What We Think · Benchmarking

Before committing AI budget, an enterprise should know where it sits relative to the firms in its sector that are already operating AI in production. This is that data.

Access the benchmarks Download the AI Outlook 2026

Elixir Industry Benchmarking uses production outcome data from the AI Hypercases and the AI Outlook 2026 research to produce sector-specific benchmarks across AI readiness, deployment rate, and production outcome metrics. Healthcare, BFSI, Manufacturing, and Procurement benchmarks currently available.

Production benchmarks

Where the sector leaders are. And what they did to get there.

Sector	AI readiness (top quartile)	Outcome achieved in production	Platform	Time to production
Healthcare RCM	Claim data governed and accessible in SAP MedTech before build	37% reduction in claims processing timeDenial cycle: 14 days to under 9	SAP MedTech	90 days
BFSI Finance Operations	Invoice data clean and accessible in NetSuite with three-way match rules defined	11x faster invoice reconciliation3-day cycle to under 6 hours	Oracle NetSuite	90 days
Manufacturing ERP	Data readiness confirmed, delivery lead named, scope locked before sprint one	14-week S/4HANA go-liveZero production downtime. SLA met.	SAP S/4HANA	14 weeks
Procurement Automation	SAP Ariba Guided Sourcing live, preferred supplier data clean in SLP	80% POs without human intervention5-8% sourcing savings on tail spend	SAP Ariba	10 weeks
HR and Payroll	HROPAL deployed, LIXI activated across payroll and compliance modules	50% reduction in HR admin workloadPayroll run without manual exception review	HROPAL	4-6 weeks activation

What the benchmarks show

Three observations from the production data.

01/ 03

Data readiness is the differentiator

Across every sector, the single variable that most consistently predicts whether a programme reaches production in 90 days is whether data readiness was confirmed before the build began. Programmes that confirmed data readiness pre-build hit the 90-day target. Programmes that discovered data issues mid-build averaged 8 additional months to production.

Platform-native produces the benchmark outcomes

Every benchmark outcome in the table above came from an AI deployment built inside the enterprise platform (SAP, Oracle, HROPAL), not alongside it. Platform-adjacent deployments in the same sectors produced materially lower outcome figures. The structural difference is consistent enough to be the basis of the AI-First Manifesto Principle 2.

Permanent operations sustains the outcome

Programmes handed over to client teams post-go-live showed an average 22% degradation in outcome metrics within six months. Programmes under Elixir Permanent Operations showed continued improvement over the same period, as the agents accumulated production data and were optimised by the operations team against the monthly outcome report.

Know where you sit relative to the sector leaders before committing the budget.

Access the full Industry Benchmarking dataset. Take the AI Readiness Assessment to score your enterprise against the production benchmarks in your sector.

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Sectors benchmarked

Healthcare RCMBFSI finance operationsManufacturing ERPProcurement automationHR and payroll

Benchmark basis

Elixir AI HypercasesAI Outlook 2026 researchAgentic Operations data

Annual AI OutlookAI Readiness Assessment (Elixir AI)AI Value Calculator

Assessment

AI Readiness AssessmentAI Business AssessmentAI Value Calculator