Is AI Paying Off Yet? CFOs Want Proof, Not Promises
Finance leaders are seeing early efficiency wins from AI, but few can point to a clean, defensible ROI today. Budgets are under tight review, pilots are everywhere, and the bill for data, integration, and governance is bigger than headline software fees suggest. The mood: cautiously optimistic, financially strict.
Where finance teams are seeing traction
- Month-end close: faster reconciliations, variance commentary drafts, and automated tie-outs for high-volume accounts.
- FP&A: scenario summaries, faster driver-based models, and automated board-pack narratives.
- Working capital: cash application, dispute classification, and invoice coding with human review.
- Controls: first-pass policy checks, anomaly flags, and audit evidence preparation.
Why ROI is still fuzzy
- Hidden costs: data cleanup, model governance, security reviews, and vendor management add up.
- Measurement lag: quality gains and cycle-time compression show up over multiple closes, not week one.
- Process debt: AI highlights broken workflows; fixing them is where the savings come from, but that takes time.
- Usage volatility: token/compute charges spike during peak periods without clear chargeback rules.
A practical ROI model you can use
- Start with a single workflow. Define the unit (invoice, journal line, forecast version) and the target metric (cost per unit, cycle time, error rate).
- Baseline with a time-and-motion sample of real work. No vendor slides. Record rework and exception rates.
- Total cost of ownership: include data prep, integration, security, model oversight, and training time-not just licenses and compute.
- Risk adjustment: apply a haircut for model drift, hallucinations, and control exceptions until stability is proven.
- Decision gates: Pilot (30-60 days), Prove (one full quarter), Scale (after two clean closes). Stop if the unit economics don't improve by a pre-set threshold.
Cost control playbook for AI spend
- FinOps for AI: set usage caps, shadow billing, and project-level budgets; treat tokens and GPUs like any variable utility.
- Vendor rationalization: prefer platforms that integrate with your data and identity stack to avoid tool sprawl.
- Internal chargebacks: make business units own usage beyond baseline allocations.
- Clear success criteria: no scaling without a documented reduction in unit cost or cycle time and no increase in control exceptions.
Build vs. buy
- Buy for horizontal tasks (document processing, search, summarization) where proven options exist.
- Build where your proprietary data is the edge and workflows are unique (pricing, risk signals, underwriting rules).
- Keep models close to data to reduce egress and latency. Cache frequently used prompts and outputs.
Risk, compliance, and audit readiness
- Model risk management: document training data, prompts, and guardrails; log decisions and overrides.
- Data governance: classify PII and sensitive data; enforce access controls and retention policies.
- Human-in-the-loop: require dual-control on material entries, disclosures, and client communications.
- Regulatory watch: align programs with emerging rules such as the EU AI Act.
Private credit and Asia: what Colin Simpson is watching
Private credit continues to draw institutional capital, and Asia is getting more attention. Borrowers facing tighter bank lending see private lenders as flexible on structure and speed, especially in upper mid-market and asset-backed deals. The opportunity is real, but so are the guardrails.
- Opportunities: senior secured loans to cash-generative businesses, diversification across markets, and financing for transitional sectors and infrastructure.
- Risks to price: legal enforceability across jurisdictions, FX exposure, sponsor quality, collateral valuation, and covenant discipline.
- What matters: downside protection first, consistent monitoring, and a clear exit path under different rate scenarios.
What to watch over the next 6-12 months
- Vendor consolidation and clearer unit economics for AI platforms.
- Compute pricing and availability trends that hit TCO.
- Regulatory guidance on AI controls, auditability, and data provenance.
- Credit conditions in Asia as refinancing needs meet tighter liquidity.
90-day action plan for CFOs
- Days 0-30: Pick two finance workflows with measurable units. Baseline costs, errors, and cycle times. Approve a capped pilot budget.
- Days 31-60: Run pilots with human review and audit logging. Stand up FinOps controls for usage and vendor spend.
- Days 61-90: Scale only if unit cost drops and control health is stable for one full close. If not, stop and reallocate.
Upskill your team
If your controllers and FP&A leads need hands-on training for practical AI use in finance, these resources can help:
The takeaway: AI can reduce cycle times and lift quality in targeted finance tasks, but ROI is earned through disciplined scope, hard baselines, and tight cost control-not promises. In parallel, private credit in Asia offers yield with structure, provided you price legal and FX risk with zero wishful thinking.
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