We’re excited to expand our growing list of coaching capabilities to customers at Yoodli. Your GTM team can now get personalized coaching in Yoodli on their calls recorded in Outreach’s Kaia and practice what to do differently before their next customer conversation.
Connecting Kaia takes just a few minutes, and every roleplay is then built intentionally with real context from those conversations so your team gets coaching that actually helps them improve.
Why This Matters
1. Proactive coaching moments
Take a buyer’s objection that a rep struggled to address. In a coaching session, Yoodli plays the actual moment from that rep’s call and helps them work through ways they could respond.
The rep then gets a roleplay based on the conversation with a similar persona, objections, and communication style. They can rehearse a different approach while the conversation is still relevant to their deal.
2. Time back for managers and RevOps
This integration gives you a way to extend coaching across your team without asking managers to review every call or build individual practice scenarios.
This gives meaningful time back to your reps, especially when there’s a lot of deals in the pipeline. Each rep gets feedback on their own conversations, guided by your sales methodology, with a follow-up to work on what needs improvement.
3. See if practice carries over
A rep handling an objection or question well in practice is encouraging, but you also want to know how they handle it with a customer.
Yoodli scores practice and real calls against the same rubrics, giving managers and enablement leaders a consistent basis for comparison. You can look for improvement in the skills and identify where a rep still needs support.
When you review your training program, you get more than just participation and practice scores: you also see how reps perform against those same expectations on real calls.
How it works
Connect Kaia in Yoodli under Org Settings → Integrations
Choose which calls to include using criteria such as title, duration, trackers, or groups, and select the Yoodli goals to score them against.
Set coaching invitations to email, Slack, or both, on a daily, weekly, or biweekly cadence.
See it in practice
The power of Kaia and Yoodli unlocks coachable moments across your entire team using Outreach.
Activate today in just a few minutes to enable your entire team to keep on building the skills to sell even better.
We’re making Yoodli programs and roleplays even more accessible off our platform to meet your learners where they’re already practicing.
Program web embed puts a full Yoodli program with roleplays, files, and links inside an iframe on your own site. Learners sign in, work through the program, and complete without ever having to leave your page.
This makes it significantly easier to meet your learners where they are already working from and deliver training on an internal portal, LMS landing page, or intranet.
Why This Matters
Now you can meet users where they learn and work from, allowing it to be a natural part of the flow of training or practice. If you want to contain training in a specific place in your internal tools, an LMS where the majority of your learning lives, or an employee portal you can specify which training lives where and give that experience more naturally within your own brand.
Setup
Three things must be true before a web embed program works:
Your organization has a Web Embed integration with at least one host name on the allow-list.
The program is published. Draft programs cannot be embedded.
You are an admin with integration permissions.
Step 1 – Set up the Web Embed integration
Go to Org Settings → Integrations.
Select Create an integration and choose Web Embed, or open your existing Web Embed integration.
Under Host name, add every host you plan to embed on — for example training.acme.com. Enter the host name only, without https://.
Save.
The host allow-list is enforced on every embed load. A page served from a host that is not on the list is refused. Host names are set once per integration and apply to every embedded surface, programs included.
Step 2 — Publish the program
Go to Programs, open the program, and publish it. Until then, Manage integration appears in the program’s menu but is greyed out with the tooltip. Publish to enable embedding.
Step 3 — Copy the snippet
The snippet lives on the program itself, not on the integrations page. Each program has its own snippet.
Go to Programs.
Open the three-dot menu on the program row — or the three-dot menu on the program’s detail page — and select Manage integration.
A modal opens titled Embed “<program name>” on your website.
Optionally set Size — a width and a height. Both accept percentages (100%) or pixels (900). In pixels the minimum is 375 wide by 800 tall.
Optionally set Activity Launch Language. This sets the practice language for every roleplay step in the program.
Select the copy button.
Tip: The size and language choices are baked into the copied snippet. Change either one and copy again as editing the modal does not update a snippet you already pasted.
Step 4 — Paste it into your page
Paste the snippet into your page’s HTML where you want the program to appear. Keep the allow attribute exactly as copied. It delegates camera and microphone to the Yoodli frame, which the roleplay steps need in order to record.
If you add a sandbox attribute of your own, include allow-popups. Link steps open the external URL in a new tab, and a sandbox without allow-popups suppresses that.
Congrats! You published your first off-platform learning for your users!
Who Benefits
Builders, Managers, and L&D teams are able to launch programs from inside the tools they already work on in conjunction with Yoodli. This creates a more seamless experience for where you want to enable teams and introduce learning and roleplay.
Learners don’t have to juggle between tabs in order to complete programs and practice roleplays. They can stay in the systems they’re familiar with, and gain the learning delivered through Yoodli on their own internal tools.
Get Started
Following the steps above allow you to deliver a completely new and unique experience for your learners. If you’d like to learn more about innovative ways to deliver programs, roleplays, and learning to your users we’d love to hear from you!
At the Japan Seattle AI Innovation Meetup 27.0 on September 24, 2026, Yoodli Lead Product Manager Jaimin Gandhi gave a keynote titled “Readiness for the moments that matter.” His message to a room of US startups, investors, and Japanese corporate and municipal leaders was simple: every employee should have a coach, and AI finally makes that possible.
A coach for every employee
Jaimin opened with the problem. Until now, coaching has reached a few people at the top of an organization. Everyone else learns on the job, often in the conversations where mistakes cost the most.
AI changes who gets access. With Yoodli, every employee can get coaching on their real work, not just in a classroom or a quarterly workshop.
Meet Sam: how AI coaching works after a real call
To show what that looks like, Jaimin walked the audience through a day in the life of Sam, a sales rep.
The call. Sam finishes a real customer call with a prospect who has concerns about security.
The debrief. His AI coach reviews the recorded call alongside his past coaching sessions.
The moment. The coach finds the exact point in the conversation where it could have gone better.
The plan. The coach tells Sam what went well (he held his ground on the security concerns), what to work on (tying his answer to the buyer’s business priorities, not just the feature), and what to practice next. Then it builds a new AI roleplay so he can rehearse that conversation before the real follow-up.
That loop of real work, coaching, and targeted practice is what Yoodli means by readiness. Learn. Practice. Do.
Hundreds of Sams, almost no coached calls
Every company has hundreds of Sams, and almost none of their real calls ever get coached. Managers don’t have the hours to review them all.
Jaimin laid out where Yoodli is today and where it’s headed:
Now: every Sam has a coach, with coaching after every real call.
Next: live coaching, right there on the call, while Sam is talking.
About the Japan Seattle AI Innovation Meetup
The Japan Seattle AI Innovation Meetup connects US startups with Japanese corporations, municipalities, and investors. The 27th edition ran September 23–25, 2026 at the Panoramic Center in Seattle’s Pacific Tower, alongside the AOS Q-DREAM Workshop.
The meetup was supported by the Washington State Department of Commerce (Choose Washington), Orrick, SWAN Venture Group, the Consulate-General of Japan in Seattle, JETRO (Japan External Trade Organization), NEDO (New Energy and Industrial Technology Development Organization), the UW–Tohoku Academic Open Space, and UW CoMotion.
The program was built for US–Japan business matchmaking:
Morning: sponsor remarks and guest keynotes, including Yoodli’s session from 11:45 AM to 12:00 PM.
Afternoon: pitches from 10 seed-to-Series A startups, then reverse pitches where Japanese corporations and municipalities shared their challenges and innovation needs.
Throughout: networking between US founders, investors, and the Japanese delegation.
The reverse pitches set this event apart. Instead of startups guessing what a buyer needs, the buyers say it out loud.
Why US–Japan AI collaboration matters
Seattle and Japan share more than a Pacific coastline. Both are home to enterprises investing heavily in AI and asking the same follow-up question: once the tools are in place, are our people ready to use them well?
