Pilots do not enter production
The team sees AI’s potential but cannot establish ownership, quality criteria, integrations or ongoing operations.
The AI pilot looks convincing, but doesn’t become a workflow.
Assistants answer without governed sources
The employee receives the text, but does not understand which version of the document is used and whether it is possible to rely on the answer.
AI works outside the process
The result remains in a separate chat and then manually transferred to a task, document, card or analytical material.
Access and action risks are not bounded
Roles, logs, confirmation rules, escalation and conditions for stopping the use case after an error are not defined.
Why this happens
Many companies have tested AI for search, documents, enquiries and analytical briefs. Initial demonstrations look promising, but the pilot often leaves questions unanswered: which work does the assistant actually take on, where is the result recorded and who is responsible for errors?
An AI use case depends on enterprise knowledge, access permissions, data quality and the operational workflow. If an assistant gives answers without sources, works outside the process or takes actions without oversight, it soon becomes another unmanaged channel for processing information.
This practice turns AI experiments into a managed operational framework: defining the task, connecting authorised knowledge and data, limiting authority, setting quality standards and incorporating results into the working system.
AI only works within a given operation.
Operation and outcome
What kind of work does AI do?
A use case is defined by its inputs, expected result, current effort, place in the process and the next employee or system action.
The operation, output, workload, input source and location where the answer is recorded.
Knowledge, data and permissions
Which sources may the assistant access?
AI only accesses authorized documents, data, and systems, uses current versions, and shows the source of the response.
Sources, access roles, versions, data limitations, and citation rules.
Control and accountability
Who confirms the result?
AI’s authority depends on the risk of error, from suggestions for employees to an agent with limited permissions, logging and mandatory confirmation of critical actions.
Quality criteria, levels of autonomy, owner, activity log and escalation rules.
More about the model
The model creates value through a specific task: reducing the work involved in searching, comparing, classifying, preparing a response or carrying out a limited action.
Each use case has defined limits of autonomy, knowledge sources, access permissions, quality criteria and a destination for its results.
How we bring an AI use case into operation
We start with the workload and enterprise knowledge, design a secure AI environment, test quality against routine and critical examples, and then integrate the use case into the process.
Select operations with measurable value
Identify where employees regularly search, match, classify, retrieve, and transfer information between documents, systems, and communications.
More about our work
Starting point
AI is seen as a common tool, not as a way to change a specific operation with an understandable load and result.
What we do
We capture scenarios, transaction volume, error risk, data availability, process owner, and expected effect.
How to check the results
For each scenario, it is clear what work it cuts, what result it creates, and where it is used.
Connect authorised sources and enterprise knowledge
Form a managed knowledge base: documents, regulations, contracts, project materials, data and system events with versions and access rights.
More about our work
Starting point
Answers are built on disparate files and do not always take into account the current version, the owner of the document and access restrictions.
What we do
We define sources, update rules, access roles, source citations and procedures for removing outdated material.
How to check the results
The answer comes with a source, version, and scope, and the assistant doesn’t see more data than an employee with the same role.
Designing scenarios, prompts, tools and constraints
We configure AI as part of the operational workflow. Within defined limits, it can prepare a response, extract facts, complete a record, create a task or propose an action.
More about our work
Starting point
The AI outputs the text in a separate interface, and the employee manually transfers the result to the system or document.
What we do
We define the model’s role, tool-use rules, output format, confirmation points and actions that require human involvement.
How to check the results
The assistant transfers the result into the required document, task, record or analytical material and stays within the use case’s scope.
Establish quality controls and ongoing support
An AI use case is supported as a business-critical system, with a test dataset, error monitoring, logs, an owner and stop rules.
More about our work
Starting point
Quality is judged by several successful responses, and once launched, model, data, and process errors are mixed together.
What we do
We define tests, accuracy criteria, activity logs, error review procedures, escalation and regular use case reviews.
How to check the results
It is clear why AI got it wrong, who is addressing the root cause, and at what level of risk the scenario should be limited or stopped.
Services within this practice
Engagements and deliverables range from selecting AI use cases to enterprise assistants, connected knowledge sources, AI integration into operations and ongoing support.
Selecting AI use cases for business
A portfolio of AI use cases with clear benefits, limitations, data sources, expected impact, risks and launch priorities.
Designing enterprise AI assistants and agents
A target model for AI assistants and agents supporting employees, managers, project teams, customer service, document workflows and internal functions.
