AI Forecasting 2026: Pigment, Anaplan, Planful, Clari, Netstock and Excel Compared

AI forecasting uses machine learning and statistical models to estimate future business outcomes from historical data and relevant signals.
These 6 tools apply it to cash and financial planning, sales pipelines, inventory demand, or forecasts you manage in Microsoft Excel with Copilot.

Maker pages and published prices verified on 28 October 2026. This is a documentation-based comparison, not a hands-on forecast-accuracy test. Team-size recommendations below are editorial judgments about workload and ownership, not vendor seat minimums.

AI Forecasting Tools at a Glance

Start with Pigment for a flexible finance-model shortlist, Clari for a sales forecast, Netstock for inventory decisions, and Excel with Copilot for a forecast a small team can own in a workbook. Anaplan earns a place when forecasting must connect complex departmental plans; Planful deserves a finance-led evaluation when the forecast belongs beside reporting and close.

The deciding question is what happens after the number changes. Does someone revise hiring, inspect a deal, place a purchase order, or edit a spreadsheet? That action determines the right category.

Microsoft's displayed promotion ends 31 December 2026 and applies to eligible existing Business-plan customers. Netstock's entry price does not establish the cost of every AI or forecasting capability. Confirm the required features in the written quote.

Cash and FP&A: AI Financial Forecasting

Financial planning and analysis, or FP&A, connects revenue, spending and operating assumptions so you can decide what the business can afford. The best starting point is a model whose inputs someone can explain. A forecast that anticipates sales growth but ignores hiring commitments or delayed customer payments can still lead to a bad cash decision.

For a finance lead, the useful AI work is generating a baseline, exposing unusual patterns and reducing repetitive forecasting work. You still supply changes that history cannot know, such as a planned price increase or a contract ending.

1. Pigment: Best Starting Shortlist for Flexible Finance Models

Pigment is a business-planning platform whose Predictions feature generates forecasts inside its existing models. Choose it when finance needs to connect a forecast to operating assumptions across departments, and someone can own that model. A founder with a single cash worksheet should first establish a repeatable forecasting process before taking on a broader planning platform.

What its AI does. Pigment says Predictions uses historical values, optionally with external factors, to generate a baseline and upper and lower estimates. Its AutoML option compares forecasting models using historical backtesting and selects a model for each series, meaning each separately forecasted sequence of values. That gives the finance owner a specific prediction workflow to inspect. Predictions documentation, model selection.

The data it needs. The source must be a numeric Metric, Pigment's modeled measure, with a native calendar dimension and empty future periods. For a revenue forecast, that could be monthly revenue by product and region. Optional drivers such as planned prices or campaigns must also be prepared as suitable Metrics. Pigment checks whether those inputs are complete and relevant; adding a driver does not guarantee it will influence the result. External-factor requirements.

Consider a software company revising its hiring plan. Finance can start by evaluating the recurring-revenue baseline, then apply an explicit assumption for an upcoming packaging change. Keep those layers separate. Otherwise, a manager cannot tell whether a lower revenue number came from the model or from a commercial decision.

Team size and ownership. The strongest fit is a small-to-midsize FP&A group coordinating several department owners, with a named model administrator. Company headcount matters less than whether revenue, headcount and spending assumptions need to stay connected. A larger company can also fit, but scale alone does not make its data more predictable.

The wall. Predictions requires activation through Support and appropriate permissions. Pigment also warns that abrupt structural changes and irregular histories can defeat the forecast. If explainability matters, ask about the exact selected model: the external-factor documentation says impact scores are available for Prophet, but not for Chronos-2 or AutoML. Do not assume every model produces the same explanation. Access and limitations, impact-score limitation.

Best for: A finance-owned model that needs input from several departments.
Standout: Model comparison and predictive baselines inside the planning application.
Pricing: No public starting price verified; contact Pigment through its trial page for commercial terms.
Free trial: A 14-day private workspace without a credit card; confirm Predictions activation and limits for your evaluation.

A Useful Pigment Evaluation

Evaluate a revenue or expense series that already has a responsible owner. Avoid beginning with the entire company's model: a small, understood series makes it easier to investigate a surprising result.

1. 1

   Prepare one eligible Metric

   Have the administrator confirm Predictions access. Use a numeric Metric with a calendar dimension, reconciled historical values and empty forecast periods. Keep the evaluation actuals outside the training input.

2. 2

   Generate a baseline

   Open Predict, choose + New, then select the source, Predict until and Model. Start with the documented AutoML option if you want Pigment to compare supported models. Turn on Calculate accuracy when appropriate for the evaluation.

