When people hear AI they often imagine a full digital transformation project with consultants, large budgets and months of change management. In reality most small and mid sized companies get more value from a handful of simple, boring automations in the back office. This is the practical lane I like to stay in.
Why pragmatic beats grand
The back office is full of work that nobody sees but everyone depends on. Invoices, purchase orders, credit checks, freight bookings, customs documents, support emails and status updates. This work is predictable, often repetitive and very rule based. That makes it a perfect match for pragmatic AI.
The problem with big AI projects is not the technology, it is the distance to value. Long roadmaps, complex integrations and unclear owners make it hard for a normal company to stay patient. Back office teams usually want three things instead:
- Less manual copy paste between systems
- Fewer small mistakes that create rework later
- More time for the tricky cases that really need judgement
Pragmatic AI focuses on these small wins. You ship something useful in a few weeks. You keep the humans in the loop. You measure the impact in hours saved and errors avoided, not in grand visions.
Step one, map the real work
The first step is not to pick a tool. It is to understand how work really flows today. Not the process diagram on the wall, but what people actually do between incoming request and completed task.
When I help a team with this, we keep it very simple:
- List the five to ten most common back office flows, for example new customer, new supplier, new order, new shipment, after sales support.
- For each flow, write down the steps in plain language, including who touches the work and which system is used.
- Mark steps that are repetitive, rule based or text heavy, for example reading incoming emails, extracting data from attachments, updating the ERP or writing similar responses.
This exercise usually takes one or two sessions and it already creates value. People finally see the whole picture, and overlap between roles becomes visible. Only after that do we start talking about AI and automation.
Good candidates for simple AI automations
The goal is not to replace a role. The goal is to make each person deal with fewer routine steps and more decisions. Here are typical candidates that work well in many companies.
Email triage and routing
A shared inbox is often the nervous system of the back office. It is also where time goes to die. AI can help by:
- Categorising incoming emails by intent, for example new order, shipment problem, invoice question, product information.
- Extracting key data, such as order number, company, requested date and attaching it to a ticket or record.
- Suggesting a draft reply that a human can quickly review and send.
The important detail is that people still stay in control. The AI helps with sorting and writing, the team decides what actually goes out.
Document extraction
Many back office teams spend time reading PDFs. Purchase orders, packing lists, certificates, contracts and so on. AI tools can now extract structured data from these documents with high accuracy.
A simple win looks like this, a user drags a PDF into a small web form, the AI reads it, validates key fields and sends the result into your ERP or a spreadsheet. The person only checks exceptions or items that fail a rule.
Internal knowledge on demand
Another sweet spot is internal knowledge. Policies, freight terms, product specifications and supplier agreements live in many folders and inboxes. New colleagues often do not know where to look.
With a private AI assistant connected to your own documents, a back office person can ask natural questions, for example:
- What are our payment terms for customer X in Germany
- Which Incoterms do we prefer for shipments from Shanghai to Gothenburg
- What are the rules for warranty claims on product line Y
The assistant links back to the source document so the person can verify details before answering a customer or a supplier.
Picking tools you can actually maintain
Once you see a few clear candidates, tool choice becomes much easier. I try to match the solution to the skills already inside the company.
- If your team lives in spreadsheets and email, low code tools and add ons are often enough.
- If you already use a ticketing or CRM system, start with whatever automation and AI features are built in.
- If you have an internal developer or a close partner, a small custom integration can create a big leap in quality.
The main test is simple, if the enthusiastic person who leads the first experiments leaves the company, can someone else still understand and adjust what you built. If the answer is no, the solution is too clever.
Roll out in very small steps
Many people are tired of big system changes. They have seen new platforms arrive together with big promises and then quietly fade away. To avoid that pattern, I like to roll out AI in very small, honest steps.
For example:
- Week 1 to 2, small pilot with two or three people and one clear task, such as classifying incoming emails.
- Week 3 to 4, adjust prompts, rules and interface based on real feedback.
- Week 5 to 6, expand to a larger part of the team and start measuring concrete outcomes, such as time spent in the inbox or number of messages answered same day.
Back office teams are often very pragmatic. If they see that a tool helps, they will use it. If it fights them, they will quietly ignore it. Small rollouts give you a chance to fix problems before people lose trust.
Risk, data and simple guardrails
Even if your AI use is modest, you still need basic guardrails. The goal is not to create a wall of policy text. The goal is to give people clear and practical rules.
- Decide which systems and documents are allowed as data sources and which are not.
- Keep customer and supplier data inside your own environment. Avoid pasting sensitive details into random web tools.
- Require human approval for anything that leaves the company, such as emails, invoices and contracts.
- Log usage in a simple way, so you can investigate if something strange happens.
With these basics in place you can move faster with a clear conscience. People know where the lines are and you avoid most of the common mistakes.
Common pitfalls to avoid
When companies start to experiment with AI in the back office I see the same patterns repeat. Most of them are simple to avoid if you talk about them before you switch anything on.
- Starting with the hardest process first. It is tempting to attack the biggest pain, but early wins come from simple flows where success is easy to see.
- Letting a single enthusiast own everything. You want a champion, but you also need at least one backup who understands the setup.
- Automating broken steps. If a manual step confuses everyone today, AI will only confuse it faster. Fix the step first, then automate.
- No feedback loop. Teams need an easy way to say when something feels wrong so you can adjust prompts, rules or tools quickly.
Talking through these pitfalls openly makes people more relaxed. They understand that AI is another tool in the toolbox, not a magic black box that nobody is allowed to question.
Where to start this month
If you want to bring AI into your back office without a huge project, I would start here:
- Spend one or two sessions mapping your real flows, from incoming request to completed work.
- Pick one use case that is small, visible and text heavy, for example email triage or document extraction.
- Run a pilot with a tiny group. Measure hours saved and errors avoided rather than vanity metrics.
- Write down what you learn and treat it as a template for the next automation.
Over time these small wins add up. People in the back office get more time for customers and exceptions. New colleagues ramp up faster because tools help them with routine decisions. You build confidence in AI step by step instead of promising a revolution that never arrives.
If you want help to find those first pragmatic wins, I like this kind of work. It sits in the sweet spot between process, technology and real life customer work.