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Automation

Process automation and applied AI

I take work off the team that consists of moving data from one place to another. I start by assessing where automation actually pays for itself, and where it would just be cost wrapped in a fashionable label.

When it makes sense to get in touch

  • Every week the team re-types the same data between the system, a spreadsheet and a deck.

  • A report that should generate itself costs someone a day a month.

  • Data lives in several places and each one shows a slightly different number.

  • Someone proposed “adopting AI” but nobody can name the problem it solves.

  • The off-the-shelf tool does not fit the process, and the process will not bend to the tool.

Scope of work

Assessment

  • Reviewing the process for repetitive work and measuring how long it actually takes
  • Deciding what to automate, what to simplify first, and what to drop altogether
  • Judging where AI gives an edge and where a plain rule is cheaper and more reliable
  • Estimating the return before anything gets built

Implementation

  • Automating repetitive work in quality processes and reporting
  • Cleaning up data and preparing it to support decisions
  • Tools built for a specific process when an off-the-shelf answer does not fit
  • A pilot on one area before extending to the rest

Running it

  • Handing the solution over to the team together with documentation
  • Agreeing what happens when the automation jams — because one day it will
  • A review after a few months: does it still pay for itself

What you are left with

  • A list of candidates for automation ranked by return, with the reasoning

  • A working solution running on your data, not on sample data

  • Documentation and handover to the team, including the failure scenario

  • A before-and-after measurement, so it can be said whether it was worth it

How we work together

  1. Understanding the problem and its context

    Talking to the team and management, mapping the constraints: people, systems, deadlines and budget.

  2. Analysing the data and the current process

    Mapping how the process actually runs, not how the procedure describes it.

  3. Setting priorities and a plan

    What gives the largest effect for the smallest cost, in what order, and who owns what.

  4. Delivering the solution

    Working with users, piloting, training, correcting based on what practice shows.

  5. Measuring effectiveness and stabilising

    Checking whether the change actually worked, then locking it into the standard.

Frequently asked questions

Where do we start if we have no experience with automation?

By measuring how long the repetitive work takes. Without that number every automation decision is guesswork, and afterwards nobody can say whether anything improved. The first step is usually one report or one instance of re-typing data — small scope, measurable effect.

Is AI needed here?

Often not. Most of the work worth taking off a team is moving data and applying repeatable rules — plain automation is cheaper, faster and predictable there. AI earns its place with text, documents, or classification that cannot be written as rules. I say so plainly when it would only add cost.

What about company data when using AI tools?

That is a question to settle before picking a tool, not after. We establish which data may leave the organisation at all, and choose the solution to match that answer. A good share of use cases can be handled without sending anything outside.

Do you build tools from scratch?

When an off-the-shelf solution does not fit the process — yes. Solvio for complaint handling and DocVerify for document compliance came about exactly that way: from a specific problem in a process, not from a product idea. But I check first whether an existing tool solves it more cheaply.

Tell me what you are working on

Describe what is happening in the process and what you have already tried. I reply within one business day.