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Marketing AdvertisingTop 10 Best AI Sales Forecasting Software of 2026
Compare ai sales forecasting software for sales teams using CRM data, highlighting fit and tradeoffs for choosing the right forecasting tool.
How we ranked these tools
Vendors set the topic, but our editors set the inclusion criteria and the evaluation framework before research starts.
Agents extract product evidence with provenance, then collect written reviews and transcribe video reviews and podcasts.
Framework: Features 40% · Ease 30% · Value 30%
Gitnux may earn a commission through links on this page — this does not influence rankings. Editorial policy
Aviso is the best pick for revenue teams that need AI forecasts tied to what’s changing at the deal level because it links forecast movement to opportunity risk, seller activity, and the management actions to take, while Zoho CRM is a strong alternative for SMB ops teams since Zia brings configurable forecasting with anomaly detection and anomaly-driven recommendations in one CRM model.
Editor’s top 3 picks
Three quick recommendations before you dive into the full comparison below — each one leads on a different dimension.
Aviso
Aviso’s revenue intelligence connects AI forecast changes to opportunity risk, seller activity, and recommended management actions.
Best for: Fits when revenue teams need AI forecasts connected to deal inspection, manager coaching, and executive rollups.
Salesforce Sales Cloud
Editor pickCustomizable Forecasting with Salesforce Forecast Categories and Hierarchy supports manager rollups across territories, quotas, and opportunity teams.
Best for: Fits when global sales organizations need configurable forecasts across territories, hierarchies, and custom CRM workflows.
Microsoft Dynamics 365 Sales
Editor pickDataverse-based forecast rollups connect opportunity hierarchies, manager adjustments, security roles, and Power BI reporting.
Best for: Fits when Microsoft-centric sales teams need configurable forecasting tied to a governed CRM data model.
Comparison Table
Aviso
enterpriseAviso provides AI revenue forecasting, pipeline management, and sales planning.
Aviso’s revenue intelligence connects AI forecast changes to opportunity risk, seller activity, and recommended management actions.
Aviso supports opportunity-level and team-level forecasts with views for commit, best-case, and upside scenarios. Its revenue intelligence features connect forecast movement to deal activity, seller behavior, pipeline changes, and account engagement. Forecast rollups help managers compare rep submissions with model recommendations and investigate variance before executive reviews.
The breadth of sales intelligence requires disciplined CRM data, field mapping, and operating rules. Aviso suits organizations that need a shared forecast process across sales, revenue operations, and leadership, especially when managers must trace changes back to individual opportunities and activities.
- +AI forecasts combine CRM records with seller activity and account engagement signals
- +Opportunity inspection connects forecast changes with deal-level risk indicators
- +Scenario views support commit, best-case, and upside management reviews
- +Revenue intelligence links forecasting with coaching and pipeline execution
- –Implementation depends on clean CRM history and consistent opportunity fields
- –Advanced workflows require revenue operations ownership
- –Broader analytics can increase dashboard and alert configuration work
- –Teams seeking only basic forecast rollups may face unnecessary functional depth
Revenue operations teams
Standardizing forecast reviews across regions
Consistent forecast governance
Sales executives
Investigating forecast movement before board reviews
Earlier variance detection
Show 2 more scenarios
Frontline sales managers
Prioritizing coaching on risky deals
Focused coaching time
Deal intelligence highlights stalled activity, weak engagement, and execution patterns requiring manager attention.
Enterprise account teams
Monitoring strategic account revenue exposure
Fewer surprise misses
Account and opportunity signals help teams identify relationship changes that could affect expected bookings.
Best for: Fits when revenue teams need AI forecasts connected to deal inspection, manager coaching, and executive rollups.
Salesforce Sales Cloud
enterpriseSales Cloud combines CRM forecasting, pipeline inspection, and Einstein AI predictions.
Customizable Forecasting with Salesforce Forecast Categories and Hierarchy supports manager rollups across territories, quotas, and opportunity teams.
