An idea without a process
The use case sounds attractive but has no sponsor, user, baseline or decision about who will maintain it.
Quick definition: AI in the organisation is a designed way of using artificial intelligence in specific business processes—with a clear objective, data, human role, owner, measure, operating rules and risk controls. It is not simply access to a chatbot.
Digitalisation and AI · advisory and implementation service
AI in the Organisation – From Pilot to Everyday Work
We help you select the right process, launch a measurable AI pilot, establish rules for use and risk assessment, and then scale only the solutions that genuinely improve work.
Review processes for automation and decision support
Pilot one process, measure the effect, then scale
Define AI operating rules, ownership and risk controls
Process before toolWe begin with a business problem and the user's work.
Owner and measureEvery pilot has an accountable owner and a baseline.
Human in the loopWe define where AI proposes and where a person approves.
Scale after evidenceOnly solutions that demonstrate value are expanded.
A practical definition
What does AI in an organisation mean?
AI in the organisation is not simply access to a chatbot. It is a designed way of applying artificial intelligence in specific processes—with an objective, relevant data, a defined human role, ownership, a measure, operating rules and risk controls.
Technology becomes part of the operating system. It may support knowledge retrieval, data analysis, content preparation, document handling, problem solving or task coordination. It must still fit the process and the capabilities of the people using it.
Our principle: technology strengthens people. We first define customer value and the business problem. Only then do we select the model, tool, integration or automation.
From experiment to standard
Why does a promising pilot fail to become everyday work?
The model itself is rarely the only issue. Organisations struggle when processes, data, ownership, user adoption and maintenance are not designed as one system.
Tools without rules
Employees use random applications while the organisation cannot see which data, providers and risks are involved.
A pilot without a measure
The demo works, but nobody knows whether it reduced time, errors or manual work, or improved decision quality.
Training without work redesign
The team learns a tool, but the process standard, roles, instructions and controls remain unchanged.
AI without context
The solution cannot reach the right knowledge or requires so many manual steps that it does not survive in practice.
Change without control
Nobody monitors versions, output quality, incidents, cost or the effect of provider changes on the process.
Service scope
How we support AI implementation in the organisation
We can lead the whole journey or join at a selected stage—from process review and pilot design to governance, adoption, scaling and orchestration.
Opportunity map
We review processes, decisions, information flows and repetitive work. The output is a list of useful applications—not a list of fashionable tools.
Priorities and business case
We assess value, feasibility, data readiness, risk and change effort, then select the first process for a pilot.
Pilot design
We define scope, owner, users, baseline, expected outcome, acceptance criteria and review rhythm.
Rules and risk controls
We establish permitted data and tools, human oversight, testing, escalation and supplier requirements before launch.
Adoption and capability
We build role-specific work standards, instructions, workplace learning and a feedback rhythm.
Scale and orchestration
We connect proven solutions with data and systems, designing controlled workflows for people and AI agents.
Practical applications
AI can support manufacturing, office and service work
A good first use case is frequent, has a clearly defined user, can be measured and does not require a complete IT architecture rebuild on day one.
Manufacturing and operations
shop floor- Search knowledge in work instructions, standards and technical documentation.
- Support analysis of quality problems, breakdowns and process deviations.
- Prepare work cards, shift reports and action summaries.
- Support trainers, onboarding and competence management with eTWI.
- Develop Kaizen ideas and problem-solving dialogue with KaizenUp AI.
Office and services
office- Classify documents, e-mails and requests and prepare response drafts.
- Retrieve knowledge from procedures, contracts and internal databases.
- Create analyses, proposals, reports and first versions of materials.
- Check data completeness and route cases between roles.
- Orchestrate repeatable steps across people, agents and systems.
Automation and oversight are designed around the consequences of an error, the type of data and applicable requirements. Not every process should be automated.
Implementation model
From one process to a scalable way of working
We do not start by purchasing a platform. We first confirm value and learn within a controlled scope.
Objective and sponsor
Define the business problem, expected result, owner and decision boundaries.
Process review
Observe the work, data, exceptions, roles and points where waste or delay is created.
Use-case selection
Compare value, feasibility, risk and the readiness of people and data.
Rules before launch
Set the approved tools, data boundaries, human oversight, tests and documentation.
Pilot and measurement
Test with users, measure the result and capture exceptions and failure modes.
Standard, adoption and scale
Update the way of working, build capability and extend the solution only where evidence supports it.
AI governance and risk assessment
Freedom to experiment needs clear boundaries
Rules should enable safe work, not create a document nobody uses. We connect them with processes, roles and management decisions.
