Repetitive tasks
Moving data, preparing reports and sending notifications. Where clear rules are enough, simple automation may be a better choice than an AI model.
Automation and AI
Less repetition. More time for the work that matters.
01 / AREAS TO EXPLORE
Start with a task that keeps coming back.
These uses are a starting point for analysis. Not every one requires AI or will be worthwhile for your company.
Documents and data
Initial extraction, classification and organisation of information. We assess formats, material quality and which results need human review.
Working with information
Finding information, compiling it and preparing draft summaries. Source access, verifiable answers and handling uncertainty are essential.
Internal knowledge assistants
Prototypes using a selected collection of materials. We define their knowledge scope, permissions and how answers are updated and assessed.
Connecting tools
Information flows between systems where their interfaces allow it. We account for limits, errors and the option for a person to take over.
AI solution prototypes
A limited pilot for a chosen task. We compare results with the current way of working before deciding on wider use.
02 / FROM IDEA TO DECISION
First, we assess value and limitations.
We choose one task and agree how to evaluate the result. A pilot should show how useful the solution is and what daily operation will cost before a broader rollout.
- 01
Task and baseline
How often the task occurs, how long it takes and what would count as a useful result.
- 02
Data and access
Which materials are needed, where they may be processed and who should have access.
- 03
Pilot and quality assessment
A sample of real cases, errors, exceptions and the human oversight required.
- 04
Costs and next steps
The cost of preparation, use and maintenance compared with the solution’s usefulness.
03 / INFORMED USE
AI can help. Results still need judgement.
Models can return incorrect or incomplete answers. We agree which tasks can be assisted automatically, where human approval is needed and how users can check the source of information.
We consider options without AI too. If a simple rule, a better form or organised data solves the problem, we do not add a more complex tool. A pilot can lead to a decision not to proceed.
05 / AUTOMATION QUESTIONS
Test first. Expand later.
How do we know whether a task is worth automating?
We start with frequency, time spent, the number of exceptions and the quality of available data. We compare the potential improvement with development and maintenance costs. A small, infrequent task may not justify a separate solution.
Does automation always need AI?
No. A repetitive process with clear rules can often be handled by a simpler tool. We consider AI when the task involves less structured content and we can assess the quality of the results.
Can an AI assistant use our documents?
We can review that scenario. First, we need to agree the materials, user permissions, processing location and how knowledge is updated. A pilot should test whether answers are useful and verifiable.
What happens when the model or automation makes a mistake?
We agree error handling before wider use. It may involve human approval, stopping an action, recording an event or returning to a manual process. Using AI does not remove the need for oversight.
THE NEXT GOOD PROJECT STARTS WITH A CONVERSATION.
What can we create together?
↗Have an idea, a challenge or just a question? Let’s start by understanding what you need.
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