AI Readiness Checklist: 5 Areas to Check Before You Build
Most companies have more AI pilots than AI systems in production. According to Deloitte’s State of AI in the Enterprise 2026, only 25% of organizations have moved 40% or more of their AI pilots into production. Respondents reported that while their AI strategy is prepared, they felt less confident on infrastructure, data, risk, and talent. Strategy has moved ahead of the operational groundwork, which is where an AI investment either pays off or stalls.
More often, an AI project begins before the team has defined the business value, confirmed that the right data is available, or agreed on how the application will be secured and operated.
This AI readiness checklist is a quick way to pressure-test one specific use case before committing budget or engineering time to a proof of concept. It helps identify whether the team can move into delivery now or needs groundwork first, such as clearer data ownership, an approved security path, defined operating responsibilities, or a production-ready cloud environment.
Why most AI projects stall between pilot and production
Production takes away the conditions a pilot was built under. A pilot typically runs on a clean sample of data, in a sandbox account, with a handful of friendly users, and no security review. Moving to production means connecting to live systems with messy data and real permission boundaries, passing a security and compliance review, assigning an owner to run the system, and paying for inference at full volume.
Pilots stall at each of those steps. Deloitte’s respondents named data privacy and security as their top AI risk (73%), followed by legal and regulatory compliance (50%) and governance oversight (46%). Only about one in five companies reported a mature governance model for autonomous AI agents, even though close to three-quarters plan to deploy agentic AI within two years. Deloitte also found that insufficient worker skills are the biggest barrier to integrating AI into existing workflows.
A readiness review surfaces the groundwork before the pilot is built: data access and quality, security approval, governance controls, delivery ownership, and the environment needed to operate the system. That allows the proof of concept to test the remaining product questions with real users and real data, rather than becoming something that has to be rebuilt for production.
AI readiness checklist
Use the checklist for a single use case, such as:
- A company knowledge assistant that answers employee questions from internal documents
- Document processing that extracts and routes information from incoming files
- Natural-language access to operational or customer data
- An AI assistant that supports a defined customer-service or internal workflow
Do not answer based on whether your company is generally “good at AI.” A team can have strong cloud and engineering capabilities while a particular use case still lacks the data access, security approval, or business ownership needed to reach production.
For a scored view across all five areas, take the 11-question AI Readiness Assessment
1. Is the use case clearly defined and worth pursuing?
A useful AI project starts with a business problem, not a model or a feature list. The team should be able to describe the work being improved, who owns that work, and what success looks like.
- Can you name one specific workflow the AI would improve, along with the business outcome you expect?
- Have you decided whether a custom solution is necessary or an existing tool could meet the need?
- Is there enough volume, value, or risk reduction to justify building and operating the solution?
A use case becomes easier to fund and deliver when the expected improvement is measurable. That might mean fewer manual review hours, faster response times, fewer errors, more complete documentation, or a better experience for a specific group of users.
This is also the right stage to make a build-versus-buy decision. Off-the-shelf tools can be the better answer for common needs such as meeting summaries, coding assistance, or general document chat. A custom solution is more likely to earn its cost when it depends on proprietary data, a distinctive workflow, or integration with your own applications.
2. Is the required data ready to use?
AI applications are only as useful as the information they can reach and trust. Before building, confirm that the needed data exists, that its owners can authorize use, and that access controls can carry into the new system.
- Do you know where the required data lives, including every system or repository the use case depends on?
- Is the data trusted, current, and owned by someone who can authorize its use?
- Can the AI application reach the data while preserving the permissions that apply in the source systems?
Many high-value use cases need both structured and unstructured data. An employee assistant might need policy documents from object storage or a document platform, as well as live account, inventory, or ticket information from a database or SaaS application.
Permissions deserve particular attention. An AI application should not become a shortcut around the access controls already in place. If it retrieves documents, the retrieval layer needs to respect the same identity and authorization boundaries as the source system.
For a deeper look at the technical choices behind these systems, see our guide to Amazon Bedrock data ingestion best practices and our Peachjar case study.
3. Is there an approved security and governance path?
A prototype can run with sample data in a controlled sandbox. A production system needs an approved way to handle prompts, documents, outputs, identities, logging, and human oversight.
- Has your security or compliance team agreed on how AI can use the data for this use case?
- Have you defined what the AI can do on its own and where a person must review its output?
- Are logging, monitoring, and a process for correcting errors or misuse part of the plan?
Governance is not a separate exercise that begins after the application works. It shapes what the application is allowed to access, which tasks can be automated, and what evidence the team needs to retain.
For example, a system that drafts an internal response may need different controls from one that changes a customer record, approves a transaction, or sends an external communication. The more consequential the action, the clearer the review point and escalation path should be.
When AI workloads run in AWS, services such as Amazon Bedrock can operate under the organization’s existing identity, encryption, network, and logging controls. That can simplify the security path, but the team still needs to define how the specific application will use those controls.
