The 7 Pillars of AI Readiness.

Core Question

Why pursue AI?

What we believe

AI is a means to a business outcome — not an outcome in itself.

What AIRIS evaluates

Clarity of intent, alignment between AI ambition and corporate strategy, executive sponsorship, and the existence of a real decision AI would help make.

Common failure mode

AI initiatives launched as a response to industry FOMO, with no clear owner, success metric, or business hypothesis.

Core Question

Where does value get lost?

What we believe

AI improves processes; it does not invent them. A weak process automated is a weak process at scale.

What AIRIS evaluates

Process maturity, handoffs, exception rates, cycle times, the role of human judgement, and which steps would genuinely benefit from intelligence.

Common failure mode

Layering AI on broken or undocumented workflows — accelerating error and confusion rather than removing it.

Core Question

Do you have usable data?

What we believe

Most AI ambitions are constrained by data reality, not algorithmic capability.

What AIRIS evaluates

Availability, accessibility, accuracy, completeness, lineage, governance, and lawful basis for the data the proposed use case depends on.

Common failure mode

Assuming data is 'there' because a system exists — discovering only at pilot time that the data is partial, stale, or unusable.

Core Question

Can it actually be implemented?

What we believe

An AI capability that cannot be integrated into how work happens is a demo, not a solution.

What AIRIS evaluates

Architecture, APIs, identity, security, observability, change windows, vendor lock-in, and the realistic engineering cost of integration.

Common failure mode

Proof-of-concept success that never reaches production because integration was treated as an afterthought.

Core Question

Can it be done responsibly?

What we believe

Responsibility is a precondition for adoption, not a constraint on it.

What AIRIS evaluates

Regulatory exposure, data protection, model risk, explainability, audit trail, human-in-the-loop design, and incident response.

Common failure mode

Deploying systems that cannot be explained, audited, or corrected when they make consequential errors.

Core Question

Will people actually adopt it?

What we believe

Adoption is the hardest part of the work — not the model, not the integration.

What AIRIS evaluates

Skills, capacity, cultural appetite for change, sponsorship, communication channels, training pathways, and incentives.

Common failure mode

Delivered capabilities sitting unused because the operating model, training, or trust were never built.

Core Question

Is it sustainable value?

What we believe

AI economics must hold up under honest accounting — including run-rate, drift, and oversight cost.

What AIRIS evaluates

TCO, value attribution, build vs buy, vendor dependency, unit economics at scale, and the path to ongoing ROI.

Common failure mode

Headline ROI claims that ignore ongoing cost of data, infrastructure, oversight, and re-training.