What Rilla and Siro Don't Tell You: The Coaching Gap in D2D Sales Tools
TJ
Founder
Rilla and Siro give D2D sales managers visibility into what happens at the door. But after the AI analysis runs, every coaching decision still falls on a human. Here is why that gap matters, and what a closed coaching loop actually looks like.
The Tool Everyone Says They Need
If you manage a D2D sales team right now, you have almost certainly been pitched on Rilla or Siro. Both companies have strong marketing, real case studies, and a compelling core promise: stop guessing about what your reps are doing in the field, and start knowing.
The pitch lands because the problem is real. Ride-alongs don't scale. Managers running 15-20 reps can't shadow every pitch. Without field visibility, coaching becomes reactive -- you find out a rep is struggling after they've missed quota for two months, not when they're struggling on a doorstep on a Tuesday afternoon.
So you buy the tool. Reps start recording. The data flows in. And then the question nobody asked before the purchase comes into focus: now what?
What Recording Tools Actually Do
To be clear: Rilla and Siro are legitimate products that solve a real problem. Conversation recording and analysis gives managers visibility they didn't have before.
Rilla transcribes in-person conversations, scores performance, surfaces patterns, and flags coaching moments. The AI identifies what top reps do differently and holds the rest of the team to that standard. Managers get analytics dashboards and can review flagged clips instead of riding along in person.
Siro does similar work. It records conversations, transcribes them, auto-drafts follow-up messages, and surfaces a scorecard showing where each rep performed well or fell short. The "Ask Siro" feature lets managers query the AI directly -- "what could this rep have improved?" -- without reviewing the entire recording.
Both products address the observation gap. Managers can now see what happens at the door without being physically present. That is genuinely useful, and teams that adopt these tools consistently report better visibility into field performance than they had before.
The question is what happens next.
The Step Both Tools Skip
Both companies are transparent about this if you read their materials carefully. The value proposition is not "we coach your reps." It is "we give you the data to coach your reps better." That is an important distinction -- and one that gets glossed over during a product demo when the dashboard looks clean and the insights look actionable.
Here is what Rilla's own materials describe as the manager's role after the AI analysis runs:
Review the insights. Identify the coaching opportunities. Deliver targeted feedback. Hold reps accountable. Design training interventions. Manage by numbers instead of by gut.
Every one of those steps requires a human manager to take action.
Siro's success data is even more revealing. According to Siro's own benchmarks, customers who see strong returns on investment spend at least two hours per week listening to recordings and send five to ten comments to reps weekly. For a team of ten reps, that's a meaningful time commitment. For a team of twenty-five, it becomes a second job.
The tools do the recording and the analysis. Then they hand the coaching work back to you.
This is not a criticism. It's a design choice. Both platforms were built around the insight that managers need better data to coach more effectively. They deliver on that. The gap is not in what they built -- it's in what they assumed: that the manager has the bandwidth to act on everything the AI surfaces.
The Math Behind the Bottleneck
Research from the Alexander Group on first-line sales manager (FLSM) span of control found that FLSMs spend roughly 16% of their time on coaching activities -- about four hours per week. That is across all their reps, all their administrative work, and all their other management responsibilities.
According to the State of Sales Coaching report from MySalesCoach, 37% of reps receive insufficient coaching and one in seven receive no meaningful coaching at all -- not because their managers don't care, but because the math doesn't work. There are only so many hours.
Now add a recording and analysis tool. The visibility improves. The coaching recommendations pile up. The scorecard flags six reps who need feedback on their opener this week. The AI identifies that three of them are losing prospects during the value proposition stage. Two more are overtaking. One has a talk-to-listen ratio that's been trending in the wrong direction for three weeks.
The tool surfaced all of it. Now the manager has to prioritize it, schedule it, deliver it, and follow up on it. All within the same four hours per week.
The bottleneck did not move. It got more information.
Why This Breaks as Teams Scale
At five or eight reps, a skilled manager can absorb the feedback from a recording tool and stay on top of coaching needs. The data helps them focus. They spend less time in the field and more time reviewing clips and coaching strategically. The tool works.
At fifteen or twenty reps, the math gets harder. More recordings, more flags, more coaching moments to triage. The manager starts prioritizing the highest performers and the most urgent problems, and the middle of the roster -- the reps who could improve significantly with consistent attention -- gets less of the coaching they need.
At twenty-five to fifty reps, the model breaks. This is the scaling wall we wrote about in the context of scaling a pest control D2D team from 5 reps to 50. You either add managers -- expensive, slow, and dependent on finding people who can coach -- or you accept that most reps are getting visibility without the follow-through.
The tools help managers be better coaches. They do not change the ratio of manager hours to reps.
The Roleplay Problem Makes It Worse
Both Rilla and Siro now market AI roleplay as a feature. Siro uses context from recorded conversations so reps can practice an objection right after a tough call. Rilla's "Hey Rick" feature lets reps run through scenarios before heading out.
We covered the limitations of this approach in depth in our post on why most AI sales roleplay is just voice-mode ChatGPT. The short version: a rep practicing with an AI that concedes too easily, responds with a five-second delay, or only covers one objection at a time is not building real muscle memory. The scenario feels like practice without functioning like it.
But even setting aside the quality of the roleplay, the structural problem remains. Both tools present the AI practice as something a rep can do voluntarily after reviewing their analysis. The manager still has to identify that a specific rep needs to practice a specific objection, direct them to do it, follow up to see if they did, and review whether it helped.
The insight and the training are not connected. The loop is not closed.
What a Closed Coaching Loop Actually Looks Like
The alternative model flips the assumption. Instead of surfacing data for managers to act on, the system acts on the data itself.
A closed coaching loop works like this: the platform records and transcribes conversations, identifies what each rep is struggling with at a stage level (opener, value prop, objection handling, close), automatically builds training targeted at those specific weaknesses, and assigns it directly to each rep's coaching plan in the app -- without the manager having to make any of those decisions.
The rep who keeps losing prospects during the value proposition on NEM 3.0 pricing objections gets a targeted roleplay scenario built from how top reps on the team actually handle that specific situation. Not a generic "practice objection handling" prompt. The exact conversation pattern, the exact objection, the exact language that works.
The manager still plays a role. They review overall patterns, handle escalations, and guide strategy. But the per-rep training triage -- the part that consumes the four hours per week and still leaves 37% of reps undercoached -- runs automatically.
This is the capability described in detail at roonly.ai/#how-it-works, and it is the model we explored in terms of field sales data that drives coaching decisions. The data does not just inform the manager. It drives the training.
What to Ask Before You Buy Any Coaching Tool
If you are evaluating recording and analysis tools right now, the question to ask is not "does it record conversations" or "does it have a scorecard." Every platform at this tier does those things.
The question is: after the analysis runs, who has to do the work?
If the answer is "the manager reviews the insights and coaches accordingly," you have a visibility tool. That is useful, and if you have the manager bandwidth to act on what it surfaces, it will improve performance.
If the answer is "the system identifies what each rep needs, builds the training, assigns it, and tracks whether it worked," you have a coaching infrastructure. That is what scales.
Both categories have legitimate use cases. The mistake is buying a visibility tool and expecting it to function as a coaching infrastructure -- and then wondering why coaching rates stay low and the bottleneck did not move.
Platforms like Roonly were designed to close that gap by automating the step between analysis and training. The visibility problem and the coaching bandwidth problem are both real. The tools that solve only one of them leave the harder problem intact.
Sources
TJ
Founder
Technical founder with 6+ years building AI-native B2B platforms. Previously led product at an enterprise tech company and founded multiple startups. Passionate about using AI to help sales teams perform at their best.