Pillar 3: Workloads

Develop and deploy workloads that meaningfully impact the organisation’s mission.

Executive Snapshot

We’re not concerned with one app; we’re interested in one thousand workloads.

Balance Incremental, Extensible and Differential AI to hedge risk and maximise ROI.

Empower Power Users and Communities of Practice to extend AI themselves.

Why the Workloads Pillar Matters

Workload is not a throw-away word; it is the front- and back-end combination required to produce an AI-driven response or action.

Strategy & Vision set aspiration; Ecosystem Architecture built foundations. Workloads answer the question: how will we use AI to solve real-world challenges?

Workload Prioritization

Our goal is a prioritised roadmap of workloads to modernise with AI or build anew—core driver of ROI.

Techniques to rank workloads

  • Alignment to executive vision

  • Legacy location / technology sunset

  • Usage telemetry (MAU/WAU, data volume)

  • Security or compliance risk

  • Target technologies investment

  • Qualitative Assessment: compare impact vs. complexity → Quick Wins, Big Wins, Nice-to-Haves, Wastes of Time

Incremental AI

Incremental AI applies intelligence to tasks a human already performs
- speed, efficiency, scale.

How to source Incremental workloads

  1. Turn on AI you already own – deploy Microsoft Copilots with high impact.

  2. Ask your people – pain-points and wish-lists.

  3. Rationalise workloads – bake AI into every app rationalisation exercise.

  4. Mine processes – use Process Advisor to surface automation candidates.

  5. Benchmark peers – monitor competitor adoption.

Extensible AI

Extensible AI occupies the broad middle range where Incremental workloads are extended with your data and context.

Example spectrum → Deploy Copilot for M365 → Custom Agent over Blob → Full RAG agent over OneLake

Architect’s mantra: Seek first to extend, then build bespoke.

Key extensibility tools: Graph Connectors, Teams Message Extensions, API Plugins, Copilot Studio Agents.

DiFFerential AI

Differential AI encapsulates moon-shot workloads that humans alone could not achieve - your secret sauce.

Hallmarks: creative thinking, mixed data sets, time-sensitive computation, higher implementation risk.

Example: DeepMind AlphaFold predicting protein structures beyond human timescales.

Rule of thumb: Bake Differential workloads into the roadmap early, but hedge with less complex bets.

Power Users

Power Users bridge general staff and technical teams, pushing AI tools beyond default use-cases and driving adoption.

Enablement checklist

  • Establish clear roles & Communities of Practice

  • Provide Copilot Studio and Azure AI Studio access

  • Train in prompt-engineering & data-security

  • Use rings-of-release and WAU metrics to track uptake

Success Checklist

✔ Roadmap ranks workloads by impact & complexity

✔ Portfolio balanced across Incremental, Extensible, Di8erential

✔ Extensibility patterns standardised (Graph Connectors, Agents)

✔ At least one Differential “moon-shot” in active discovery

✔ Power Users active; CoPs sharing best practice

FAQs

  • Incremental augments existing tasks; Extensible adapts vendor AI with your data; Differential delivers novel outcomes once impossible - see spectrum diagram.

  • Provide Copilot Studio, set clear roles, enforce data-security training and monitor adoption via rings-of-release before scaling.