For CFOs putting AI on a defensible budget.
Unit economics, cost attribution, cheap-first routing and monthly governance rhythms — for CFOs and Heads of FinOps who need enterprise AI spend to be defensible to the board.
Long-form thinking for CFOs
Enterprise LLM Cost & TCO Benchmarks — 2026
Cost-per-resolution, token and run-cost bands by workload — the numbers to challenge an inflated business case and set budget guardrails that survive the jump from pilot volume to production volume.
The FinOps playbook for LLM + CCaaS spend
Per-workload attribution, cheap-first cascade routing and the monthly governance rhythm that turns AI from an unallocated cost line into a variable cost you can forecast and defend.
Generative AI ROI Benchmarks — Financial Services 2026
Realized versus projected returns by use case, with payback periods and the discount factors most business cases omit — exception volume, human review and integration run cost.
Outcome Pricing & Gain-Share Contracting for Enterprise BPO — Buyer's Playbook 2026
How to convert a provider's automation gains into contracted savings, and write terms that let you exit at renewal instead of funding someone else's margin expansion.
What we've shipped for peers
Cutting LLM spend 41% at a Fortune 100 insurer
A Fortune 100 insurer had 40+ AI workloads across Azure and Bedrock with no unified cost view, no routing controls and a spend curve that was going to cross eight figures inside 18 months. pronix.ai stood up an LLM FinOps program that cut spend 41% with no measurable quality regression.
32% forecast-accuracy gain for a top-10 BPO — AI-driven workforce management
A top-10 BPO's staffing model was rebuilt every Monday in spreadsheets and always wrong by Wednesday. pronix.ai shipped an AI forecasting engine with intraday re-optimization across 60 sites — 32% forecast-accuracy improvement, 9% shrinkage reduction and $11M in avoided overstaffing.
47% faster denial resolution at a multi-hospital system — RCM workqueue automation
A multi-hospital health system had $34M in denied and underpaid claims stuck in manual workqueues, with an average 23-day resolution cycle. pronix.ai deployed an agentic RCM workqueue that classified denials, drafted appeals, gathered evidence and tracked status — cutting resolution time by 47% and recovering $11M in the first year.
72% straight-through prior authorizations at an 11-hospital system — agentic RCM
An 11-hospital IDN was losing scheduled procedures to prior-auth delays and paying overtime to clear a growing backlog. pronix.ai deployed a multi-agent prior-auth system that read the order, pulled clinical evidence, matched payer policy and submitted through Availity or payer portals — 72% of authorizations completed straight-through with turnaround compressed from six days to under 24 hours.
Early-out self-pay AI lifts collections 29% at a 900-provider physician group
A national physician group was placing early-out self-pay accounts with agencies at 90 days because in-house outreach couldn't keep pace. pronix.ai deployed an agentic early-out program on voice, SMS and email with propensity-to-pay scoring, self-service payment plans and financial-assistance screening — collections lifted 29% and cost-to-collect dropped 38%.
Start-here reading
Agentic AI for Healthcare Providers — A Practical Guide for Hospitals, Health Systems and Physician Groups
A practical guide to deploying agentic AI across provider operations. Covers scheduling and access agents, prior authorization automation, revenue cycle and denials, ambient clinical documentation, and patient contact center — with HIPAA, HITRUST and safety governance patterns.
AI automation for finance operations: AP, AR and the close
A finance-specific view of AI automation: the processes with real return, the control requirements that cannot be relaxed, and how to build a case a CFO and an external auditor will both accept.
Back-office automation ROI: building a case that survives review
The financial mechanics of back-office AI automation — how to measure the baseline, categorise benefit honestly, account for retained and run cost, and defend the case at review.
What CFOs ask us first
- How should CFOs budget for enterprise AI?
- Per-workload, not per-project. Attribute inference, retrieval, platform and run cost to the workflow it serves, review monthly against the value it produces, and use guardrails with alerting rather than hard caps that stall delivery.
- What drives enterprise AI unit costs down?
- Cheap-first cascade routing, prompt and context caching, retrieval that returns less but better, batching where latency allows, and retiring workloads that don't clear their value threshold. Together these routinely halve cost per resolved task.
- How do we build an AI business case the board will accept?
- Baseline the current cost to serve, model savings per workflow with explicit assumptions, discount for exception volume and human review, and commit to a measurement plan that reports realized — not projected — savings.
- How should AI vendor contracts be structured?
- Term and commitment sized to observed volume, portability at the model and platform layer, published rate cards for overage, and exit terms that don't require re-platforming to leave at renewal.
Solutions, practice research and free downloads for this role
AI Transformation solutions
Research practices
Free downloads
For CIOs standing up the enterprise AI operating model.
For Chief AI Officers running an agentic portfolio.
For contact center leaders moving to an AI-first operating model.
Customer experience transformation, implemented — not just designed.
Back office automation that survives audit and peak volume.
An AI strategy you can implement, not a deck you present.
Get a briefing curated to your role and program.
We run 60-minute sessions with executive teams on the priorities above — leaving you with a shortlist, a business case or a roadmap you can defend.