AI Readiness Checklist: Is Your Company Ready for AI?

Why AI Readiness Matters More Than AI Hype

Every week, another vendor promises that AI will transform your business. But the organizations that actually succeed with AI - the credit unions reducing member churn, the healthcare systems streamlining clinical documentation, the universities predicting student retention - share one trait: they were ready before they started.

AI readiness is not about having the latest technology. It is about having the fundamentals in place so AI projects succeed instead of becoming expensive experiments that go nowhere. This AI readiness checklist gives you an honest assessment across five dimensions. No fluff, no sales pitch - just a practical framework you can use this week.

Dimension 1: Data Readiness

Data is the foundation. Without usable data, AI is just software with nothing to learn from. Most organizations overestimate their data readiness because they confuse "having data" with "having usable data."

Checklist

• Data inventory exists: Can you list your major data sources, what they contain, and who owns them? If the answer is "sort of" or "it is in someone's head," you are not ready.
• Data is accessible: Can a technical person query your key datasets without filing three tickets and waiting two weeks? Data locked in legacy systems with no API or export capability is a blocker.
• Data quality is known: Do you know your missing value rates, duplication rates, and consistency issues? You do not need perfect data - you need to know how imperfect it is.
• Historical data is available: Most AI models need at least 12-24 months of historical data to train on. If you only have 6 months, your options narrow significantly.
• Data is documented: Is there a data dictionary? Do field names make sense to someone outside the team that created them? Undocumented data doubles the cost of every AI project.
• Sensitive data is identified: Do you know which datasets contain PII, PHI, or other regulated information? Compliance requirements shape every AI architecture decision.

Scoring

• 5-6 items checked: Strong data readiness. You can move to AI projects with confidence.
• 3-4 items checked: Moderate readiness. Plan for data preparation work before or alongside your first AI project.
• 0-2 items checked: Low readiness. Invest in data infrastructure first. An AI strategy engagement can help you prioritize.

For credit unions, your core banking system (Symitar, DNA, Corelation, etc.) is usually a solid foundation. The challenge is typically getting data out of the core and into an analytics-friendly format. Community banks face similar dynamics with their core providers.

Dimension 2: Infrastructure Readiness

AI workloads have different infrastructure requirements than traditional business applications. You do not need a GPU cluster on day one, but you do need a few basics.

Checklist

• Cloud access or on-premise compute: Can you provision a virtual machine or container with 16+ GB RAM and reasonable CPU? Cloud platforms (AWS, Azure, GCP) make this trivial. On-premise requires planning.
• Development environment: Is there a place where a data scientist or engineer can write and run code (Python, R, SQL) without going through a procurement process?
• Version control: Do you use Git or similar version control for code? AI models are code - they need the same discipline as software.
• Data pipeline capability: Can you move data from source systems to an analytics environment on a regular schedule? Even a nightly SQL export counts.
• Security and access controls: Can you grant controlled access to data without giving someone the keys to everything? Role-based access is essential for AI work involving sensitive data.
• Monitoring and logging: Do you have basic application monitoring in place? AI models in production need performance monitoring - accuracy can degrade over time.

Scoring

• 5-6 items checked: Your infrastructure supports AI development and deployment.
• 3-4 items checked: You can start with development and prototyping. Plan infrastructure upgrades for production deployment.
• 0-2 items checked: Infrastructure gaps will slow down AI projects significantly. Address cloud access and development environments first.

Dimension 3: Talent Readiness

You do not need a team of PhD data scientists. But you need people who can work with data and technology at a level beyond Excel pivot tables.

Checklist

• SQL proficiency: Do you have at least one person who can write SQL queries to extract and analyze data? This is the minimum viable skill for AI readiness.
• Programming capability: Does anyone on your team write Python, R, or similar code? Even basic scripting capability accelerates AI projects dramatically.
• Statistical literacy: Can your team interpret a confusion matrix, understand what a p-value means, or explain the difference between correlation and causation? AI outputs require statistical reasoning to evaluate.
• Domain expertise is documented: Do your subject matter experts have time allocated to support AI projects? The best model in the world is useless without domain context. A credit union's lending team knows which variables actually predict default - that knowledge is irreplaceable.
• Leadership sponsor exists: Is there a C-level or VP-level sponsor who will champion AI initiatives, remove blockers, and defend the budget? Without executive sponsorship, AI projects die in committee.

Scoring

• 4-5 items checked: You have the talent foundation to run AI projects, potentially with consulting support for specialized ML work.
• 2-3 items checked: You can participate in AI projects but will need significant external support for technical execution.
• 0-1 items checked: Start with AI literacy training for your team before launching projects. A consulting partner can run initial projects while you build capability.

Dimension 4: Cultural Readiness

This is the dimension most organizations ignore, and it kills more AI projects than bad data does.

