# AI Forecasting 2026: Driver-Based & More Accurate | Dexity

> AI replaces the annual spreadsheet forecast with continuous, driver-based forecasting. See how it improves accuracy and how to start.

Upskilling Reality

# AI Forecasting Is a Skill Now — Continuous, Driver-Based, and 23 Points More Accurate

The annual spreadsheet forecast is being replaced by something continuous and driver-based: AI learns the relationships that move the P&L and updates as new signals arrive. The accuracy gap is the headline — 42% of organizations rate their forecasts great or good on average, but that jumps to 65% for teams using AI/ML. The catch: your data readiness, not your tool, sets the ceiling. Here's what an AI forecasting copilot actually is, and how to build one in a single sitting.

27 July 2026

7 min read

Author

[Mrudang Vora](https://www.linkedin.com/in/mrudangvora/)

Sr. Software Engineering Manager

![Two finance analysts stand before a wall display where demand, price and hiring drivers feed an AI forecast line with a confidence band extending past today.](/assets/intel-ai-forecasting-hero-8d7c95.webp)

On this page

1.  [Why is the forecast no longer an annual event?](#why-is-the-forecast-no-longer-an-annual-event)
2.  [How much more accurate is AI forecasting?](#how-much-more-accurate-is-ai-forecasting)
3.  [What does an AI forecasting copilot actually do?](#what-does-an-ai-forecasting-copilot-actually-do)
4.  [How do you build an AI forecast without a data-science team?](#how-do-you-build-an-ai-forecast-without-a-data-science-team)
5.  [Build it live](#build-it-live)

Key facts

-   Per Infosys BPM, just **42%** of organizations rate their forecasts as great or good on average.
-   For teams using AI/ML forecasting, the great-or-good rating jumps to **65%**, per Infosys BPM.
-   That leaves a **23-point** forecast-accuracy gap between teams forecasting by hand and teams using a model that learns, per Infosys BPM.
-   Infosys BPM finds **data readiness** — a consistent chart of accounts, near-real-time ERP feeds, and 2–3 years of history — predicts forecast accuracy more than tool choice does.
-   In Dexity's AI for Forecasting workshop, you build a working AI forecasting copilot live in **90 minutes**, guided by a practicing FP&A/RevOps analyst.

## Why is the forecast no longer an annual event?

For most finance teams, "the forecast" is still a quarterly ritual — a spreadsheet reopened, rows re-keyed, assumptions re-argued, and a number shipped that's stale by the time the deck is printed.

AI changes the shape of the exercise. Instead of a periodic snapshot, it makes forecasting **continuous and driver-based** — [learning the relationships that move the P&L and updating as new signals arrive](https://www.cubesoftware.com/blog/ai-forecasting), rather than rebuilding from scratch each cycle.

The reason to care isn't elegance. It's accuracy. On average, **42% of organizations rate their forecasts as great or good — but that figure jumps to 65% for teams using AI/ML**, [per Infosys BPM](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html). That's a 23-point gap between the teams doing it by hand and the teams doing it with a model that learns.

## How much more accurate is AI forecasting?

The case for AI forecasting rests on two things: a measurable accuracy lift, and a clear-eyed view of what actually drives it.

| Signal | Figure | Source |
| --- | --- | --- |
| Organizations rating their forecasts great/good, on average | **42%** | [Infosys BPM](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html) |
| The same rating, for teams using AI/ML | **65%** | [Infosys BPM](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html) |
| What predicts the accuracy ceiling more than tool choice | **Data readiness** | [Infosys BPM](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html) |

The lift comes from a shift in method, not just a faster spreadsheet. AI [surfaces the variables that truly matter — price elasticity, regional win rates, ad-spend decay, renewal probabilities — and recalibrates as conditions change](https://www.cubesoftware.com/blog/ai-forecasting). A static model can't do that; it assumes last quarter's relationships still hold.

## What does an AI forecasting copilot actually do?

Strip away the jargon and it's a concrete tool that does four things a spreadsheet can't do on its own:

1.  **It forecasts from drivers, not from last quarter's number.** Rather than trending a total upward, it models the underlying variables that move it — [price elasticity, regional win rates, ad-spend decay, renewal probabilities](https://www.cubesoftware.com/blog/ai-forecasting) — and rolls those up into the P&L. Change a driver, and the forecast responds the way the business actually would.
    
2.  **It runs scenarios on demand.** Best case, base case, worst case aren't three separate weekend rebuilds. Because the forecast is [continuous and driver-based](https://www.cubesoftware.com/blog/ai-forecasting), you flex the inputs and read the output in minutes.
    
3.  **It explains the drivers.** A number is useless in a board meeting if you can't say why it moved. A copilot attributes the change — this much from softer renewals, that much from ad-spend decay — so the forecast is a story, not a guess.
    
