AICost Industry Solutions

Industry AI workflows where a wrong answer costs more than the tokens

Pharma, hospitals, clinics, health plans, banks, insurers, retailers and manufacturers. Each workflow lists the rules that apply and the AICost engine chain that prices the work, tests it and keeps a human where the law expects one.

Business functions Industries

24reference automations

8industries

34engines in these chains

124engines to build your own

1Find your process

Start from the work your team already does: the ticket, the invoice, the claim, the visit note.

2See the engine chain

Each step maps to an AICost engine that prices it and a control that keeps it safe: eval, human review, guardrails, budgets.

3Make it yours

Run the same chain over MCP from Claude, Codex or your app, or open each calculator. We help you adapt it to your stack.

Top 10 AI automations in 2026

Ranked from public spend, adoption and scale data. Many figures are vendor-published, so treat them as directional.

01 Ambient clinical documentation 🏥 Hospitals

Largest healthcare AI spend category (about $600M in 2025), now scaling enterprise-wide (Northwell, Kaiser TPMG)

Source: healthcare.digitalSee the chain → 02 Revenue cycle coding, denials and appeals 🏥 Hospitals

Second-largest healthcare AI spend (about $450M); a provider-vs-payer 'battle of the bots'

Source: hfma.orgSee the chain → 03 Prior authorization automation 📋 Health plans

Market growing about 10x year over year; CMS-0057-F deadlines (2026 decision clocks, 2027 APIs) force automation

Source: medicaleconomics.comSee the chain → 04 KYC/AML investigation agents 🏦 Banking

Up to 10-15% of bank FTEs in KYC/AML; agentic squads promise 200-2,000% productivity

Source: mckinsey.comSee the chain → 05 Claims FNOL, triage and fraud ☂️ Insurance

Bain sizes P&C claims genAI at a 20-25% LAE cut and over $100B value

Source: bain.comSee the chain → 06 Contact center agent assist and wrap-up 📋 Health plans

High-volume, low-risk start; Elevance runs AI wrap-ups on about 1M interactions a month

Source: medcitynews.comSee the chain → 07 Patient message triage and draft replies 🏥 Hospitals

In-basket overload is a top clinician pain point; Providence deflects 30% of administrative messages

Source: beckershospitalreview.comSee the chain → 08 Product content and catalog enrichment 🛒 Retail and e-commerce

Proven at massive scale (Walmart: 850M data points, about 100x productivity)

Source: retaildive.comSee the chain → 09 Pharmacovigilance case intake 🧪 Pharma and biotech

Case volumes up 10-15% a year and 40-85% of PV budgets go to case processing; Oracle, Veeva and ArisGlobal ship AI intake

Source: thepharmaletter.comSee the chain → 10 Advisor/banker knowledge assistant and meeting notes 🏦 Banking

Near-universal adoption at Morgan Stanley (over 98% of advisor teams), a template for front-office copilots

Source: openai.comSee the chain →

Browse by area

Pharma and biotech

Pharmacovigilance case intake and ICSR processing5 cost + 5 governance engines
Clinical study report (CSR) and regulatory document drafting6 cost + 3 governance engines
MLR (medical, legal, regulatory) content review4 cost + 3 governance engines

Open pharma and biotech →

Hospitals

Ambient clinical documentation6 cost + 4 governance engines
Patient message (In Basket) triage and draft replies3 cost + 4 governance engines
Revenue cycle: coding, denials and appeal letters5 cost + 3 governance engines

Open hospitals →

Open banking →

Insurance

Claims FNOL, triage and fraud flags4 cost + 3 governance engines
Commercial underwriting submission intake and triage4 cost + 3 governance engines
Claims and policyholder communications drafting4 cost + 3 governance engines

Open insurance →

Retail and e-commerce

Product content and catalog enrichment4 cost + 2 governance engines
Conversational shopping assistant (agentic commerce)5 cost + 2 governance engines
Customer service agent (WISMO, returns, refunds)3 cost + 3 governance engines

Open retail and e-commerce →

Manufacturing

Predictive maintenance with technician copilot5 cost + 2 governance engines
Visual quality inspection4 cost + 1 governance engines
Industrial copilot for automation engineering and operations4 cost + 3 governance engines

Open manufacturing →

Reference chains are a starting point. Your volumes, models and vendors change the numbers; the engines recompute them from live prices.

Your process is not on the list, or runs differently? These chains are a starting point. We assemble the right engines for your workflow, your models and your vendors, and you run it over MCP or in the calculators.

