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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 und eins process, which leads each phase cleanly, from requirements through tests and verification to acceptance. Those who understand both see immediately: AI is no longer a toy today, but real value creation. Very few companies have worked this way so far. That is exactly where your advantage lies.

Wohin es gehtIndustry forecasts predict for around 2026 40% of enterprise software with AI agents that complete tasks independently. A year earlier it was still unter 5%. Der Sprung geschieht gerade jetzt, und er belohnt diejenigen, die verstehen, wie saubere KI-Arbeit entsteht.

Die vier Treiber

Four things determine whether good work comes from an AI, just like with a machine and a human. They determine the first, the goal, in conversation with us. When the other three are given, 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
  • Querying and filling databases
  • Web und Browser bedienen
  • Controlling the screen for programs without an interface
  • E-Mail, Teams, Kalender
  • Spezialisierte Systeme wie SAP, SharePoint, Power BI
  • Creating, testing, and deploying code

Wissen → Unternehmenswissen

Woraus die KI schöpft

Damit sie basierend auf dem eigenen Wissen des Unternehmens antworten kann und nichts erfinden muss.

  • Company folder: documents, manuals, presentations
  • Emails und Sitzungsprotokolle
  • Technische Zeichnungen und Spezifikationen
  • Standards, Vorschriften und Richtlinien
  • Stammdaten und Datenbanken
  • Durchsuchbar, mit Quellenangabe

Budget → Modell und Rechenaufwand

Wie viel Rechenaufwand eine Aufgabe erhält

Speed versus quality, consciously chosen. A short question does not require a large model.

  • Kleines, schnelles Modell für einfache Aufgaben
  • Großes Modell für schwierige Aufgaben
  • More or less thinking, depending on difficulty
  • Lokal auf deiner eigenen Hardware oder in der Cloud
  • Kosten pro Durchlauf, sichtbar in Zahlen
Der Kern in einem SatzIn the conversation, we only determine the first driver, the assignment. The other three are craftsmanship and environment. When all four align, the AI takes care of the rest, and the human decides on the points that are important.

Der gesamte Prozess

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

6 / 6Rollen-Gruppen darstellen
16Phasen, alle von der KI ausgeführt
10Übergaben zwischen den Gruppen

Jede Rollengruppe ist besetzt, nicht beansprucht:

  • Produkt & Management Phases
  • Area of expertise Phases
  • Entwicklung Phases
  • Qualität & Testing Phases
  • Dokumentation & Support1 Phase
  • Betrieb & DevOps Phases
  1. 1
    Idea and assignmentKI abgeschlossen

    FachbegriffKickoff

    Produkt & Management
  2. 2
    AnforderungenKI abgeschlossen

    FachbegriffURS, Anforderungskatalog

    Area of expertise
  3. 3
    SpezifikationKI abgeschlossen

    FachbegriffFunctional and data specification

    Area of expertise
  4. 4
    PlanungKI abgeschlossen

    FachbegriffBacklog, Sprint, Refinement

    Produkt & Management
  5. 5
    programmingKI abgeschlossen

    FachbegriffImplementation

    Entwicklung
  6. 6
    VersionskontrolleKI abgeschlossen

    FachbegriffGit

    Entwicklung
  7. 7
    Writing TestsKI abgeschlossen

    FachbegriffUnit, Integration, E2E

    Qualität & Testing
  8. 8
    Test runKI abgeschlossen

    Fachbegriffautomated test run

    Qualität & Testing
  9. 9
    Review and approvalKI abgeschlossen

    FachbegriffCode review, gates

    Qualität & Testing
  10. 10
    Feedback and bug fixingKI abgeschlossen

    Fachbegriffiterative

    Entwicklung
  11. 11
    traceabilityKI abgeschlossen

    FachbegriffTraceability matrix

    Qualität & Testing
  12. 12
    documentationKI abgeschlossen

    Fachbegrifftechnical and user

    Dokumentation & Support
  13. 13
    build pipelineKI abgeschlossen

    FachbegriffCI

    Betrieb & DevOps
  14. 14
    release and deliveryKI abgeschlossen

    Fachbegriffdeployment

    Betrieb & DevOps
  15. 15
    acceptanceKI abgeschlossen

    Fachbegriffacceptance protocol

    Produkt & Management
  16. 16
    operations and feedbackKI abgeschlossen

    Fachbegriffoperations

    Betrieb & DevOps
the pointAn AI can not only write code. It covers the entire spectrum, from requirements to acceptance, just like a well-rehearsed team. All six role groups are represented, each phase is fully executed by the AI, and at each role transition, it hands over smoothly to the next group.

And in your company?

This very process is implemented by TB-Software for SMEs: from supporting individual steps to fully taking over entire processes, locally, verified, with clear guidelines.

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