Best AI Sales Forecasting Software 2026

AI sales forecasting software helps revenue teams call a more defensible number by combining pipeline data, rep inputs, historical win patterns, deal-risk signals, activity capture, conversation data, and planning context. The best tools do more than show a weighted pipeline report. They help RevOps, sales leadership, and finance-adjacent teams inspect what changed, where the forecast is exposed, which deals need action, and how much confidence belongs behind a commit number.

This guide is written for teams comparing forecasting, pipeline inspection, revenue intelligence, CRM-native forecasting, and planning-adjacent tools in 2026. It is intentionally narrower than a broad AI sales tools list and separate from a general AI CRM tools comparison.

Quick picks

What AI sales forecasting software actually does

Traditional sales forecasting often starts with CRM stages, close dates, opportunity amounts, manager judgment, and rep commits. That process can work for simple teams, but it breaks down when CRM data is incomplete, late-stage deals slip quietly, sales cycles vary by segment, usage-based revenue changes the model, or finance needs a more explainable bridge between pipeline and plan.

AI sales forecasting software adds pattern detection and workflow structure around that process. A strong product can:

• Aggregate forecast submissions across reps, managers, segments, regions, products, and time periods.
• Compare current pipeline to historical deal progression and prior-quarter patterns.
• Detect stale opportunities, pushed close dates, missing activity, single-threaded deals, stalled buying committees, and other risk signals.
• Use activity capture, call data, email data, meeting data, CRM fields, product usage, or consumption signals to support forecast confidence.
• Give leaders a drill-down path from the top-line number to the specific deals, assumptions, and risks behind it.
• Create a repeatable cadence for forecast calls, pipeline reviews, QBRs, board reporting, and cross-functional revenue planning.

The important word is "support." AI forecasting should not be treated as an autopilot for revenue decisions. It is an evidence layer for human forecast calls, not a guarantee that a quarter will land.

Comparison table

Best AI sales forecasting software for 2026

1. Clari

Clari is the strongest default shortlist pick for enterprise RevOps teams that want a dedicated forecasting and revenue orchestration layer. Its official Forecast page positions the product around forecasting intelligence for every revenue model, including subscription and consumption revenue. It also emphasizes real-time data insights, configurable forecasting processes, automated roll-ups, scenario modeling, and integrations with Salesforce and other revenue-critical systems.

Clari fits teams that have outgrown a CRM-only forecast process. If your managers spend forecast calls reconciling spreadsheets, chasing updates, arguing over deal health, or translating rep commits into board-ready numbers, Clari is built for that operating rhythm.

Use Clari when you need:

• Structured revenue cadences and forecast governance.
• Forecast roll-ups across teams, segments, regions, and revenue models.
• Pipeline inspection tied to slipped deals, stalled opportunities, and execution risk.
• Executive-ready drill-down from forecast number to deal evidence.
• RevOps ownership over a more consistent forecast process.

Ask before buying:

• Which CRM, data warehouse, ERP, product-usage, and customer-success systems will feed the forecast?
• How does Clari handle consumption, expansion, renewal, and new-logo revenue in the same forecast?
• Which accuracy claims are based on customers similar to your business model?
• How much RevOps administration is required after implementation?

2. Aviso

Aviso is a strong fit for teams that want AI-guided forecasting tied closely to deal execution. Its official revenue forecasting page says the platform tracks deals, analyzes quarter progression, keeps forecasts automatically updated, supports complex forecast models, surfaces risk signals, and provides WinScore-style explanations based on CRM data, engagement, conversations, and historical deal trends. The page also describes consumption forecasting for usage-based businesses.

That makes Aviso especially relevant for companies where forecast misses come from deal movement rather than only reporting discipline. If the team needs guidance on which deals to pull into commit, which opportunities are weakening, and where consumption patterns affect ARR or MRR, Aviso deserves a serious look.

Use Aviso when you need:

• AI-guided forecast calls and deal reviews.
• Risk signals connected to deal execution.
• Forecast workflows that work beyond desktop dashboards.
• Consumption forecasting alongside traditional opportunity forecasting.
• Enterprise CRM and data-lake context.

