INNOVAREModule 9 · AI in Human Resources & Organisational Processes

Case 5: The Organisation Machine

The second half of the module: AI beyond HR, in organisational processes — People/Process/Technology, the TOE readiness framework, and how you actually scale AI.

August 2026 · Case 5 of 6
As you read — hold this question

Most AI pilots never scale. Is that a technology problem, or an organisation problem?

~80%
A large majority of AI projects stall before they deliver value — and the blocker is rarely the model. It is data, process, governance and readiness.

The module widens from HR to the whole organisation. The examinable frameworks here are the People/Process/Technology view, the TOE adoption-readiness model, an AI roadmap, and the discipline of scaling — the difference between a demo and an operating capability.

The frameworks
The textbook versionQuiz: TOE + People/Process/Tech

People / Process / Technology, TOE, and the roadmap

People / Process / Technology (Medium 2024): three overlapping circles. People∩Process = Governance & Ownership; People∩Technology = Service Model; Process∩Technology = Automation & Operations; the centre where all three meet = Thinking.

TOE framework (Felemban et al. 2024) — three contexts feed one outcome, “organisation readiness to adopt AI”: Technological (data availability, compatibility, relative advantage); Organisational (senior-management support, resources, process); Environmental (competitive pressure, government support).

AI roadmap (InfoTech): align AI & strategy → establish responsible-AI principles → assess maturity (exploration→incorporation→proliferation→optimisation→transformation) → prioritise (value vs feasibility) → policies → roadmap.

Carry this

TOE has three contexts — Technological, Organisational, Environmental — feeding one thing: readiness. That is the most examinable diagram in the back half.

Scaling — the part demos skip
The rest of the story

From a clever model to an operating capability

Organising AI for scale (Cloudflight 2021): realise the need for change; get employees engaged; organise to scale; educate the organisation; track and facilitate adoption. Key indicators of AI at scale (Datacamp 2024): Impact, Pervasiveness, Consistency, Sustainability.

Why pilots die
A pilot proves a model can work once; scaling requires clean data pipelines, changed processes, trained people and governance that holds. The frameworks agree the binding constraints are organisational — data quality and change management — not model cleverness.
Govern first
The other side

Readiness is a governance question

The evolved view puts governance before capability: the NIST AI Risk Management Framework centres a Govern function, and readiness (the TOE outcome) is really a question of whether the organisation can be accountable for what it deploys. Government also plays a role — safety nets and retraining to cushion automation's labour impact, which the deck notes explicitly.

The other side

“Ready to adopt AI” is not a technology score. It is whether People, Process and governance can carry the Technology — and answer for it when it fails.

Quick recall — without looking back

Test yourself on this case

Question 1 of 3

Name the three TOE contexts and the single outcome they feed.

Technological, Organisational, Environmental → organisation readiness to adopt AI (Felemban et al. 2024).
Question 2 of 3

In the People/Process/Technology diagram, what sits where all three overlap?

Thinking. (People∩Process = Governance & Ownership; People∩Tech = Service Model; Process∩Tech = Automation & Operations.)
Question 3 of 3

Name the four indicators of “AI at scale.”

Impact, Pervasiveness, Consistency, Sustainability (Datacamp 2024). Scaling fails on organisational constraints — data and change management — not model quality.
Module 9
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Case 6: It's People, Not the AI →

Module 9 Videos

Module 9 · Short · The Algorithmic Boss
Module 9 · Long Form · The Algorithmic Boss (Short-Doc)

Sources

Module content
BUSN9049 Module 9 — organisational processes: People/Process/Technology, TOE (Felemban et al. 2024), InfoTech roadmap, Cloudflight scaling, Datacamp indicators (deck). Flinders University, 2026.
Governance
NIST AI Risk Management Framework (Govern/Map/Measure/Manage), 2023.