4. Is the team ready to build and adopt it?
A technically sound application still needs clear ownership, enough delivery capacity, and users who understand how it fits into their work.
- Are data engineering, AI engineering, platform operations, and security responsibilities covered for this use case?
- Does a business owner have accountability for the outcome, and does the team have dedicated time to deliver it?
- Have the people who will use the system helped define how it should work and how success will be evaluated?
No one person needs to perform every role. A smaller company may rely on a partner for platform engineering, AI implementation, or security support. What matters is that responsibilities are explicit and that the business owner is involved from the beginning.
Teams should also decide how they will evaluate quality. For a knowledge assistant, that could mean a representative set of real questions and expected answers. For document processing, it could mean accuracy, exception rates, review time, and the number of cases that still require manual handling.
A proof of concept should create evidence that helps the team make a decision. It should not become an isolated demo that is difficult to maintain or adopt after the initial excitement fades.
5. Can you run the application reliably and affordably?
Moving from a proof of concept to a useful production system requires a place to run it, a repeatable way to make changes, and visibility into whether it is delivering value at a sustainable cost.
- Do you have an approved cloud environment and a repeatable way to deploy AI application changes?
- Can you track AI usage, performance, and cost at the workload level after launch?
- Do you know what the workflow is worth, so you have a practical cost ceiling for the AI solution?
The goal is not to predict every dollar before any code is written. Running cost becomes clearer when the team tests real prompts, real data, and realistic usage patterns during the proof of concept.
What should be clear before launch is how the team will measure usage, latency, errors, and cost, and who will respond when those measures move in the wrong direction. Infrastructure as Code, automated deployment, observability, and basic cost controls help a successful proof of concept become the first version of a maintainable production system.
Want a scored view of one AI use case? Take the AI Readiness Assessment →
Ready to Build, or Ready to Prepare?
The checklist is not a pass-fail test, and a “no” does not make a use case a bad idea. It shows where groundwork is needed to give the project a path to production.
To read your results, look at each of the five areas. If you answered “yes” to all three questions in an area, that area is ready. Any “no” means that area still has open work.
Ready to scope a proof of concept (POC)
If you answer “yes” across all five areas, you have a solid foundation to scope a focused proof of concept. A POC can then validate the remaining unknowns: output quality, user adoption, workload cost, and the architecture that best fits the job.
Ready to proceed with targeted groundwork
One or two areas with “no” answers often point to focused work that can happen before or alongside the POC. For example, the team may need to establish data access, define human-review controls, assign an operating owner, or automate deployment into an approved environment.
Ready to prepare before building
“No” answers to three or more areas suggest the team should establish the underlying foundations first. That may mean narrowing the workflow, creating a data-access plan, defining security and governance controls, assigning delivery responsibilities, or building the cloud and delivery foundation the application will need after launch. These investments often support future AI use cases, not only the first one.
Stratus10 helps teams complete this groundwork as part of AI strategy, proof-of-concept, and implementation work. That can include data integration, secure AWS architecture, deployment automation, governance controls, and the operating model needed to run the application after launch.
Build your AI proof of concept with AWS funding
Stratus10’s AI strategy and opportunity assessment helps teams prioritize use cases, complete the groundwork needed to move forward, and scope a proof of concept for the strongest candidate. As an AWS Advanced Consulting Partner, we also assess whether qualifying proof-of-concept projects can receive up to $25K in AWS funding and help manage the application process.
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FAQs
An AI readiness checklist is a structured review of whether your organization can take specific AI use cases from idea to production. It covers five areas: which use cases are worth funding, whether the data behind them is accessible and usable, whether security and governance can support them, whether your team has the skills to build and adopt them, and what the architecture, delivery process, and running costs will look like.
A readiness checklist is different from a generic AI maturity model, which scores an organization on a standard scale. A useful checklist is tied to real use cases and ends with a clearer picture of which projects to start, which need groundwork first, and which to drop.
An AI readiness checklist evaluates whether a specific use case has the business value, data, security controls, team ownership, and the technical foundation needed to move from idea to production. Going deeper, a checklist may then examine the systems behind the use case, including data sources and quality, access controls, integrations, approved model and cloud environments, deployment automation, monitoring, and cost management. How those technical requirements connect to the business case may also be covered, such as identifying the workflow owner, success measures, operating responsibilities, and the gaps most likely to delay the project.
Start with a specific use case. A company can have strong cloud and engineering capabilities while one proposed AI project still lacks the data access, security approval, owner, or operating plan required to move forward.
The checklist works best when the business owner of the use case and a technical lead complete it together. The business owner can answer whether the use case is worth pursuing and who will adopt it. The technical lead can answer the data, security, and infrastructure questions. If neither can answer a question with confidence, treat it as a “no.” That gap is usually the first piece of groundwork.