Applied AI for Finance
Give us the finance workflow.
We put AI to work on it.
Finance already knows the number moved. The work is explaining why, in operational terms, before the review. Next Quarter reconciles the variance against account reality and produces an explanation you can defend line by line.
Q3 commit · EMEA enterprise
Variance detected · $2.1M below plan
Variance
EMEA commit down $2.1M against plan
Pipeline
Three strategic renewals slipped a quarter
Usage
Consumption flat on two expansion accounts
Plan
$14.8M
Current
$12.7M
At risk
$3.4M
Coverage
2.1x
Variance Reconciliation Agent
Idle
Variance explanation
Pending- $1.6M of the gap traces to two renewals moved by customer procurement cycles.
- $0.5M reflects expansion assumptions that consumption never supported.
- Recoverable this quarter: $0.9M, concentrated in three accounts with active sponsors.
Human review
Finance lead reviews the attribution
Forecast reconciled
Explanation and evidence attached to the commit record
Live loop: a variance is detected, explained in account terms, approved and reconciled.
40%
less manual effort in the monthly forecast reconciliation cycle
Reduction in analyst hours spent assembling variance explanations, self-reported in active deployments.
60 days
to measurable adoption inside existing planning workflows
Typical elapsed time from kickoff to sustained use in existing FP&A and revenue-planning cycles.
11%
improvement in win rate on the strategic accounts finance flags early
Relative improvement in closed-won rate versus the prior comparable period.
The gap
The number is accurate. The explanation is still assembled by hand.
FP&A can produce the variance in minutes and spends the rest of the week asking sales what caused it. That round trip is the workflow worth automating.
How it works today
- Variance is known long before anyone can explain it operationally.
- Explanations come from conversations, so they are inconsistent between regions.
- Forecast confidence rests on judgement that is never written down.
- Board questions trigger a fresh manual reconstruction each time.
With applied AI on the workflow
- Variance is attributed to named accounts and drivers as soon as it appears.
- Every explanation carries the account evidence behind it, consistently.
- Confidence is stated with its basis, so it can be challenged properly.
- Board-level questions are answered from the same reconciled record.
Workflows we take on
The finance workflows we take on
Each one follows the same grammar: a signal arrives, context is applied, an agent reasons, a business output is produced, and a person decides what happens next.
Variance explanation
Signal
01A material gap between plan and current commit in a segment or region.
Context applied
02Account-level pipeline, usage, renewal timing and recent sales activity.
Agent reasoning
03The agent decomposes the gap and tests each candidate driver against observed activity.
Business output
04A written variance explanation with the amount attributed to each driver.
The finance lead approves or corrects the attribution before it enters the record.
Why it works
Context is the reason the output is usable
Generic AI summarises. Applied AI reasons against the specific reality of your accounts, your positioning and your numbers.
Finance and revenue data joined
Plan, commit, contracts and usage are read as one connected picture.
Operational attribution
Movement is explained in account terms a business partner recognises.
Auditable by design
Every explanation keeps its sources and its approver.
In the planning cycle
Output arrives in the models and reviews finance already runs.
Human control
Finance signs off on every number that moves
Agents assemble and explain. Attribution, adjustment and disclosure remain a human decision with a name against it.
Every recommendation carries its sources, so the owner can judge it instead of trusting it.
Nothing reaches a customer, a CRM record or a board pack without a named human approving it.
Agents operate inside your permission model — people see what their role already allows.
Outcomes feed back in, so the workflow gets sharper without anyone rewriting a prompt.
Where it is used
Where finance teams put it to work
Every engagement starts with one workflow. These are the ones teams bring us most often.
Monthly close commentary
Operational commentary is drafted from account reality, not from memory.
Quarterly business reviews
Finance and sales enter the review with one reconciled version of the quarter.
Scenario planning
Downside cases are built from named account exposure rather than flat haircuts.
Working capital management
See where cash is tied up across receivables, payables and operations before it becomes a constraint.
Renewal revenue assurance
Contracted revenue at risk is visible early enough to protect.
Investor reporting
Reported figures carry an evidence trail that survives follow-up questions.
How we engage
Give us the workflow. We put AI to work on it.
This is a service-led engagement, not a licence and a login. We do the mapping, the building and the review with your team.
- 1
Workflow session
We sit with your team and map one finance workflow end to end — the trigger, the judgement calls and the output that matters.
Week 1
- 2
Context connection
We connect the systems that already hold the truth: CRM, documents, meeting history, product and finance data.
Weeks 2–3
- 3
Agent build and review
We build the agents against your definitions, then review the reasoning and outputs with the people who own the work.
Weeks 3–5
- 4
Run and expand
The workflow runs in your existing tools. Once it holds, we take on the next one.
Week 6 onward
The rest of the family
Applied AI across the revenue organisation
The same context layer, pointed at a different function's workflows.
Give us the workflow
Bring us the reconciliation your team dreads every month
We map it with your controllers, build the agents against your definitions, and keep every explanation auditable.