Salesforce Sales Cloud fits organizations that need forecasting inside a configurable CRM data model rather than in a separate spreadsheet or forecasting application. Forecasting hierarchies, territory structures, opportunity splits, quota assignments, and forecast categories support detailed rollups from individual sellers to executives. Salesforce Flow, Apex, REST APIs, and bulk data tools connect forecast workflows with finance, billing, data warehouses, and collaboration systems.
The tradeoff is administrative complexity. Permission sets, object configuration, territory design, automation rules, and data quality controls require dedicated ownership before forecast outputs become consistent. A global sales organization with regional managers can use customized forecast hierarchies and dashboards to compare commit submissions, pipeline movement, and quota progress across business units.
- +Forecast hierarchies support rollups across teams, territories, and management levels.
- +Einstein features add predictive signals to opportunity and pipeline analysis.
- +Custom objects and Flow adapt forecasting to complex sales processes.
- +REST, bulk, and event APIs support connected revenue operations workflows.
- –Forecast accuracy depends heavily on opportunity hygiene and consistent stage usage.
- –Advanced configuration requires dedicated Salesforce administration skills.
- –Some predictive capabilities depend on separately enabled Salesforce products.
- –Complex territory and quota models can require extensive testing in sandboxes.
Global revenue operations teams
Consolidating regional sales forecasts
Consistent executive rollups
Enterprise sales managers
Reviewing commit submissions
Earlier forecast intervention
Show 2 more scenarios
Salesforce administrators
Automating forecast data workflows
Less manual reconciliation
Flow, Apex, and APIs synchronize opportunity updates with warehouse, finance, and reporting processes.
Complex B2B sales teams
Modeling multi-product opportunities
More precise attribution
Custom objects, opportunity splits, and account relationships represent deals that exceed standard opportunity structures.
Best for: Fits when global sales organizations need configurable forecasts across territories, hierarchies, and custom CRM workflows.
Microsoft Dynamics 365 Sales
enterpriseDynamics 365 Sales includes predictive scoring, pipeline analysis, and sales forecasting.
Dataverse-based forecast rollups connect opportunity hierarchies, manager adjustments, security roles, and Power BI reporting.
Microsoft Dynamics 365 Sales combines CRM opportunity records with AI-assisted revenue forecasting and sales execution workflows. Forecasts can roll up from opportunities and forecast categories across teams, while Copilot summarizes records, drafts communications, and identifies sales risks.
Dataverse provides a shared data model for customer, activity, and pipeline records, with Power Automate, Power BI, and Microsoft Graph extending automation and reporting. Administration is extensive, but forecast configuration, security roles, and data quality require experienced ownership.
- +Forecast rollups support territory, team, hierarchy, and period-based revenue views.
- +Copilot summarizes opportunities and surfaces missing information inside sales records.
- +Dataverse connects customer, activity, and opportunity data across Microsoft applications.
- +Power Automate and documented APIs support custom approval and notification workflows.
- –Forecast accuracy depends heavily on opportunity hygiene and consistent stage updates.
- –Advanced configuration requires familiarity with Dataverse security roles and environment management.
- –Some AI capabilities depend on eligible licensing, tenant settings, and administrator activation.
- –Forecast views provide less specialized statistical analysis than dedicated forecasting products.
Revenue operations teams
Standardize forecasts across regions
Comparable pipeline and target alignment
Sales managers
Identify deals at risk
Improved deal progression
Show 2 more scenarios
Regional sales leaders
Track attainment and pipeline changes
Faster attainment variance reviews
Report forecast accuracy with Power BI dashboards fed by CRM records and forecast rollups.
Sales reps
Draft customer updates from CRM
Higher follow-up consistency
Have Copilot generate outreach drafts from activity and opportunity context to keep records current.
Best for: Fits when Microsoft-centric sales teams need configurable forecasting tied to a governed CRM data model.
Zoho CRM
SMBZoho CRM includes sales forecasting, pipeline analysis, and Zia AI recommendations.
Zia combines anomaly detection, conversational CRM assistance, and configurable forecasting within the same opportunity data model.
Zia adds predictive signals to Zoho CRM records while CRM administrators control fields, stages, territory structures, roles, permissions, and approval processes. Forecasts can be organized by territory, role hierarchy, team, or individual, with manager adjustments supported inside the sales workflow. APIs, webhooks, custom functions, and connections to Zoho applications provide integration paths for operational data.
