Turn growing AI adoption into a strategic investment Finance can forecast, govern and fund with confidence.
One live financial position · updated between closes, not at them.
The AI Financial Control Check · nine questions, about three minutes · no cloud access, no sales call.
Room left inside the approved guardrail · forecast €2.14M of €2.35M
Finance can keep funding 3 initiatives. One decision needs review, while €210k of capacity remains inside the approved plan.
96% of spend owned · forecast remains within plan
Four steps, in order. Each one earns the next, and the last one is the point: funding more of what is working.
See the spend Name the owners Forecast ahead Approve the growth
Every AI provider, cloud platform and coding assistant in one picture, mapped to teams, products and initiatives.
Forecast per initiative and business unit, with the assumptions behind it visible and open to challenge.
Guardrails — pre-approved budget boundaries — plus thresholds and approvals bring Finance into the few decisions that are material, and stay quiet on everything else.
Each initiative carries a documented value case and a value KPI, so every additional euro of AI investment is a decision Finance can justify.
Three quarters of growing adoption, still inside the approved guardrail.
One initiative moved outside its plan. It surfaces as a decision, not an alarm.
Deflection rate is holding above the value case. Finance has already cleared the next increment.
As adoption grows, ownership, forecasts, guardrails and value evidence need to keep pace.
A few teams, a few thousand euros. Formal control stays light.
More providers, more teams, more use cases — spend appears in several places at once.
Usage turns variable. The forecast starts moving between reviews.
AI is now a material investment Finance plans, explains and backs to the board.
Control is cheapest to establish early — establishing the model now makes later scaling decisions easier to approve.
When AI is financially under control, Finance becomes an accelerator — not a bottleneck.
Ownership, forecast and value evidence sit behind every initiative — so approving the next increment of AI investment stops being a judgement call and starts being a decision Finance can make quickly, with the evidence already in hand.
Next year's AI budget gets planned this quarter, on evidence you already hold.
Cost per outcome is measured, the owner is named and the KPI is moving. Finance says yes with the evidence already behind it.
Spend is tracking inside its guardrail and the forecast is stable. The team keeps moving; Finance stays out of the way.
A forecast moved beyond its threshold, with the driver already explained. One decision, made early, instead of a surprise at close.
One flow, run with you during onboarding. No engineering project required.
Day 14: AI and cloud spend mapped to teams and initiatives. Day 60: owners, budgets, guardrails, forecasts and exception routing live. Miss either, and the exit right with a pro-rated refund is in the contract.
Model providers, the infrastructure underneath, and the finance systems it all has to land in — one path, one number.
OpenAI · Anthropic · Gemini · Vertex AI · Amazon Bedrock · Azure OpenAI · Cursor · Claude Code · GitHub Copilot
Google Cloud · AWS · Microsoft Azure
NetSuite · Exact Online · DATEV
So the numbers Finance reports and the numbers Finance controls are the same numbers. Coverage varies by plan and provider API availability. See the full integration list →
InstantViewAI is built for Finance and shared across AI, Engineering and FinOps. Engineering keeps shipping. FinOps keeps optimising. Finance gets a position it can defend — from the same set of numbers.
It does not replace your FinOps or cost-optimization tooling. Those lower the bill; this makes the remaining spend governable.
See how each team uses it →
Read-only access, EU residency and traceability from board number to source billing.
We never need write access to your cloud or AI accounts. Because InstantViewAI uses read-only permissions, it cannot start, stop, or modify anything in your environment.
Append-only, access-controlled record of every change, so every allocation, assumption and approval stays explainable down to the line item that produced it.
Data stays in the EU, encrypted in transit and at rest, with GDPR-aligned processing terms available before you connect anything.
Nine questions across spend visibility, ownership, forecasting, guardrails and value. You get a control-maturity read and the specific gaps that tend to become variances first. No cloud access, no sales call required.
Nine questions, about three minutes.
The questions finance leaders ask us most when scoping financial control over AI.
Cloud cost management is the operational work of tracking cloud costs, cloud usage and cloud resources day to day. AI financial control is the finance discipline on top: knowing where AI and cloud spend is heading, who owns it, what it is producing, and which decisions need approval. InstantViewAI is built for that control layer, not just monitoring.
No — keep your existing optimization tools. FinOps optimizers and native tools like AWS Cost Explorer or Google Cloud cost management lower the bill through rightsizing and storage-cost cleanup. InstantViewAI sits alongside them as the financial control layer that Finance owns, and FinOps teams use it as their shared view with Finance.
We forecast per initiative, team and business unit with transparent assumptions, so Finance can see exactly why spend is rising — and challenge it. AI and cloud spend follows usage, not the calendar, so the method is published rather than proprietary. — why you can trust the forecast →
Finance gets control of AI spend while AI adoption keeps its pace. Every material AI investment has an owner, a forecast, a guardrail and a documented value case — so growing AI expenditure can be defended to the board instead of explained after the fact.
It means five things are agreed with you and running in the product: your AI initiatives are defined, each material initiative has a named owner, budgets and financial guardrails are set, forecasts run against those guardrails, and the exception and approval routing is live.
It is the control model you and we agreed in scoping — not a claim that every financial risk is under control. The scope is written into the contract, so “live” is testable rather than a matter of opinion.
Something narrower than ROI. Each initiative carries a value case — an expected outcome, a value KPI and an owner — and an evidence status showing how far that case has been substantiated: value case documented, KPI instrumented, outcome measured, value validated.
For example: Customer Support AI — expected outcome “deflect 30% of tier-1 tickets”, value KPI “deflection rate”, owner VP Support, evidence status “outcome measured”.
InstantViewAI reports that evidence status next to the spend. It does not calculate a return on your behalf, and it does not present an expectation as a realised result.
Both. AI expenditure increasingly includes model providers, coding assistants and the cloud infrastructure supporting them. InstantViewAI is a cloud and AI financial control plane: AI providers, cloud platforms and your finance systems in one model.
Spend, forecast, ownership, guardrails and value in one model — so the next AI investment is a decision Finance can back, not a number it explains afterwards.