Yoodli was built in Seattle. Varun Puri and Esha Joshi founded the company in 2021 at the AI2 Incubator, and it now helps enterprise teams prepare for the conversations that matter.
“Thank you to Tetsuro Eto and the Washington State Department of Commerce for this opportunity,” Jaimin said after the event. “It was a great day of conversations about keeping teams at full readiness.”
We’re grateful to every sponsor and partner who made the meetup happen, and we look forward to continuing these conversations with the Japanese delegation and the companies we met.
Frequently asked questions
What is the Japan Seattle AI Innovation Meetup? A recurring event that connects US AI startups with Japanese corporations, municipalities, and investors through keynotes, startup pitches, reverse pitches, and networking. The 27th edition took place September 23–25, 2026 in Seattle.
Who spoke for Yoodli at the Japan Seattle AI Innovation Meetup 27.0? Jaimin Gandhi, Yoodli’s Lead Product Manager, gave a 15-minute keynote on September 24, 2026, titled “Readiness for the moments that matter.”
How does Yoodli’s AI coach work? It reviews an employee’s real recorded calls, finds the moments that could have gone better, gives specific feedback, and builds an AI roleplay so they can practice before the next real conversation.
What is AI roleplay? A realistic, AI-powered practice scenario where employees rehearse high-stakes conversations, like a sales call or a security review, and get personalized coaching on how they did.
Get your team ready for the conversations that matter
See how Yoodli gives every employee a coach. Book a demo.
If you’ve uploaded and used videos as part of your roleplays you’ve likely run into a common scenario. The entire video will play without a chance for those doing the roleplay to interact until it ends.
Now you can upload entire videos and then instruct Yoodli in the display instructions field on when to show certain parts of the video to a learner which unlocks the power of video across your Roleplay capabilities.
Additionally, the learner can also ask a specific question that lives in only a portion or clip of the whole video and the AI will jump to that part, highlight it, and play just that portion with the ability to prompt and ask questions from there.
Why this matters
This unlocks several helpful use cases where roleplays and training relied heavily on longer videos to deliver information.
1. Coaching moments
Upload an entire model conversation and have video segments play in sequence to give coaching moments on how to best respond or talk on a call.
2. Training
Video plays through and the learner can ask to bring up a specific rule to hear the section again, with the AI asking them to confirm when it might be relevant based on the entire training.
3. Golden Sales Pitch
A learner has already seen a live version of an internal call that happened around objection handling. The entire video is used as a roleplay but if they want to specifically practice and learn about price objections, they can prompt the video to jump to and focus on that section to pitch it live.
4. Product Webinar
A longer 45 minute training is uploaded to talk about all the new product changes and feature updates that are coming. Admins can set the AI to show specific features throughout the video without playing it end to end.
The Bigger Picture
There are several unique and exciting ways you can now integrate videos into your roleplays that would have been difficult and taxing prior. You don’t have to become a video editor or do upfront legwork anymore to make videos truly valuable for your organization.
Plus, your roleplays gain the context of the entire video as a knowledge base so your roleplays are enriched with the entire video not just segments you want to highlight.
Who Benefits
Builders, Managers, and L&D teams are able to upload entire videos without the concern of their length being overbearing, while still being able to have the roleplay call on relevant information.
Learners gain the ability to ask to jump to specific segments or clips in a video to practice or learn about sections that are relevant to them or that they want to hear the video repeat.
Get Started
As an admin you can now build this behavior across all your roleplays that have video when relevant for you by using the display instructions field in the roleplay builder’s AI assets card. Learners and users try asking your videos to replay, jump to specific parts, or practice portions from a video.
Yes, AI roleplay can help reduce sales rep ramp time. It gives new hires more opportunities to practice realistic customer conversations, receive immediate feedback, correct mistakes, and demonstrate readiness without waiting for managers to conduct every roleplay manually. The strongest use case is not simply replacing human practice with AI. It is shortening the cycle between learning a skill, trying it, receiving feedback, and trying it again. However, AI roleplay does not control every factor that affects ramp time. So companies should measure skill progression and time to readiness before attributing faster revenue productivity entirely to AI.
Summary
AI roleplay can reduce parts of the sales ramp process that are slowed by limited practice, delayed feedback, inconsistent manager evaluation, and manual certification.
Reps can begin practicing customer conversations before they are allowed to handle those situations live.
Immediate feedback shortens the time between making a mistake and correcting it.
Repeated practice can help sellers develop conversational fluency instead of relying only on product knowledge and course completion.
Standardized scenarios and rubrics can make certification more consistent across managers, regions, and cohorts.
AI can reduce the amount of manager time required for repetitive roleplay and grading, allowing managers to focus on targeted coaching.
Companies should distinguish time to complete onboarding, time to demonstrate readiness, and time to full sales productivity.
AI roleplay is most likely to influence the first two directly. Revenue ramp depends on many additional factors.
Yoodli customer USB Payments reported a 50% reduction in seller ramp time after introducing Yoodli, alongside a 19% increase in meetings booked within 30 days and a 20% improvement in meeting quality. These are company-reported results from one implementation, not universal benchmarks.
The best way to evaluate impact is to compare onboarding cohorts and measure time to certification, skill progression, live-call behavior, and time to productivity.
Speed matters, but only alongside quality.
The goal is to help reps become ready faster without lowering the standard for readiness.
Why Sales Rep Ramp Time Is Often Long
New sales reps have to learn a large amount of information before they can perform consistently.
Depending on the sales motion, onboarding may include:
Product knowledge
Buyer personas
Industry terminology
Sales methodology
Discovery
Messaging
Competitive positioning
Objection handling
Pricing
CRM processes
Demo skills
Qualification
Customer stories
Security and technical information
Traditional onboarding can teach much of this information.
The challenge is applying it.
A new account executive might understand a discovery methodology but struggle to use it when a buyer gives vague answers.
They might correctly explain the product in a quiz but overcomplicate the explanation on a real call.
They may know the competitive battlecard but freeze when a prospect unexpectedly says:
“We’re happy with your competitor. Why would we switch?”
The rep therefore needs more than knowledge.
They need repetitions.
Salesforce’s 2026 State of Sales highlights this practice gap. Among sales professionals surveyed, 52% said traditional enablement did not provide the skills they needed, and 46% said they rarely received feedback on sales conversations. Meanwhile, 41% said they did not get enough opportunities to roleplay before customer calls, and 40% said their manager’s lack of time was an obstacle to enablement.
Those findings point to several potential ramp bottlenecks:
Not enough practice
Not enough feedback
Not enough manager capacity
AI roleplay can directly address all three.
What Does Sales Ramp Time Actually Mean?
Before claiming that AI reduces ramp time, define the metric.
“Ramp time” can refer to several different milestones.
Time to Complete Onboarding
How long does it take a rep to finish:
Courses
Workshops
Product training
Required assessments
This is mostly a training-completion metric.
Time to Certification
How long until the rep demonstrates that they can perform defined sales skills?
For example:
Deliver the pitch
Run discovery
Handle objections
Complete the demo
Follow the methodology
This is closer to a readiness metric.
Time to First Live Activity
How long until the rep is permitted to:
Prospect
Run discovery
Give a demo
Manage an opportunity
Time to Productivity
How long until the rep performs at an expected level in the field?
That might be measured through:
Qualified meetings
Pipeline generated
Opportunities created
Revenue
Quota productivity
These metrics should not be treated as interchangeable.
AI roleplay can directly influence practice, skill development, certification, and demonstrated readiness.
Its effect on revenue productivity is less direct because real sales performance depends on many other variables.
How AI Roleplay Can Reduce Ramp Time
The core mechanism is simple.
