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
72% straight-through prior authorizations at an 11-hospital system — agentic RCM
72% straight-through prior authorizations — cash acceleration and denial avoidance modelled against a pre-program baseline.
Early-out self-pay AI lifts collections 29% at a 900-provider physician group
29% collections lift on self-pay balances at lower cost to collect, with compliance evidence attached to every contact.
47% faster denial resolution at a multi-hospital system — RCM workqueue automation
47% faster denial resolution — working capital freed by clearing the queue, not by adding revenue-cycle headcount.
Cutting LLM spend 41% at a Fortune 100 insurer
The unit-economics view of AI: 41% lower model spend, with cost per resolved interaction reported monthly like any other line.
Start-here reading
Enterprise AI & CX Outlook 2027
The annual pronix.ai outlook: ten dated predictions for enterprise AI and CX in 2027, the evidence behind them, and the planning implications for CIOs, CX leaders, COOs and CFOs.
The cost curve of enterprise AI: what leaders should assume through 2029
Model prices fall every year and enterprise AI budgets keep growing. Our view of where the cost really sits through 2029, and how to build a plan that does not depend on the price of tokens.
The 2027 CIO agenda: where enterprise AI budget should go next
Our view of how a 2027 technology budget should be allocated for AI: the long-lead investments that determine everything downstream, the line items to stop funding, and the decisions that cannot be deferred another year.
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.
AI for HR and employee service — from case volume to workforce readiness.
AI for the IT service desk — resolve more incidents without adding headcount.
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.