Connecting enterprise knowledge and data
A framework for AI access to internal documents, procedures, knowledge bases, correspondence, reference data and business data, with access permissions taken into account.
Embedding AI in business operations and processes
AI use cases within actual work: search, classification, data extraction, drafting, document analysis, request processing, analytical briefs and task creation.
Establishing AI quality and security controls
Rules for data use, response verification, action logging, access permissions, owner accountability and limits on AI use in business processes.
Operating and developing AI solutions
An operating model for AI support: roles, metrics, user support, quality monitoring, use case development and the transition from a pilot to reliable ongoing use.
What the management team receives
Portfolio of AI use cases
A prioritized set of operations with an assessment of load, risk, data availability, and expected effect.
Corporate knowledge map
Document, data and system event sources, with owners, versions, permissions and update rules.
AI architecture model
The roles of models, assistants, agents, tools, integrations and human oversight, with limits on autonomy.
Security and access rules
Roles, authorised sources, prohibitions, activity logs, confirmation requirements and use case stop procedures.
A set of quality tests
Typical and critical examples, accuracy criteria, rules for evaluating answers and analysis of error categories.
Commissioning plan
Pilot phases, integrations, user training, launch metrics, support ownership and change procedures.
What changes in daily work
Results are visible when enterprise knowledge is used in routine operations with defined permissions, cited sources and clear accountability. The number of connected models is not the measure of success.
AI is tied to a specific operationAn assistant or agent works where there is a clear input, result, and owner of the process.
Starting point
AI is used as a general chat, and the effect depends on the skills of the individual employee.
What's changing
Each use case has a defined operation, output format, recording location and next workflow step.
How to check the results
The number of operations delegated to AI and the share of outputs accepted without rework can be measured.
Responses are based on corporate sourcesThe employee sees the source, version, and limitations of the response.
Starting point
The assistant forms text without clear origin and can use outdated material.
What's changing
AI works with a managed knowledge base, respects access rights and shows the source of the response.
How to check the results
Every answer can be traced to a document, record, version or system event.
Documents and requests are processed uniformlyClassification, fact extraction and draft answers are created according to established rules.
Starting point
Each employee reads documents in their own way, determines the topic of request and forms a response.
What's changing
AI applies uniform rules for extracting, verifying, classifying and preparing a draft result.
How to check the results
Errors are categorised by data source, use case logic, model or process.
Critical actions remain under controlThe degree of autonomy depends on the consequences of the error.
Starting point
It is unclear what actions AI can perform itself, and where the confirmation of an authorized employee is required.
What's changing
Levels of autonomy, confirmation rules, an action log and escalation of non-standard cases are set.
How to check the results
No critical change of obligation, payment, status or data is carried out without established control.
The pilot becomes an operational serviceThe use case has an owner, tests, monitoring and support rules.
Starting point
After the demonstration, there is no clear plan to maintain quality or extend AI use.
What's changing
An AI use case has an owner, metrics, a set of tests, an order of changes, and regular error analysis.
How to check the results
Use case quality is monitored after launch; changes to knowledge, data and rules are controlled.
When this practice is especially useful
This practice is needed when a company sees AI’s potential and needs to connect models securely to enterprise knowledge, processes, roles and operational results.
Lots of corporate knowledge
Employees spend time searching for information in documents, regulations, contracts and project materials
More details
- Experts respond to recurring questions
- Search depends on knowledge of folder structure and systems
- Different versions of the same document are used
- The answer cannot be quickly confirmed by the source.
- New employees take time to understand the context
Documents and requests
Teams manually read, classify and match a large flow of documents and queries.
More details
- Requests need initial classification and routing
- Contracts and sets of documents are checked manually
- Terms have to be entered into records and spreadsheets
- Typical responses are being prepared anew
- Non-standard cases are not separated from typical cases.
AI is being used informally.
Units use public AI services without uniform rules of access and responsibility
More details
- Confidential data may be transferred to external services
- There is no general list of permitted scenarios
- There are no rules for verifying the result
- AI results are not logged
- Errors cannot be attributed to the source or owner.
Pilots don't scale
The company has conducted several AI experiments, but they are not included in the regular work.
More details
- Not measured actual load before and after launch
- There is no use case owner after the pilot
- Integration with work systems is not defined
- Test sample does not cover critical cases
- It is unclear how to maintain quality after launch.