Agentic FP&A: Using AI Agents to Supercharge Rolling Forecasts

TL;DR

AI agents now manage the tedious tasks in rolling forecasts, including data prep, driver updates, initial forecasts, QA checks, scenarios, and draft narratives, freeing humans to focus on decision-making. Using a hybrid modelling approach with human-in-the-loop controls, clients usually cut cycle time from about 10 days to 3 to 5 days, gaining better forecast quality and quicker business responses.

Key takeaways

Hybrid beats single method: combine statistical baselines with ML and keep sensible business rules.
Throughput improves: agents automate repeatable steps so Finance can partner earlier in the month.
Controls are designed in: tests, drift monitors, reconciliations, and approval gates are standard.
Start small and scale: pilot three agents first - DataOps, Forecast, Narrative - then add Scenario and QA.

The business problem we are solving

Most FP&A teams still dedicate too much time to data collection, mapping, and fixing. This hampers the monthly cycle and pushes back discussions that should happen while there is still time to respond. Forecast accuracy varies across different lines and business units. Narratives are rewritten each month from scratch. Scenario analysis is uncommon because the team is busy preparing the report.

Numerroo addresses these constraints with a set of small, safe steps that add value immediately and then scale across the organisation.

What is an AI agent in this context

An AI agent is a software agent that can plan tasks, call tools safely, and report back with a record of what happened. Agents are not a black box. Each step is logged and testable. In finance planning, we use five agents.

DataOps Agent - prepares data, maintains mappings and driver dictionaries, and raises exceptions for human review.
Forecast Agent - runs classical statistical models and ML boosters, blends the results, and returns values with prediction intervals.
QA and Controls Agent - runs reconciliation and reasonableness checks and blocks publication until issues are reviewed.
Scenario Agent - runs agreed playbooks such as price or demand changes, hiring delays, or wage pressure.
Narrative Agent - drafts variance commentary and slide bullets with links to the underlying tables and charts.

Why now

Cloud platforms now offer managed runtimes for agents, tracing, and policy controls. Forecasting research indicates that combinations and hybrids generally outperform individual methods. FP&A leaders seek cycle time reductions and more time for decision-making. All three trends align, making agentic workflows a practical and low-risk step forward.

Architecture overview

Data sources: ERP and GL actuals, CRM or pipeline tables, HR and staffing data, driver tables.
Processing: DataOps Agent applies schema checks, outlier tests, mapping diffs, and GL reconciliation.
Model layer: The Forecast Agent runs ETS or ARIMA as a baseline, adds ML where drivers introduce signals, and blends models using constrained rules.
Quality and governance: QA Agent enforces unit tests, drift monitors, and reconciliation.
Scenarios: Scenario Agent runs approved playbooks and explains drivers of change.
Narratives and pack: Narrative Agent drafts commentary and a one-page exec summary for review.
User experience: Finance and business managers work inside Numerroo pages that are consistent with your existing look and feel.

Operating rhythm that teams can follow

Day 0 to 1: Data cut, checks, and exceptions are created automatically.
Day 1 to 2: First cut rolling forecast is produced with intervals and diagnostics.
Day 2 to 3: Scenarios are requested and compared.
Day 3: Narrative draft is reviewed and the pack is published.

Figures

Figure 1 Forecast Accuracy vs Maturity (illustrative)

Comment: Hybrid and combination methods are usually more reliable than a single method when conditions shift.

Figure 2 Monthly Cycle Time Compression (illustrative)

Comment: Agents automate the heavy lifting, which shortens the time from close to published forecast.

Figure 3 ROI Components (illustrative)

Comment: Value comes from hours saved, fewer reworks, and faster decisions. Costs are mainly licenses and a modest implementation.

Methodology in more detail

DataOps Agent

Profiles inputs and checks schema.
Highlights mapping changes and suggests updates.
Detects outliers using a simple robust method and flags them.
Reconciles to the GL and opens exceptions with clear messages and links.

Forecast Agent

Builds a classical baseline for every line using ETS or ARIMA with damped trends where needed.
Adds ML boosters such as gradient boosting when exogenous drivers carry a signal.
Blends results using a simple weight scheme that prefers stability unless there is clear evidence of improvement.
Returns a value and a prediction interval and stores diagnostics for the dashboard.

