Thinklytics

Texas Health and Human Services · Government · Austin, TX · 20 weeks

AI readiness assessment completed across 8 program areas

Texas Health and Human Services wanted to evaluate AI readiness across eight programs before investing $12M in modernization. We assessed their current capabilities, uncovered $7.3M in potential automation savings, and created a clear, prioritized 24-month plan to guide implementation.

Challenge

HHSC faced a $12M AI modernization decision without clear insight into which programs could effectively use AI and which required data cleanup first. Past tech projects failed because they ignored underlying data quality issues. We delivered a simple evaluation to identify readiness gaps and prioritize foundational data work before any AI rollout.

Approach

We evaluated AI readiness in 8 program areas by measuring data quality, completeness, process documentation, staff skills, and governance maturity. We scored each area on these factors to pinpoint exact gaps blocking AI adoption. Then, we aligned $7.3M in automation opportunities with only the areas that met readiness criteria.

Outcome

We pinpointed three programs ready for AI deployment right away and five others that required 6 to 18 months of data and process improvements first. We mapped out $7.3M in automation opportunities into a 24-month plan. The agency shifted $4.2M from AI projects to foundational data work, preventing common deployment failures.

We caught the risks and gaps early, so nothing slipped through the cracks or drained the budget.

Out of the eight program areas, five had data so messy or incomplete that any AI project would’ve been dead on arrival. We caught these problems early, which saved the agency a ton of time and money chasing AI that wasn’t going to work. Spotting this upfront also kept them from making the same tech mistakes they’d run into before.

How We Dug Into Five Key Areas to See If They Were Ready

We rolled up our sleeves and dug into every program area. We checked if the data was accurate and complete, scoured the documentation, sized up the team’s skills, and looked at governance. That gave us a clear picture of where things were falling short. So, the agency knew exactly what to fix before they kicked off their AI project.

We moved our budget to the stuff that actually drives the business.

So, here’s what happened. Five programs were totally struggling because their data was all over the place. We figured the best move was to shift $4.2 million away from AI projects and put it into cleaning up that messy data first. The agency agreed right away. Meanwhile, the three programs that already had good data just went ahead and launched their AI solutions without a hitch.

Results

  • $7.3M Automation opportunities identified
  • 8 Program areas assessed
  • 3 Areas ready for immediate AI
  • 24 months Implementation roadmap

Before, our tech investments didn’t work out because the data was unreliable. Thinklytics helped us identify which programs were actually ready and which ones weren’t. Their clear, no-nonsense approach stopped us from making the same mistakes again.

Deputy Executive Commissioner for Technology, Texas Health and Human Services

In the client's terms

The question

Is the data in a state that will survive what we want to build on it?

The situation

A $12M AI modernisation decision was in front of the agency with no view of which of eight programme areas could actually support AI and which needed data work first. Previous technology programmes had failed by skipping that question.

What we found

Readiness was not uniform and nobody had measured it. Three programmes could carry AI immediately. Five could not, and building on those five would have repeated the failure pattern the agency already had.

What it produced

Three areas were cleared for immediate deployment and five were given 6 to 18 months of foundation work first. The agency moved $4.2M out of AI build and into the data work underneath it, which is the decision the assessment existed to inform.

The roadmap

Eight programme areas scored on data quality, completeness, process documentation, staff capability and governance maturity, which produces a specific gap per area rather than a general verdict. $7.3M in automation opportunity was then mapped only onto the areas that cleared the bar, and sequenced over 24 months.

The work behind this result

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Frequently asked questions

What does an AI readiness assessment actually produce?

A named gap per programme area and a sequencing decision. Texas Health and Human Services had eight areas scored on data quality, completeness, process documentation, staff capability and governance maturity. Three were cleared for immediate deployment and five needed 6 to 18 months of foundation work first.

Should you fix the data before building AI?

Where the data cannot support it, yes, and the assessment is how you tell which is which. This agency moved $4.2M out of AI build and into the data work underneath it, against a $12M modernisation decision, because five of eight programme areas would have failed after the money was spent.

How long does an AI readiness assessment take?

Twenty weeks here for eight programme areas, producing $7.3M of mapped automation opportunity sequenced over 24 months. Scope drives the duration: the assessment covers data, process documentation, staff capability and governance rather than data alone.