Ask before buying:

• How transparent are the WinScore explanations and risk drivers?
• Which deal signals are native, and which require extra integrations?
• How does the platform handle multiple forecast cadences and overlays?
• Which published metrics are vendor-reported averages versus customer-specific proof?

3. Gong Forecast

Gong Forecast is best for teams that already use Gong or want conversation intelligence to influence forecast confidence. Gong's current revenue forecasting page positions Forecast as an AI sales forecasting solution that gives visibility across deals, pipeline, accounts, and execution, with support for spotting risks, coaching reps, and calling the number using customer-data-trained AI.

The key advantage is context. Forecast risk often lives outside static CRM fields: pricing objections, procurement delays, quiet champions, competitor mentions, missing next steps, or weak executive alignment. Gong is strongest when those signals come from actual customer interactions and can be brought into forecast inspection.

Use Gong Forecast when you need:

• Forecasting connected to call and conversation intelligence.
• Deal-risk visibility that managers can use for coaching.
• A forecast process tied to real buyer engagement rather than only CRM hygiene.
• A single environment for revenue intelligence and sales execution review.

Ask before buying:

• Is the team already committed to Gong's data capture and coaching workflow?
• How will conversation insights map to forecast categories and manager reviews?
• What happens to forecast quality when reps do not record or sync all key interactions?
• How much forecast configuration is available for complex enterprise models?

4. Salesforce Sales AI

Salesforce is the natural forecasting option for teams that want to keep forecast workflows inside Sales Cloud. Salesforce's current Sales AI page describes AI for pipeline management, deal insights, predictive scoring, Sales Analytics, and forecast accuracy support. Its pricing section shows forecasting in Pro Suite and predictive AI in higher tiers, but packaging should always be rechecked before import or purchase because Salesforce names, editions, and add-ons change frequently.

Salesforce fits teams that already trust Salesforce as the operating system for pipeline, opportunities, quotas, forecasts, and sales leadership reporting. It is usually simpler to govern than a separate platform when the sales process is straightforward and CRM data quality is high.

Use Salesforce Sales AI when you need:

• CRM-native forecasting and pipeline management.
• Predictive scoring and deal insights inside the seller workflow.
• Forecasting connected to Salesforce permissions, records, reports, and analytics.
• A lower integration burden than a separate RevOps platform.

Ask before buying:

• Which Sales Cloud edition or add-on is required for the forecast and predictive features you need?
• Does your forecast require custom objects, product lines, overlays, or multi-business-unit logic?
• Are reps and managers keeping opportunity data clean enough for AI predictions to be useful?
• Do finance leaders need planning features that Salesforce alone does not cover?

5. HubSpot Sales Hub forecasting

HubSpot is the best CRM-native forecasting option for many SMB and mid-market teams. HubSpot's forecasting product pages position the tool around pipeline data, team visibility, sales analytics, and forecasting inside the customer platform. Its knowledge base also says Breeze AI can project future sales based on recent closed-won deals, but that AI forecasting feature is currently described as beta with eligibility requirements and a warning that projections are estimates.

That positioning matters. HubSpot is not trying to be a heavy enterprise revenue orchestration platform. It is more compelling when a team wants a practical forecast view inside the same CRM where reps manage deals, sales leaders review pipeline, and managers run one-on-ones.

Use HubSpot when you need:

• Forecasting inside a simpler CRM workflow.
• Pipeline visibility for SMB or mid-market sales teams.
• Forecast categories, sales analytics, and manager-friendly reporting.
• AI projections as an additional perspective, not the sole forecast source.

Ask before buying:

• Is Breeze AI forecasting available for your subscription and account setup?
• Does your team meet HubSpot's eligibility requirements for AI forecasting?
• Are monthly forecast cadence and recent closed-won data sufficient for your model?
• Would a dedicated revenue intelligence platform be needed as complexity grows?

