A tool without a work task
The company buys licences. People do not know which part of their work to change, let alone how to tell the result is better.The company buys licences. People do not know which part of their work to change.
AI ADOPTION IN THE COMPANY
One procedure that holds up in operation.
We start with one task and measure the result.
PROBLEMWhere AI adoption
Where AI adoption
usually fails.
Licences, a one-off demo or an order to use AI never say who changes which work and how a better result will be recognised.Licences, a demo or an order to use AI never say who changes which work and how to spot a better result.
Training without a follow-up
Participants try something and the training ends. No owner, no template and no date emerge for checking whether people use it at all.Participants try something and the training ends. No owner, template or check date emerges.
Verification without rules
The team reaches for sensitive data or automation before the environment, responsibility and checking method are clear.The team uses sensitive data or automation before environment, responsibility and checks are clear.
PROCESSFive steps from an idea
Five steps from an idea
to a measured decision.Five steps to a measured decision.
No licences for the whole company until you know what you are actually changing.
Work and constraint map
We list recurring tasks, roles, inputs and systems. We find where waiting, retyping or outputs that look different every time arise today.We list recurring tasks, roles, inputs and systems, and find where waiting and retyping arise.
Output: an opportunity and constraint map.Choosing one priority
We compare every option by value, frequency, data quality, risk and change effort. The company verifies one procedure at a time.We compare options by value, frequency, data quality and risk. One procedure is verified at a time.
Output: a decision matrix and a verification owner.Training people for the chosen procedure
The team learns on tasks close to its real work. Rules for data, output verification and error escalation are included.
Output: working procedures, templates and training records.Limited verification
The verification has a clear scope, trial or approved data, a checkpoint and a way back if something does not fit.
Output: a verified procedure and a record of issues.Measurement and decision
We compare time, quality, error rate and actual use. The result can be expansion, adjustment, or calmly ending with no further investment.We compare time, quality, error rate and use. The result can be expansion, adjustment, or an end.
Output: a decision to continue, change or stop.DECISION MATRIXA good first verification is a task
A good first verification is a task
where an error does not hurt.Start where
an error does not hurt.
Value
What improves and for whom: for the customer, the team, or a decision?
Frequency
How many times a week or month does the task repeat?
Data
Are the inputs available, correct and usable in the given environment?
Check
Can the result be quickly verified against a source or a sample?
Risk
What is the impact of an error, a leak, or a wrong decision?
Change
Who takes over the procedure and what must they change in their work?
Our hands-on answers about using AI and model and product comparisons with methodology and measurement date help with the choice.Practical answers and model comparisons with methodology and measurement date help you choose.
MEASUREMENTModel speed says nothing.
Model speed says nothing.
We measure the whole work result.We measure
the whole result.
Time for the whole task
We measure the whole procedure, from input preparation to output checking.
Quality and rework
We track interventions, errors and cases where a human must redo the output or find a missing source.
Actual use
Success is whether people use the approved procedure long after the training ends.
Safe exceptions
We record where the procedure must not be used, when escalation is needed, and whether the checkpoints actually caught the problem.We record where the procedure must not be used, when to escalate and whether checkpoints worked.
OUTPUTS
What stays after the pilot, even if the cooperation ends.What stays after the pilot, without us.
- an opportunity map ordered by role and work task;
- a decision matrix for the first verification;
- a described working procedure with input, check and output;
- rules for data, tools, human decisions and escalation;
- a syllabus and training records for the people involved in the verification;
- a measurement sheet before and after verification;
- a decision to expand, adjust, or stop.
If the team first needs a shared foundation, it starts with AI training for companies, CIAD publishes its own data and methodology in its studies.AI training for companies gives a shared foundation, and our data and methodology are in the studies.
QUESTIONS
Questions asked before the first pilot.
Do we have to buy licences for the whole company first?
+
No. First pick a work task and a small group. Choose the tool and tariff only after the data, environment and verification scope are clear.
Which process works well as the first one?
+
Usually a recurring task with available inputs, a verifiable result and limited impact of an error. High frequency alone is not enough if quality cannot be checked.
How long does adoption take?
+
The scope depends on the process, data and integrations. You verify a simple procedure faster than automation connected to internal systems. The timeline only emerges after a concrete priority is chosen.
Are the AI Act and data protection included?
+
Yes, as operating guardrails. We define roles, permitted inputs, checks, training records and who is responsible for what. But we never confuse hands-on adoption with an individual legal opinion.
What if verification brings no value?
+
Stopping is also a valid result if measurement backs it. That is why we verify small: so the company does not roll out a tool with no proof it works.
NEXT STEPStart with one task.
DISCUSS VERIFICATIONStart with one task.
The rest waits for measurement.Start with one task.
Measure, then decide.
Describe your team, tools and the work you want to change. The first consultation shows whether training makes sense, direct limited verification, or tidying data first.Describe the work you want to change. The consultation shows whether to start with training, verification or data.