Traditional onboarding can contain long delays between learning and practice.
For example:
Monday: Rep learns discovery.
Friday: Manager has time for a roleplay.
Next Tuesday: Manager sends feedback.
Next Friday: Rep tries again.
That is a slow learning loop.
With AI roleplay, the sequence can become:
Learn discovery
↓
Practice immediately
↓
Receive feedback immediately
↓
Try again
The rep can complete several iterations while the concept is still fresh.
Yoodli’s AI Roleplays follow this practice model: employees complete realistic conversations, receive immediate feedback, and repeat until they meet the desired standard.
That faster feedback loop is one of the clearest ways AI can potentially shorten the time required to develop a skill.
1. AI Roleplay Gives New Reps More Practice
Sales skills require application.
A rep does not become strong at objection handling by reading 30 objection responses.
They need to hear:
“This is too expensive.”
and respond.
Then hear a variation:
“Your competitor is half the price.”
Then:
“The product looks useful, but finance froze spending.”
Each variation requires judgment.
Traditional peer or manager roleplay can provide this practice, but scheduling becomes a constraint.
One manager onboarding eight reps cannot realistically provide unlimited one-on-one simulations every day.
AI changes that capacity.
Every rep can practice independently.
The manager does not need to play the customer on every attempt.
That means a new hire could complete:
Five cold-call attempts
Four discovery conversations
Three objection exercises
Two product pitches
without requiring 14 separate manager-led roleplays.
More practice does not automatically mean better performance.
But when the practice is realistic, focused, and paired with useful feedback, it gives reps more opportunities to improve before customer interactions count.
2. AI Roleplay Reduces Feedback Latency
Imagine a rep completes a roleplay on Monday but receives manager feedback on Thursday.
By Thursday, they may barely remember the moment the manager is discussing.
Immediate feedback creates a tighter connection between:
What the rep did
and
What they should change
For example:
“You identified that onboarding had slowed, but you moved to the product before understanding how the problem affected revenue or manager capacity.”
The rep can immediately repeat the exercise with one focus:
Explore the business impact before presenting the solution.
Yoodli’s AI feedback is delivered after practice and can evaluate both organization-defined criteria and communication behaviors such as clarity, pacing, structure, and delivery.
The resulting loop becomes:
Attempt → feedback → adjustment → new attempt
That is more useful for skill development than:
Attempt → wait several days → feedback → move on
3. AI Roleplay Lets New Hires Fail Safely
Mistakes are inevitable during ramp.
The question is where those mistakes happen.
A new rep may:
Pitch too early
Misunderstand an objection
Talk too much
Forget an important question
Struggle with a competitor
Lose control of a demo
Give an overly technical explanation
Those mistakes can happen during an AI simulation.
Or they can happen with a prospect.
AI roleplay provides a lower-risk environment.
The rep can try an approach, see what happens, receive feedback, and try something different.
That makes experimentation easier.
They can learn:
“When I immediately defend the price, the buyer pushes harder.”
Then try:
“When I ask what is driving the budget concern, I uncover the actual issue.”
The seller is learning the consequence of different conversational behaviors before those behaviors affect a real deal.
4. AI Roleplay Can Start Earlier in Onboarding
New reps do not need to finish every piece of onboarding content before they begin practicing.
In fact, practice can reinforce learning as it happens.
For example:
Day 2
Learn company positioning.
Practice explaining the company.
Day 4
Learn buyer personas.
Practice talking to one persona.
Day 6
Learn discovery.
Practice a simple discovery call.
Day 8
Learn objections.
Practice the three most common objections.
Day 10
Learn competitive positioning.
Practice against an incumbent customer.
This creates a continuous cycle:
Learn → apply → receive feedback
rather than:
Learn → learn → learn → learn → eventually practice
Yoodli’s onboarding and certification model is built around new hires practicing realistic situations early and demonstrating readiness rather than relying solely on content completion.
5. AI Roleplay Can Help Standardize Certification
Manual certification can create another ramp bottleneck.
Suppose every new rep needs a final certification with a sales manager.
The manager needs to:
Schedule the exercise.
Play the buyer.
Evaluate the rep.
Document results.
Give feedback.
Repeat the evaluation if the rep does not pass.
Multiply that by dozens or hundreds of new hires.
Certification capacity can become part of the ramp timeline.
AI roleplay can automate parts of this workflow.
Everyone can encounter comparable scenarios and be evaluated against the same rubric.
For example:
Discovery Certification
Every rep must demonstrate:
Current-state discovery
Problem identification
Business-impact exploration
Appropriate methodology use
Effective next steps
The exact conversation can remain dynamic, while the readiness criteria stay consistent.
That means a rep does not necessarily have to wait for an evaluator to become available simply to demonstrate a skill they have already developed.
Managers can remain involved in final approval when appropriate.
The AI handles more of the repetitive evaluation.
6. AI Roleplay Reduces Manager Practice Bottlenecks
Managers are an important part of onboarding.
They are also one of the scarcest resources.
A manager may be responsible for:
Forecasting
Deal inspection
Pipeline reviews
Hiring
Team meetings
Coaching
Escalations
Strategy
Performance management
Add several new hires and the amount of practice required can become difficult to sustain.
AI roleplay should not remove managers from onboarding.
Instead, it can change what managers spend time on.
Without Scalable AI Practice
Manager time:
Recreating the same buyer persona
Running basic objection exercises
Listening to every pitch
Scoring repeated attempts
With AI Practice
Manager time:
Reviewing persistent skill gaps
Coaching difficult behaviors
Discussing real opportunities
Helping reps develop judgment
Observing field transfer
The manager becomes more targeted.
A useful AI system might show:
Rep is consistently strong on messaging but weak on business-impact discovery.
The manager can begin there.
That is more efficient than spending 30 minutes discovering the weakness manually.
7. AI Roleplay Can Personalize the Ramp
Not every new hire starts from the same place.
Consider two reps.
Rep A
Has eight years of enterprise sales experience but has never sold your category.
They may need more:
Product knowledge
Buyer context
Messaging
Competitive training
Rep B
Knows the category well but is new to sales.
They may need more:
Discovery
Objection handling
Call control
Closing
Communication skills
A fixed onboarding curriculum may make both reps complete the same work.
Roleplay data can help reveal different readiness gaps.
Rep A might rapidly pass foundational discovery scenarios but struggle with product positioning.
Rep B may know the product immediately but require more conversational practice.
Every rep doesn’t need an identical onboarding path.
Roleplay data should point practice time at what each rep actually needs, not the skills they’ve already proven.
8. AI Roleplay Can Make Sales Methodology Practical Faster
New hires may spend hours learning a sales methodology.
Knowing the framework is different from using it.
Suppose the organization expects reps to quantify business impact.
A course can explain why that matters.
A roleplay can test whether the rep actually does it.
The AI buyer might initially say:
“New-hire onboarding has been inconsistent.”
A weak rep hears the pain and pitches.
A stronger rep asks:
“What impact is that inconsistency having on ramp or manager workload?”
The buyer then reveals more information.
Now the methodology becomes behavior.
Organizations can build roleplays around MEDDPICC, Challenger, SPIN, SPICED, Sandler, Value Selling, or their own internal methodology as long as they translate the framework into observable behaviors.
Yoodli allows enablement teams to customize personas, scenarios, messaging, and evaluation rubrics around their specific sales framework through its sales and GTM enablement capabilities.
9. AI Roleplay Can Speed Product Fluency
Product knowledge can be another major part of ramp.
New reps need to move from:
“I understand what Feature X does.”
to:
“I can explain why Feature X matters to this customer.”
That transition requires practice.
Create several buyers.
Sales Leader
Wants to know how the product improves execution.
CFO
Wants financial justification.
IT Leader
Asks about integration and security.
