Introduction: The New Era of Forecasting in B2B Sales

Revenue forecasting has always been a challenging yet mission-critical function for enterprise sales and RevOps teams. With rapidly shifting markets, elongated deal cycles, and rising buyer sophistication, the margin for error in forecasting is thinner than ever. Enter AI: today’s leading sales teams are leveraging AI-powered tools to transform forecasting from a rearview exercise into a proactive, data-driven discipline. This article explores the top 7 AI tools for forecasting, actionable best practices, and how to embed these innovations into your GTM motion.

1. Proshort: Contextual AI for Revenue Forecasting and Enablement

Overview

Proshort is an AI-powered Sales Enablement and Revenue Intelligence platform designed for modern GTM teams. Proshort distinguishes itself by combining deep CRM integration, contextual AI agents, and advanced meeting intelligence for a holistic forecasting approach.

Key Capabilities

• Deal Intelligence: Aggregates CRM, email, and meeting data to surface deal sentiment, risk, and MEDDICC/BANT coverage.

• Meeting & Interaction Intelligence: Automatically records and analyzes calls, extracting action items, risks, and key moments.

• AI Roleplay: Simulates buyer conversations to identify rep readiness and likely deal outcomes.

• CRM Automation: Syncs notes, updates opportunity fields, and maps meetings to deals, ensuring pipeline data accuracy.

• RevOps Dashboards: Identifies high-risk deals, stalled opportunities, and rep skill gaps affecting forecast reliability.

How Proshort Improves Forecasting

1. Real-time Risk Identification: Contextual AI agents flag deals at risk based on multi-channel signals—missed follow-ups, buyer disengagement, or negative sentiment.

2. Automatic Pipeline Hygiene: Eliminates outdated close dates and ghost deals by syncing meeting and email insights directly to CRM.

3. Behavioral Coaching: Surfaces rep-level patterns (talk time, objection handling) that correlate with win rates, delivering coaching to drive forecast accuracy.

Best Practices with Proshort

• Use the Deal Agent to audit pipeline weekly—prioritize follow-ups for deals showing risk signals.

• Leverage call and meeting summaries to validate the true stage and health of every opportunity.

• Enable video snippet sharing to spread best-practice deal navigation across teams.

“Proshort elevated our forecast accuracy from 61% to 83% in two quarters by eliminating data blind spots and surfacing actionable risks in real time.”
— VP Revenue Operations, Fortune 500 SaaS

2. Clari: Predictive Pipeline Management at Scale

Overview

Clari is a pioneer in AI-driven revenue operations and forecasting. It integrates deeply with CRM, email, and calendar data to deliver predictive insights and automated forecasting workflows for large sales organizations.

Key Capabilities

• AI Forecasting Engine: Predicts deal outcomes with machine learning models trained on historical and live data.

• Activity Capture: Ingests seller-buyer interactions (emails, meetings, calls) to augment CRM opportunity data.

• Pipeline Inspection: Automated pipeline reviews highlight deal slippage and forecast risks.

• Roll-up Forecasts: Provides real-time visibility from rep to CRO, eliminating manual spreadsheet rollups.

How Clari Improves Forecasting

1. Deal Movement Tracking: Flags sudden changes in deal size, stage, or engagement that could impact the forecast.

2. Historical Pattern Analysis: Surfaces win/loss predictors based on thousands of closed opportunities.

3. Commit Forecast Automation: Streamlines manager reviews and roll-up processes for more consistent forecast cadences.

Best Practices with Clari

• Set up automated pipeline inspection alerts for all managers prior to weekly forecast calls.

• Use Clari's AI recommendations to recalibrate pipeline coverage ratios and commit thresholds.

• Align sales and finance by sharing real-time forecast dashboards.

3. Gong: AI-Powered Conversation Intelligence for Forecast Accuracy

Overview

Gong is a leader in conversation intelligence, harnessing AI to analyze sales calls, meetings, and emails. Its forecasting module integrates conversational data to surface deal risks and validate pipeline stages, making it a favorite for teams seeking to improve forecast reliability through better deal inspection.

Key Capabilities

• Deal Insights: Analyzes call and meeting content for buying signals, next steps, and deal risks.

• Forecast Workspace: Centralizes forecast management and roll-ups, with AI-driven risk scoring for every deal.

• Rep Activity Tracking: Measures rep engagement and coaching opportunities tied to forecasted deals.