QA and Controls Agent

Runs three families of tests: reconciliation, structure and shape checks, and stability checks.
Applies change limits where the business has a constraint, such as capacity or supply.
Opens a review task if any test fails and blocks the publish until approved.

Scenario Agent

Supports a set of playbooks written as templates so business users can run them without code.
Examples: price up or down, demand shock, hiring lag, wage increase, FX move, and supplier change.
Produces a small table and a chart that explains where the difference comes from.

Narrative Agent

Writes a first pass variance story in plain language and adds references to the underlying tables.
Uses a controlled template for tone and structure and keeps every statement linked to a source.

What is different for people and process

Analysts spend less time on repetitive steps and more time on exceptions and business partnering.
Business managers see scenarios sooner and agree on actions while there is still time to act.
The monthly pack is smaller and more focused because the core story is clear and consistent.
Audit and governance improve because every step is logged, and review decisions are captured.

KPIs and service levels

Cycle time: close to published forecast. Target 3 to 5 days.
Accuracy: MAPE by line and by business unit. Track trend and variation.
Adoption: percent of lines with prediction intervals and diagnostics.
Exceptions: open exceptions by type and average time to close.
Scenario usage: number of scenarios run and how often decisions change.

Typical pilot scope

2 business units or 2 major revenue streams plus a small opex set.
3 agents in phase 1: DataOps, Forecast, Narrative.
2 playbook scenarios agreed with business owners.
2 full cycles run in the pilot with a live publish and a short lessons learned workshop.

Implementation plan

Weeks 1 to 2 - Readiness and ROI
Inventory data, agree on drivers, set success metrics and a baseline for cycle time and accuracy.

Weeks 3 to 6 - Pilot build
Connect sources, set up three agents, wire tracing and evaluation, and enable human-in-the-loop approvals.

Weeks 7 to 10 - Expand and harden
Add Scenario and QA agents, build reconciliations and change logs, and publish two cycles.

Weeks 11 to 13 - Productionisation
CI/CD, cost controls, dashboards for accuracy and cycle time, playbooks for rollback, and handover training.

Risks and how we control them

Data quality: schema and mapping tests run every month, and reconciliation to the GL is mandatory.
Model drift: diagnostics and drift monitors highlight changes. Baselines are stable, and ML is added only when it improves performance.
Spreadsheet risk: critical steps move into controlled workflows while Excel remains a presentation layer if needed.
Security: Access is role-based, and every agent step is logged. Data leaves the boundary only where approved.

Change management and enablement

Short demos for each role with practice tasks and cheat sheets.
A one-page guardrail document that explains exceptions and approvals.
Three worked examples for everyday situations, such as a demand shock.
Monthly show and tell that reviews accuracy and exception themes.

Cost and ROI framing

Pilot: a small cloud spend and platform subscription, plus enablement and advisory time.
Benefits: time saved across analysts and managers, accuracy improvements that reduce rework and waste, and decisions made earlier in the month.
Payback: most pilots recover the implementation cost inside the first year if the playbook is followed.

Technology choices

AWS path: Bedrock Agents and standard serverless or container options.
Google Cloud path: Vertex AI Agent Builder with built-in evaluation and memory.
On platform: Numerroo pages for data entry, driver management, scenarios, and the monthly pack.

What success looks like

Forecast cycle time is consistently below 5 days.
Accuracy improves on the lines that matter, and the team can explain why.
Scenarios are used in regular meetings and lead to clear actions.
Audit and governance questions are easy to answer because every step is logged.

Glossary

Rolling forecast: a plan that always looks a fixed number of months ahead, such as 12 or 18.
Agent: a software worker that can plan, call tools, and return results with a trace.
MAPE: mean absolute percentage error, a standard way to measure forecast accuracy.
Hybrid model: a combination of statistical and machine learning methods with business rules.

Sources and further reading

FP&A Trends Survey 2024 - time allocation and data quality in planning and forecasting.
Deloitte CFO Signals 2024 - interest in GenAI talent and the current execution gap.
M4 Competition - combinations and hybrids outperform single methods in many settings.
AICPA and CIMA - guidance on rolling plans and forecasts.

See it in action
Book a short demo to see how agentic rolling forecasts plug into your P&L and monthly cadence.
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