r/AI_Sales

Having 0 luck with sales these days and trying to figure out the best way to utilize AI to help get more responses from media planners… any advice would be appreciated! What are you doing differently and which AI tools are you using to help find contacts or send emails, etc. Thanks in advance!

There's a gap between what reps say they do before a discovery call and what actually happens when they have 10 minutes and a Zoom link.

Do you look up the company? The person? Their LinkedIn activity from the last month?

I'm in sales tech so I have obvious biases here. But I'm genuinely curious about the real answer, not the aspirational one.

What's actually in your prep stack before a first call?

Over the last few weeks, we’ve all seen the posts.

Founders bragging about replacing their sales teams with AI.

Faster deals. More output. “Smarter” outbound.

And every time I read one, I think:
You’re probably not telling the full story.

Because behind closed doors, I’m hearing something very different.
Sales leaders are worried.

Then I see this:
‼️ Anthropic is paying ~$330K to manage 10 SDRs. ‼️

The company building the AI… is doubling down on humans ‼️ HUMANS ‼️

That tells you everything.

In the last 18 months, outbound production became cheap.

Prospecting, emails, follow-ups, CRM updates all near zero cost.

So the scarce resource changed.

It’s not messages anymore.

It’s trust.

Everyone can send outreach now.

Almost no one earns attention.

Trust comes from relevance:
One message that actually understands the buyer
Follow-ups that add value
Real effort, not templates
AI lowered the cost of noise — and buyers’ tolerance for it dropped to zero.

The real risk isn’t AI.

It’s scaling bad outreach faster.

Trust is the only thing left worth building.

For two years our marketing visuals were entirely stock photography and template based graphics. Clean, professional, completely forgettable. Every competitor in our space was using the same Unsplash images and the same Canva layouts and our content looked exactly like theirs even when the copy was genuinely differentiated. 6months ago we started incorporating original hand drawn style illustrations into our social content and blog posts. The engagement shift was noticeable within the first month. Comments went up. Save rates on Instagram improved significantly. People started sharing our posts in a way they never did with stock imagery.

The reason makes intuitive sense when you think about it. Custom illustrated content is visually distinctive in a way that stock photography can never be because it is by definition unique to your brand. Nobody else has those exact characters, that exact visual style, those exact scene compositions. It signals a level of creative investment that audiences respond to even if they cannot articulate why.

The challenge is production. Getting a consistent illustrated visual style produced at the volume a content marketing operation needs is genuinely hard. Illustration takes longer than photography or graphic design. Style consistency across multiple pieces requires either one illustrator doing all the work or extremely detailed style guides that are difficult to brief and maintain. For brands that have successfully built illustration into their regular content output, what did the production model look like and how did you maintain style consistency at volume?

I’ve been working on my pitch deck for fundraising, but I’m struggling to understand how founders actually judge whether it’s “good enough” before sending it to investors. Everyone says the deck should be clear, structured, and compelling, but there’s rarely any objective way to measure that.

In reality, feedback from peers is usually very general like “looks good” or “maybe improve storytelling,” which doesn’t help much. And when investors don’t respond, it’s hard to know if the issue is the idea, the presentation, or just timing.

I’m wondering if there is any structured way founders validate their pitch decks before going into outreach. Do people use frameworks, scoring methods, or tools to simulate investor feedback? Or is it mostly based on trial and error until something starts working?

Would love to hear how others have approached this stage, especially before their first serious investor conversations.

Hey all,

I’ve been tasked with exploring effective ways to enhance our sales training programs using AI roleplay. We’re a mid‑sized team with both in‑office and remote sales reps, and we want something that can help improve real‑worlds selling skills, objection handling, and real customer conversations.

For us, the ideal solution would:

✅ Provide realistic AI‑drivin sales roleplay scenarios
✅ Support skills like objection handling, negotiation, and discovery
✅ Be easy for sales reps to use regularly without heavy setup
✅ Integrate with our LMS or learning stack

Has anyone implemented AI roleplay for sales training or seen tools that work really well in this space? I’d appreciate recommendations, lessons learned, or pitfalls to avoid.

Thanks in advance!

#SalesTraining #AI #LearningAndDevelopment #SalesEnablement #CorporateTraining

Few months ago I changed my display name on a conference networking app to "AI Stanislav". pure SEO - when people searched for "AI" to find experts or partners, I showed up near-first positions. always working well.

