Today, most finance organizations have a handful of artificial intelligence (AI) pilots running across the business. Chatbots might be operating in shared services. A forecasting proof‑of‑concept might exist in just one region. And perhaps a variance‑explanation assistant is used for a single profit and loss (P&L) statement. The result? Activity without impact.

For meaningful, defensible return on investment (ROI) in 2026, finance needs to stop scattering attention across “interesting” experiments. Finance must instead go deep into one financial planning and analysis (FP&A) domain that truly matters.

Pilot sprawl is the biggest threat to AI ROI. Here’s how to break the sprawl.

The Problem With Lots of AI Pilots

Pilots rarely survive contact with reality.

Typically, they’re run on clean data in one market, with a champion team, and a vigilant engineer lurking behind the scenes. The problem comes when you try to roll such pilots into production. Why? You collide with messy data, inconsistent definitions, security reviews, change control, and fragile handoffs across finance, IT, and the business.

According to leading surveys, most organizations are still stuck between experimentation and true transformation. Many initiatives are failing at the “pilot‑to‑production” bridge because operating models, governance, and data foundations aren’t ready. Survey data underscores why fixing this disconnect is critical. For example, “Worker access to AI rose by 50% in 2025, and expectations for scale the number of companies with ≥40% projects in production is set to double in six months.”

To close the pilot-to-production gap, organizations must re‑engineer the underlying operating model to ensure pilots aren’t just promising experiments, but scalable, durable capabilities.

Pilots create local wins, not enterprise capabilities. Per Accenture’s research on scaling AI, 80% – 85% of companies remain in a proof‑of‑concept factory, running many tests with low scaling success. Meanwhile, the few that do scale reported achieving materially better returns. The lesson? Value comes from designing for scale early, not from amassing more pilots.

Culture and ways of working are also painful blockers, with culture being an even bigger challenge than technology. Without cross‑functional collaboration, redesigned processes, and sustained leadership sponsorship, even the best models can wither.

Why Pilots Are So Alluring

They feel safe. A contained pilot limits exposure while giving leaders “evidence” of progress. But safety without a scale plan becomes an excuse to defer the hard choices about data, platforms, and governance.
They demo beautifully. Generative AI can make even thin use-cases look magical. Meanwhile, cloud consumption and integration costs show up later as bill shocks when pilots scale. Capgemini’s research highlights these cost surprises and the need to architect for scale from the start.
They satisfy many stakeholders. A pilot per function spreads budget and goodwill. But diluted attention prevents any one capability from maturing into an operating advantage, which is what fuels the actual return on investment.

The Solution? Choose One FP&A Domain and Go Deep

To escape pilot sprawl, pick one FP&A domain and build an end‑to‑end capability you can scale across business units and geographies. Here are some good candidates to help you get started:

Revenue and demand forecasting (short and medium term)
Operating expense planning (driver‑based budgets with continuous re‑forecasting)
Cash and working capital forecasting (collections, days sales outstanding/aging, liquidity)
Scenario planning (policy‑driven, multi‑scenario stress tests)
Close‑to‑plan integration (variance explanation and commentary automation)

How to Select Your Domain (A Quick Scorecard):

Profitability metrics: Can improvements move enterprise metrics (revenue, margin, cash)?
Data addressability: Do you control or have reliable access to the signals required?
Process repeatability: Is the workflow stable enough to standardize and automate?
Governance readiness: Can you satisfy model risk, privacy, and audit requirements?
Executive sponsorship: Do the chief finance officer (CFO) and domain owner agree on key performance indicators (KPIs) and thresholds?

After answering those questions, prioritize the domain with the highest combined score, not the one with the flashiest demo.

A Practical Playbook for FP&A

1. Define the “Minimum Lovable Product” (MLP) for One Domain

Document the single decision you’re improving (e.g., weekly revenue forecast lock). Specify inputs, latency, controls, and the human handoffs. Coach the team to design for operability (alerts, lineage, explainability) rather than just model accuracy. Here’s guidance from the Harvard Business Review (HBR): build the organizational backbone alongside the tech.