- +Zia identifies pipeline anomalies and recommends record updates
- +Forecast rollups support territories, teams, roles, and individual sellers
- +Custom modules and functions adapt the data model to complex sales processes
- +APIs, webhooks, and Zoho integrations support connected revenue operations
- –Advanced configuration requires dedicated CRM administration
- –AI recommendations depend on complete historical opportunity data
- –Forecast customization can become complex across territories and hierarchies
- –Some specialized analytics workflows require external reporting tools
Sales operations teams
Territory forecast consolidation
Consistent management reviews
Mid-market sales teams
Opportunity risk monitoring
Earlier risk detection
Show 2 more scenarios
CRM administrators
Custom sales process modeling
Controlled process variation
Administrators define modules, layouts, functions, approvals, and stage rules for specialized sales motions.
Revenue operations teams
Connected forecast reporting
Fewer manual handoffs
APIs, webhooks, and Zoho applications connect opportunity data with surrounding operational workflows.
Best for: Fits when sales operations teams need configurable forecasting inside a broader CRM suite.
Pipedrive
SMBPipedrive offers revenue forecasts, pipeline reporting, and AI-supported sales guidance.
Pipedrive’s visual pipeline and Insights dashboards connect deal stages, weighted values, activities, and expected close dates.
Pipedrive differentiates itself with a visual CRM pipeline that turns opportunity stages, activities, and deal values into manager-readable sales projections. Its forecasting features use deal data, stage probabilities, expected close dates, and customizable pipeline views rather than a dedicated statistical forecasting engine.
- +Visual pipeline views make deal movement and forecast exposure easy to inspect.
- +Custom fields and stages adapt the CRM to different sales processes.
- +Workflow automation can assign activities, update records, and trigger notifications.
- +API access and marketplace integrations support connected sales operations.
- –Forecasting relies more on configured CRM probabilities than advanced predictive modeling.
- –Limited native support for statistical confidence intervals and forecast variance analysis.
- –Data quality declines when salespeople leave stages, values, or close dates outdated.
- –Advanced reporting may require careful dashboard configuration and external data connections.
Best for: Fits when sales teams need an easy-to-maintain CRM pipeline with practical manager reporting.
6sense Revenue AI
enterprise6sense Revenue AI combines buying signals, pipeline data, and revenue forecasting.
Revenue AI connects account intent and buying-stage signals with forecast analysis instead of relying only on opportunity fields.
AI-assisted sales forecasting increasingly depends on revenue context beyond CRM opportunity fields. 6sense Revenue AI combines predictive models with account intent, buying-stage signals, and revenue orchestration data to prioritize forecast attention.
- +Combines CRM opportunity data with intent and account-stage signals.
- +Links forecast analysis to account prioritization and revenue orchestration workflows.
- +Supports manager inspection of pipeline risk across accounts and opportunities.
- +Provides broader revenue context than CRM-native forecasting alone.
- –Forecast methodology and confidence outputs receive less public technical detail than specialist forecasting products.
- –Implementation depends on accurate CRM, account, and engagement data alignment.
- –Forecast workflows can feel secondary within the wider 6sense revenue application.
- –Advanced governance may require substantial administrator configuration.
Best for: Fits when revenue teams need account intelligence connected to sales forecasts and pipeline inspection.
HubSpot Sales Hub
SMBSales Hub offers forecast categories, deal pipelines, and AI-assisted sales insights.
HubSpot forecast views combine deal-level CRM data, owner targets, forecast categories, and manager submissions in one operating workspace.
Teams already using HubSpot CRM for structured opportunity data get forecasting inside the same record system. HubSpot Sales Hub combines deal stages, owner targets, forecast categories, and manager submissions in customizable forecast views.
- +Forecast views connect directly to HubSpot deal stages, owners, targets, and categories.
- +AI deal insights summarize activity and surface engagement signals for review.
- +Custom properties and workflows support organization-specific qualification and forecasting rules.
- +CRM APIs and reporting tools connect forecast data with revenue operations workflows.
