AI and Technology
The company's technology architecture and AI use cases built into real processes. We start with the task and the cost of the current process, and choose the model and the tool at the end.
Common requests:Employees spend hours processing the same texts and data, Someone is offering you AI, Requests are handled slowly because of manual sorting, Data has piled up
Services in this area
4 services
01AI and Technology · 01
Technology Architecture and AI
Which technologies are needed where across the whole company, and where AI fits in that picture. We start with the task and the cost of the process.
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02AI and Technology · 02
AI Consulting
Choosing AI scenarios by four criteria, checking data, risks and controls, and a pilot plan with a stop criterion.
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03AI and Technology · 03
AI Tools Implementation
Integrating AI into specific workflows: scenario, roles, a pilot on a limited area, training and quality control.
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04AI and Technology · 04
AI in Workflows
Task chains where AI handles the repeatable steps and a person checks at the right points: context handoff, result checks, pilot.
Learn moreWhat people come with
Does this sound familiar?
- Employees spend hours processing the same texts and data.Situation 01AI doesn't create data and doesn't replace a decision.
- Someone is offering you AI, but it's unclear what will change in the numbers.Situation 02AI doesn't create data and doesn't replace a decision.
- Requests are handled slowly because of manual sorting.Situation 03AI doesn't create data and doesn't replace a decision.
- Data has piled up, but no decisions are made from it.Situation 04AI doesn't create data and doesn't replace a decision.
The core idea of this area
AI doesn't create data and doesn't replace a decision.
The self-assessment for this area checks readiness for AI by three signs: whether the data is usable, whether the process that will receive the result has been named, and whether there is a number that will show the use case has worked.
- 01Management data
- 02Repeatable processes
- 03Technology backbone
- 04Reliable records
- 05Accountability
How we see it
AI changes the economics where a task repeats, a mistake is cheap to spot, the result can be checked without an expert and there is enough data. Elsewhere it creates new work in checking.
The cost of that checking is almost never budgeted for. We budget for it from the start.
When this area won't help
- We don't use AI where a mistake is expensive and can't be checked.
- We don't use AI to automate a process that isn't described: the result would be the same, only more expensive.
- We don't consider a pilot successful without a stopping criterion named in advance.
Questions
How can we tell whether a task suits AI?
Check four signs: repetition, a cheap and visible mistake, a result that can be checked, enough data or context. Three out of four: you can run a pilot. Two: better to wait.
Will AI replace employees?
In repetitive tasks it reduces manual work. In tasks that need judgment it shifts the work into checking. Plan for the second; it's more realistic.
How long does a pilot take?
Four to six weeks on one task, with a baseline before the start, criteria for success and for stopping, a person in the loop and a calculation of the full cost of ownership.
Do we need our own AI department?
Not necessarily. You need owners: for the data, for the use case and for the quality of the result.
Related articles and case analyses
AI in business: where it really changes the economics
The question isn't whether to use AI. The question is where it changes the numbers and where it just adds costs and a nice presentation.
Ask your contractor when a task shouldn't be given to a model.
If the answer is "never", you're talking to a salesperson. The first meeting with us is free, and we'll go through that question together.