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When a new Enterprise Account Executive (AE) joins your sales org, the clock starts ticking. Every day without deals is an expensive one. Traditionally, ramp plans have been rigid, manual, and inconsistent. Some AEs thrive, others flounder. But today, with AI-powered sales onboarding templates and AI-guided AE ramp plans, the process can be precise, personalized, and measurable.
This post introduces an AI-assisted 30-60-90 day plan for SaaS AEs that accelerates readiness and ensures a faster path to revenue. You’ll walk away with a sample schedule, a certification rubric, a first 10 accounts research template, and even a manager dashboard for tracking ramp KPIs.
Key takeaways:
The 30-60-90 framework isn’t new. Sales leaders have used it for decades to onboard reps. The issue is not with the structure itself but with how it has been applied.
Most traditional plans are too static, meaning every AE—whether a seasoned enterprise seller or a first-time SaaS hire—gets the same assignments. This one-size-fits-all approach doesn’t acknowledge different learning curves or prior experience. They’re also too manual, relying on spreadsheets, checklists, and subjective manager assessments that drain leadership time without providing consistent visibility into progress. Finally, they’re too shallow, often measuring success only by whether someone “completed training modules” rather than by whether they can actually run a discovery call, pitch a solution, or advance a deal.
AI flips this on its head by introducing adaptive, AI-scored sales onboarding competencies that flex with rep performance and manager guidance.
Picture an AE’s first month in your company. Instead of combing through static training decks, they’re engaging in dynamic learning pathways where AI evaluates their comprehension and automatically adjusts the difficulty of modules. Rather than shadowing any random call, AI highlights the most valuable conversations based on sentiment analysis and calls where buyers asked the most challenging questions or displayed strong interest signals.
Meanwhile, instead of spending hours researching accounts, the AE’s first 10 opportunities are pre-loaded with intelligence: buying committee structures, relevant trigger events, and AI-drafted opening outreach. On the management side, leaders no longer need to manually sift through call notes or activity logs. They receive weekly coaching nudges, like “Review objection handling from Tuesday’s call,” along with KPI dashboards tracking time-to-first-meeting, time-to-opportunity, and time-to-close.
This isn’t theory, it’s the reality of modern enterprise sales onboarding with AI checklists.
The first month is all about establishing a strong baseline. AEs need to build foundational product knowledge, understand the competitive landscape, and start practicing customer-facing conversations. AI ensures this stage is both efficient and adaptive.
During this phase, the priority is to immerse the AE in the company’s world. That means gaining fluency in product features, understanding how the solution solves customer pain points, and internalizing the messaging framework. Traditionally, this has been achieved through thick binders or a barrage of slide decks. With AI, however, the process becomes interactive. Progressive learning modules dynamically adjust based on how well the AE performs on quizzes or simulations. If an AE struggles with competitive positioning, the system can automatically assign extra practice scenarios or recommend shadowing relevant recorded calls.
Certification no longer means a manager checking off boxes. Instead, AI evaluates real performance by scoring discovery call role-plays or objection handling exercises. Call shadowing becomes smarter too. AI doesn’t just suggest “shadow five calls.” It flags the ones where customer engagement was highest, ensuring AEs learn from meaningful interactions rather than random recordings. By the end of the first month, reps aren’t just “trained.” They’re already demonstrating core competencies that predict success.
Week by week, the plan builds momentum. In week one, AEs complete a product overview and their first certification test, which AI scores in real-time. In week two, they dive into shadowing at least five high-value calls flagged by AI, followed by structured debriefs with their manager. Week three introduces a mock discovery call where AI provides granular scoring and feedback, highlighting not just what went well but where improvement is needed. By week four, the AE reviews their first 10 assigned accounts, using AI-generated briefs to accelerate territory planning and pipeline activation.
With foundational knowledge in place, the second month shifts focus from learning to applying. AEs begin entering the field, prospecting into accounts, and taking early customer meetings. AI acts as both a safety net and a coach.
This is the transition period where AEs move from observers to participants. They’re expected to engage in outreach, deliver product pitches, and begin generating pipeline. But they’re not left to figure it out alone. Instead, every step is reinforced by AI-driven tools and manager oversight.
Discovery calls are supported by AI-guided talk tracks that adapt in real time, helping AEs respond to buyer objections with confidence. AI automatically surfaces POV/PoC exposure opportunities from deal data, giving AEs direct visibility into how other enterprise deals are being structured. Managers, meanwhile, receive weekly AI coaching prompts, such as reminders to review an AE’s executive prep or to role-play a competitive objection. This transforms 1:1s from generic check-ins into targeted skill-building sessions. Here's a closer look at what AI coaching looks like.
By week five, AEs are executing their first outbound sequences with AI-generated email drafts. Week six sees them running their first discovery call, with the conversation automatically summarized by AI for coaching review. In week seven, they prepare for their first executive meeting and prep materials are generated automatically, including industry insights, persona pain points, and suggested talk tracks. By week eight, the AE shadows a live proof-of-concept engagement, then delivers their own analysis back to the manager for feedback.
The final stage of onboarding is all about independence. By now, AEs should be capable of managing their own book of business, but with predictive AI, managers can still ensure no one slips through the cracks.
At this point, AEs should be running full discovery and demo calls without hand-holding. They’re expected to begin building a real pipeline in their territory and ideally advancing at least one deal toward close. This phase is less about learning tasks and more about developing rhythm and confidence.
AI continues to provide support in the background. Predictive analytics highlight whether an AE is on track to hit early milestones by comparing their activity and performance against benchmarks from past successful reps. If sentiment analysis shows that discovery calls consistently stall at budget conversations, AI will prompt targeted objection-handling coaching. KPIs like time-to-first-meeting and time-to-first-close are automatically tracked, giving managers visibility into whether the ramp is happening at the right pace.
Week nine is often a turning point, when AEs lead their first executive meeting using AI-generated prep briefs. In week ten, they present a full opportunity strategy during a manager 1:1, backed by data pulled from CRM and AI analytics. By week eleven, they’re running independent customer demos, with predictive coaching surfacing areas of improvement based on performance patterns. Finally, week twelve culminates in a QBR-style review, where the AE presents their pipeline, learnings, and next steps—closing out onboarding as a fully ramped rep.
Ramp success isn’t just about the AE. It’s equally about the manager’s ability to coach effectively. With AI dashboards, leaders gain unprecedented visibility. Rather than spending time digging through call recordings or activity logs, managers receive weekly nudges like: “AE struggled with budget objections this week—schedule a role-play on handling CFO pushback.”
The dashboard also provides a consolidated ramp KPI pack, tracking time-to-first-meeting, time-to-first-opportunity, and time-to-first-close across the team. This means managers can instantly identify who is accelerating and who may need intervention. Additionally, AI competency snapshots highlight strengths and weaknesses across skills like discovery, demoing, and competitive messaging, allowing for targeted coaching rather than generic feedback.
At Pod, we’ve built Onboarding Boards specifically for this challenge. These boards combine Deal Coach + Briefs into a single experience, giving AEs everything they need to succeed faster.
Before every call, AI generates prep materials so reps walk in informed and confident. During calls, AI-powered talk tracks ensure consistent messaging. Afterward, automatic summaries highlight what worked and where coaching is needed. For managers, this creates a closed loop where ramp is not only accelerated but continuously measured and improved.
The result? Enterprise AEs reach self-sufficiency faster and prioritize better, shortening time-to-first-deal and improving retention.
Book your demo with Pod today to help ramp AEs and boost sales performance in no time.