3. 3

   Review, save and challenge it

   Review the baseline and bounds, save the result Metric, and compare it with your existing forecast and held-out actuals. Pigment says the temporary result disappears after one day unless saved. Add business overrides separately, with a reason and owner.

Verdict: Put Pigment first on the finance shortlist when the problem is maintaining a shared operating model. Its value depends on that model being worth maintaining.

2. Anaplan: Best for Complex Connected Planning

Anaplan is an enterprise-planning platform, and PlanIQ is its forecasting capability combining statistical, AI and machine-learning methods. It belongs on the shortlist when finance, sales and operations need forecasts connected to a larger planning process. For a founder updating one monthly spreadsheet, the broader planning setup is difficult to justify.

What its AI does. Anaplan describes a workflow that validates collected data, trains a forecast model, generates performance measures and brings results back into plans. PlanIQ can incorporate related data and attributes, and its product page describes explanations of important forecast drivers. The useful promise is a repeatable planning workflow; a favorable error metric still has to be demonstrated for your business. Anaplan PlanIQ.

The data it needs. The historical input requires an item identifier, a time dimension and the value to predict. An item might be a product-location combination or a business unit. Related inputs add changing drivers; attributes describe items. These are different jobs: a promotion changes over time, while a product category describes what the item is.

Anaplan's validation documentation gives concrete warnings. It expects at least an average of five historical entries per item, and fewer than 300 historical values across all series prevents certain advanced algorithms, including Anaplan AutoML. Those are processing thresholds, not evidence that your history is long enough to forecast seasonality. Missing or inconsistent dates also matter. PlanIQ data errors and warnings.

Consider a distributor trying to connect regional demand, purchasing and its financial plan. The hard problem may be that each department uses a different product hierarchy. Buying a forecasting model before resolving that hierarchy creates competing answers with different meanings. Anaplan is worth evaluating when the business is prepared to standardize those definitions and keep them maintained.

Team size and ownership. Best for an enterprise or operationally complex mid-market organization with a planning lead, a model administrator and contributors in several functions. That describes the operating capacity needed to get value, not a minimum number of licenses. A lean organization already using Anaplan should evaluate PlanIQ before moving data into a separate forecasting product.

The wall. An automated input check can identify a missing column; it cannot decide that a sales territory was redefined or a product code was reused. Assign ownership of those changes before accepting the forecasts. Ask the demonstration team to trace a changed business input through the forecast and into the plan someone will act on.

Best for: Connected forecasts across functions, regions or business units.
Standout: Data validation, model evaluation and integration with the planning process.
Pricing: No public starting price verified; Anaplan directs buyers to its contact team.
Free trial: A demo is offered; a public self-service PlanIQ trial was not stated on the reviewed pages.

Verdict: Choose Anaplan when coordination across plans is the expensive problem. Do not select it solely because its forecasting model sounds more sophisticated.

3. Planful: Best for a Finance-Led Forecasting Cycle

Planful is a financial-performance platform whose AI Projections builds a starting forecast from historical performance. It is a strong candidate when finance wants to spend less time reconstructing the baseline and more time reviewing assumptions. The adoption case is particularly clear when planning needs to sit beside reporting, consolidation and close.

What its AI does. Planful says Projections generates baselines for revenue, expenses or headcount using historical trends and seasonality. ALGO mode allows a chosen model or automatic selection. It also describes upper and lower boundaries, scenario comparison and adjustments by amount or percentage. Treat the output as a proposed starting point for a finance review. Planful AI Projections.

The data it needs. The maker identifies historical performance as the input. For your evaluation, prepare reconciled actuals for the accounts and organizational units you want to forecast, plus a record of known business changes. The public Projections page reviewed here does not establish a universal minimum number of months or a complete input schema, so request those requirements for the precise model the vendor proposes.

Planful's broader platform describes connections to enterprise resource planning systems, which hold operational and financial records, customer relationship management systems, and data tools. An integration is a route for moving data; your accounting definitions still determine whether that data is comparable. Planful platform and integrations.

Consider a finance lead preparing an expense reforecast after a reorganization. A historically sensible baseline might continue spending under the old departmental structure. The lead needs to map the changed cost centers and mark which spending ended, moved or continues. AI can reduce baseline preparation work without knowing the management decision behind the reorganization.