6. BoostUp.ai

BoostUp.ai is a RevOps-oriented option for teams that want machine forecasting, deal intelligence, conversation intelligence adjacency, and pipeline inspection. Its current public site positions BoostUp around revenue agents that help teams nail forecasts and close deals, with machine forecasting, deal intelligence, conversation intelligence, pipeline inspection, and revenue insights.

BoostUp fits buyers who want a system of action around forecast execution rather than only dashboards. It belongs in the shortlist when the sales leadership problem is not "we lack reports" but "we cannot see risk early enough or turn forecast findings into action."

Use BoostUp.ai when you need:

• Machine forecasting and forecast inspection.
• Deal intelligence linked to revenue execution.
• Pipeline risk visibility for RevOps and sales managers.
• A platform that can sit between forecast calls and seller action.

Ask before buying:

• Which forecasting workflows are native versus configured during implementation?
• How does BoostUp calculate machine forecast outputs and explain deal risk?
• Which CRM, call, email, and activity systems are required for the best signal quality?
• How does it compare with Clari, Aviso, or Gong for your exact sales motion?

7. People.ai / Backstory

People.ai's public URL currently redirects to Backstory, so buyers should verify current product and legal naming before procurement. The current Backstory page positions the product as an answers platform for revenue leaders, focused on buyer engagement data, forecast integrity, deal risk, pipeline blind spots, and direct answers to questions like what changed in the forecast or which deals are real.

This is a useful angle because many forecast problems start with missing activity data. If CRM fields are incomplete and leaders cannot see what actually happened across buyer meetings, emails, calls, and account activity, forecast confidence suffers.

Use People.ai / Backstory when you need:

• Activity-data-backed forecast confidence.
• Deal-risk answers grounded in buyer engagement.
• Better visibility into pipeline blind spots and missing activity.
• A way to challenge CRM-only commit numbers.

Ask before buying:

• What is the current product name, commercial package, and data model?
• Which activity sources are captured automatically?
• How does the platform distinguish real buyer engagement from routine activity noise?
• Does it own the forecast workflow, or does it feed answers into another forecasting tool?

8. InsightSquared

InsightSquared remains relevant for teams that want AI sales forecasting tied to revenue analytics. Its current official site describes AI sales forecasting, forecast submission automation, machine-learning validation, RevOps dashboards, activity capture, conversation intelligence, guided selling, and revenue analytics. The site also carries Mediafly/Revenue360 navigation, so final CMS naming should stay current with the vendor's public structure.

InsightSquared is strongest when sales leaders need analytics and forecast process improvement together. It is less about pure planning and more about giving sales management a better way to inspect submissions, validate assumptions, and connect activity to revenue outcomes.

Use InsightSquared when you need:

• Forecast submission workflows plus analytics.
• Machine-learning validation against human forecast inputs.
• RevOps dashboards for recurring sales management meetings.
• Pipeline and activity context in the same reporting layer.

Ask before buying:

• How is InsightSquared packaged within Mediafly or Revenue360 today?
• Which reports and forecast workflows are available out of the box?
• Can the team customize forecast categories, hierarchy, products, segments, and time periods?
• How much historical data is needed before machine-learning validation is useful?

9. Mediafly

Mediafly is broader than forecasting, but it is worth including for teams that want forecasting connected to revenue enablement, coaching, content, value selling, buyer engagement, and deal inspection. Mediafly's older but official revenue intelligence release described forecasting, pipeline management, and sales execution in the same platform, and the current InsightSquared pages keep forecasting and analytics inside the broader Mediafly ecosystem.

Mediafly fits organizations that see forecast quality as a sales execution and enablement problem, not just a RevOps analytics problem.

Use Mediafly when you need:

• Forecasting connected to enablement and buyer engagement.
• Deal inspection plus coaching and content context.
• A broader revenue enablement environment rather than a forecasting-only tool.
• Integration between value selling, conversation intelligence, and revenue analytics.

