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You set the goal. The AI builds the rest.

AI that not only answers but works

Most know AI as a clever chatbot: you ask a question and get an answer. But that is already yesterday. Modern AI not only answers, it works. It reads your documents, operates your programs, writes, checks its own output, and delivers. It carries out an entire task from the idea to the finished result, just like a well-rehearsed team would.

Whether good work is really produced in the end is not a coincidence. It is determined by four drivers and one Process, which leads each phase cleanly, from the requirement through testing and review to acceptance. Those who understand both see immediately: AI is no longer a toy today, but real value creation. Very few companies work this way yet. That is exactly where your advantage lies.

Where it is goingIndustry forecasts predict around 2026 40% of enterprise software with AI agents that complete tasks independently. A year earlier, it was still under 5%. The leap is happening right now, and it rewards those who understand how clean AI work is created.

The four drivers

Four things determine whether a good job comes from an AI, just like with a machine and a human. You determine the first, the goal, in conversation with us. If the other three are in place, the AI takes care of the rest, and you only decide where it matters.

Good result
AssignmentThe goal
ToolsThe hands
KnowledgeThe source
BudgetThe effort

Four drivers collectively determine the result. If one fails, the quality tips.

Order → Prompt and specification

The goal and the guidelines

What should come out, and what must not happen. We determine this driver in the conversation.

  • The goal in one sentence, the actual requirement
  • Acceptance criteria: when is the result considered correct
  • Architecture, rules, and test specifications as files
  • Examples, counterexamples, and clear boundaries
  • Priorities and order of steps

Tools → Tools

What the AI acts with, instead of just talking

The hands and eyes. Without tools, a model remains an advisor; with them, it becomes a collaborator.

  • Read and write files and folders
  • Query and fill databases
  • Operate web and browser
  • Control the screen for programs without an interface
  • E-mail, Teams, Calendar
  • Specialized systems like SAP, SharePoint, Power BI
  • Build, test, and deliver code

Knowledge → corporate knowledge

What the AI draws from

So that it can respond based on the company’s own knowledge and doesn’t have to invent anything.

  • Company folder: Documents, manuals, presentations
  • Emails and meeting minutes
  • Technical drawings and specifications
  • Standards, regulations, and guidelines
  • Master data and databases
  • Searchable, with source citation

Budget → Model and computational effort

How much computational effort a task receives

Tempo versus quality, consciously chosen. A short question doesn’t need a large model.

  • Small, fast model for simple tasks
  • Large model for difficult tasks
  • More or less thinking depending on the difficulty
  • Locally on your own hardware or in the cloud
  • Costs per run, visible in numbers
The core in one sentenceIn the conversation, we only determine the first driver, the assignment. The other three are craftsmanship and environment. If all four align, the AI takes care of the rest, and the human decides at the points that matter.

The entire process

Not just code. An AI follows the same arc as a well-rehearsed team, from requirements through testing and review to release and acceptance. Each phase has its technical term and role, and each is fully executed by the AI.

6 / 6Represent role groups
16Phases, all executed by the AI
10Handovers between the groups

Each role group is occupied, not claimed:

  • Product & Management phases
  • Specialist area phases
  • development phases
  • Quality & Testing phases
  • Documentation & Support1 Phase
  • Operations & DevOps phases
  1. 1
    Idea and AssignmentAI completed

    Technical termKickoff

    Product & Management
  2. 2
    RequirementsAI completed

    Technical termURS, Requirements catalog

    Specialist area
  3. 3
    SpecificationAI completed

    Technical termFunction and Data Spec

    Specialist area
  4. 4
    PlanningAI completed

    Technical termBacklog, Sprint, Refinement

    Product & Management
  5. 5
    programmingAI completed

    Technical termImplementation

    development
  6. 6
    Version ControlAI completed

    Technical termGit

    development
  7. 7
    Writing TestsAI completed

    Technical termUnit, Integration, E2E

    Quality & Testing
  8. 8
    Test runAI completed

    Technical termautomated test run

    Quality & Testing
  9. 9
    Review and approvalAI completed

    Technical termCode review, gates

    Quality & Testing
  10. 10
    Feedback and bug fixingAI completed

    Technical termiterative

    development
  11. 11
    traceabilityAI completed

    Technical termTraceability matrix

    Quality & Testing
  12. 12
    documentationAI completed

    Technical termtechnical and user

    Documentation & Support
  13. 13
    build pipelineAI completed

    Technical termCI

    Operations & DevOps
  14. 14
    release and deliveryAI completed

    Technical termdeployment

    Operations & DevOps
  15. 15
    acceptanceAI completed

    Technical termacceptance protocol

    Product & Management
  16. 16
    operations and feedbackAI completed

    Technical termoperations

    Operations & DevOps
the pointEine KI kann nicht nur Code schreiben. Sie zieht den ganzen Bogen durch, von der Anforderung bis zur Abnahme, so wie ein eingespieltes Team. Alle sechs Rollen-Gruppen sind vertreten, jede Phase führt die KI vollständig aus, und an jedem Rollenwechsel übergibt sie sauber an die nächste Gruppe.

Und in deiner Firma?

Genau diesen Ablauf setzt TB-Software für KMU ein: von der Unterstützung einzelner Schritte bis zur kompletten Übernahme ganzer Prozesse, lokal, geprüft, mit klaren Leitplanken.

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