Team size and ownership. Best for a lean finance team in a mid-market or enterprise business, with a controller or FP&A lead responsible for the forecast. Planful itself identifies mid-market and enterprise organizations as its ideal audience. The buying question is whether that team also needs a structured financial planning and reporting environment. Company positioning.

The wall. A revenue or expense projection is not automatically a collections forecast. If your immediate concern is paying suppliers before customers pay you, the demo must show receipt and payment timing at the frequency you use. Ask the vendor to demonstrate that requirement instead of assuming every financial-planning feature includes it.

Before selecting Planful, ask for an exception review on your data. Have the finance owner identify a discontinued expense, an unusual revenue period and a planned hiring change. The useful outcome is a baseline with understandable adjustments and responsibility for each one. A smooth curve without that review process will not settle the forecast meeting.

Best for: Finance-owned forecasting connected to the financial planning cycle.
Standout: A historical baseline with model selection and scenario adjustments.
Pricing: No public starting price verified; Planful describes subscriptions tailored to company size, users and modules.
Free trial: A demo is offered; a public self-service Projections trial was not stated on the reviewed pages.

Verdict: Favor Planful when improving the finance forecasting cycle is the main job. Favor a broader operational-model evaluation when the harder problem sits across departments.

AI Cash Flow Forecasting: Ask When the Money Arrives

A cash forecast needs receipt and payment dates, not just a projected revenue total. A deal can close before it is invoiced, and the invoice can remain unpaid after the business recognizes revenue. The same gap appears on the spending side when a purchase commitment precedes the supplier payment.

For a cash-focused evaluation, bring opening bank balances, receivables, payables, expected collection dates, payroll and committed payments. This is a buyer's input checklist, not a claim that every platform above ingests those fields automatically. Ask the vendor to show where each input enters the model and how a late payment changes the balance.

The decision rule: if a forecast cannot show the cash consequence of a delayed collection, it has not yet answered a founder's runway question. A reliable receipts-and-payments model can be more useful than a more elaborate revenue prediction.

Sales Pipeline: AI Sales Forecasting

4. Clari: Best for a Sales Organization Calling a Revenue Number

Clari Forecast is sales-forecasting software for a revenue team that needs to call a number, inspect pipeline risk and act on individual deals. Its current maker page is on Salesloft's site and still names the product Clari Forecast. Use it when the forecast depends on how opportunities move through a sales process.

What its AI does. The maker says Forecast AI learns from historical data and predicts outcomes using deal size, stage and velocity, meaning how quickly a deal progresses. Its AI Forecast Agent also uses live rep activity. The product describes individual and team forecasts, pipeline changes, at-risk deals and recommended next actions. Current Clari Forecast product page.

The data it needs. Prepare historical deal outcomes and trustworthy pipeline records, along with the activity signals your organization will connect. The listed predictive inputs make stage consistency and meaningful timestamps particularly important. A pipeline that gets rewritten only before the forecast meeting supplies a poor account of how deals developed.

The public page reviewed does not provide a minimum number of months needed for your deployment. Ask about the minimum useful history, which signals are required versus optional, and how a changed sales process affects the model. Do not assume a large CRM export means the historical snapshots needed for evaluation still exist.

Consider a sales leader whose expected quarter depends on a handful of large contracts. A model can help identify deals moving differently from past wins, but an owner still has to confirm procurement, legal review and the customer's decision date. The operational payoff is a better inspection agenda: which deals need attention and what action could change their outcome.

Team size and ownership. Best for a multi-manager sales organization with a revenue-operations owner, commonly called RevOps. A small sales team may also have enough deal volume to benefit, but a founder managing a short list of unique opportunities should first ask whether the sales process produces learnable patterns. Rep count alone is an unhelpful buying threshold.

The wall. Bookings are not collected cash. Nor does a model trained on yesterday's sales motion automatically understand a move into a new customer segment. Evaluate the forecast separately by segment and deal type, and preserve the manager's override with its reason. That makes disagreements with the model useful evidence instead of arguments about whose number looks better.

In the demo, request a deal whose expected close date moved and a deal that stayed in the same stage while activity changed. Ask what the forecast would have shown before the outcome was known. A current pipeline view with the final outcome already attached cannot demonstrate historical predictive performance.

Best for: Sales leaders and RevOps teams managing a recurring forecast cadence.
Standout: Deal-history predictions connected to live activity and pipeline action.
Pricing: No public starting price verified; the current commercial route is Salesloft's sales-contact page.
Free trial: A demo is offered; public self-service trial terms were not stated on the reviewed pages.