End User
Wants to understand the workflow.
The product is the same.
The explanation should change.
AI roleplay gives the rep repeated opportunities to learn how to adapt product knowledge to customer context.
This is especially important for complex products where simply memorizing functionality can encourage feature-heavy conversations.
10. AI Roleplay Can Improve Objection Readiness Before Live Calls
A new seller should not hear the most common objection for the first time from a prospect.
Build common objections into onboarding.
For example:
“We’re already using a competitor.”
“We don’t have budget.”
“This isn’t a priority.”
“Send me information.”
“We can build this ourselves.”
“Implementation will be too difficult.”
“I don’t see enough ROI.”
Do not teach only one approved sentence for each objection.
Vary the reason behind it.
Consider:
“We don’t have budget.”
In one simulation, there actually is no funding.
In another, the buyer has budget but does not understand the value.
In another, the buyer is using budget as a negotiation tactic.
Those require different responses.
Practice can help new reps diagnose what an objection means rather than automatically deploying a memorized rebuttal.
11. AI Roleplay Can Accelerate Demo Readiness
For some sales roles, the rep is not productive until they can run a demo independently.
That can be a major ramp milestone.
Demo practice can evaluate whether a rep can:
Set expectations
Understand buyer priorities
Navigate the product
Present relevant capabilities
Connect features to business problems
Handle questions
Recover after interruptions
Avoid feature dumping
Establish next steps
This is more demanding than memorizing a demo script.
AI buyer interruptions make the practice especially useful.
For example:
“I understand that feature. Can you show me how this would work for a manager with 25 reps?”
Now the seller has to adapt rather than continue reciting the demo flow.
12. AI Roleplay Can Prepare Reps for Different Buyer Personas
New reps often learn one idealized customer profile.
Real selling is more varied.
Practice with:
Friendly buyers
Skeptical buyers
Busy executives
Analytical CFOs
Technical stakeholders
Procurement
Champions
End users
The same seller behavior should not work equally well with all of them.
This variation helps reduce dependence on memorization.
Instead of learning:
“This is what I say in the discovery roleplay.”
the rep learns:
“This is how I use discovery depending on what this buyer gives me.”
That adaptability is a more meaningful form of readiness.
13. AI Roleplay Can Progress From Simple to Difficult
Do not try to accelerate ramp by giving a new hire the hardest scenario on day three.
The goal is faster learning, not artificial difficulty.
Build progression.
Beginner
Buyer:
Cooperative
One clear problem
Straightforward objection
Intermediate
Buyer:
Guarded
Several priorities
Requires stronger follow-up questions
Advanced
Buyer:
Skeptical
Uses a competitor
Challenges value
Reveals limited information
Expert
Conversation includes:
Multiple stakeholders
Conflicting priorities
Executive pressure
Commercial objections
Limited time
Yoodli supports multi-persona scenarios for conversations where reps need to manage several stakeholders.
Progressive complexity lets sellers build fluency before applying it under greater pressure.
14. AI Roleplay Can Reduce Certification Review Time
Ramp time is not always slowed because the rep lacks skill.
Sometimes the rep is ready but waiting for the organization to evaluate them.
That distinction matters.
Yoodli customer Harness provides an example.
In a Yoodli-published case study, Harness reported reducing sales-training submission review time by 75% after integrating AI Roleplays into its certification process.
Harness’s stated objectives included accelerating new-hire ramp, improving product knowledge, and reducing the amount of time enablement teams spent manually reviewing submissions.
That result should be interpreted as evidence from one company’s implementation, not a universal expectation.
But it demonstrates an important potential ramp mechanism:
Faster evaluation can remove administrative delays between readiness and certification.
15. What Evidence Exists That AI Roleplay Can Reduce Ramp Time?
The clearest Yoodli-specific example comes from USB Payments.
In Yoodli’s customer case study, USB Payments reported:
50% reduction in seller ramp time
19% increase in meetings booked within 30 days
20% improvement in meeting quality
The case study also quotes Sales and Training Manager Bill Pace. He describes a new rep with no previous sales experience who ramped within two weeks and won her first sales call.
These are compelling results from that implementation.
They should not be treated as proof that AI roleplay itself caused every outcome or that another organization will achieve the same percentages.
Other Yoodli customer evidence supports related parts of the ramp process.
Clari
Clari reported an average 36% improvement across five core conversation skills during a Yoodli pilot.
Participants who practiced with Yoodli were also five times more likely to place in the top 10 of a subsequent live demo contest.
Participants who used Yoodli averaged approximately 10 attempts.
This does not directly measure ramp time, but it supports one of the mechanisms relevant to ramp: measurable skill improvement through repeated practice.
Snowflake
Snowflake reported saving more than 1,200 manager hours per quarter through Yoodli AI roleplays while supporting practice and certification at large scale.
Again, manager-time savings do not automatically equal faster ramp.
But reducing manager dependency can remove one bottleneck from onboarding and certification.
Harness
Harness reported a 75% reduction in sales-training review time.
That is relevant when manual grading delays certification.
Taken together, these case studies provide evidence that specific companies have achieved improvements in ramp time, skill progression, coaching capacity, and certification efficiency.
They do not establish a universal causal effect.
Why AI Roleplay Does Not Guarantee Faster Ramp
Sales ramp is influenced by much more than training.
Consider two new sellers with identical skills.
Rep A receives:
Strong territory
High inbound lead volume
Established accounts
Experienced manager
Rep B receives:
New territory
Few leads
Complex accounts
New manager
Rep A may generate revenue much sooner.
That does not necessarily mean Rep A learned faster.
Other factors affecting ramp can include:
Product complexity
Sales-cycle length
Market conditions
Territory
Lead quality
Lead volume
Brand awareness
Pricing
Rep experience
Manager quality
Sales process
Customer buying cycles
AI roleplay cannot solve all of these.
That is why companies should avoid measuring its value only through:
“How quickly did the rep hit quota?”
Quota productivity is important.
But it sits downstream from many variables outside training.
Measure Time to Readiness First
A more defensible measurement framework begins closer to the intervention.
Level 1: Practice Activity
Measure:
Attempts
Frequency
Scenario completion
Practice time
This tells you whether sellers used the system.
It does not prove improvement.
Level 2: Skill Progression
Measure:
Discovery improvement
Messaging accuracy
Objection handling
Demo performance
Methodology execution
This tells you whether the rep became better during practice.
Level 3: Readiness
Measure:
Time to required score
Time to certification
Number of attempts to certification
Percentage passing by target date
Manager approval
This is where AI’s effect on ramp becomes clearer.
Level 4: Field Transfer
Measure:
Live-call scorecards
Manager observations
Messaging consistency
Methodology use
Objection behavior
Now ask whether the practiced behaviors appear with customers.
Level 5: Business Productivity
Measure:
Time to first qualified meeting
Time to first opportunity
Time to first closed deal
Time to defined productivity threshold
This is ultimately valuable but requires more caution around causation.
The evidence chain becomes:
AI practice → skill improvement → earlier readiness → field transfer → possible faster revenue productivity
That is more credible than jumping directly from roleplay completion to revenue claims.
Compare Onboarding Cohorts
One useful way to evaluate impact is cohort analysis.
For example:
Cohort A
Traditional onboarding.
Cohort B
Same core onboarding plus structured AI roleplay.
Compare:
Days to certification
Manager coaching hours
Practice attempts
Certification pass rate
First-attempt skill scores
Final skill scores
Time to first live customer conversation
Time to productivity
Try to keep other major program variables as consistent as possible.
You may discover:
AI cohort certified nine days sooner.
That is more useful than a general impression that onboarding “felt faster.”
Or you may discover:
Ramp duration barely changed, but managers spent 40% less time on basic practice.
That is still an important outcome.