2. Establish a Cross‑Functional Capability Pod

Staff the MLP with FP&A power users (process owners), data engineering, machine‑learning operations specialists, finance systems, and risk/compliance. Co‑locate (physically or virtually), give them a backlog, and rotate business stakeholders through sprint reviews. Per Deloitte’s 2024 series, data foundations and governance, risk, and compliance (GRC) are two make‑or‑break enablers for scaling generative AI.

3. Fix Data at the Source

Map the canonical definitions (customer, product, region, calendar). Implement data contracts for critical tables and instrument quality checks (completeness, timeliness, drift) in the pipeline, not in a downstream “cleaning” notebook. Through this process, leaders avoid the proof‑of‑concept trap.

4. Choose a Scale‑Ready Architecture

Favor a common feature store for forecasting signals. Standardize model packaging; build continuous‑integration and continuous‑deployment pipelines for models and prompts; and centralize observability for data, model, and business KPIs. Per Capgemini,platformization, built on shared components and consistent patterns, is key to managing cost and driving reuse.

5. Bake in Responsible AI

Document use‑case risk, approvals, and human‑in‑the‑loop thresholds. Provide forecast explainability and audit trails (e.g., what changed, who overrode, and why).

6. Operationalize Adoption, Not Just Models

Train planners and business partners on how to use, challenge, and override outputs. Measure adoption and override rates. Run monthly model councils with FP&A and operations.

7. Prove Value, Then Replicate

Once your first domain hits target thresholds, templatize the data products, governance package, and runbooks. Then clone the pattern into the next domain (e.g., move from revenue to working capital) with 60% to 80% reuse.

What “Good” Looks Like: Concrete Targets for FP&A

While your mileage will vary, leading programs report gains along these lines:

Forecast accuracy: Reduction in mean absolute percentage error (MAPE) for short‑term revenue or cash forecasts.
Cycle time: Faster closes and re‑forecast cycles through automation and anomaly detection.
Time to insight: Hours to minutes for variance analysis with AI‑generated commentary and drill‑downs.
Adoption: A majority of planners regularly using AI‑augmented forecasts with declining override rates over time.

IBM IBV highlights measurable productivity and quality improvements across FP&A and record‑to‑report when AI is embedded end‑to‑end. When paired with stronger data and workflow design, those gains are even more pronounced.

Common Pitfalls (And How to Avoid Them)

“Cool problem, low value.” Resist novelty. Instead, solve the expensive problem first (cash, revenue, OpEx).
Underestimating cost to run. Track cloud and model‑serving costs per forecasting cycle and per business unit. Set budgets and autoscaling policies.
Data quality whack‑a‑mole. Don’t “clean” data downstream forever. Put ownership and SLAs on source systems. Then instrument automated checks.
Governance bolted on late. Involve risk, compliance, and audit at design time. Maintain model cards and decision logs from day one.

Getting Started in the Next 90 Days

Weeks 1 – 2: Decide the domain and KPIs
Pick one FP&A domain. Define the single decision you’ll improve (e.g., weekly cash forecast lock) and 3 – 5 KPIs. Potential KPIs include accuracy (MAPE), cycle time, override rate, adoption, and cost to serve.

Weeks 3 – 4: Stand up the capability pod
Name accountable owners in FP&A, data, platforms, and risk. Publish a RACI. Confirm access to data, and establish data contracts for the top 10 features.

Weeks 5 – 8: Build the MLP and governance
Deliver a working slice: data pipeline with quality checks, baseline model/prompt with explainability, controls for overrides, and dashboards for both business KPIs and model health.

Weeks 9 – 12: Pilot at production quality
Run the solution in parallel with your current process. Track accuracy, overrides, and adoption. Hold weekly operating reviews. Tune, document, and prepare the rollout plan.

If you do only one thing this quarter...

...commit to a single FP&A domain for AI. Go deep enough to make it part of how your finance team works every week.

The research is clear. Companies that move from scattered experiments to scaled, AI‑led processes see outsized gains in productivity, growth, and decision quality.

Want more insight into how to build your FP&A organization? Read the 2026 Finance Leader’s Guide to FP&A.