Verdict: Choose Clari when the business needs a better sales call and a clearer response to pipeline risk. Finance must still translate that call into revenue and cash assumptions.

Inventory and Demand: AI Demand Forecasting

5. Netstock: Best for Inventory Decisions Built on ERP Data

Netstock is inventory and supply-chain planning software for operators who need forecasts to become replenishment decisions. Its AI page explicitly describes forecasting from historical patterns, with variables for seasonality, promotions and trend shifts. It is the most direct fit here when the immediate problem is excess stock, stockouts or poorly timed orders.

What its AI does. Netstock describes AI that identifies inventory risks, prioritizes opportunities and recommends actions. Its data explanation also says the underlying planning numbers come from deterministic calculations, meaning the same inputs produce the same result. The AI helps explain and prioritize those numbers. That distinction matters when you ask which part of a proposed order is calculated, forecasted or explained. AI approach, how Netstock uses data.

The data it needs. Netstock lists product and location records, current stock, monthly sales or issues, open orders, suppliers and delivery lead times. Bills of materials and replacement-product relationships matter where relevant. It says ERP data syncs nightly and planning calculations refresh daily. This is an ongoing operating feed, so evaluate it using the records your buyers actually maintain. Data inputs and refresh process.

A SKU, or stock-keeping unit, identifies a distinct inventory item. For each SKU and location, distinguish a true zero-demand period from a period with no stock available to sell. Netstock says it adjusts history for stockout distortions and highlights data-quality problems. The planner should still review how those corrections affect important items; an estimated lost sale is not an observed transaction.

Consider a small distributor that repeatedly buys more of a fast seller while slow stock accumulates elsewhere. A demand estimate alone will not fix that. The buyer also needs current stock, incoming orders, transfer possibilities and supplier reliability. This is why an ERP-linked planning process can be a better fit than a spreadsheet assistant that only sees last month's export.

Team size and ownership. Best for a small purchasing or planning team at an established small or midsize business with an ERP and repeat inventory decisions. Netstock explicitly positions its AI around SMB inventory planning. Give a purchasing or supply-chain lead ownership of the forecast and the ordering policy, with finance involved in working-capital tradeoffs.

The wall. Bad lead times and stale open orders can ruin a sensible demand estimate. Netstock says planners retain control over recommendations and approvals before orders reach the ERP. Keep that review meaningful: the operator should be able to explain why an order changed and whether it reflects demand, safety stock or supply risk. Controls and data quality.

What to Ask in a Netstock Demo

Ask the vendor to show a seasonal item, an intermittently purchased item and an item affected by a stockout. Then inspect the recommended order, not only the forecast chart. The acceptance question is whether the process helps you make the replenishment decision at the right time with the information then available.

The public pages reviewed do not state a universal minimum history. Request requirements for your mix of items, the intended planning horizon and new-product handling. Also confirm the bundle: the pricing page separates basic forecasting and AI Opportunities from the fuller AI Pack and advanced forecasting capabilities. Netstock packages.

Best for: Buyers and planners managing recurring stock decisions from ERP records.
Standout: Forecasting and inventory-risk guidance connected to replenishment work.
Pricing: From $900/month on an annual subscription; the product and bundle determine the quote.
Free trial: A demo is offered; a public self-service trial was not stated on the reviewed pages.

Verdict: Choose Netstock when the forecast needs to change a buying decision. Evaluate it with your purchasing lead, not solely through a finance presentation.

Spreadsheet-First: Microsoft Excel With Copilot

6. Microsoft Excel With Copilot: Best for a Forecast You Can Own in a Workbook

Microsoft Excel with Copilot combines spreadsheet forecasting with an AI assistant for preparing, editing and understanding the workbook. It is the sensible first evaluation for a founder or small finance team whose model already lives in Excel and has a clear owner. The key qualification is that assistance with forecasting is different from a dedicated forecasting system.

What its AI does. Microsoft's forecasting page says Copilot supports the workflow and does not replace Forecast Sheet for building projections. It describes using the assistant to help clean formatting, investigate patterns and analyze forecast results. Microsoft's broader Copilot page adds formula suggestions, workbook editing and visualization. Check the actual calculation behind a forecast, even when the assistant explains it confidently. Forecasting in Excel, Copilot capabilities.

The data it needs. Start with dated historical values at a consistent frequency. For a monthly sales forecast, use a stable date column, a numeric sales column and a clear definition of what counts as sales. Microsoft's workflow describes selecting the range, choosing Data and Forecast Sheet, reviewing the end date and intervals, then using Copilot to analyze the result. Availability and exact controls depend on the Excel environment you use. Microsoft's forecasting workflow.