Ask before buying:

• Which forecasting capabilities are part of the current Mediafly package versus InsightSquared-specific modules?
• How does the platform support forecast calls compared with enablement workflows?
• Does your team need enablement and buyer engagement enough to justify a broader suite?
• Which modules are required for conversation intelligence, activity capture, and forecast validation?

10. Anaplan

Anaplan belongs in this guide because many enterprise forecast decisions cross from sales into finance. Its current revenue planning page positions Anaplan around revenue planning and forecasting, AI-driven predictions, rolling forecasts, what-if scenario modeling, top-down and bottom-up alignment, and linking revenue drivers to business objectives.

That is different from a sales manager's weekly forecast call. Anaplan is better for planning, modeling, and finance-owned alignment than for coaching reps on a specific at-risk deal. It should be evaluated when revenue forecasting must connect to capacity, territory, headcount, targets, financial plans, and cross-functional assumptions.

Use Anaplan when you need:

• Finance-owned revenue planning and scenario modeling.
• Rolling forecasts and what-if analysis.
• Alignment between sales, finance, operations, and executive planning.
• A planning model that connects top-down targets to bottom-up inputs.

Ask before buying:

• Is the primary pain daily sales forecasting or enterprise revenue planning?
• How will CRM opportunity data flow into Anaplan models?
• Who owns the model: finance, RevOps, sales operations, or an implementation partner?
• How quickly can business users adjust scenarios without breaking governance?

11. Pigment

Pigment is another planning-adjacent option. Its current public site positions Pigment as an AI platform for real-time business planning, and its sales planning messaging emphasizes aligning revenue targets, territories, capacity, and financial outcomes in real time.

Pigment should not be framed as a pure sales forecasting platform. It is more relevant when RevOps and finance need to model the revenue plan, territory capacity, quota coverage, hiring assumptions, and financial outcomes together.

Use Pigment when you need:

• Sales planning connected to finance and operations.
• Revenue targets, territory planning, capacity, and scenario modeling.
• A flexible business planning layer for cross-functional forecast assumptions.
• Planning workflows that go beyond opportunity-level sales inspection.

Ask before buying:

• Do you need a planning platform, a forecast call tool, or both?
• How will Pigment connect to CRM and sales performance data?
• Which assumptions need to be owned by finance versus RevOps?
• Can frontline sales managers act on the model, or is it mainly for planning leadership?

CRM-native forecasting vs revenue intelligence platforms

The biggest buying decision is not which vendor has the boldest AI claim. It is whether the forecast workflow should live primarily inside the CRM, inside a dedicated revenue intelligence platform, or inside a planning platform.

CRM-native forecasting is usually best when:

• Most pipeline data lives cleanly in Salesforce or HubSpot.
• The sales process is relatively simple.
• Managers already run forecast calls from CRM views.
• The team wants less integration and less change management.
• Forecasting is mostly about pipeline roll-ups and manager judgment.

Dedicated revenue intelligence platforms are usually best when:

• CRM data is incomplete or late.
• Forecast calls need deal-risk signals, activity context, and pipeline inspection.
• Leaders need explainability across regions, overlays, product lines, segments, or revenue models.
• Consumption, expansion, renewal, or usage-based revenue affects the forecast.
• RevOps needs a more governed process than spreadsheet roll-ups.

Planning platforms are usually best when:

• Finance owns the revenue model.
• Forecasting needs to connect to capacity, territories, quotas, headcount, and scenario planning.
• Leadership needs a plan-vs-actual model rather than only a sales commit view.
• The forecast must bridge sales inputs with financial assumptions.

Many enterprise teams eventually use more than one layer. Salesforce or HubSpot may remain the CRM of record, Clari or Aviso may run forecast inspection, Gong may supply conversation and deal-risk context, and Anaplan or Pigment may own the finance planning model. The key is to define which system owns which decision.

Forecasting features that matter

Forecast roll-ups and hierarchy

A forecasting tool should support the way your business actually calls the number: reps, managers, regions, territories, overlays, products, segments, new business, expansion, renewals, services, and consumption. If the hierarchy does not match the operating model, leaders will export to spreadsheets.

