With insights from Emanuel Paz, Head of Data at Distillery, drawing on experience leading data platform and analytics initiatives for Fortune 500 and high-growth technology companies.
AI pilots are getting easier to run, but far harder to scale. Teams experiment with models, prompts, and tools, only to discover that moving AI into production exposes deeper issues with data, ownership, and execution.
This AI readiness assessment is designed to help data and engineering leaders understand whether their organization is actually prepared to put AI to work in a real, repeatable way.
This assessment reflects many of the same questions we ask clients before recommending any AI investment, because the fastest way to fail with AI is skipping the fundamentals.
1. AI Strategy and Business Value: Defining the “Why”
AI without a clear decision engine is just an expensive science experiment.
Successful AI initiatives start with a clear connection between AI strategy and business value. When that link is missing, teams often end up experimenting without impact.
Which scenario best describes your organization’s AI planning?
The Experimenter
You feel pressure to adopt AI, but it has not yet been tied to a specific business outcome or measurable impact.
The Scaling Block
You have strong ideas, like predicting churn or improving forecasts, but you are unsure whether your data history is deep or reliable enough to support a production model.
The Legacy Trap
You have identified high-value AI use cases, but technical debt makes implementation costly and difficult to justify.
Emanuel’s advice:
“Stop asking what AI can do and start asking what it should decide. If you cannot finish the sentence, ‘If this model is 90 percent accurate, we will stop doing X and start doing Y,’ you are building a toy, not a tool.”
2. Data Foundations for AI: Is Your Data Actually Ready?
AI does not fix bad data. It amplifies it.
Strong data foundations are required for any AI-enabled organization. Even the best models struggle when data is fragmented, unreliable, or slow to arrive.
How would you describe your current data accessibility?
The Data Silos
Data lives across SaaS tools and spreadsheets. Getting a complete picture requires manual exports, custom queries, and workarounds.
The Trust Gap
You have a central data warehouse, but pipelines break often. Dashboards exist, yet stakeholders hesitate to use them because they do not trust the numbers.
The Speed Wall
Your data is accurate and well modeled, but it is not timely. By the time it is processed, the opportunity for an AI-driven response has already passed.
Emanuel’s advice:
“AI is a mirror. If teams cannot agree on how to calculate revenue or churn, AI will reflect that confusion with speed and confidence. Clean your logic before you automate your intelligence.”
3. AI Execution, Governance, and MLOps: Moving from Pilot to Production
A pilot is a win. Production is a process.
Many AI initiatives stall after the proof of concept phase. Reaching production requires clear execution paths, ownership, and governance.
What typically happens after an AI pilot is complete?
The Prototype Ceiling
Teams can experiment in notebooks or prompts, but there is no clear way to integrate models into real products or workflows.
The Resource Bottleneck
The data team spends most of its time fixing reports and pipelines, leaving little capacity to support production AI systems.
The Governance Gap
Models can be deployed, but guardrails are missing. Monitoring is limited, ownership is unclear, and sensitive data risks being exposed.
Emanuel’s advice:
“If an AI pilot takes more than three months to reach a real user, it is no longer a pilot. It has become a research project. Ship early, then build the guardrails.”
What This AI Readiness Assessment Tells You
If you identified with mostly first-option scenarios, you are likely in the Foundational Stage.
Your best return on investment may come from centralizing data, aligning definitions, and reducing manual workflows before introducing more advanced AI systems.
If you identified with second or third-option scenarios, you are in the Scaling or Optimization Stage. You have the data, but need stronger execution, governance, and operational practices to make AI reliable and repeatable in production.
Get an Expert AI Readiness Review
If you are working toward becoming an AI-enabled organization, a second opinion can help. We offer a no-pressure AI readiness walkthrough to help teams:
- Identify where AI initiatives are likely to stall
- Prioritize data and engineering investments
- Build a realistic roadmap from pilot to production