Before forecasting, ask the assistant to identify duplicate dates, inconsistent units and missing periods. Review every proposed correction. A blank cell could mean a failed import; replacing it with zero would claim that nothing happened. The model cannot resolve that business meaning from formatting alone.

Consider a founder deciding whether next quarter's service revenue can support a planned hire. An understandable workbook can separate historical revenue, the statistical baseline and a specific assumption about signed work. The founder can then explain the model to a finance adviser. The useful output is an editable calculation and a traceable assumption, not just a paragraph predicting growth.

Team size and ownership. Best for a solo founder, an individual analyst or a small finance team with one accountable workbook owner and a manageable review process. Move toward a specialist platform when the pain comes from coordinating many contributors, refreshing many forecasts or maintaining a governed planning process across departments.

The wall. Copilot does not remove the need to verify formulas, units, date ranges and assumptions. Ask it to show which cells supplied the history and which method produced the projection. Microsoft documents FORECAST.LINEAR for steady trends and FORECAST.ETS for seasonal patterns; the presence of AI in the workflow does not mean every result was produced by machine learning. Forecast methods.

Licensing also affects the evaluation. Microsoft's Copilot page specifies qualifying subscription entitlements for workbook editing. Access to a general chat experience is not sufficient evidence that the precise Excel feature you want is included. Have the administrator demonstrate it in your account before basing a process on it. Editing requirements.

Best for: A founder or small team with an existing, understandable Excel model.
Standout: AI-assisted preparation and analysis around editable spreadsheet calculations.
Pricing: From $18/user/month for the Copilot Business add-on, paid yearly under a first-year promotion through 31 December 2026; an eligible existing Business subscription costs extra.
Free trial: Trials are offered with a card and automatic paid renewal; the promotional offer has separate eligibility and excludes trial subscriptions.

Verdict: Start with Excel when your main obstacle is workbook work. Move on when your main obstacle is coordinating the forecasting process. For a separate, occasional analysis of an export, see the AI data-analysis comparison.

What AI Forecasting Models Can Do Today

A predictive model estimates a future value; an assistant helps you work with the data and the estimate. Several products combine both. Ask a vendor to identify which component generated the number, which inputs it used and how performance was evaluated.

Machine learning can use patterns across historical observations and relevant drivers. Statistical methods can also model trends and recurring seasons. Neither approach automatically knows that a key customer will leave or that a supplier has changed its terms. The business must provide that context, or the owner must apply a documented override. IBM's explanation of AI demand forecasting.

The practical benefit can be faster baseline preparation, more consistent updates or attention directed toward exceptions. Better accuracy is a separate question. A complex model can lose to repeating last year's seasonal pattern; only a fair test on unseen outcomes settles that comparison.

One common mistake is supplying a historical test with information that would not have existed at the time. A promotion schedule approved before the forecast date is a legitimate input. Actual sales from the future promotion are the answer you are trying to predict. Keep that distinction explicit when evaluating any model.

Which AI Forecasting Software Should You Pick?

For a founder, start with the smallest process someone can maintain. If the forecast lives in a comprehensible workbook and only a few people review it, evaluate Excel with Copilot. If finance is struggling to connect changing assumptions across departments, shortlist Pigment. If the finance cycle also needs a broader reporting and consolidation environment, include Planful.

For a complex organization, evaluate the planning environment before the model. Anaplan belongs on the shortlist when finance, sales and operations need a shared set of planning definitions. An existing planning-platform customer should first check its own forecasting capability, because moving the data and rebuilding ownership can cost more effort than changing the algorithm.

For a sales leader, choose the pipeline workflow. Clari fits when the meeting ends with a revenue call and actions on deals. For a buyer or operations manager, choose the replenishment workflow. Netstock fits when the output should change what you order, transfer or expedite.

The rule that flips the choice is ownership of the next action. A sales forecast feeds the finance plan; it does not become the cash plan merely because both use currency. A demand forecast informs purchasing; it does not determine the purchase order without stock and supply information.

Before You Buy: History, Ownership and a Fair Test

Count Clean History at the Level You Will Forecast

For a seasonal monthly pilot, my recommendation is to prepare at least 24 clean months for training, with additional history reserved for evaluation. This is a planning target, not a universal software minimum. Pigment's own guidance says four years is ideal and improvement may be possible with two, equivalent to 48 and 24 months. The other tools' public pages reviewed do not establish a shared requirement. Pigment's history guidance.

