of AI use cases are now purchased, not built in-house (2026)
We evaluate your current data sources for completeness, accuracy, consistency, and freshness. We document every gap that would cause an AI agent, automation workflow, or analytics initiative to fail before it starts.
We review your current infrastructure: databases, warehouses, pipelines, and integration points. We identify what needs to change before you can run reliable AI workflows or autonomous agents at scale.
AI agents and automation workflows inherit the definitions your data layer uses. If your metric definitions are inconsistent or undocumented, every downstream AI output will be wrong. We find and fix this before you build.
We review your current dashboards, reports, and analytics tools. We identify what is trusted, what is not, and what needs to be rebuilt before you can feed reliable signals into any AI or automation layer.
You receive a written report with every finding documented, prioritized by business impact, and mapped to a specific fix. No verbal summaries. No slide decks that disappear after the meeting.
We give you a sequenced 90-day roadmap to fix the highest-impact issues. Scoped by effort, ordered by dependency, and written for both technical and business stakeholders so everyone knows what comes next.
The model worked in the demo. In production, the data was too inconsistent to trust the output. This is the most common pattern we see.
Lead qualification, support triage, onboarding automation, RevOps routing. The agent is only as reliable as the data it reads. We assess that layer first.
Salesforce Einstein, Microsoft Copilot, HubSpot AI. These features require clean, structured, consistently defined data. Most environments are not there yet.
You are migrating to Snowflake, Fabric, or a modern data stack
Platform migrations do not fix data quality problems. They move them. We assess what you have before you move it so you do not rebuild the same problems on new infrastructure.
It is a structured 30-day assessment of your current data layer: data quality, data architecture, metric definitions, reporting environment, and governance. The output is a written findings report and a 90-day fix roadmap. It is the starting point for any AI, automation, or analytics initiative that needs to work in production, not just in a demo.
Internal data teams are often too close to the environment to see it clearly. They know what the data is supposed to do. We assess what it actually does. We also bring a cross-industry view of what breaks AI and automation initiatives at the data layer, which is different from what breaks standard reporting.
A standard data audit checks for completeness and accuracy. The AI Readiness Assessment goes further: it evaluates whether your data layer can support autonomous workflows, agent-based automation, and AI-driven analytics at production scale. The questions we ask are different because the failure modes are different.
You receive a written report and a 90-day roadmap. Many clients then engage Thinklytics to execute the roadmap. Others take the findings and implement them with their own team. Either way, you leave with a clear picture of what needs to change before your AI investment can deliver.
Most assessments are completed within 30 days. We typically work with a data or IT lead, a business stakeholder who owns the reporting, and whoever manages the current analytics tools. We keep the process lightweight and do not require weeks of your team's time.
Structured AI readiness assessment to evaluate your data foundation for AI deployment. Identify gaps in pipelines, governance, and metric definitions.
Evaluate your data foundation for AI. Identify gaps in pipelines, governance, and metrics.
You are evaluating AI agents for lead qualification, support, or RevOps workflows
You are migrating to Snowflake, Microsoft Fabric, or a modern data stack
You have Tableau or Power BI but the reports are slow, wrong, or both
Readiness is not one number. We assess each dimension and return a maturity score with a prioritized path to close the gaps.
Whether the data an AI use case needs exists, is complete, and is trustworthy.
Whether your pipelines can feed a model reliably and on time.
Who owns the use case, and whether the workflow can absorb AI in the loop.
A maturity score and a prioritized roadmap to close the gaps that block your AI.
A scored, evidence-based assessment scales with scope. These are the factors that move the effort.
The assessment scales with how much of your data estate and how many AI use cases are in scope.
A board-ready scored assessment with a remediation roadmap is more than a checklist review.
More teams and data owners to interview lengthens discovery.
Scoring readiness for a specific AI use case differs from a general readiness baseline.
You want a scored, evidence-based read before you commit budget.
You already know the gaps and want them fixed and run: see Managed Data Readiness.
The issue is undefined metrics specifically: see Semantic Layer Engineering.
AI Readiness Assessment We dive in to see how ready your business really is for AI. No fluff, just a simple look at your data, tools, and team setup. We check the gaps, spot the opportunities, and map out what needs to happen next. Think of it as a health check, but for your AI game. By the end, you’ll know exactly where you stand, and what to tackle first.
It will fail because the data feeding it is inconsistent, undocumented, or structurally wrong. In 2026, 76% of AI use cases are purchased rather than built in-house. The bottleneck is no longer the model. It is the data layer underneath it.
The Thinklytics AI Readiness Assessment tells you exactly what your data layer can and cannot support before you commit budget to AI agents, autonomous workflows, or platform migrations.
An AI readiness assessment scores whether your data can support AI in production. Most initiatives fail because the data feeding the model is inconsistent, undocumented, or structurally wrong, not because of the model itself. Thinklytics audits the data layer underneath your use cases and returns a prioritized plan to close the gaps before you build.
These are not edge cases. They are the rule. If any of these match your situation, the assessment will tell you exactly what needs to change.
If any of these sound familiar, start here before anything else.