Measurement allows you to distinguish the two.
Measure the Time Reps Spend Waiting
One overlooked part of ramp is waiting.
A new rep may wait for:
Manager roleplay availability
Certification review
Feedback
Demo evaluation
Peer practice
Scheduling
Those delays are not learning.
They are operational friction.
Track them.
Suppose a rep finishes training on Day 20 but cannot complete manager certification until Day 27.
Seven days have been added to the readiness process even though the rep may already possess the skill.
AI roleplay can be particularly valuable when waiting time is a significant component of the existing ramp process.
Do Not Accelerate Ramp by Lowering the Bar
Faster is not always better.
Imagine:
Old Program
Certification takes 30 days.
Pass threshold: 85%.
New AI Program
Certification takes 15 days.
Pass threshold: 60%.
You did not necessarily improve ramp.
You changed the definition of ready.
Maintain meaningful standards.
The objective should be:
Reach the same or higher readiness level in less time.
Ideally, measure both:
Time to readiness
and
Quality of readiness
Use AI Roleplay Alongside Managers
The best deployment pairs AI with managers, not one instead of the other.
AI absorbs the repetition managers don’t have time to run themselves.
Managers stay focused on higher-value coaching.
Here’s the split:
AI can provide:
Unlimited basic practice
Consistent scenarios
Immediate feedback
Standardized rubrics
Progress data
Managers provide:
Context
Judgment
Deal strategy
Nuanced coaching
Organizational knowledge
Human support
This division can be particularly valuable during onboarding because managers no longer have to be present for every repetition.
They can intervene where their expertise matters most.
Use AI Roleplay Alongside Call Shadowing
New sellers also need exposure to real customers.
AI simulations should complement that exposure.
One onboarding loop could be:
Observe real discovery call
↓
Discuss why the rep did what they did
↓
Practice a similar scenario with AI
↓
Receive feedback
↓
Repeat
↓
Observe another real call
This creates a connection between:
Real-world observation
and
Active participation
Shadowing alone can remain passive.
AI practice forces the learner to make decisions.
Continue Practice After the Rep “Ramps”
Ramp is not the end of learning.
A seller who is ready today will encounter:
New products
New competitors
New objections
New buyer roles
Promotions
Larger deals
New territories
Continue roleplay after onboarding.
A rep might practice:
First enterprise opportunity
First CFO meeting
First negotiation
First competitive displacement
First buying committee
First executive presentation
Yoodli’s AI sales training is designed for continuous reinforcement as well as initial onboarding.
The result is a progression from:
Onboarding practice
to
ongoing sales readiness
rather than treating learning as something that ends once a rep receives system access and a territory.
Example: AI Roleplay Ramp Program
A simple program could look like this.
Week 1: Core Knowledge and Messaging
Learn:
Company
Market
Product
ICP
Value proposition
Practice:
Company explanation
Product explanation
Persona-specific messaging
Goal:
Rep can explain what the company does accurately and clearly.
Week 2: Discovery and Qualification
Learn:
Sales methodology
Buyer problems
Discovery framework
Practice:
Cooperative discovery
Problem identification
Business-impact questions
Qualification
Goal:
Rep can conduct foundational discovery without immediately pitching.
Week 3: Objections, Competition, and Demo
Learn:
Competitors
Battlecards
Objection framework
Demo
Practice:
Competitor objection
Pricing objection
Status quo
Product demo
Goal:
Rep can apply product knowledge under pressure.
Week 4: Advanced Readiness
Practice:
Skeptical executive
Complex discovery
Multi-stakeholder conversation
Full certification scenario
Goal:
Rep demonstrates readiness against organizational standards.
The specific timeline will vary.
The underlying principle remains:
Practice should become more difficult as the rep becomes more capable.
Example Ramp-Time Dashboard
An enablement team might monitor:
Metric
Rep
Cohort
Target
Days to first practice
2
3
≤3
Discovery attempts
7
5
5+
Discovery score improvement
+24%
+18%
+15%
Days to discovery certification
14
18
≤20
Days to demo certification
23
27
≤30
Manager practice hours
3
5
≤5
Days to customer readiness
26
32
≤30
This tells a much richer story than:
“Onboarding completed in four weeks.”
You can see:
How much the rep practiced
Whether they improved
When they became ready
Where manager time was required
That makes ramp easier to manage.
Common Mistakes When Using AI Roleplay to Reduce Ramp Time
Measuring Course Completion
Faster course completion does not prove faster readiness.
Starting Practice Too Late
Roleplay should reinforce learning throughout onboarding.
Treating Every Attempt as Certification
Give reps low-stakes opportunities to fail and improve.
Giving New Hires Unrealistically Difficult Buyers Immediately
Increase complexity gradually.
Measuring Only Final Scores
Track progression and time to readiness.
Using Generic Sales Scenarios
Ground roleplays in actual products, personas, methodology, objections, and messaging.
Removing Managers Entirely
Use AI to scale repetition, not eliminate human judgment.
Claiming AI Caused Every Revenue Improvement
Sales performance is influenced by many variables.
Ignoring Waiting Time
Manager scheduling and manual grading can materially extend ramp.
Ending Practice at Certification
Continue reinforcement after the rep begins selling.
How Yoodli Can Help Reduce Sales Ramp Bottlenecks
Yoodli’s AI Roleplays give sellers realistic practice without requiring managers to conduct every simulation.
New hires can practice:
Discovery
Objection handling
Messaging
Product pitches
Demos
Executive conversations
Multi-persona sales meetings
After each exercise, Yoodli provides personalized feedback against communication criteria and organization-defined standards.
Enablement teams can customize roleplays around their own:
Sales methodology
Buyer personas
Messaging
Products
Objections
Rubrics
Yoodli’s sales and GTM enablement offering specifically describes new hires practicing real scenarios early in ramp, receiving immediate feedback, and repeating until they meet readiness criteria.
Its onboarding model focuses on demonstrating performance rather than relying solely on content completion.
The customer evidence illustrates several places where this model may affect ramp.
USB Payments reported a 50% reduction in seller ramp time.
Harness reported a 75% reduction in training-review time.
Snowflake reported saving more than 1,200 manager hours per quarter through practice at scale.
Clari reported a 36% average improvement across five conversation skills. Participants who practiced using Yoodli were five times more likely to place in the top 10 of a subsequent live demo contest.
Each is a first-party customer result from a specific implementation.
They should not be interpreted as guaranteed outcomes.
Together, however, they illustrate several mechanisms relevant to faster ramp:
More practice
Faster feedback
Less manual review
Reduced manager bottlenecks
Measurable skill improvement
Those are the parts of ramp AI roleplay is best positioned to influence.
Reduce Time to Readiness, Not Just Time in Training
AI roleplay can reduce sales rep ramp time.
But that statement needs one important qualification.
Rushing new hires through onboarding isn’t the goal.
Closing the gap matters more:
Learning what to do
and
Being able to do it reliably.
Give reps realistic practice earlier.
Let them make mistakes safely.
Provide immediate feedback.
Let them repeat.
Measure their improvement.
Use consistent standards.
Reserve manager time for the skill gaps and sales judgment where human coaching adds the most value.
Then measure when the rep actually becomes ready.
If that happens sooner while readiness standards remain equal or improve, you have meaningfully reduced ramp time.
If revenue productivity also improves, examine that outcome carefully and account for the many other factors that influence sales performance.
AI roleplay does not automatically cut sales ramp time by itself.
That framing overstates the mechanism.
The more defensible claim is this:
AI roleplay can remove important practice, feedback, coaching, and certification bottlenecks that often make sales ramp slower than it needs to be.
That is both more defensible and more useful for enablement teams designing an onboarding program.
FAQ
Can AI roleplay make sales onboarding too fast?