Count usable observations for each forecasted product, account or business unit. A company with a long trading history may still have a new product with almost none. Keep dates, units and definitions consistent; identify missing periods, stockouts and structural changes. Reserving a full year for evaluation requires additional history: 36 months leaves 24 for training and 12 for testing.

Name the Owner Before Connecting Data

Finance owns a financial or cash forecast, sales leadership and RevOps own the pipeline forecast, and the purchasing or planning lead owns demand decisions. IT supports connections and access. Write down who approves overrides, resolves data errors and investigates misses. Buying software before assigning those responsibilities leaves the most important part of the process undefined.

Compare Against Last Year's Actuals Without Revealing Them

To test against 2025, recreate the information available at the end of 2024. Generate the forecast, then compare it with 2025 actuals. For a process that forecasts monthly, move the cutoff forward each month and retain the same forecast horizon you would use in practice. Forecast evaluation requires observations that were not used to fit the model. Forecasting: Principles and Practice.

Compare the AI with the forecast your team would normally make and a seasonal baseline, such as the corresponding month from the previous year. Measure errors in useful units, then inspect whether the model repeatedly forecasts too high or too low. Include the items and periods that caused operational pain, not only an aggregate where mistakes can cancel out.

For illustration, if actual demand is 120 units, the AI predicts 100 and the baseline predicts 110, their absolute errors are 20 and 10 units. The simpler baseline wins that observation. This is arithmetic, not a measured result for any product. Average absolute errors across the held-out periods, and examine the business consequence of misses. Percentage-error measures need care when actuals are zero. Error measures and limitations.

Keep a final untouched period after choosing models and settings. Repeatedly tuning against the same test year can turn the evaluation into another training exercise.

How These Were Picked

Every included tool has a forecasting role verified on its maker's current page. The comparison evaluates the specific AI function, the required input data, the person who will own the output, and the operational decision it supports. Prices come only from maker pages; missing prices remain missing.

The recommendations separate direct predictive engines from AI-assisted spreadsheet work. They also distinguish a platform's advertised capability from what your account, selected model or quoted bundle includes. No unsupported accuracy ranking or third-party contract estimate was used.

Each of the six tools has a documented forecasting role, with the spreadsheet-assistance distinction stated explicitly. The work performed here was a dated documentation and commercial-terms review. A product trial, implementation assessment and accuracy test on your own data remain the evidence needed to choose a winner for your business.

The Ones to Avoid

Avoid Anaplan for a lone spreadsheet problem unless an existing implementation makes the added forecasting capability an easy evaluation. Its connected-planning rationale needs a connected-planning problem.

Avoid Clari as your only cash forecast. Deal progression and expected bookings leave collection and payment timing unresolved.

Avoid Netstock before your inventory records can support a buying decision. A sophisticated forecast cannot make a stale open order or an incorrect supplier lead time harmless.

Avoid treating an Excel Copilot answer as an approved forecast. Inspect the calculation, data range and assumptions. An explanation is useful evidence only when it describes work you can check.

For every candidate, reject a demonstration that shows historical accuracy after the model has already seen the evaluation outcomes. Also reject a proposal that leaves nobody responsible for reviewing the forecast once the software is installed.

Frequently Asked Questions

AI forecasting tools free

Free evaluation access and a permanent free forecasting service are different offers. Pigment publishes a limited workspace trial, and Microsoft publishes trial and subscription terms for Copilot. The reviewed pages do not establish a permanently free specialist forecasting plan across this shortlist. Check access to the exact predictive feature, not merely access to the product or a general chat assistant.

AI forecasting companies

Pigment, Anaplan and Planful provide business-planning platforms with predictive forecasting capabilities. Clari Forecast, now presented on Salesloft's product page, addresses sales opportunities and pipeline outcomes. Netstock focuses on inventory and demand decisions. Microsoft provides Copilot assistance around Excel's spreadsheet forecasting workflow. Choose the company whose workflow matches the decision your forecast owner needs to make.

Best AI for prediction

There is no single best model for every business series or forecast horizon. For a practical buying decision, compare the proposed model with your existing forecast and a simple seasonal baseline, using historical outcomes the model has not seen. Select a tool only after considering prediction errors, the consequences of those errors and the effort needed to maintain the forecasting process.

For Monday, name the forecast owner, select one useful series and prepare the history without the evaluation outcomes. Use the AI Tools Map for Business Owners to place that forecasting workflow within your wider AI tool choices.