Deal inspection and risk signals

Useful AI forecasting surfaces the deals that changed the number. Look for slipped close dates, inactivity, missing next steps, weak engagement, single-threaded accounts, discounting, competitor mentions, procurement delays, or late-stage amount changes.

Activity and conversation capture

Forecast quality improves when the system can see real work: meetings, emails, calls, buyer engagement, call topics, objections, next steps, and executive involvement. Activity capture is not enough on its own, but it gives managers evidence beyond a rep's last update.

Explainability

Executives need to understand why a forecast moved. A useful platform should show the inputs, risk drivers, model assumptions, confidence level, and deal-level evidence behind the number. Avoid black-box scores that cannot be challenged in a forecast call.

Consumption and usage-based forecasting

For SaaS and cloud businesses, traditional opportunity forecasting may not be enough. If usage, consumption, expansion, or renewal behavior materially affects revenue, shortlist vendors that explicitly support those revenue models.

CRM hygiene and writeback

Every AI forecast depends on data quality. Confirm whether the tool only reads CRM data, writes suggested updates, syncs activity automatically, or helps managers enforce stage, close date, next step, amount, and forecast category discipline.

Planning integration

Finance will ask how the sales forecast maps to the plan. If the forecast affects hiring, cash planning, quota capacity, product investment, or board reporting, evaluate how the tool connects to planning systems and financial models.

Pricing and implementation realities

Most enterprise forecasting vendors do not publish simple self-serve pricing for serious deployments. Expect pricing to depend on seats, modules, data sources, CRM instances, revenue model complexity, implementation services, support tier, and contract length.

Implementation is not just a technical integration. It usually requires agreement on:

• Forecast categories and definitions.
• Manager and rep submission cadence.
• Opportunity stage rules and required fields.
• Data sources that should influence forecast confidence.
• Who can override, commit, or adjust the forecast.
• Which meetings will use the tool and which spreadsheets will be retired.
• How success will be measured after one or two quarters.

Do not buy a forecasting platform before defining the operating cadence. Software can make a forecast process more visible and evidence-based, but it cannot fix unclear stage definitions, inconsistent manager behavior, or a sales culture that rewards sandbagging and last-minute surprises.

Common mistakes when buying AI sales forecasting tools

Mistake 1: Treating vendor accuracy claims as universal facts

Vendors often publish impressive forecast accuracy, win-rate, or productivity metrics. Treat those as vendor-reported claims. Ask for proof from customers with similar sales cycles, deal sizes, data maturity, and revenue models.

Mistake 2: Confusing dashboards with forecast governance

A dashboard can show pipeline. Forecast governance defines who submits what, when managers inspect it, how changes are explained, and how leadership commits the number. Prioritize workflow, not only charts.

Mistake 3: Ignoring CRM data quality

AI does not rescue bad inputs by default. If opportunity amounts, close dates, stages, next steps, contacts, and activities are unreliable, the first implementation milestone should be data quality and adoption.

Mistake 4: Buying a planning platform for a sales execution problem

Anaplan and Pigment can be excellent planning systems, but they are not replacements for deal-level sales inspection when managers need to coach reps and de-risk specific opportunities.

Mistake 5: Buying a revenue intelligence platform for a simple CRM problem

If a small team only needs basic forecast categories, pipeline views, and manager roll-ups, HubSpot or Salesforce native forecasting may be enough. Add a dedicated platform when complexity creates enough pain to justify it.

Evaluation checklist

Before shortlisting vendors, collect three recent quarters of forecast history and a representative pipeline sample. Ask each vendor to show how its product would handle real deals, not demo data.

