Case Studies

AI readiness assessment: 10 engagements

• We consolidated 14 regional patient encounter definitions into one standard in 11 weeks, cutting reconciliation labor costs by $2.1 million. , Kaiser Permanente, Healthcare
• We recovered $4.8M a year in misrouted claims by lifting member match accuracy from 75 to 94 of every 100 records, restarting three stalled ML pilots. , Express Scripts, Healthcare
• We improved patient data quality from 58 to 91 in 12 weeks, enabling a $3.2M population health management platform to move forward. , St. David's Medical Center, Healthcare
• We evaluated AI readiness in 8 program areas, uncovered $7.3M in automation potential, and created a detailed 24-month AI rollout plan. , Texas Health and Human Services, Government
• We improved policy data quality from 61 to 94, unlocking $8.4M in AI underwriting projects that were stalled. , Employers Holdings Inc., Financial Services
• We audited and fixed research data systems in 14 departments to secure $6.8M in federal AI research funding. , Texas Tech University System, Higher Education
• We found three stalled ML projects and mapped out clear 12-week plans to get each into production. , Mid-Market SaaS Platform, Technology & SaaS
• We made inventory and customer data migration-ready in 7 weeks, before a go-live date was ever set. , Regional Wholesale Distributor (representative), Distribution & Logistics
• We made finance and supply chain master data audit-ready for S/4HANA in 12 weeks, with the first close on time. , Regional Healthcare System (representative), Healthcare
• We carried asset and reporting data through an S/4HANA migration with zero reporting blackout, in 16 weeks. , Mid-Market Energy & Utilities Company (representative), Energy & Utilities

Where the work sat

Work by capability

Sixty-four engagements, each recorded with the question it existed to answer, the situation it arrived in, and what the work produced. Filter by stage, industry or question type.

Sixty-four engagements and the question each one was answering.

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Every entry records the question the work existed to answer, the situation it arrived in, the roadmap that answered it, and what that produced. Filter by the stage the work sat in, by industry, or by the kind of question.

A question rarely turns into one kind of work, and the stage an engagement started in is usually not the stage the client expected it to. Select one to narrow the library.

Build is the largest of these because it is where clients arrive. Seven of sixty-four started at Assess, and every one of those seven cost less than the engagement the client came in asking for.

That ratio is the thing the first movement exists to change.

If one of those engagements reads like the situation you are in right now,

An hour, no charge, usually enough to say whether the thing you are about to commission is the thing you need.