Checklist

• Data-driven decision making: Does leadership actually use data to make decisions, or do they request reports and then go with their gut? If data is decorative, AI will be too.
• Experimentation is tolerated: Can a team try something, fail, learn, and try again without political consequences? AI development is iterative - first models are rarely production-ready.
• Cross-functional collaboration works: Can IT and business units work together on a shared project without turf wars? AI projects require tight collaboration between technical teams and domain experts.
• Change management is a practice: When you introduce new tools or processes, is there a structured approach to training, communication, and adoption? AI tools that nobody uses are expensive shelf-ware.
• Realistic expectations exist: Does leadership understand that AI is not magic? That it requires good data, time, and iteration? Unrealistic expectations ("just plug in AI and it will fix everything") set projects up for perceived failure even when they deliver real value.

Scoring

• 4-5 items checked: Your culture supports AI adoption. This is a significant competitive advantage.
• 2-3 items checked: Cultural barriers exist but are manageable. Invest in education and quick wins to build momentum.
• 0-1 items checked: Cultural transformation should precede or accompany AI investment. Start with executive education and pilot projects that demonstrate tangible value to skeptics.

Dimension 5: Budget Readiness

AI does not have to be expensive, but it is not free. Honest budget planning prevents the most common failure mode: starting a project and running out of funding before it reaches production.

Checklist

• Initial project budget is allocated: Do you have $15,000 to $100,000 earmarked for a first AI initiative? This covers either internal time allocation or external consulting for a strategy-through-prototype engagement.
• Ongoing costs are understood: Have you budgeted for cloud compute, model maintenance, and potential consulting support after the initial project? Plan for $1,000 to $10,000 per month depending on scale.
• ROI expectations are defined: Have you quantified the business problem you are solving? "Save money" is not an ROI case. "Reduce member churn by 2 points, saving $480K annually" is.
• Funding timeline is realistic: Is the budget available for 6-12 months, or does it expire at fiscal year-end in 8 weeks? AI projects need sustained funding through development and validation.
• Build vs. buy analysis is done: Have you compared the cost of building custom AI versus buying a vendor solution? Sometimes a $50K/year SaaS tool is cheaper than a $150K custom build - and sometimes it is the opposite.

Scoring

• 4-5 items checked: You have the budget foundation for a successful AI initiative.
• 2-3 items checked: Budget constraints will shape your approach. Start smaller, prove value, then expand funding.
• 0-1 items checked: Focus on building the ROI case before requesting budget. A lightweight strategy assessment ($15,000-$25,000) can produce the data you need to justify larger investment.

Your Overall AI Readiness Score

Add up your checked items across all five dimensions (28 items total):

• 22-28: High readiness. You are positioned to launch AI projects now. Focus on selecting the right use case and partner.
• 14-21: Moderate readiness. You can start AI initiatives but should address gaps in parallel. A phased approach works well - use early projects to build capability in weaker dimensions.
• 7-13: Developing readiness. Invest in fundamentals before committing to large AI projects. Data infrastructure, talent development, and executive education should come first.
• 0-6: Early stage. You are not behind - you are just starting. Focus on data basics, build internal literacy, and consider a consulting engagement specifically to assess and improve readiness.

The most common pattern we see across credit unions, healthcare, and higher education: strong domain expertise and data assets, but gaps in infrastructure and talent. That is a solvable problem - and it is exactly where an AI consulting partner adds the most value.

What To Do With Your Score

Do not treat this as a gate. A score of 14 does not mean "wait until you hit 22." It means "start, but start smart." Pick a project that works within your current readiness level, use it to improve your weakest dimension, and reassess in six months.

The organizations that never start are the ones that fall behind. The ones that start recklessly waste money. The ones that assess honestly and act deliberately are the ones we see succeed - whether they are a $500M credit union or a 200-bed hospital system.

Frequently Asked Questions

How long does an AI readiness assessment take?

A self-assessment using this checklist takes 1-2 hours with the right people in the room (IT lead, business lead, data owner). A formal assessment conducted by a consulting firm typically takes 2-4 weeks and includes interviews, data audits, and a detailed findings report.

What is the most common AI readiness gap?

Data accessibility. Most organizations have data, but it is trapped in siloed systems with no easy way to extract and combine it. Solving this one issue - even partially - unlocks multiple AI use cases.

Can we start AI projects if our readiness score is low?

Yes, but scope accordingly. With low readiness, start with a strategy engagement or a small proof of concept that simultaneously addresses readiness gaps. Do not attempt a production deployment until your fundamentals are stronger.

How often should we reassess AI readiness?

Every 6-12 months, or after completing a major AI initiative. Readiness improves with practice - your second AI project will go faster than your first because your data, talent, and processes improve with each iteration.

Does company size determine AI readiness?

Not as much as you would think. We have seen 100-person credit unions with better data readiness than billion-dollar enterprises. Size affects budget and talent availability, but culture and data quality matter more.

Related Resources

• What Does AI Consulting Cost?
• How to Choose an AI Consulting Partner
• AI Readiness: Addressing Your Data Should Be Step #1
• Where to Begin with AI: Why Decision Makers Are Stuck
• Get a Free AI Readiness Assessment