Your AI Readiness Checklist
Ready to roll AI out across your teams? This checklist reflects how Neoma assesses AI readiness across organisations.
Neoma [nee-OH-ma] /niːˈoume/ — n. [Greek neos (new) + mene (moon)] New moon; symbolising renewal, transformation, and new beginnings.
Be clear on why you're doing this.
AI should be tied to specific business outcomes, not because right now you can't walk down the street without hearing the word. If you can't articulate ROI in plain English, AI will become an expensive toy used by three enthusiasts, and ignored by everyone else.
Find your high-friction processes.
Map 3–5 use cases. These should be repeatable, high-volume processes where people are spending a disproportionate time on low-value work.
Put a number on the pain.
Time wasted, operating cost, rework and error rates, delayed decisions, and missed or deferred revenue. If it can't be quantified, it's not ready for automation.
Determine what "good" looks like.
Faster cycle times, lower unit cost, reduced rework, improved accuracy, or increased throughput. Name the metric before you start.
Confirm the AI exec committee.
This structure exists to prevent stalled pilots, fragmented ownership, and uncontrolled AI usage across the organisation.
AI Team Lead
The structured, detail-obsessed person who gets stuff done.
Owns the day-to-day of the AI rollout. Translates business priorities into use cases, defines success metrics, and drives adoption across teams.
- Own delivery of prioritised use cases from pilot through to adoption
- Decide what good looks like for adoption and use
- Support CTO in platform decision
- Measure and benchmark adoption, surfacing risks and resistance early
CTO
The person who understands your tech stack and can separate hype from reality.
Existing CTO, or a GM appointed to manage the rollout. Accountable for ensuring AI solutions integrate cleanly with the existing stack and meet security, privacy, and compliance requirements.
- Sign off on selected platform/s
- Integrate with your existing stack
- Ensure security and compliance
AI Exec Sponsor
Usually the CEO or business unit lead. Someone with authority, appetite, and backbone.
Promotes and mandates AI adoption. The evangelist who champions AI as a useful business tool, and uses it every day.
- Set clear expectations for AI adoption across the organisation
- Remove organisational and budgetary blockers to delivery
- Hold leaders accountable for adoption and realised impact
- Ensure AI investment aligns with strategic and commercial priorities
- Confirm your AI Steering Committee
- Assign clear ownership and decision rights
- Define success metrics for adoption and impact
Without this structure: stalled pilots, no clear owner, and AI tools proliferating across the org with nobody accountable for what happens next.
Augment, Accelerate, or Adapt?
Before selecting tools or training teams, organisations must identify where AI will create measurable impact, and which mode of deployment fits.
| Dimension | Augment Optimise work | Accelerate Scale performance | Adapt Transform the model |
|---|---|---|---|
| When this makes sense | Knowledge work dominates time | Speed limits revenue or CX | Core model under AI pressure |
| Typical pain | 20–40% time on low-value work | Backlogs, SLA breaches, leakage | Margin erosion, disruption risk |
| AI role | Assist humans, retain decisions and accountability | Remove bottlenecks at scale | Enable new models or offerings |
| Minimum readiness | Employee buy-in, approved tools | Product ownership, usable data | Capital, exec sponsorship |
Set your AI operating stance.
This stance aligns leaders and teams on what AI is used for, what remains human-owned, and how decisions are made as AI adoption scales.
Human-owned vs AI-supported work
AI assists drafting, summarising, and analysis. Humans retain accountability for decisions and outcomes. This line needs to be explicit, not implied.
Expectations for day-to-day use
Teams use approved AI tools to reduce manual effort or improve quality. Outputs are reviewed before being finalised or shared externally.
AI and customer experience
Customer-facing AI is implemented deliberately, monitored for quality, and escalates to humans where judgement, sensitivity, or risk is high.
Tools, data, and budget boundaries
Only sanctioned tools may be used with company data. Tooling decisions balance security, cost, and integration with the existing stack.
Ways of working
Experimentation is encouraged where impact can be measured. Wins are shared. Failed experiments are reviewed quickly and retired without blame.
Ownership and decision rights
AI ownership sits with the nominated AI sponsor, CTO, and AI lead. New tools, use cases, and exceptions are reviewed centrally before scale.
Benchmark AI capability before you scale.
Neoma's AI Proficiency Assessment goes deeper than "how many times a week do you use AI?" but it's still a great place to start. Know what you have before you assume what's missing.
Skills and Usage
- Frequency of use
- Quality of prompting
- Ability to review, interrogate, and improve outputs
- Confidence in applying AI to real workflows
Attitudes and Mindset
- Anxiety levels
- Openness to experimentation
- Perceived risks vs. opportunities
- Beliefs about how AI affects their role
Capability Gaps vs Business Needs
- Team-by-team mapping
- Role-specific proficiency
- Alignment with your operating mode
Run follow-up interviews. Speak to 2–3 people across levels.
- Hidden fears that won't surface in a survey
- Unspoken resistance that will quietly kill adoption
- Brilliant use cases no one has told leadership about
The survey tells you what people are willing to say. The conversation tells you what's actually happening.
This is exactly what we do.
Neoma can benchmark and upskill your teams across all five of these steps through our AI Capability Uplift program. No expensive roadmaps that sit in drawers.
