Planning More Often Isn’t Enough: Continuous Planning Needs a Rethink, and AI Is a Key Enabler

Volatile markets require finance teams to reassess their objectives continuously and adjust them when needed. Plans and forecasts need to be updated more often, but every update takes a lot of work and time and ties up resources. Annual plans and rigid structures are a poor fit for these conditions. Rising volatility is one of the strongest drivers of a long-overdue transformation of corporate planning away from a cyclical routine toward a continuous, event-driven approach.

Why doesn’t more frequent planning alone speed things up?

Among organizations that had already taken steps to make planning more resilient, 49% (n=131) had changed the frequency of their planning and forecasting cycles. That makes it the most common measure in BARC’s study Resilient Planning in Volatile Markets. The same study found that 35% of respondents said adjusting plans and forecasts takes too long, while 37% considered the effort involved in planning and forecasting too high (n=247).

Shorter intervals produce fresher plans and forecasts. But if each cycle takes as long as before and ties up the same resources, little is gained. In the worst case, switching from quarterly to monthly planning means running the same manual process three times as often. This calls for a rethink and a new operating model in which planning runs as an ongoing process rather than in calendar-driven cycles.

Continuous Planning as a new operating model

Continuous Planning organizes planning as an ongoing process. Instead of planning a few times a year in great detail, teams incorporate new information as it emerges, monitor deviations, and trigger selective replanning when events warrant it. Planning becomes an always-on activity: it detects change and models scenarios and trade-offs, and the results feed straight into agreed decisions and actions.

Continuous Planning is not a new planning method as such but a question of process and organization: an operating model supported by modern software and AI. It has four stages:

1. Detect signals: Monitor events and operating data continuously. Automated methods identify variances, demand shifts, and cost spikes as they occur, rather than at the next planning date. Teams then decide which changes warrant a planning update. Machine-learning models track actuals as they come in and flag outliers.
2. Update the forecast: Use detected deviations to revise forecasts for the affected drivers. The challenge is to update forecasts quickly and incorporate new information from business functions. A statistical forecast provides a baseline, which planners review and supplement with what they know about orders, planned initiatives, and market developments. The result is a continuously updated forecast with a range of scenarios that teams can turn into actions with clear owners.
3. Evaluate scenarios: Quickly test alternative assumptions and courses of action, then assess their implications. AI can support scenario creation and evaluation. Continuous Planning depends on flexible, driver-based models that carry a changed assumption through all subplans. A faster cadence only becomes economically viable with less granularity and detail.
4. Align decisions: Planning leaders remain responsible for this step. AI can prepare variance commentary and draft decision papers, while people weigh the options and sign off.

Most companies expect AI to improve planning efficiency and productivity. This includes automating previously manual tasks and freeing planners from routine work to give them more time for analysis. AI is a key enabler of Continuous Planning: it automates data preparation, detects deviations on its own, provides baseline forecasts, and generates scenarios. This makes it economically feasible to plan at a cadence no manual process could sustain.

What Continuous Planning means for traditional planning

Annual budgets and quarterly forecast updates are losing relevance in volatile times. They are often too static and too slow to keep up with rapidly changing market conditions. Continuous Planning does not make the annual budget obsolete. The budget still sets the targets, while the ongoing forecast guides day-to-day management. Continuous Planning also means more than updating the detailed plan 12 times a year instead of 4. Planning continuously means never fully closing the plan, and a higher frequency only becomes achievable with less granularity.

Conclusion: Planning as an ongoing process

Many companies have responded to volatility with more planning cycles, additional scenarios, and more finance headcount. All of these measures are expensive and don’t scale, and they may still leave planning too slow. The current situation therefore calls for a rethink: a continuous approach that treats planning as an ongoing process.

That is where the transformation has to begin if companies want to keep pace with volatile markets. Continuous Planning starts with process and organization. Modern software platforms and technology are what make it scalable. AI is central here: by automating tasks, it makes an otherwise unmanageable planning cadence affordable. This can speed up the shift to the new operating model considerably.

BARC’s study Agentic AI in Corporate Planning shows where AI and AI agents already support corporate planning in practice.

Get the full findings on volatility, scenarios, and software support in BARC’s study Resilient Planning in Volatile Markets.