Architecture model for decision support and prioritisation
Model, architecture and workflow

AI architecture for decision support and commercial prioritisation

How can data, analysis and AI be connected to better priorities and leadership decisions?

The project shows a model for how organisations can connect data, insight, analysis, business rules and AI more closely to prioritisation and decisions. The architecture distinguishes between reporting what happened, analysing why, and recommending what should be prioritised next — with human decision-making at the centre. Developed as an architecture model, not a fully implemented enterprise system.

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The solution is developed as a concept prototype and portfolio demonstration — not presented as a production-ready system.

The challenge the project addresses

Strategy documents, KPI structures, sales processes, tender documents and regulatory requirements can create more complexity than progress. The problem is often not a lack of information, but that information is scattered, heavy to interpret and difficult to translate into priorities and next actions.

The architecture and decision logic

The model distinguishes three levels in decision support:

  1. Reporting

    What happened — structured overview of data and status.

  2. Analysis

    Why it happened — patterns, gaps and deviations.

  3. Prioritisation

    What should be prioritised next — recommended actions for leadership.

Human decision and effect measurement are retained as concluding and learning steps.

What has been developed

  • Architecture model for structuring complexity
  • Logic for gap identification and pattern analysis
  • Link between data sources, business rules and analysis
  • Basis for prioritisation and recommended actions
  • Transfer value to other AI projects as technical foundation

Core components

Data sources

Developed

Documents, processes, sales data and strategic goals.

Structuring

Developed

Normalisation and systematic breakdown of complexity.

Analysis and patterns

Developed

Identification of gaps, deviations and action alternatives.

Leadership information

Developed

Prioritised decision foundation for human assessment.

How the model works

  1. Input

    The architecture starts from relevant data sources and requirements.

  2. Structure

    AI structures the material and extracts patterns.

  3. Prioritise

    Gaps and action alternatives are presented for assessment.

  4. Decide

    Human assessment and decision at the decision point.

Possible application areas and relevance

Commercial prioritisation

Pipeline, customer insight, margin, resource use and sales leadership.

Leadership support

Strategy, reporting, governance and leadership team decision work.

Status, maturity and limitations

Developed architecture model and decision logic for how data, analysis and AI can support commercial prioritisation and leadership decisions. Model and architecture track under development — not presented as a fully implemented enterprise system.

Further development and scalable application

Further development of the architecture

  • Dashboards and scenario analysis
  • Alerts and prioritisation engine
  • Decision log and explainable AI
  • Role-based leadership information

Adaptation to functions and industries

  • Gap analysis and use case prioritisation
  • Sales and market overview
  • Leadership tools for strategy and commercial governance

Marius Ottesen

Commercial leader and AI strategist

Develops business-oriented models, workflows and concepts at the intersection of commercial leadership, decision support, knowledge work, technology and practical AI.

Explore the project further

Get in touch if you would like to map how AI can be used for better decision support, commercial prioritisation or leadership support.

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