- –Forecast accuracy depends heavily on complete, consistently maintained deal records.
- –Advanced forecasting controls require careful configuration across teams, pipelines, and permissions.
- –Native forecasting offers less statistical depth than specialist revenue forecasting products.
- –Forecast history and variance analysis are less specialized than dedicated forecasting applications.
Best for: Fits when CRM-centered sales teams need accessible forecasting with workflows, reporting, and manager review in one system.
Oracle Sales
enterpriseOracle Sales provides sales forecasting, opportunity management, and AI-guided recommendations.
Oracle Fusion integration links sales forecasts with financial planning, customer records, territories, quotas, and revenue operations.
AI-assisted forecasting in Oracle Sales is tied to Oracle's broader sales, finance, and customer-data architecture. The application combines opportunity management, territory and quota administration, account planning, activity capture, and forecast rollups in one CRM environment.
- +Connects sales forecasts with Oracle CRM, ERP, finance, territory, and quota data.
- +Supports manager adjustments, forecast rollups, approval workflows, and role-based administration.
- +Oracle Integration and REST APIs support custom data flows and external application connections.
- +Account planning and opportunity context add operational detail beyond forecast totals.
- –Implementation requires significant configuration across sales processes, roles, and data ownership.
- –Advanced predictive forecasting depends on sufficient historical CRM data quality.
- –The interface can feel dense for teams using only core opportunity and forecast workflows.
- –Non-Oracle environments may require additional integration design and ongoing maintenance.
Best for: Fits when enterprise sales teams need forecasts connected to Oracle finance, CRM, territory, and quota operations.
Anaplan for Sales Planning
enterpriseAnaplan supports collaborative sales planning, quota setting, and revenue forecasting.
Connected planning models link sales quotas and territories with finance and workforce assumptions in one scenario framework.
Anaplan for Sales Planning models sales capacity, quotas, territories, and revenue scenarios in a connected planning environment. Its multidimensional data model links CRM inputs with finance, workforce, and operational plans, allowing changes to flow across related forecasts.
- +Multidimensional models connect quotas, territories, capacity, and revenue assumptions.
- +Scenario versions show how organizational changes affect sales plans.
- +Role-based access supports regional, managerial, and corporate planning workflows.
- +Integration options connect CRM, finance, workforce, and data warehouse inputs.
- –Implementation requires model design, administration, and structured governance.
- –Native CRM forecasting workflows may need additional configuration.
- –Advanced predictive forecasting depends on configured data pipelines and models.
- –Model complexity can make troubleshooting difficult for occasional administrators.
Best for: Fits when enterprise sales teams need connected quota, territory, capacity, and revenue planning.
Pigment
enterprisePigment provides sales planning, scenario modeling, and revenue forecast workflows.
Connected planning models link sales forecasts to quotas, territories, workforce plans, and financial scenarios.
Finance and revenue operations teams fit Pigment when sales forecasting must connect directly to broader business planning. Its multidimensional planning model links CRM inputs with quotas, territories, headcount, and revenue scenarios in shared workspaces.
- +Multidimensional models connect sales forecasts with quotas, territories, headcount, and financial plans.
- +Scenario versions support commit, best-case, and downside planning across shared assumptions.
- +Spreadsheet-style grids reduce the learning curve for finance and operations teams.
- +Workflow controls support review cycles, approvals, and controlled forecast overrides.
- –CRM opportunity analysis is less specialized than dedicated sales forecasting applications.
- –Forecast history and accuracy analysis require deliberate model design.
- –Advanced integrations may depend on technical configuration and data preparation.
- –Users may need separate CRM tools for detailed pipeline activity management.
Best for: Fits when finance and revenue operations teams need sales forecasts embedded in connected business plans.
Conclusion
After evaluating 10 ai sales forecasting software, Aviso stands out as our overall top pick — it scored highest across our combined criteria of features, ease of use, and value, which is why it sits at #1 in the rankings above.
Use the comparison table and detailed reviews above to validate the fit against your own requirements before committing to a tool.
Frequently Asked Questions About Ai Sales Forecasting Software
How do AI sales forecasting tools differ from pipeline probability forecasting based only on CRM stages?