4.  **It's defensible and reviewed.** The point isn't to hand the model the keys. It's to produce a forecast a human analyst has checked, understands, and can stand behind — faster than doing it by hand, and more accurate.
    

That last point matters. The [65% figure](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html) belongs to teams who use AI/ML as part of a reviewed process — not teams who outsourced judgment to a black box.

## How do you build an AI forecast without a data-science team?

You don't need a data-science team to build the first version. You need a real driver tree and a copilot to do the modeling. Here's the loop, built with Claude:

1.  **Identify the real drivers.** Ask Claude to work backward from your P&L: what handful of variables actually move revenue and cost? For most businesses it's a short list — [price elasticity, regional win rates, ad-spend decay, renewal probabilities](https://www.cubesoftware.com/blog/ai-forecasting) — not the hundred line items in the model.
    
2.  **Build a driver-based forecast.** Wire those drivers to the outputs so the forecast is [driven by the relationships that move the P&L](https://www.cubesoftware.com/blog/ai-forecasting), not by copy-pasting last period forward. Now the model reasons the way the business does.
    
3.  **Run best / base / worst scenarios.** Flex each driver and let the copilot recompute all three cases side by side. This is where continuous forecasting earns its keep — the scenarios are minutes apart, not rebuilds apart.
    
4.  **Auto-explain what moved the number.** Have Claude attribute the change between any two versions of the forecast: which drivers pushed it up, which pulled it down, and by how much. That attribution is what turns a number into a decision.
    
5.  **Add a review step.** Bake in a human checkpoint — an analyst who sanity-checks the drivers and the output before it ships. The accuracy lift lives in the _reviewed_ AI forecast, not the unattended one.
    

Data quality is the accuracy ceiling — no tool clears it for you. [Infosys BPM is explicit](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html): data readiness — a consistent chart of accounts, near-real-time ERP feeds, and 2–3 years of history — predicts forecast accuracy _more than tool choice does_. If your chart of accounts is a mess, fix that before you shop for software.

The 23-point accuracy gap isn't a reason to buy an expensive platform. It's a reason to learn the method — drivers, scenarios, attribution, review — because that method is what the [65% of high-accuracy teams](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html) have in common, whatever tool they run it on.

## Build it live

The distance between "AI forecasting is more accurate" and "I can build an AI forecast" is one working session — because the copilot does the modeling once you know the loop. In Dexity's **AI for Forecasting workshop**, you build your AI forecasting copilot live in 90 minutes — guided by a practicing FP&A/RevOps analyst — walking away with it working: real drivers, best/base/worst scenarios, auto-explained variances, and a review step you can defend in a board meeting.

## Frequently asked questions

What is driver-based AI forecasting?

Instead of reopening a spreadsheet each quarter and re-keying assumptions, AI makes forecasting continuous and driver-based: it learns the relationships that move the P&L and updates as new signals arrive, rather than rebuilding from scratch each cycle.

How much more accurate is AI/ML forecasting?

On average, 42% of organizations rate their forecasts as great or good, but that figure jumps to 65% for teams using AI/ML, per Infosys BPM. That is a 23-point accuracy gap between doing it by hand and doing it with a model that learns.

Do I need a data-science team to build an AI forecast?

No. You need a real driver tree and a copilot to do the modeling. The article outlines a five-step loop you can build with Claude: identify the real drivers, wire them to outputs, run best/base/worst scenarios, auto-explain what moved the number, and add a human review step.

What does an AI forecasting copilot actually do?

Four things a spreadsheet cannot do on its own: it forecasts from drivers rather than last quarter's number, runs best/base/worst scenarios on demand, explains which drivers moved the number and by how much, and stays defensible because a human analyst reviews it before it ships.

Does the tool or the data matter more for accuracy?

Data readiness matters more than tool choice. Infosys BPM is explicit that a consistent chart of accounts, near-real-time ERP feeds, and 2 to 3 years of history predict forecast accuracy more than which tool you pick. If your chart of accounts is a mess, fix that before you shop for software.

Do I need to buy an expensive platform to get the accuracy lift?

No. The 23-point gap is a reason to learn the method, not to buy a platform. Drivers, scenarios, attribution, and review are what the 65% of high-accuracy teams have in common, whatever tool they run it on.

* * *

_Sources: [Infosys BPM — AI-driven FP&A forecasting](https://www.infosysbpm.com/blogs/finance-accounting/ai-driven-fp-and-a-forecasting.html) (accuracy figures and data-readiness); [Cube — AI forecasting](https://www.cubesoftware.com/blog/ai-forecasting) (continuous, driver-based forecasting and the variables that matter)._

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