphol turnaround case. Some benefits are estimates.

What should you do next?

Pick one recurring workflow and collect a few real examples. Bring in the person who knows the exceptions and someone who knows the systems. Answer the four questions, choose the type of offer, and only then the supplier.

If you want a second opinion, talk through one workflow with us. We'll help you work out whether you need training, a pilot or a build, and if another provider fits better, we'll say so.