Earlier this year, Donald Trump took to Truth Social, questioning the need for corporations to report quarterly to the Street. Further, the SEC acknowledged that it was fast-tracking a proposal to review reporting frequency. The quarterly cadence of the 10-Q, set in 1934, has remained almost cast in stone ever since. While this move may appear counterintuitive in a fast-paced, AI-driven world, where quick commerce and real-time data are table stakes, it may well shape the tone and tenor of future boardroom conversations.

The stated intent is to allow management teams to remain focused on becoming globally competitive, rather than giving in to the instant gratification that comes with quarterly earnings calls. Naturally, this has drawn criticism from transparency activists who fear such a shift could lengthen information cycles, leaving stakeholders with stale data from the previous season. Time will tell whether U.S. corporations eventually adopt a semi-annual reporting cycle. But come to think of it, this may well be an indirect provocation from the POTUS to the FP&A fraternity:

Are we checking on the cake too often while it’s still baking? Looking inward, how frequently should organisations update their financial forecasts?

“Plans are worthless, but planning is everything,” Eisenhower famously remarked when asked for frequent updates on his D-Day invasion progress [1]. 

That sentiment resonates deeply with FP&A and Control executives across the world. Access to real-time information creates a compelling case for increasing reporting and forecasting frequency. But do periodic refreshes truly improve tactical execution or do they force leadership into knee-jerk reactions?

Between Rainbows, Unicorns, and Reality

In an ideal, AI-driven world, where it’s all rainbows, unicorns, and cheerful songbirds, forecasts would be auto-regressed from pristine historical data, fed into a perfectly trained model that spits out real-time P&L, balance sheet, and cash flow positions. Fortunately, or unfortunately, reality bites. Fortunate, because in such a world, there wouldn’t be too many FP&A jobs left. Unfortunately, because those AI models rely on high-quality data inputs, it is easier said than done.

Many organisations boast exemplary end-to-end automation, able to deliver results every single time. However, once we accept the need for a forecast, the question that begs clarification is: How often should we update the annual forecast?

Keeping It Real vs Drip-Feeding Bad News

Bottom-up builds presume robust actualisation and mindfully crafted functional forecasts that paint a credible picture of the near term. Hand on heart, is the organisation truly equipped to provide a reliable estimate of what is likely to unfold over the next 6–8 months? 

Typically, this plays out in one of two ways: 

1. The truth is laid threadbare, however unpleasant.
2. Bad news is released in instalments, carefully phased to blend into the momentum narrative.

Working with manufacturing organisations rooted in Deming principles, the credibility of each forecast depended on how clearly variances from the previous build were explained with root-cause analyses. For instance, torrential rainfall was once cited as the primary reason for a slump in two-wheeler sales in India. While that was a hard-hitting reality, the leadership now expected subsequent forecasts to incorporate seasonality and weather system indicators from the local meteorological department. That is professional scepticism being put to good use. That made the forecast “water-tight”, but regional heads thought a million times over before taking cover under the weather Gods for loss of sales. 

Bottom line: If an organisation lacks scientific data sources and standardised methods for at least 80% of its P&L variables, does it really matter how often the forecast is refreshed?

Assimilation vs Action

How does the organisation react to a revealing forecast? Do revised forecasts trigger meaningful course corrections?

C-suite reactions to “best and latest thinking” are the cornerstone of forecasting. Are the right levers pulled to ensure the “worm” on the graph moves up again? Or does “reforecast” simply mean pressing the reset button on the year’s expectations? Too often, the yardstick that was once the Operating Plan quietly shifts to the latest forecast.

Momentum vs Policy

How time-sensitive is the business model? Are major contracts or renewals clustered in specific months? Are seasonal patterns strong enough that certain forecasts matter more than others?

For example, beverages fly off the shelves during summer, often contributing disproportionately to the year’s P&L. Any change to summer execution plans merits a forecast reset just before the season begins. Would a February forecast really make a difference in this case?

Effort vs Value: The Forecasting Equation

While forecasting frequency directly impacts the workload of FP&A teams, forecasting value depends on how leaders use the insights. Senior executives naturally focus on the 3–5-year horizon but rely on a forecasting rhythm that balances effort, volatility, systems maturity, and decision-making speed.

What Does the Future Hold?

A suite of AI solutions can eliminate manual intervention entirely, enabling functional heads to build digital air-castles. Having had a front-row seat to strategy and vision document discussions, I once heard a chairman remark at an IT strategy meeting, “No automation for the sake of automation.” Can your customer feel the change? Or is the automation merely an internal bonfire of an IT budget that Finance couldn’t claw back in time?

Value Stream Mapping (VSM), though traditionally alien to Finance, holds immense relevance. On the shop floor, if a product took one hour to manufacture, actual value-addition occurred for barely ten minutes. This meant that the rest of the time, the raw material was stored in various places as semi-finished goods or was being transported as Goods in Transit. Remember, time is money when it comes to Activity-Based Costing. Similarly, in Finance, what percentage of your process actually enhances customer experience? Which AI interventions are strategic, not cosmetic?

What portion of your process actually contributes to improving customer experience? In the same breath, what AI interventions are strategic to your business? For instance, the ability to capture real-time demand signals is a key process enhancement that many organisations cannot afford to delay. Capturing real-time demand signals is not the future. It is NOW. From real-time inventory replenishment to mapping consumer preferences, AI is continually redefining the landscape. Walmart’s agentic AI push (source: Forbes, July 2025) is a case in point. ‘Sparky’, the customer AI agent, is likely to make the search bar redundant for its customers as the AI agent executes your shopping list based on a broad shopping mission statement that you outline. For instance, you might be planning your child’s birthday party, and that’s all the agent needs to know.

Archaic inventory and supply planning systems cannot hide behind the shining exteriors of Agentic AI chatbots in the hands of their consumers. I recall my boss from earlier quipping, “The exterior is doing TED Talk; the interior is struggling with stage fright.” Supply chains and planning systems need to stay in tune with evolving demand patterns. Walmart, through its Intelligent Retail Labs, is piloting vision inspection systems that identify real-time shelf gaps using ML-based triggers for replenishment.   

Assuming limited means, would you trade in a demand automation solution for a financial software that is built to wire P&Ls faster?

Closing Thought

Eisenhower’s wartime frustration still echoes today: Plans matter only because they prepare us for what comes next. Between the strategic stability of Operating Plans and the tactical agility of frequent forecasts lies the true art of modern FP&A, knowing when to adjust course and when to simply check the compass.

How does your organisation institutionalise its interim forecasts? Is it a compliance ritual, or a strategic intervention?
 

Sources:

1. Dwight D. Eisenhower, “Remarks at the National Defense Executive Reserve Conference,” November 14, 1957, in Public Papers of the Presidents of the United States: Dwight D. Eisenhower, 1957 (Washington, DC: Government Printing Office, 1958), 818.

2. Walmart’s agentic AI push (source: Forbes, July 2025) is a case in point.