Practical AI · processes · pilot
Implementing AI:
from task to working process
A useful AI project does not start with model selection. First you need a repeatable task, defined data and quality criteria — then embed the result into a real process with clear autonomy boundaries.
Anton Konnov · 18 August 2026 · 10 minutes
Scenario selection
Which task is suitable for a first pilot
A good pilot works with a repeatable operation where the input, expected outcome and process owner are known. Tasks that benefit include document search, draft preparation, ticket classification, data extraction and preliminary checks against defined rules.
Rare decisions with a high cost of error, undocumented criteria and no verifiable examples are poor candidates. In such a context it is impossible to distinguish technology quality from a random impression.
Six steps
How to implement AI in a business process
Select a process and a useful scenario
Document the current route, the manual operation and its consumer. Record what the AI component must produce and what action follows the result.
Gather context and data
Define documents, reference data, rules, examples and access constraints. Separate knowledge that can be passed to the model from data requiring a controlled loop or additional approval.
Set quality criteria
Assemble a set of real examples and pre-define the correct result, acceptable errors and cases requiring mandatory human escalation. Evaluate not the beauty of the answer, but its fitness for the next step in the process.
Build a limited prototype
Test one scenario against available data without premature integration across all systems. At this stage compare model variants, search, prompts and tools against the same test set.
Embed control and actions
Categorise actions as safe and automatic, requiring review, or prohibited without a separate decision. Preserve sources, execution status and stop reason so the result can be audited and reproduced.
Evaluate operation
Measure quality, speed, cost, proportion of genuinely used results and volume of manual corrections. You also need monitoring, error logging, knowledge updates and a clear rollback path.
Quality gate
What to check before handing to operations
Accuracy
Correctness of results on the test set and stability against edge cases.
Verifiability
Sources, facts and intermediate actions available for human audit.
Safety
Access, personal data and external actions restricted to the chosen scenario.
Economics
Model, infrastructure and review costs proportionate to the result's usefulness.
Architecture
The model is only one component
A working system includes the input channel, context, memory, tools, routing rules, quality control and observability. The model may change provided interfaces and result criteria are described independently of any specific vendor.
For simple operations a small local or inexpensive solution suffices. A more capable model makes sense only where complexity and error cost justify the escalation.
Pilot complete
When to move to operation
Quality criteria tested against real examples, not demonstration questions.
Failure cases are known and there is a hand-off route to a human.
Access is restricted, external actions are observable and reversible.
The process user actually applies the result in daily work.
There is an owner, monitoring, knowledge updates and a way to stop the loop.
First step
Choose one manual operation
We will define the process, data, quality criteria and safe boundaries for the first AI pilot.