Applied AI Methodology
How dedicated, working tools emerge from a department's processes and data.
What Applied AI is
Applied AI is the practice of deploying artificial intelligence within a company's specific processes, as distinct from general experimentation with tools. The starting point is not the technology but the process: a specific task performed daily by a specific team.
Before an AI tool enters a process, the data underlying that process must be structured in a way AI can read. In many companies this data is scattered — across spreadsheets and systems that do not talk to each other. That is why the first weeks of work are often devoted to assessing data sources together with the team and structuring them for the solutions to be built later. This stage is called context engineering and forms the foundation of every subsequent AI implementation.
In practice, Applied AI means working directly alongside the process: observing how the team performs a task and identifying where time is lost or errors occur. Building on a context structured beforehand, the resulting tool is fitted to that specific workflow rather than to a generic case.
Context engineering
The stage preceding every implementation — a plant's scattered knowledge consolidated into a single, AI-readable source.
Structured context
Result
A tool fitted to the process
Three pillars of the methodology
The Applied AI methodology rests on three pillars that together set it apart from a classic software implementation.
- 01
Work inside the company, not from outside
The consultant works physically at the client's premises for most of the engagement, alongside the department's team — at their workstations, in direct contact with end users. On-site work makes it possible to map the real course of the process: the successive steps and handoffs between systems where data is created and flows. This is also where context engineering begins — identifying where data originates and what needs to be structured before the first tool can be built.
- 02
Built with the team, not instead of the team
Solutions are built together with the department's team, on a context structured in advance. Employees co-create successive versions of the tool and validate them against their own data and cases. This produces an understanding of how and why the tool works — a precondition for independent development after the engagement ends.
- 03
The competence stays with the company
Transferring competence is part of the engagement. The team learns to identify further areas for automation and to maintain a structured data context for new tools on its own. The choice of the path forward belongs to the company, not to a technological lock-in.
Two solution modes
Not every problem calls for artificial intelligence. The methodology distinguishes two solution modes, chosen to fit the nature of a given problem.
Mode 1
AI-based solutions
Tools in which artificial intelligence is the core of the solution. These include domain assistants answering questions based on company documentation, classifiers processing incoming requests, or systems analyzing technical documents. This mode applies where the problem involves understanding language or analyzing unstructured data.
Mode 2
Solutions built with AI
Tools in which artificial intelligence is a development tool rather than part of the end product. These include business applications, system integrations and automation of repetitive tasks, built faster with the help of AI but operating deterministically. This mode applies where the problem involves data flow and integration of existing systems.
Most engagements combine both modes. A domain assistant (Mode 1) needs an interface and integration with the company's systems (Mode 2). The choice of mode follows from the nature of the specific problem identified while working with the team, not from a technological assumption set in advance.
Each such tool is a dedicated micro-solution for one specific process. Over time, successive micro-solutions begin to connect and form the company's internal ecosystem, built on a shared, structured data context. In parallel, the team builds the competence to think in “AI first” terms — recognizing which tasks can be taken over by an in-house tool before the company turns to an off-the-shelf product from outside.
The three-role model within a department
An Applied AI implementation within a department engages three roles with different levels of involvement. The model allows solutions to be deployed without halting the department's ongoing work.
Recipient
Most of the department's team. Uses the deployed tools in daily work and reports issues and needs for subsequent versions. Involvement is limited to ordinary work with the new tools and occasional feedback. Recipients do not take part in building the tools.
Process guide
One or two people from the department who know the process best. They take part in interviews and workshops and validate early versions of the tools against the real course of the work. Their knowledge underlies the process mapping and the assessment of data sources — the starting point for context engineering.
Independent builder
A person from the department or IT with a technical bent who wants to learn to build solutions independently. Works side by side with the consultant and gradually takes over the building of further tools. This is the role in which the company retains the competence to think “AI first” and to maintain a structured data context after the engagement ends. The independent builder role is optional. Where it is present, it forms the basis for the company's independent development after the engagement ends.
Not every department fills all three roles. The Recipient and Process guide are present in every implementation. The Independent builder appears where a company wants to build internal competence, and its presence is usually what determines whether the company continues to develop on its own or continues the collaboration under the Implementation Partnership.
Four stages of delivery
Every implementation, regardless of the package, goes through four stages.
- 01
Diagnosis
Identifying the department's process and its data sources. Interviews with the team and on-site observation of the work reveal recurring problems and where time is lost. The state of the data is also assessed — where it originates and what needs to be structured. The result is a process map and an assessment of data sources — the starting point for context engineering.
- 02
Design
Translating the identified problems into concrete solutions. At this stage the mode for each solution is chosen and solutions are prioritized. The result is a plan of solutions with priorities and a preliminary feasibility assessment.
- 03
Build
Iterative building of tools in collaboration with the team. Successive versions are tested by Process guides and Recipients, and the tools are adjusted based on their feedback. The result is working tools deployed in the company's operating environment.
- 04
Transfer
Training the team and documenting the tools. Where the department has an Independent builder, joint work on the final features and the transfer of skills to develop and maintain the data context. The result is a team capable of using the tools and, optionally, of developing them further.
Long-term goal: an adaptive organization
The Applied AI methodology produces dedicated micro-applications that connect into a coherent ecosystem built on a single, unified source of truth for the company's data and processes. Alongside them, the company retains internal technical competence and infrastructure ready for further solutions built on “AI first” logic. The long-term goal is, however, organizational in nature: shaping a company able to identify and remove its own process bottlenecks on its own, with AI as a tool of everyday work.
In the target model, an employee who sees an operational problem formulates it in terms of a possible solution rather than waiting for an external supplier. The department holds enough internal technical competence to maintain and develop existing tools and, in selected cases, to build new ones. The relationship with the external partner shifts from that of a contractor toward that of a technical mentor for the internal team.
The methodology makes sense in contact with a specific process
The fastest way to assess how it will work for your company is a short conversation or the online test.
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