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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 Prozess, der jede Phase sauber führt, von der Anforderung über Tests und Überprüfung bis zur Abnahme. Wer beides versteht, sieht sofort: KI ist heute kein Spielzeug mehr, sondern echte Wertschöpfung. Sehr wenige Unternehmen arbeiten bisher so. Genau dort liegt Ihr Vorteil.

Wohin es gehtBranchenprognosen sagen für etwa 2026 voraus 40% von Unternehmenssoftware mit KI-Agenten, die Aufgaben unabhängig erledigen. Ein Jahr zuvor lag es noch unter 5%. Der Sprung geschieht gerade jetzt, und er belohnt diejenigen, die verstehen, wie saubere KI-Arbeit entsteht.

Die vier Treiber

Vier Dinge bestimmen, ob eine gute Arbeit von einer KI kommt, genau wie bei einer Maschine und einem Menschen. Sie bestimmen das erste, das Ziel, im Gespräch mit uns. Wenn die anderen drei gegeben sind, kümmert sich die KI um den Rest, und Sie entscheiden nur, wo es wichtig ist.

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
  • Abfragen und Datenbanken füllen
  • Web und Browser bedienen
  • Den Bildschirm für Programme ohne Schnittstelle steuern
  • E-Mail, Teams, Kalender
  • Spezialisierte Systeme wie SAP, SharePoint, Power BI
  • Code erstellen, testen und bereitstellen

Wissen → Unternehmenswissen

Woraus die KI schöpft

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

  • Unternehmensordner: Dokumente, Handbücher, Präsentationen
  • 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 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.

Discuss process

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