Business solutions → Build my chain

Vendor / Model	Field	Why it’s inferred
Anthropic — Claude Sonnet 4.6	`cachedInput`	Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5	`cachedInput`	Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5	`batchInput`	Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5	`batchOutput`	Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5	`cachedInput`	Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini	`cachedInput`	Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano	`cachedInput`	Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano	`batchInput`	Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano	`batchOutput`	Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro	`cachedInput`	Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro	`batchInput`	Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro	`batchOutput`	Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2	`cachedInput`	Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.2	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5	`cachedInput`	Derived at 10% of input.
OpenAI — GPT-5	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.5 Pro	`cachedInput`	Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.5 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.2 Pro	`cachedInput`	Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.2 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5.1	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5.1	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5 Pro	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5 Pro	`batchOutput`	Derived at 50% of output.
OpenAI — GPT-5 Nano	`cachedInput`	Derived at 10% of input.
OpenAI — GPT-5 Nano	`batchInput`	Derived at 50% of input.
OpenAI — GPT-5 Nano	`batchOutput`	Derived at 50% of output.
Google — Gemini 3 Flash	`cachedInput`	Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite	`cachedInput`	Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite	`batchInput`	Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite	`batchOutput`	Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro	`cachedInput`	Derived at 10% of input.
Google — Gemini 2.5 Flash	`cachedInput`	Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite	`cachedInput`	Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite	`batchInput`	Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite	`batchOutput`	Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy)	`cachedInput`	Extrapolated at 25% of base.

Vendor / Model

Field

Why it’s inferred

Anthropic — Claude Sonnet 4.6

cachedInput

Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.

Anthropic — Claude Sonnet 4.5

cachedInput

Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.

Anthropic — Claude Sonnet 4.5

batchInput

Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Sonnet 4.5

batchOutput

Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.

Anthropic — Claude Haiku 4.5

cachedInput

Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.

OpenAI — GPT-5.4 Mini

cachedInput

Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.

OpenAI — GPT-5.4 Nano

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Nano

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Nano

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

cachedInput

Derived at 10% of input — OpenAI 90% cache-hit convention.

OpenAI — GPT-5.4 Pro

batchInput

Derived at 50% of input — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.4 Pro

batchOutput

Derived at 50% of output — OpenAI Batch API uniform 50% discount.

OpenAI — GPT-5.2

cachedInput

Derived at 10% of input; no residency uplift.

OpenAI — GPT-5.2

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2

batchOutput

Derived at 50% of output.

OpenAI — GPT-5

cachedInput

Derived at 10% of input.

OpenAI — GPT-5

batchInput

Derived at 50% of input.

OpenAI — GPT-5

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.5 Pro

cachedInput

Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.

OpenAI — GPT-5.5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.2 Pro

cachedInput

Derived at 10% of input — pro-tier convention.

OpenAI — GPT-5.2 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5.2 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5.1

batchInput

Derived at 50% of input.

OpenAI — GPT-5.1

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Pro

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Pro

batchOutput

Derived at 50% of output.

OpenAI — GPT-5 Nano

cachedInput

Derived at 10% of input.

OpenAI — GPT-5 Nano

batchInput

Derived at 50% of input.

OpenAI — GPT-5 Nano

batchOutput

Derived at 50% of output.

Google — Gemini 3 Flash

cachedInput

Derived at 10% of input — Google caching discount convention ~90%.

Google — Gemini 3.1 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 3.1 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 3.1 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Pro

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash

cachedInput

Derived at 10% of input.

Google — Gemini 2.5 Flash-Lite

cachedInput

Derived at 10% of input — Google caching convention.

Google — Gemini 2.5 Flash-Lite

batchInput

Derived at 50% of input — Google Batch API uniform 50% discount.

Google — Gemini 2.5 Flash-Lite

batchOutput

Derived at 50% of output — Google Batch API uniform 50% discount.

xAI — Grok 4 (legacy)

cachedInput

Extrapolated at 25% of base.

Industry AI workflows where a wrong answer costs more than the tokens

Top 10 AI automations in 2026

Browse by area

Pharma and biotech

Hospitals

Clinics and practices

Health plans

Banking

Insurance

Retail and e-commerce

Manufacturing

Methodology

Primary sources

Inferred values (marked with * in calculator tables)

Immediate steps you can take

Industry AI workflows where a wrong answer costs more than the tokens

Top 10 AI automations in 2026

Browse by area

🧪Pharma and biotech

🏥Hospitals

🩺Clinics and practices

📋Health plans

🏦Banking

☂️Insurance

🛒Retail and e-commerce

🏭Manufacturing

Methodology

Primary sources

Inferred values (marked with * in calculator tables)

Immediate steps you can take

Pharma and biotech

Hospitals

Clinics and practices

Health plans

Banking

Insurance

Retail and e-commerce

Manufacturing