For decades, finance teams have dreamed of a budget that “runs itself.” Such budgets automatically adapt to shifting business priorities like profitability and agility, without constant manual intervention. The rise of artificial intelligence (AI) and automation has only intensified this vision, with vendors promising “autonomous budgeting” and hands-off forecasting. But as any chief financial officer (CFO) or financial planning and analysis (FP&A) leader knows, the reality is more complicated — and more human.

Today, most advanced organizations aren’t chasing a future where algorithms replace human judgment. Instead, these organizations are building annual operating plans (AOPs) that combine AI’s speed and scenario agility with the transparency, oversight, and accountability that only people provide.

For instance, finance teams using OneStream can immediately model the impact of FX volatility. Flow Traders (a global trading firm) used OneStream to automate FX corrections and consolidate financial data, reducing reporting time from days to minutes. The shift not only accelerated scenario modeling but also improved data reliability and transparency, allowing finance leaders to prioritize strategic decisions rather than manual checks.

With that type of rapid scenario modeling, leaders can quickly assess risks, test responses, and pivot strategies in real time. Leaders can thus transform budgets from static documents into dynamic decision-making tools. The result? Budgets that don’t just spit out numbers but also explain them, empowering leaders to pivot, challenge, and steer the business.

Let’s explore why explainability is the new gold standard for AOPs, how to debug black-box budgets, and what’s required to build modern, human-centered planning processes.

Why “Autonomous Budgeting” Is Overhyped

The idea of a fully autonomous budget (one where AI ingests data, spits out a plan, and finance simply “accepts” the result) sounds appealing. But Pras Chatterjee, Product Marketing Director at OneStream, dispelled this notion in a recent interview:

“The most overhyped thing is autonomous budgeting. I don’t think AI is going to replace the judgment of the finance team. It’ll help augment it… but it can’t replace accountability.”

This sentiment is echoed by leading analysts. For instance, Harvard Business Review warns that “AI’s trust gap” is real. The magazine further emphasizes how organizations must empower humans to manage and oversee AI tools — not just blindly accept their outputs. Meanwhile, Gartner flags “Responsible AI” and “augmented analytics” as top priorities in its 2024 Hype Cycle for Finance AI, emphasizing transparency, explainability, and human-in-the-loop controls.

The bottom line? AI is a powerful co-pilot, but it’s not ready, nor suited, to fly solo. Instead, the future belongs to AOPs that keep humans in the loop, blending automation with oversight.

The Case for Explainable AOPs

1. Trust and Accountability

Finance leaders trust what they can see and explain. In black-box models, the logic is hidden, the drivers are unclear, and the outputs can’t be challenged. These models thus breed skepticism and risk. In contrast, explainable AOPs do the following:

Show how forecasts are built, such as by highlighting prediction accuracy, scenarios considered, and factors that influenced results (e.g., sales trends, market changes)
Allow users to override or annotate forecasts
Provide guardrails to prevent bias or over-reliance on any single model

As Chatterjee notes, “OneStream provides transparency into the model drivers… Users can override and annotate forecasts to preserve accountability, and we can also create guardrails to prevent bias or over-reliance on different outputs.”

2. Scenario Agility with Human Judgment

The real world doesn’t stand still — markets shift, supply chains break, tariffs emerge, and new opportunities reveal themselves overnight. Accordingly, the best AOPs are living, breathing plans that can be rapidly updated with new scenarios by humans, not just machines.

OneStream’s approach enables “rapid what-if analysis” using driver-based levers (price, volume, FX rates) in a sandbox environment. With such analysis, finance can test scenarios in minutes, see the full profit and loss (P&L) and cash flow impact, and make decisions without waiting for IT.

But crucially, these scenarios aren’t just generated. They’re governed. Scenario libraries, approval workflows, and lineage tracking ensure creativity doesn’t devolve into chaos.

3. Unified Data, Shorter Cycles, Broader Participation

Disconnected models and siloed data crush agility. When AOPs are built on a unified platform, finance, operations, human resources (HR), and sales all work from the same data. The result is shorter cycle times, improved accuracy, and more people meaningfully participating.

In the interview, Chatterjee cites OneStream customers who reduced cycle times by 30% – 50%, improved forecast accuracy, and expanded participation from 4,000 to 5,000 planners. All that was achieved by moving to a unified, explainable platform.

Deloitte’s research reiterates such benefits, stating that organizations that pair AI with strong data governance and cross-functional collaboration see faster cycles and deeper insights.

4. Continuous Planning, Not Shelfware

Ineffective AOPs become “shelfware” by Q2 — irrelevant as soon as the ink dries. Explainable, unified AOPs feed directly into rolling forecasts and in-year replans. As a result, assumptions flow seamlessly, variances trigger scenario updates, and plans remain relevant all year. Gartner’s 2024 survey found that finance leaders see GenAI’s biggest near-term impact in “explaining forecast and budget variances” — not just generating them. This impact closes the loop between planning and action.

5. Building Trust in AI for the Whole Organization

Finally, explainability isn’t just for the CFO. Non-finance users like budget owners, department heads, and business partners also need to understand, trust, and engage with the plan. With OneStream’s guided workflows, plain-language templates, and transparent dashboards, everyone can easily see how their inputs drive outcomes.

In other words, when able to see, challenge, and improve the plan, people are more likely to own it.

How to Debug Your Black-Box Budget

Ready to move from black-box budgeting to explainable AOP? Here’s a practical checklist:

1. Demand Transparency from Your Tools

Can you see which drivers and assumptions power each forecast?
Are error rates, confidence intervals, and model logic visible?
Can users override, annotate, or challenge AI-generated outputs?

2. Keep Humans in the Loop

Is there a clear workflow for review, approval, and escalation?
Are scenario libraries governed, with lineage and version control?
Do business users have a say in what scenarios matter?

3. Unify Data and Models

Is your AOP built on a single platform or stitched together from silos?
Can data flow seamlessly from ERP, CRM, HRIS, and other sources?
Are dimensions and hierarchies consistent across the enterprise?

4. Make Planning Continuous

Does your AOP feed rolling forecasts and in-year replans?
Are variances and new data automatically incorporated?
Can you pivot quickly when the business changes?

5. Train and Empower Non-Finance Users

Are templates and dashboards in plain language?
Is training focused on driver ownership, not technical coding?
Do users see the impact of their inputs on relevant KPIs?

The Payoff: Faster, Smarter, More Trusted Plans

Organizations that debug their black-box budgets and build explainable AOPs will be equipped to do the following:

Respond faster to change via scenario agility and rapid what-ifs
Build trust across finance and the business through transparent, accountable forecasts
Shorten planning cycles and reduce reconciliation headaches
Empower more people to participate and own the plan
Keep plans alive and relevant all year — not just at budget time