SIPOC mapping for Intelligent Agents

SIPOC mapping for Intelligent Agents
SIPOC map auto generated by the ProcessHorizon web app

While AI models are basically functions that map inputs to outputs, the SIPOC model extends this model notion by adding stakeholders in AI applications.

As one and the same model will produce different results in different contexts and under different circumstances, for different individuals and stakeholders, end-to-end SIPOC process mapping will make the AI application context and its impacted stakeholders transparent.   

As LLMs have become an all-purpose or general-purpose technology, all-in-one SIPOC mapping of data input to output, algorithms & stakeholders is a precursor of envisaged AI applications.

LLMs: what you ask is what you get ?

SIPOC model

Suppliers > Input > PROCESS > Output > Customers

SIPOC mapping for AI applications

Data source>Data Input> Algorithm: objective function >Data Ouput>Destination

AI model explained

Who ? Supppliers > Stakeholders upstream

            Customers > Stakeholders downstream

What ? Input data & Output data

How ? Process > algorithmic transparency

When ? Events trigger

Where ? anywhere

Why ? SIPOC map from Suppliers > Input > Process > Output to Customers

SIPOC map for input to output transformation from data source to destination

In an age of complex algorithms and invisible decision-making, SIPOC gives us clarity. It shows us not just what happens but who makes it happen, with what and for whom.

In AI context, SIPOC is a floating signifier leading the way from sophisticated simplicity to a sustainable humanity model (UN SDGs).

Using the following link you can access this sandbox SIPOC model in the ProcessHorizon web app and adapt it to your needs (easy customizing) and export or print the automagically created visual AllinOne SIPOC map as a PDF document or share it with your peers: https://app.processhorizon.com/enterprises/zoQdqZmSmc1vEQUcjgNYDYPD/frontend

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Integrated Quality Management within GRC: A Foundation for Responsible Agentic AI

Integrated Quality Management within GRC: A Foundation for Responsible Agentic AI

Governance defines what should be achieved. Risk Management determines what could go wrong. Compliance establishes what must be respected. Quality Management ensures that processes and their outcomes consistently fulfill requirements & intended objectives. Together, they establish an integrated management system for responsible human & AI-driven enterprise operations. Event-driven SIPOC model

By Peter Haenni