AI that actually helps with the work.
Not another chat window. AI that genuinely moves the work forward.
Inside the process. With the right data. Under your control.
With Lens · takes about 3 minutes
Less manual work
AI takes on reading, searching, sorting and transferring.
Knowledge becomes usable
The right context comes from the right sources.
Control stays with you
It is clear what AI may do, and when people decide.
When AI still sits beside the work
Where AI can take real work off your team
The lever is not the prompt. The lever is the work that no longer has to happen by hand afterwards.
How AI-ready is your use case?
Idea
AI is interesting, but the problem, the benefit and the priority are not defined clearly enough yet. There are possibilities, but not yet a use case you can stand behind.
- Many possibilities
- Use case still open
- Benefit unclear
With Lens · takes about 3 minutes
Problem → process → data → decision → AI.
AI-ready does not mean using as much AI as possible. It means a relevant use case is prepared so AI can create value under control, and keep doing so.
How AI becomes a use case in production
Find work with leverage
We do not hunt for AI ideas. We look for recurring work with volume, friction or a quality problem.
Make context usable
Data, knowledge and systems are opened up only as far as the use case actually needs them.
Build it into the flow
An AI result does not stay in the chat window. It lands in the right task, the right system or the right decision.
Design the control
Approvals, limits, failure paths and monitoring follow the risk and the responsibility.
Der Ablauf zuerst
A good model is only the engine. The value appears when the process runs on it.
So we do not measure how impressive the AI sounds. We measure what gets better in the work afterwards.
What purposeful AIcan already change
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More cases per hour.
In an NBER study of 5,179 support staff, an AI assistant raised the number of issues resolved per hour by 14% on average.
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Knowledge work gets faster.
In an HBS and BCG field experiment, consultants working on tasks inside the AI performance boundary were more than 25% faster.
0 %
Quality can rise.
In an MIT study, the quality of professional writing tasks, as rated by independent reviewers, rose by 18% with ChatGPT. Time spent fell by 40% at the same time.
0 %
Technical work can get markedly faster too.
In a controlled GitHub experiment, developers completed a defined coding task with Copilot about 55% faster than the control group.
Calculate manual knowledge effort
- How long does a recurring knowledge task take by hand today?
- How often does it happen each week?
- How much human working time sits in it over a year?
15Minutes per knowledge task
40Cases per week
46Working weeks per year
- ≈ 58 workdaysat 8 hours per workday
- ≈ 11.5 work weeksat 5 workdays per week
460 hours · 58 workdays
This recurring knowledge work ties up, per year
17,480.00 €
The calculator shows addressable working volume. It does not claim that AI replaces this effort entirely.
Which part of that really needs human attention?
Lens helps you place the task, the data and a sensible use of AI.
Common questions
- Where do we start if we see AI potential everywhere?
- Do we need a perfect data foundation first?
- Do we have to develop our own AI model?
- How do we stop AI from doing the wrong thing?
- How is this different from classic automation?
- Can O/S integrate AI into existing systems?
Not with a long list of use cases. A good starting point is a frequent, information-heavy task with measurable effort and a clear follow-on process. O/S prioritises where benefit, feasibility and risk fit together.
CONTACT
Which work should AI take off your hands?
Show us a flow where people currently read, search, sort, transfer or prepare the same things again. O/S shows where AI has real leverage, and how that becomes a productive process you can control.