• AI-Powered Coaching: Identifies skill gaps that may hinder deal progression or forecast accuracy.

How Gong Improves Forecasting

1. Deal Health Validation: Compares stated pipeline stages against actual buyer reactions and engagement levels.

2. Risk Signal Surfacing: Flags deals where critical next steps or buying signals are missing.

3. Manager Coaching: Informs 1:1s with granular insights on rep pipeline and forecast confidence.

Best Practices with Gong

• Set up keyword triggers for late-stage deals to catch missing decision makers or next steps.

• Review call highlights with reps during forecast reviews to challenge or confirm deal status.

• Leverage Gong's AI recommendations to optimize sales process stages for forecasting granularity.

4. People.ai: Revenue Intelligence for Large Enterprise Teams

Overview

People.ai automates data capture across sales activities, delivering actionable insights for deal inspection, pipeline health, and forecasting. Its AI algorithms help large organizations maintain clean data and spot forecast-impacting risks that might otherwise go unnoticed.

Key Capabilities

• Automated Activity Capture: Syncs emails, meetings, and contacts directly to CRM.

• Opportunity Health Scoring: Uses AI to score deals based on engagement, buyer roles, and activity trends.

• Rep Performance Benchmarking: Compares rep activity and deal progression to historical top performers.

• Forecast Accuracy Insights: Surfaces where pipeline reality deviates from CRM data or rep input.

How People.ai Improves Forecasting

1. Contact Mapping: Ensures all relevant buying committee members are engaged, reducing single-threaded risk in forecasts.

2. Pipeline Cleanliness: Flags low-activity, low-engagement deals that artificially inflate forecasted revenue.

3. Rep Accountability: Tracks forecast submissions and changes for auditability and coaching.

Best Practices with People.ai

• Review opportunity health scores weekly to prioritize forecast-impacting actions.

• Use People.ai’s engagement heatmaps to validate pipeline stages before submitting forecasts.

• Benchmark rep activity to top performers for more confident forecast roll-ups.

5. Aviso: AI Forecasting for Global Sales Operations

Overview

Aviso is an enterprise-grade forecasting platform leveraging deep learning and advanced analytics. It is known for its global-scale capabilities, supporting complex sales organizations with multi-currency and multi-region forecasting.

Key Capabilities

• AI Forecast Roll-ups: Generates predictive forecasts at rep, manager, and CRO levels across geographies.

• Deal Risk Scoring: Applies machine learning to historical activity and win/loss trends.

• Scenario Modeling: Enables what-if analysis to assess forecast impact of pipeline changes.

• Forecast Change Tracking: Audits all forecast adjustments for compliance and variance analysis.

How Aviso Improves Forecasting

1. Predictive Accuracy: Trains models on multi-year closed-won/lost data, reducing variance in forecasts.

2. Variance Analysis: Flags forecast gaps by region, segment, or product for targeted action.

3. Scenario Planning: Supports board-level forecast presentations with defensible AI-driven models.

Best Practices with Aviso

• Run scenario models monthly to stress-test pipeline and spot forecast vulnerabilities.

• Leverage forecast change logs for coaching and process improvement in global teams.

• Integrate Aviso’s AI recommendations into QBRs and board reporting.

6. InsightSquared: Data-Driven Forecasting and Pipeline Analytics

Overview

InsightSquared delivers robust dashboards and AI-powered analytics for pipeline management and forecasting. It empowers RevOps and sales leaders to move beyond gut-feel with automated, data-driven insights and predictive modeling.

Key Capabilities

• AI Pipeline Analytics: Provides real-time dashboards on pipeline coverage, hygiene, and conversion trends.

• Forecasting Models: Runs multiple forecast scenarios (best case, commit, upside) using AI predictions.

• Sales Performance Analytics: Benchmarks rep and team performance against historical trends.

• CRM Data Quality Checks: Surfaces data hygiene issues that can undermine forecast accuracy.

How InsightSquared Improves Forecasting

1. Multi-Scenario Forecasting: Allows sales leaders to see risk, upside, and commit forecasts side by side.

2. Data Quality Management: Highlights missing fields or outdated opportunities impacting forecast trustworthiness.

3. Performance Benchmarking: Links forecast variance to rep or team-level execution gaps for targeted enablement.

Best Practices with InsightSquared

• Automate forecast scenario reporting for weekly and monthly business reviews.

• Run regular CRM data quality audits to maintain forecast integrity.