Use this checklist:

1. Define the forecast owner: RevOps, sales leadership, finance, or shared.
2. Separate forecast use cases: weekly sales call, board forecast, finance plan, territory planning, capacity planning, renewal forecast, consumption forecast.
3. Identify data sources: CRM, email, calendar, call recording, conversation intelligence, product usage, billing, ERP, data warehouse, customer success, spreadsheets.
4. Confirm hierarchy support: reps, managers, regions, overlays, products, segments, channels, and business units.
5. Require deal-level explainability for AI predictions and risk scores.
6. Test the tool against historical quarters and known misses.
7. Ask how humans override forecasts and how those overrides are tracked.
8. Review security, permissions, data retention, SOC 2 evidence, and admin controls.
9. Define adoption metrics: forecast submission completion, stage hygiene, slipped-deal reduction, inspection coverage, forecast variance, and manager usage.
10. Decide which spreadsheets will be retired and which will remain.

Recommended shortlist by company stage

Smaller teams and founder-led sales

Start with CRM-native forecasting in HubSpot or Salesforce. Add a dedicated platform only when the team has enough reps, pipeline complexity, and forecast cadence to justify implementation.

Scaling B2B SaaS teams

Compare Clari, Aviso, BoostUp.ai, Gong Forecast, and InsightSquared. The best fit depends on whether the main pain is forecast governance, deal execution, activity capture, conversation context, or analytics.

Enterprise RevOps teams

Shortlist Clari, Aviso, Gong Forecast, InsightSquared, Salesforce, and relevant planning systems. Enterprise buyers should evaluate multi-CRM support, data warehouse integration, product-line forecasting, overlay workflows, usage-based revenue, and executive explainability.

Finance-adjacent revenue planning teams

Evaluate Anaplan and Pigment alongside the sales forecasting layer. These platforms are most useful when the question is not just "what will close this quarter?" but "how does the revenue plan change if capacity, conversion, churn, pricing, or territory assumptions change?"

FAQ

What is AI sales forecasting software?

AI sales forecasting software uses CRM, pipeline, activity, conversation, historical revenue, and sometimes product-usage data to help teams project likely sales outcomes. It supports forecast calls, deal inspection, pipeline risk detection, and executive reporting.

Is AI sales forecasting the same as revenue intelligence?

No. Forecasting is one workflow inside many revenue intelligence platforms. Revenue intelligence may also include activity capture, conversation intelligence, guided selling, coaching, buyer engagement, pipeline inspection, and revenue analytics.

Can AI sales forecasting guarantee accuracy?

No. AI forecasts are estimates based on available data and assumptions. They can improve the evidence behind a forecast, but buyers should test tools against historical quarters and keep human judgment in the loop.

Should we choose Salesforce or HubSpot forecasting instead of a dedicated tool?

Choose CRM-native forecasting when your process is relatively simple and CRM data is trusted. Choose a dedicated platform when you need better deal-risk signals, forecast governance, multi-source data, complex roll-ups, consumption forecasting, or board-level explainability.

Are Anaplan and Pigment sales forecasting tools?

They are better described as planning-adjacent tools. They can support revenue forecasting, sales planning, targets, capacity, territories, and financial modeling, but they are not pure sales forecast call workbenches.

What data does AI forecasting need?

At minimum, it needs reliable opportunity data: amount, stage, close date, owner, account, forecast category, and history. Stronger systems also use activity data, call transcripts, email/calendar engagement, product usage, billing data, customer health, and historical conversion patterns.

What is the biggest implementation risk?

The biggest risk is buying software before fixing the forecast operating process. Define cadence, ownership, data quality rules, override rights, manager behavior, and success metrics before assuming AI will make the number trustworthy.

Publisher caveats

• Recheck official vendor pages and packaging on import day.
• Keep vendor accuracy, win-rate, productivity, and ROI claims attributed if any are used.
• Do not turn this into a broad sales engagement or AI CRM list.
• Do not link /reviews/best-ai-fpa-financial-analysis-tools-2026 until it returns 200.
• Consider future inbound links from /reviews/best-ai-sales-tools-2026 and /reviews/best-ai-crm-tools-2026 using anchors such as "AI sales forecasting software", "forecast accuracy tools", "revenue intelligence platforms", and "pipeline forecasting tools".