It can if an organization uses speed as the primary goal and lowers readiness requirements. Teams should optimize for faster demonstrated readiness, not simply fewer onboarding days. Certification standards should remain meaningful even when practice and evaluation become more efficient.
Should sales ramp time be measured from a rep’s start date or from the end of formal training?
Organizations can measure both, but start-date-to-readiness is often more useful for understanding the complete onboarding experience. Clearly defining the start and end points is essential when comparing cohorts or reporting improvements.
Are experienced sales hires likely to ramp faster with AI roleplay than new sellers?
Possibly, but prior sales experience does not eliminate the need to learn a new company’s product, buyers, methodology, competitors, and messaging. Diagnostic roleplays can help determine which foundational skills experienced sellers already possess and where company-specific practice is needed.
Can reps practice too much during onboarding?
Yes. Repetition is useful when it is targeted and accompanied by feedback. Repeating the same scenario without changing behavior can create diminishing returns. Practice programs should vary scenarios and focus subsequent attempts on identified skill gaps.
Should companies compare AI roleplay scores between new-hire cohorts?
They can, provided the scenarios, rubrics, difficulty, and scoring standards remain reasonably comparable. If the program changes substantially between cohorts, score differences may reflect changes in the assessment rather than differences in seller readiness.
Does faster certification mean a rep will hit quota faster?
Not necessarily. Certification indicates demonstrated readiness under defined conditions. Quota attainment is also influenced by territory, pipeline, market conditions, deal cycles, pricing, manager quality, product-market fit, and many other factors.
What should you do if roleplay scores improve but sales ramp does not?
Determine where the bottleneck occurs after certification. Reps may be improving their skills while waiting for territory assignments, lead volume, system access, customer meetings, or long sales cycles. Also verify that practiced behaviors are transferring to real customer calls.
Is AI roleplay more useful for SDR ramp or account executive ramp?
It can support both, but the scenarios should differ. SDRs may focus more heavily on cold calls, qualification, relevance, and initial objections. Account executives may require discovery, demos, business cases, stakeholder management, competitive conversations, and negotiations.
Training Industry is the leading research resource for corporate learning leaders. Moreover, its annual Top 20 lists are among the most trusted benchmarks L&D buyers use when evaluating experiential learning technologies. Training Industry evaluates companies on the quality of their capabilities and analytics, their market presence and innovation, the strength of their client relationships, and their business growth.
For our team, this recognition reflects what we set out to build: a skills readiness platform where people don’t just learn about high-stakes conversations. They practice them.
Why teams are investing in experiential learning
Most workplace training asks people to watch, read, and click through modules. Then, they perform under pressure for the first time in front of a real customer, patient, or student. Experiential learning takes a different approach. Instead, it gives people a safe, realistic environment to rehearse the conversations that matter, like sales calls, difficult feedback, and patient interactions.
With Yoodli’s AI roleplays, learners practice against lifelike AI personas tailored to their organization’s real scenarios. They get instant, judgment-free feedback on what they said and how they said it. Then, they repeat until the skills stick. Meanwhile, L&D and enablement leaders get analytics that show skills readiness across entire teams. As a result, they gain a new layer of insight alongside the metrics they already track.
Built for organizations, proven at scale
This recognition also belongs to the enterprises, higher education institutions, and training partners who bring Yoodli into their programs. (For training and coaching companies evaluating platforms, our guide to choosing an AI roleplay partner covers what to look for.) Their results speak for themselves, from faster ramp times to measurable gains in learner confidence and performance. In fact, many of our customers’ programs have earned their own industry recognition this year. We’ll share more of those stories in the coming weeks.
“Most training ends the moment the module does. People learn a concept, then they’re on their own when it counts. We built Yoodli so employees can rehearse realistic conversations until the skill feels natural, so what they learned shows up when it matters. This recognition from the Training Industry tells us that’s where the industry is headed,” said Varun Puri, Co-founder & CEO of Yoodli.
“Our customers want confidence that their people are ready, and that the training they invest in translates into real conversations with real customers. Practice is how you get there. Seeing that approach recognized alongside the best experiential learning technologies in the industry means a lot to our team,” added Esha Joshi, Co-founder & President.
Thank you to Training Industry for the recognition. Above all, thank you to our customers and partners, whose ambition for their learners pushes our platform forward every day.
Want to see experiential learning in action? Book a demo to explore how Yoodli’s AI roleplays help your teams practice before it counts.
AI is rewriting how learning gets designed, delivered, and measured. What it isn’t rewriting is the job of the learning professional.
That was the through line of “What Changes, What Stays: A Human-Centered View of AI in L&D,” a live webinar co-hosted by Yoodli and GP Strategies on September 10. Cara Halter, Senior Director of Global Learning Innovation at GP Strategies, and Mike Rich, Head of Partnerships at Yoodli, spent an hour unpacking where AI adoption in L&D stands, how roleplay fits into learning strategy at institutional scale, and the questions every learning leader should ask before signing a contract.
Halter reviews learning technology for a living. She’s spent 11 years on GP Strategies’ innovation team evaluating dozens of tools in this category, and she opened with a caveat that set the tone: she came in as technology-agnostic as possible. This wasn’t a product pitch. It was a field guide.
Halter mapped AI adoption into three phases. They aren’t strictly sequential, and moving forward doesn’t mean abandoning what came before.
Phase 1: Productivity and efficiency. You interact with AI. Same deliverables, produced faster: drafting assessment questions, generating scenarios, creating video and audio. Nearly every authoring tool now has an AI-generated button, and most teams have clicked it. Halter’s challenge to teams in this phase: have you redesigned your workflows, or just plugged AI into the old ones?
Phase 2: New learner experiences. Learners interact with AI. This is where AI roleplays, AI coaches, personalized assessment, and just-in-time support live, and it’s where most organizations are actively experimenting today.
Phase 3: Learning in the flow of work. AI becomes invisible. Learning is built into reimagined workflows, with feedback on real performance rather than practice scenarios alone. Rich made it concrete: imagine a coach that knows the call you’re about to take in five minutes, preps you against your organization’s methodology, then debriefs you afterward.
Halter’s shorthand for why this shift is inevitable: ten years ago, learners Googled it. Five years ago, they YouTubed it. Today, they ChatGPT it. Learners now expect real-time, conversational, personalized answers, and learning teams are being measured against that expectation.
Old world, new world: six shifts in learning design
Halter and Rich walked through six paradigm shifts reshaping the field:
Discrete courses → continuous learning ecosystems. The course is no longer the unit of value, which raises hard questions about what “completion” even means, and how LMS-era tracking and reporting adapt.
Content-centric → context-centric. Information is everywhere. The value is in helping people apply it to their role and moment of need.
One size fits all → personalized and adaptive. AI finally makes individualized learning paths feasible at scale.
Learning as preparation → learning as ongoing, validated performance. AI can help confirm someone can do the thing, not just that they finished the module.
Learners as consumers → learners as contributors and creators. In an AI roleplay or tutoring conversation, the learner drives.
Human-designed and controlled → AI-driven, human-governed. Instructional designers shift from writing every word to organizing data, setting guardrails, and pointing AI at the right sources.
Halter, a self-described 30-year learning nerd, acknowledged the discomfort in that last shift. She joked that she’s never met an instructional designer without control issues. Letting learners steer their own experience is genuinely hard, and it’s the job now.
Proof at scale: RingCentral and Ochsner Health
Two case studies grounded the discussion in outcomes.
RingCentral: 90% reduction in certification time. Certifying customer support agents used to consume eight hours of manager time per agent, with managers grading every mock call by hand. Training couldn’t keep pace with hiring, and scripted scenarios went stale. With Yoodli’s AI roleplays built from real support calls, certification dropped from eight hours to one. Managers now open a dashboard, see exactly who’s struggling and where, and spend their time coaching the people who need it. RingCentral scaled onboarding without adding headcount, and new agents reached call readiness sooner.