• Use AI-driven conversion analysis to recalibrate pipeline coverage ratios by segment.

7. Salesloft: AI-Driven Engagement for Forecast Confidence

Overview

Salesloft is a leading sales engagement platform that leverages AI to optimize buyer interactions and pipeline progression. By marrying engagement analytics with pipeline data, Salesloft helps sales and RevOps teams improve forecast confidence and reduce deal slippage.

Key Capabilities

• Cadence Analytics: AI-driven insights into email, call, and meeting effectiveness by deal and stage.

• Deal Engagement Scoring: Measures prospect responsiveness and activity trends impacting deal momentum.

• Pipeline Progression Alerts: Flags deals at risk of stalling or slipping from the forecast.

• Rep Coaching: Surfaces best-practice cadences and messaging for consistent execution.

How Salesloft Improves Forecasting

1. Engagement-Driven Forecasting: Uses AI to correlate buyer engagement with deal win likelihood.

2. Deal Progress Monitoring: Flags pipeline stagnation or lack of buyer response as early warning signals.

3. Messaging Optimization: Guides reps on next-best actions to accelerate deal progression and minimize forecast risk.

Best Practices with Salesloft

• Monitor engagement scores for all forecasted deals and intervene early on red flags.

• Use cadence analytics to refine outreach and improve pipeline conversion rates.

• Continuously update playbooks with AI-validated messaging and cadence strategies.

Comparing the Top 7 AI Forecasting Tools: Key Differentiators

While each platform brings unique strengths, selecting the right AI forecasting solution depends on your team’s size, process maturity, and tech stack. Here’s a quick comparison:

• Proshort: Best for organizations seeking contextual AI across enablement, deal, and rep intelligence, with deep workflow integrations and actionable coaching.

• Clari: Ideal for global enterprises prioritizing predictive pipeline management and automated roll-ups.

• Gong: Excels at conversation intelligence and validating pipeline health from buyer interactions.

• People.ai: Strong for automated activity capture and deal health scoring in large, distributed teams.

• Aviso: Suited for organizations needing multi-region, multi-currency predictive forecasting with scenario modeling.

• InsightSquared: Best for data-driven teams prioritizing pipeline analytics, scenario modeling, and CRM data quality.

• Salesloft: Ideal for teams focused on engagement analytics and pipeline progression alerts.

Actionable Forecasting Tips Leveraging AI

1. Automate Data Capture: Eliminate manual CRM updates by integrating AI tools that sync meetings, emails, and calls.

2. Inspect Pipeline Weekly: Use AI risk signals to audit deals before forecast submissions—prioritize deals with engagement or data hygiene issues.

3. Validate Forecasts with Buyer Signals: Analyze conversation and engagement data, not just rep input, to confirm deal stage and likelihood.

4. Benchmark Reps and Teams: Compare performance and forecast accuracy to historical top performers, identifying coaching opportunities.

5. Run Scenario Models: Stress-test your forecast with multiple scenarios, including best case, commit, and downside.

6. Share Forecast Dashboards: Align sales, finance, and executive teams with real-time, AI-powered forecast dashboards.

7. Continuously Coach and Refine: Use AI insights on rep behavior, deal progression, and pipeline hygiene to drive ongoing enablement and process improvement.

Embedding AI Forecasting into Your GTM Workflow

To maximize the impact of AI forecasting tools, embed them into your existing sales cadences, CRM workflows, and enablement programs. Consider the following implementation roadmap:

1. Assess Readiness: Audit your current CRM data quality, pipeline review process, and enablement maturity.

2. Select the Right AI Tool: Map platform capabilities to your team’s needs—prioritize integration, scalability, and actionable insights.

3. Pilot and Iterate: Launch with a core team, gather feedback, and refine workflows for broader rollout.

4. Coach to Adoption: Equip managers with training and real-time dashboards to drive adoption and accountability.

5. Measure Impact: Track improvements in forecast accuracy, deal velocity, and win rates post-implementation.

Conclusion: Winning with AI-Driven Forecasting

AI-driven forecasting is no longer a future state—it’s a competitive necessity for modern sales and RevOps organizations. By adopting the right mix of AI tools such as Proshort, Clari, Gong, and others, and embedding actionable best practices, leaders can transform forecasting from a guessing game into a strategic growth lever. The result: greater predictability, higher win rates, and tighter alignment across revenue teams.