1Landscape
Which systems exist, how they connect, where the data lives.
Technology Architecture and AI
Technology only where it changes the economics. We review the company's technology landscape as a whole: which systems are needed, how they connect, and where in that picture AI delivers a measurable effect.
Sound familiar?There's a lot of talk about AI, but it's unclear where it's actually needed, The team tries different tools without a shared picture, The data exists, yet decisions are still made by hand
AI and Technology
How we work
1
2Tasks with an effect
Where technology, including AI, delivers a measurable result.
3Architecture
How the new fits into existing processes and controls.
4Rules
Where the system decides and where a person must.
Sound familiar?
- There's a lot of talk about AI, but it's unclear where it's actually needed.
- The team tries different tools without a shared picture.
- The data exists, yet decisions are still made by hand.
What usually lies behind it
The main mistake is starting with the technology. First you need to define the business task, the cost of the current process and the success criterion. Only then choose a model, an integration or an automation. AI doesn't fix a bad process; it replicates it.
What you get
A map of the company's technologies and data.
A list of priority scenarios with an estimate of their effect.
Rules: where a decision can't be handed to a machine.
The cost of waiting
If nothing changes
- 01
Money will go to demos instead of working processes.
- 02
Employees will use tools haphazardly.
- 03
Quality and confidentiality risks will stay hidden.
When you don't need this service
- It's already clear which specific AI scenario you need. Then AI consulting or implementation.
- Processes aren't documented. You need to start with them.
How to tell it worked
Measurability
every scenario has a metric and a "before" value.
Human in the loop
the points where a person makes the decision are named.
Data
whether there's enough of it for a stable result.
Questions
Do we need our own AI department?
Not necessarily. You need an architecture: who owns the data, the processes and the quality of decisions.
Can we start with a single pilot?
Yes, and that's the better way. A pilot with metrics that show the result.
What if we have little data?
Data and process come first. Without them, AI amplifies noise.
Related
Services: AI Consulting · Process Automation · System Integrations Materials: AI in business: where it really changes the economics
Related to this service
Public company case analysis
Salesforce
One salesperson looked for customers, negotiated and handled the deal. An analysis of the model that split this work across four roles.