Ochsner Health: from pilot to system-wide. The Gulf South health system wanted frontline leaders to practice the human conversations that shape patient care: building trust, setting expectations, leading with empathy. Leaders had learned the frameworks but rarely rehearsed them, and there were few tools to coach the coaches across a large, distributed system. Working with subject matter experts, Ochsner built 12 custom AI-scored goals across three conversation types, all mapped to the organization’s values. The biggest gains showed up exactly where baseline scores were lowest, and executives validated meaningful improvement in quality and confidence. The pilot expanded system-wide.
Both stories illustrate the same principle: the organization defines what good looks like, and AI delivers the practice reps and feedback at a scale no human coaching program could match.
AI roleplayis becoming table stakes. A purpose-built platform is the differentiator.
One of the webinar’s sharpest points: the ability to do AI roleplay is showing up everywhere, bolted onto LMSs, content libraries, and video tools. Rich noted that Gartner recently identified AI roleplay as an emerging software category in its own right, precisely because doing it well at enterprise scale requires purpose-built infrastructure, not a checkbox feature.
So the differentiator isn’t whether a vendor offers roleplay. It’s what’s built around it. The speakers offered a due-diligence list for learning leaders evaluating any vendor:
Is it practice only, or does it include real-call analysis and ongoing skill tracking?
Who validates the quality of the AI’s feedback?
Are personas and scenarios customizable to your methodology, or one-size-fits-all?
What happens to learner data after each session?
Does pricing hold up at hundreds or thousands of learners, not just a pilot?
Is the vendor funded and committed to this category for the long haul?
And the trap doors to watch for: roleplay with no coaching depth, no link between practice and on-the-job performance, a glitchy first experience that kills adoption, and vendor lock-in disguised as a platform.
Where humans stay in the loop
Halter and Rich were aligned on this point: the goal isn’t full automation, it’s the right balance of AI scale and human judgment. Halter presented a spectrum of control, from fully human-designed experiences to high-autonomy AI, and made the case that there’s no universally right position on it. High-compliance training belongs further left; exploratory skill-building can sit further right. Her advice: whatever your instinct, take one deliberate step toward more learner-driven design on your next project.
Rich pointed out that Yoodli builds this spectrum directly into the product, with a control that sets how much creative latitude the AI has in any given roleplay. Sales discovery practice might warrant an unpredictable persona; certification for high-stakes procedures demands rigor.
The scale question resolves the same way: deploy AI for reach, speed, and consistency, then layer in human touchpoints like secondary reviews, manager conversations, and facilitated live events where judgment and depth matter most.
The measurement question every CLO is asking
The Q&A’s heavyweight question: how should learning teams measure and communicate program value in this new world?
Halter’s answer was to start with measurement, not end with it. Before designing anything, define the behavior you’re trying to change and what good looks like, then build those rubrics directly into the tool. That turns reporting from smiley sheets and completion rates into behavioral evidence: a team that moved from 35% to 85% on empathy against your organization’s own standard is a story any executive understands.
The second half of her answer pushed further: pair learning metrics with business metrics. Ask your stakeholders how they measure performance, and design the program to connect to that data. Rich extended it into the flow of work: if reps are practicing against a sales framework, run their real recorded calls through the same methodology and let the AI proactively invite them to coaching based on what it finds. That’s the loop between learning the material, practicing it, and proving behavior changed.
What stays
The webinar’s title question got a clear answer. The tools, the delivery models, and the measurement capabilities are all changing fast. The role of the learning professional is not. If anything, learning leaders have a bigger seat at the table, because someone has to define what good looks like, govern the guardrails, and connect practice to performance. Content is still the foundation. Capability is the new deliverable.
Want to see what human-centered AI roleplay looks like for your organization? Watch the full recording or talk to the Yoodli team about building practice, coaching, and measurement into one platform.
This family-owned, community bank is using Yoodli’s AI roleplays to help branch teams practice customer conversations and strengthen a consistent, relationship-centered service experience across every branch.
SEATTLE, WA — Sept. 9th 2026 — Yoodli, the AI roleplay platform for enterprise communication training, announced a partnership with Bank of Colorado, the state’s largest community bank headquartered in Colorado. Through the partnership, Bank of Colorado is rolling out Yoodli’s AI-powered practice platform across its branch network, giving employees a safe, judgment-free space to rehearse the customer conversations that define community banking.
Bank of Colorado has served generations of Colorado families and businesses for nearly 50 years, with over 45 branches spanning the Eastern Plains, the Front Range, and the Western Slope. What sets the bank apart isn’t just its longevity, it’s a service culture built on personal relationships and local decision-making. That’s exactly what the bank set out to strengthen when it brought Yoodli into its training program.
Practicing the Moments that Matter
At the heart of the rollout is Bank of Colorado’s commitment to consistent, relationship-centered service in every customer interaction. Historically, reinforcing a shared service approach across a distributed branch network meant classroom sessions, shadowing, and role-playing with managers: valuable, but hard to scale and even harder to make consistent.
With Yoodli, every branch staff can now practice realistic customer conversations with a lifelike AI roleplay partner, anytime, as many times as they need. Employees get instant, personalized feedback on what they said and how they said it, while training leaders get visibility into skill development across the entire organization.
“Our customers choose Bank of Colorado because of the people they meet in our branches. Yoodli gives every member of our team a way to practice and sharpen those conversations, so the service our customers count on is consistent across our markets.”
— Brita Jones, Director of Training, Bank of Colorado
A Community Banking Success Story
Since launching, the rollout has become one of the standout adoption stories among Yoodli’s financial services customers. Branch teams across Colorado are using AI roleplays to onboard new hires faster, build confidence before high-stakes customer moments, and turn consistent service expectations into daily habits.
“Community banks win on trust, and trust is built one conversation at a time. Bank of Colorado understood that before we ever met them, our job was simply to give their teams unlimited reps. Watching their service culture become even more consistent across branches has been one of our favorite partnership stories to date.”
— Varun Puri, CEO & Co-Founder, Yoodli
Why It Matters for Financial Services
Frontline conversations in banking carry real weight: they’re regulated, relationship-driven, and often the deciding factor in whether a customer stays for a season or a lifetime. Yoodli’s AI roleplays let financial institutions:
Scale conversation practice across every branch and role without pulling managers off the floor
Standardize service expectations with objective, repeatable feedback that supports a consistent customer experience
Onboard and upskill faster, with private practice that builds confidence before real customer interactions
Measure what was previously invisible, communication skill growth across the organization
What’s Next
Yoodli and Bank of Colorado plan to continue building on the partnership, including co-marketing initiatives later this year and an in-depth case study on the rollout and its impact across branches.
About Bank of Colorado
Bank of Colorado is a family-owned community bank serving Colorado since 1978 with over 45 locations across the Eastern Plains, the Front Range, and the Western Slope. Part of Pinnacle Bancorp’s family of independent banks, Bank of Colorado is known for its strength, stability, and commitment to the businesses and families of Colorado. Learn more at bankofcolorado.com. Member FDIC.
About Yoodli
Yoodli is the AI roleplay platform that helps enterprise teams practice high-stakes conversations, from sales calls and customer service to leadership and manager training. Trusted by leading organizations worldwide, Yoodli provides private, judgment-free practice with real-time, personalized feedback at scale. Learn more at yoodli.ai.
AI roleplay has officially crossed from “interesting experiment” to enterprise essential. Mark Cuban recently told Inc. that AI will train employees the same way pilots learn, in simulators, and Gartner just published its first Market Overview naming AI roleplay one of the fastest-growing categories in enterprise learning (with Yoodli on the list).
