The Future of AI-Driven Zero-Based Forecasting in FP&A

A Forecasting Prediction We Can’t Ignore

Over the past decade, FP&A has quietly shifted from an annual budget exercise to a continuous discipline. But the next decade will force something more radical: the rise of AI-driven zero-based forecasting (ZBF).

Traditional budgeting still carries legacy habits: incrementalism, sandbagging, and the classic “last year plus 5%.” But what happens when AI gives finance leaders the ability to rebuild forecasts from zero, continuously, with no historical bias?

That’s the prediction I want to explore here: AI-driven zero-based forecasting will redefine how companies allocate resources, price risk, and build growth strategies.

Sarah Schlott, Founder & CEO of The Schlott Company, captures it this way: “AI doesn’t just make your budget faster. It changes what you’re budgeting for — by surfacing blind spots you didn’t even know existed.”

Why This Challenge Matters

Zero-based forecasting isn’t new. Many organizations have experimented with it in budgeting cycles — starting from zero and justifying every expense.

The challenge is that it’s always been too labor-intensive. FP&A teams could handle it for one budget cycle, but not monthly, and certainly not continuously.

Enter AI. Machine learning, natural language processing, and pattern detection can now strip cost centers, revenue lines, and operational drivers down to a baseline — then rebuild forward-looking forecasts without clinging to history.

The stakes are high:

  • Fallout of Failure: Companies that cling to incremental budgets in volatile markets risk overspending in declining areas and underfunding high-growth opportunities. In a downturn, this could be existential.
  • Reward of Success: Those who adopt AI-driven ZBF gain agility. They can reallocate dollars monthly, align cost structures with real-time demand, and get ahead of shifts instead of reacting months later.

The Ripple Effects of Getting It Wrong

Before we dig into frameworks, let’s make the consequences tangible.

Imagine a global manufacturing company that kept adding 5% annually to its logistics budget. For years, it worked fine. But when shipping disruptions and tariffs spiked costs by 20%, the model broke. They had no mechanism to re-baseline midyear. By the time finance sounded alarms, millions had already been lost.

Or consider a tech company that continued funding underperforming marketing channels because “that’s what we spent last year.” Meanwhile, high-ROI digital campaigns were starved. The incremental budget didn’t just waste money — it slowed growth.

The fallout of ignoring zero-based methods in volatile markets isn’t inefficiency. It’s survival.

Framework: The AI-Driven ZBF Operating Model

To move from theory to practice, I’ve built what I call the AI-Driven ZBF Operating Model. It has four steps:

1. Decompose Spend and Revenue Drivers

AI’s first role is classification. Use it to break down costs and revenues into granular drivers.

  • Costs: vendor contracts, headcount by role, technology licenses, logistics routes.
  • Revenues: product SKUs, pricing tiers, customer cohorts, regions.

💡 ChatGPT Prompt Example:
“Break down our $50M OPEX into granular categories and identify 5 key cost drivers per department using historical spend patterns.”

This decomposition replaces the blunt “line items” of traditional budgets with driver-based clarity.

2. Baseline from Zero, Not History

Here’s the leap: instead of “last year plus,” AI re-baselines each driver at zero.

  • Do we still need this vendor?
  • Does this channel still produce ROI?
  • Does this headcount align with revenue trajectory?

💡 Excel Example:

=IF(ROI_Channel<1,0,ProjectedSpend*ROI_Channel)

This formula forces expense justification on ROI, not inertia.

3. Rebuild with Predictive Models

AI models then rebuild each forecast line forward. For example:

  • Predictive churn curves by cohort.
  • Dynamic pricing sensitivity by region.
  • OPEX regression models tied to revenue growth.

💡 Python/Excel Hybrid Example:

=FORECAST.ETS(Revenue, DateSeries, Timeline)

paired with an AI regression output adjusting for external drivers (inflation, FX, seasonality).

This creates predictive FP&A models that adapt monthly, not annually.

4. Enable Dynamic Budget Reallocation

The final step: turning insight into action. AI-driven dashboards flag underperforming spend and surface reallocation opportunities.

💡 ChatGPT Prompt Example:
“Identify top 3 areas in our budget where ROI <1 and recommend reallocation targets based on forecasted growth drivers.”

Finance no longer just reports variances — it prescribes moves.

Proof Layer: What We See at The Schlott Company

At The Schlott Company, we’ve been piloting AI-driven ZBF frameworks with mid-market clients.

What we’ve seen:

  • Time savings: A forecast cycle that once took six weeks now takes two.
  • Credibility: Boards and investors value transparency. Starting from zero shows rigor, not inertia.
  • Flexibility: Leaders can pivot budgets quarterly, sometimes even monthly, without breaking reporting.

As Sarah Schlott puts it: “The real power of AI in FP&A isn’t speed for its own sake. It’s that finance finally runs at the speed of the business.”

Tutorial Walkthrough: Applying AI-Driven ZBF

Let’s walk through a realistic example for a mid-market consumer goods company.

Step 1: Cost Decomposition

Total OPEX = $30M. AI classification splits it:

  • $12M logistics (warehousing, shipping, tariffs).
  • $8M marketing (channels, agencies, digital).
  • $10M G&A (payroll, systems, compliance).

Step 2: Baseline Zero

AI flags logistics vendors where rates rose 15% while volume held flat. Result: $2M spend reset to zero baseline.

Step 3: Predictive Rebuild

AI regression shows marketing ROI by channel:

  • Paid social: 1.8 ROI
  • Trade shows: 0.6 ROI
  • SEO: 2.2 ROI

Baseline rebuild shifts budget toward SEO and paid social.

Step 4: Dynamic Reallocation

Quarterly review: shipping costs spike due to fuel. AI suggests reallocation of $1M from paused trade shows to absorb logistics cost without raising total spend.

Result: A flexible, zero-based, predictive forecast that adapts to market shocks in real time.

Why Many Companies Fail at ZBF

Despite the potential, most companies will stumble because of:

  1. Cultural Resistance
    Managers hate re-justifying budgets. It feels like punishment. Without leadership framing, ZBF dies on impact.
  2. Data Fragmentation
    Without unified data lakes, AI can’t decompose costs accurately. Siloed ERP systems kill the process.
  3. Over-Automation
    Handing ZBF entirely to AI creates a “black box budget.” Human oversight is essential.

The Fallout of Failure

Ignore AI-driven ZBF, and companies risk:

  • Chronic misallocation: Spending on legacy costs while starving innovation.
  • Credibility loss: Boards will see through incremental budgeting.
  • Strategic lag: Competitors who reallocate faster will outpace growth.

This isn’t just inefficiency. In volatile markets, it’s the difference between surviving shocks and being blindsided.

Future-Casting Insight

Here’s the reframing:

The real promise of AI in FP&A isn’t faster closes or prettier dashboards. It’s a cultural reset.

Zero-based forecasting, powered by AI, forces leaders to confront assumptions every cycle. It removes the crutch of “last year’s budget” and replaces it with a living system of allocation, accountability, and agility.

The prediction is simple: within five years, AI-driven zero-based forecasting will be the default expectation from boards, investors, and creditors. Companies that cling to incremental budgeting will be seen not just as outdated, but as reckless.

And that’s the challenge — and opportunity — every FP&A team now faces.

/by