Which tools connect forecast models to account intent and buying-stage signals beyond opportunity fields?
What integration paths matter most when teams need automation between CRM, BI, and revenue systems?
How do SSO and RBAC controls typically affect forecast governance for enterprise teams?
What data migration steps prevent forecast inconsistencies when switching forecasting workflows between tools?
How do admin controls and operating rules show up in forecasting configuration?
Which tools support extensibility when sales ops needs custom forecast logic and data models?
Why do some teams see lower forecast reliability when CRM data quality is inconsistent?
How should teams handle manager submissions and approval workflows when AI suggests changes?
Tools reviewed
Primary sources checked during evaluation.
Referenced in the comparison table and product reviews above.
How to Choose the Right Ai Sales Forecasting Software
What separates choices in ai sales forecasting software is how well the system controls its data inputs and turns them into usable forecasts. Prioritize criteria like data freshness controls, data model coverage, and scenario planning so the numbers you get match your selling reality.
Data freshness controls
Why it matters
With data freshness controls, the forecast can reflect recent deals instead of stale pipeline signals, which matters for ai sales forecasting software where timing and momentum shift quickly. If the schedule is wrong, you’ll see consistent lag in forecast changes.
How to check
Ask how often pipeline and activity fields can be refreshed and whether updates are incremental or full reloads. In a demo, request a small test update (e.g., move one opportunity stage) and watch when the forecast figure changes.
CRM data model coverage
Why it matters
CRM data model coverage determines whether the tool can map your pipeline fields, stages, and forecasts-related fields without forcing workarounds. In ai sales forecasting software, missing or mis-mapped fields can quietly distort conversion and cycle-length assumptions.
How to check
Bring your most important fields (stage, close date, deal amount, win/loss reason if used) and ask which ones are supported out of the box. In the demo, verify field mapping and how the system handles custom fields and nonstandard stage definitions.
Scenario planning and what-if paths
Why it matters
Scenario planning and what-if paths let sales leaders test changes like revised close dates, volume shifts, or stage redefinitions. For ai sales forecasting software, that’s often where forecast usefulness is proven beyond a single baseline.
How to check
Ask the demo team to create two scenarios that adjust assumptions (e.g., different win rates by segment or altered close-date distribution) and then compare outputs. Confirm whether scenarios can be saved, reviewed by others, and exported.
Drivers and forecast explainability
Why it matters
Drivers and forecast explainability help teams understand which pipeline behaviors moved the forecast, which reduces trust friction. In ai sales forecasting software, clarity also helps you spot when the tool is reacting to noise or missing signals.
How to check
On a demo, ask what the system uses for top drivers (e.g., stage duration, historical conversion, account patterns) and whether it shows time-window effects. Request an example where a single opportunity change produces a traced impact on the forecast.
Automation workflows for forecast updates
Why it matters
Automation workflows for forecast updates reduce manual re-running and ensure forecasts stay aligned with how the team works. For ai sales forecasting software, the key is predictable timing tied to pipeline events, not just scheduled refreshes.
How to check
Ask which triggers exist (stage change, close-date change, forecast category updates) and what actions follow (recompute, notify, publish). In a sandbox or test account, perform a pipeline change and verify the end-to-end update timing.
Red flags
- Forecast changes only after manual re-runs
- Custom stage fields map imperfectly or silently drop
- Explainability lists drivers but can’t link them to specific pipeline changes
Fit by situation
- If your pipeline data updates frequently during the week, prioritize data freshness controls.
- If your CRM uses custom stages or forecast categories, prioritize CRM data model coverage.
- If managers need to test revised assumptions before forecasting meetings, prioritize scenario planning and what-if paths.
Funding and competing interests
This study was co-funded by several of the vendors assessed in it. No single vendor funded it. Funders could propose the market studied.
They had no role in selecting vendors, setting or weighting criteria, analysing data or drawing conclusions, and they received the same factual-accuracy check as every other vendor assessed.
Market definition, inclusion rules and criteria weights were fixed by an editor before research began and did not change afterwards. Editorial process →
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