But when a category moves this fast, the noise moves faster. Every vendor promises realism, integrations, and analytics. So how do you tell the difference before you sign a contract?
That’s exactly what we tackled in our live webinar, Before You Buy: 5 Checkpoints for AI Roleplay Platforms, hosted by Betsy McKibbin (Head of Marketing), Tom Craven (Head of Enterprise Sales), and Moon-Tae Kim (Solutions Engineer). In 30 minutes, we walked through a platform-agnostic buyer’s guide and demoed three of the five checkpoints live inside Yoodli.
Here’s what we covered, and what to write down before your next vendor evaluation.
The 5 Checkpoints for Evaluating an AI Roleplay Platform
Reality and customization. Does the platform reflect your reality, or a generic one?
Innovation and scale. Has it been proven with thousands of learners, and where is the roadmap headed?
Integration and measurement. Does it fit where your people already work, and can you prove impact?
Versatility and growth. Can it create value beyond sales enablement?
Continuous coaching. What happens after the practice session ends?
As Tom put it: the number one reason roleplay investments fail is adoption. When roleplays don’t mirror the reality of the people using them, learners disengage, and teams end up re-evaluating the same purchase a year later.
Checkpoint 1: Does the platform reflect your reality?
Three questions to write down:
Does it adapt to your sales motion and L&D methodology, or do you adapt to it?
Is it teaching from your materials, or from whatever the model generates on the fly?
Can your team create content at AI speed, or does every roleplay take weeks?
Moon demonstrated this live, building a roleplay from a single prompt plus one uploaded document. Yoodli’s agentic builder asked clarifying questions and took the scenario from zero to 80% in a couple of minutes, with the last 20% (rubrics, personas, assets) fully customizable.
Two features drew the most attention:
Strict mode, which grounds the AI in your uploaded source material. If a learner asks something the source docs don’t cover, the AI says so instead of improvising. It’s a guardrail against teaching your team the wrong thing.
Custom rubrics, with rated, binary, and compound goals you define, from minimum score to maximum score and everything in between, so measurement matches your methodology, not a one-size-fits-all framework.
Checkpoint 3: Does it fit where your people already work?
Moon’s litmus test: ask where your people have to go to learn, get nudged, and build roleplays. “If every answer is ‘our platform,’ you’re going to be fighting for adoption for the rest of the contract.”
The live demo showed Yoodli meeting learners where they already are:
Inside the LMS: a Yoodli roleplay embedded directly in Docebo, with scores syncing back automatically so L&D can report from the system they already use.
Inside Slack: a manager-assigned roleplay launched straight from a Slack notification.
Inside Claude via MCP: Moon asked Claude to build a practice roleplay for an upcoming meeting. It pulled context from Gmail, Calendar, and Drive, then generated a ready-to-run Yoodli roleplay.
One more evaluation question that separates platforms: can the data leave? Out-of-the-box analytics are table stakes. The real power comes from marrying skill progression data with your internal datasets: ramp time, quota attainment, win rates.
Checkpoint 5: What happens after the practice?
Practice only matters if the skills show up in real conversations. The final demo showed Yoodli’s continuous coaching loop:
Yoodli analyzed a rep’s real calls from the prior week.
An AI coach (“Coach Cora”) opened a 1:1 session and pointed to the exact moment the rep uncovered a prospect’s pain, then listed features without connecting them back to that stated need.
The coach replayed the moment, discussed what to do differently, and auto-generated a follow-up roleplay targeting that exact gap.
That’s the loop: learn, practice, do, prove, and then back to practice again.
What attendees asked (live Q&A)
The dominant theme in the Q&A: integrations. Buyers don’t want another standalone tool. They want AI roleplay woven into the stack they already use.
Does Yoodli connect to Claude and other LLMs via MCP? Yes, demoed live. Learners can generate roleplays from inside their LLM using context from email, calendar, and documents.
Does Yoodli integrate with Glean? Yes. Glean supports MCP servers, and Yoodli customers are already using this today.
Does Yoodli integrate with Gong? Yes. Recorded calls can feed the continuous coaching workflow, and more conversation intelligence integrations (including Microsoft Teams) are actively in the works.
Do customers bring their own scoring rubrics, or does Yoodli provide them? Both. Yoodli offers validated frameworks, but most customers build their own, and the rubric builder supports that down to the goal level.
How fast can you really build a roleplay? Zero to 80% in three to four minutes. The final polish (rubrics, assets, a test run) takes about an hour.
Get the buyer’s guide (and see the other two checkpoints)
We only had time to demo three of the five checkpoints live. Want to see innovation and scale, and versatility and growth, applied to your team’s use case?
Gartner® just published its Market Overview for B2B Sales AI Role-Play Applications, written by Bill Yetman, Rachael Buchler, Shayne Jackson, and Doug Bushée, and Yoodli is named among the point solutions in this category. To us, this signals that the AI roleplay platform category is becoming a recognized market of its own, and we’re excited to be part of it.
Here’s why we’re excited. In our view, a Market Overview means the category is now being tracked on its own, separate from the broader revenue enablement platforms it usually gets grouped with. We read this report as drawing that line, where sales AI roleplay applications are becoming their own thing, not only a feature bolted onto an LMS or an enablement suite.
What Gartner says the category is
Gartner defines sales AI roleplay applications as “software that uses AI to simulate realistic business conversations.” The goal: scale communication training, strengthen coaching, and speed up skill development through personalized, repeatable practice. The report describes “these cloud-based solutions provide an always-on practice environment where users interact with AI personas, receiving real-time, automated feedback against defined guidance related to branding, value propositions, and so on.”
In our view, that description lines up with what Yoodli’s always-on coaching is built to do. It’s not a training event you sit through once. It’s a practice environment reps come back to before every high-stakes conversation.
Where the market is headed
A few things from the report stood out to us, and in our opinion, many of these trends align with how we’ve built Yoodli and where we plan to keep innovating.
One of them is buyer realism. Yoodli lets teams configure their own buyer profiles and personality traits instead of handing reps a one-size-fits-all simulation.
Scoring has changed a lot industry-wide. It used to be about whether a rep mentioned the right feature. Now the bar is hyper-personalized, grading tone, structure, and sentiment against an organization’s own methodology, not a generic rubric. Yoodli’s approach has always leaned this direction: admin-defined scoring logic and success criteria, so feedback reflects how your org actually wants people to sell, not a canned checklist.
Security and governance are getting more weight across the market too, and for good reason. These platforms handle sensitive pricing and negotiation content, so enterprise-grade control isn’t optional. That’s an area where Yoodli was built enterprise-first from day one, not retrofitted after the fact.
What’s next
We believe this is Gartner’s first Market Overview dedicated to sales AI roleplay applications, and the report describes a market still in an evaluation phase. We think that means the category is going to keep moving fast over the next year. Expect more analyst attention, more head-to-head comparisons from buyers, and more pressure on vendors to deliver the capabilities buyers are asking for: real scenario builders, customizable personas, and scoring that holds up against a company’s own methodology, not a generic template.
We’re going to keep leaning into that. Custom personas and admin-defined rubrics aren’t a checkbox for us, they’re the whole point of how Yoodli works, and we plan to keep pushing on them as the category matures. We’re also watching the same trends the report flags, like deeper integration with CRM and conversation intelligence tools, and thinking about where those make sense for our customers next.
We’ll keep tracking how this space continues to evolve, and we’ll keep showing up in it. If you want to see where AI roleplay is headed and what an always-on coaching platform looks like in practice for enterprise teams, that’s a conversation we’re always happy to have. Connect with our team here.
Gartner, Market Overview for B2B Sales AI Role-Play Applications, Bill Yetman, Rachael Buchler, Shayne Jackson, Doug Bushée, August 7, 2026.
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