An operational AI management system
We build the minimum governance the organisation needs at its current level of maturity, with room to develop towards AI Act and ISO/IEC 42001 requirements.
Register of tools and use cases
Business and technical owners
Data classes and permitted use
Human oversight and escalation
Quality tests and acceptance criteria
Suppliers, versions and changes
Incidents and feedback
AI user capability
A practical solution ecosystem
The tool follows process understanding
LeanTrix combines process, implementation and technology experience. We also use solutions developed close to the real work of teams.
KaizenUp AI
An assistant supporting problem analysis, better questions and the development of Kaizen ideas. It strengthens thinking rather than replacing an expert or process owner.
Explore KaizenUp AI →eTWI
Digital work instructions, competence management and trainer support help transfer knowledge into everyday work and sustain the standard.
Explore eTWI →AI orchestration
After a pilot confirms value, we design a controlled flow across people, specialised AI agents, data sources and business systems.
Explore AI orchestration →What stays in the organisation
The result is not a presentation about AI
The team receives decisions, standards and working materials that allow the change to continue after the pilot.
AI opportunity map
Prioritised use-case backlog
Pilot charter and business case
Tool and risk register
AI operating rules
Roles and human control points
Work standard and capability plan
Scaling and integration map
lead timemanual efforterrors and reworkknowledge access timeoutput qualityuser adoptionprocess cost
Lead expert
Maciej Misiurek
Digitalisation, AI and IT transformation expert · representative of MISOFT EU
Maciej connects business, product and technology perspectives. He has more than twenty years of experience as a systems implementation consultant, software house owner, Head of IT and Engineering Director responsible for solutions that had to be selected, implemented, measured and maintained.
He is CTO and co-founder of eTWI. His experience includes applying AI in software development, test automation and an industrial digital product. He helps organisations move from an isolated pilot to everyday team use—with ownership, operating rules, risk assessment and change control.
Experience and evidence
Technology is evaluated by the value it creates in work
We connect LeanTrix process implementation with experience in digital products and work with manufacturing and service organisations.
From knowledge to an everyday standard
eTWI demonstrates how digital work instructions and competence management can enter the daily rhythm of a plant. KaizenUp AI brings artificial intelligence into problem-solving dialogue and continuous improvement. AI orchestration connects proven applications with subsequent process steps.
500+participants in the Lean AI course
8signed Lean AI references
1 processto start a measurable pilot
Related areas
AI in the organisation connects processes, people and technology
Frequently asked questions
AI in the organisation — questions before the first step
Where should an organisation start with AI?
Start with a business problem and a process, not a software licence. Choose a frequent task with a clear user and a measurable baseline, then assess data, risk and team readiness.
How do we select a good process for an AI pilot?
A strong pilot has a repeatable flow, accessible example data, a named process owner and a result that can be compared before and after. For the first pilot, avoid a scope in which one error could cause difficult-to-reverse consequences.
How is this service different from AI training?
Training builds capability. AI in the Organisation designs and implements change in a real process: opportunity mapping, operating rules, a measured pilot, a new work standard and a decision about scale. Both can complement each other.
How is AI in the Organisation different from AI orchestration?
AI in the Organisation is the broader implementation model covering processes, people, pilots, governance and risk. AI orchestration is the deeper stage in which people, specialised agents, data sources and business systems are connected in a controlled workflow.
Do we need an AI policy before starting?
At minimum, the organisation needs practical rules covering approved tools, permitted data, verification of outputs, accountability and incident reporting. Their detail should match the risk and maturity of the organisation.
How should the effect of an AI implementation be measured?
The measure must come from the process. It may be lead time, manual effort, errors, access time to knowledge, data completeness, first-pass quality or process cost. The mere use of AI is not a business result.
Does an AI pilot always require system integration?
No. A first pilot can work with a controlled dataset and a simple workflow. Integration makes sense after value is confirmed and when it reduces manual work, improves control or helps sustain the standard.
What does human-in-the-loop mean in practice?
The organisation defines where AI may propose or prepare a result and where a person must review, approve, stop or override it. The control point depends on the consequences of an error and the type of data involved.
Can the model be used in manufacturing and service organisations?
Yes. The first use case may support shop-floor knowledge, quality analysis, maintenance and work instructions, or office processes such as document handling, reporting, customer requests and knowledge retrieval.
What happens after a successful pilot?
The team confirms the result, updates the work standard, assigns ownership, prepares users and decides whether to scale. Only proven use cases move towards wider integration and AI orchestration.
Choose one process in which AI can create a measurable result
During a free consultation, we will clarify the problem, a possible pilot scope, the operating rules and a way to measure value. If AI is not the right answer, we will say so directly.