And also turned into a spam filter

Last week a "Partnership Lead" from a licensed fintech company ($2B+ annual processing volume, their words) sent me outreach. opening line: "hi AI Stanislav"

I pointed it out and shared a post I wrote about why this kind of outreach hurts fintech brands specifically. And I doubled answer to his boss via linkedin.

His leadership response wasn't "oh damn, our script is broken, thanks", it was: "we get 30+ qualified meetings a month from this. i'm not sure your post is about us"

and that's kind of the whole problem.

To get those 30 meetings they probably annoyed 3000+ people who now just associate their brand with cheap spam. In fintech this is bad because the whole pitch is "trust us with your money". If you can't clean a contact list before sending, why would I route payments through you.

When I pushed a bit more the conversation ended with "i feel your arrogance... this style of communication isn't for me".

Fine! But it tells you something. The dashboard shows 30 meetings so nobody looks at what's burning underneath.

IMHO, AI SDR tools are not bad by default, but "intent-based" has to actually mean something. knowing someone was at a conference is not intent. having a rough idea why they were there and saying something relevant - that's closer to it.

If your whole strategy depends on people not noticing they got addressed as "AI [name]" you're not building a pipeline.

I'm keeping the "AI" in name. Cheapest filter I found =)

anyone else running something like this, on purpose or by accident?

I read earlier a post on LinkedIn about a Sales VP who won't hire AE's if they aren't AI-capable.

But isn't that just a "Duh" question at this point? If you are selling tech solutions with some complexity, it makes sense to leverage everything you can.

I'm seeing this thing where common table-stakes sales skills are now considered "extra".

The other issue I see is some teams are hamstrung by what the company will allow. Like some can only use CoPilot, which is the worst. And the guidance they get is asking it to write emails 🙄 ... so it is a self-inflicted wound of disabling them from being power-users.

But this whole idea of being "extra" is really what has always been the truth. Really strong reps always leverage everything they can to increase influence/knowledge/insight into accounts.

The thing of making it new is just branding.

Gartner uses terms like:

* Adversity Quotient (AQ): Resilience and Adaptability
* High-Level "Executive Presence"
* "Sense Making" and Decision Facilitation
* Technical and Tactical Proficiency
* Adaptability: Thriving in a changing environment

But this is really:

* Know the stuff you are selling so you can talk about it like a peer
* Know how to read the room and adapt in real-time so prospects don't check out
* Do your homework on the account/team/etc.
* Don't say stupid things in the meeting

Are any of you restricted in what AI you can use with your meeting prep?

I'm an applied AI engineer spending this week talking to sales leaders to understand the gap between what conversation intelligence tools claim to do and what actually moves the needle on a sales floor in 2026.

Specifically curious about four things:

1. If you've used Gong, Convin, Avoma, Salesken, or anything similar... what did you stay/leave over? Features, price, adoption, integration, something else?

2. For teams with vernacular/code-mixed calls (Hinglish, Tanglish, regional mixes, Spanglish) ... how usable are the transcripts and insights today? Genuine question, not loaded.

3. The promise of "AI surfaces coaching moments automatically"... does this actually work in practice for your team, or do managers still end up rebuilding the picture themselves?

4. What's the real ROI threshold above which you'd swap or add a CI tool? Cost-per-seat, manager-hours-saved, rep-ramp-time ; pick your metric.

Not selling anything, not building anything yet. Just trying to understand the actual workflow before I decide what (if anything) to build. I'll share aggregated patterns back to the thread once I've had ~10 conversations with operators.

Especially curious to hear from Heads of Sales, sales ops leaders, and folks doing ground-level coaching. What's actually broken?

https://cal.eu/sasi-preetham-r

Most sales teams throw new reps into live calls before they've had real repetitions anywhere. The problem with most ai roleplay tools for sales training isn't the technology, it's that the simulated buyer feels flat enough that reps learn to perform against a script, not against human objection patterns.

After testing different setups with my team, here's how the landscape maps out.

Second Nature is the most commonly recommended ai roleplay tool for sales training. It runs simulated calls where a virtual buyer pushes back on pricing, value, and timing. Strong for early reps learning call structure, weaker for advanced objection handling because the buyer doesn't adapt to what the rep says.

Hyperbound is built specifically for cold call training. The ai persona is more aggressive and the scoring gives reps granular feedback on what to say at each stage of a cold call. Better for volume practice than presence building.

Nooks is worth mentioning for teams running parallel dialing workflows, the practice environment mirrors live prospecting queues in a way the others don't.

Tavus is different, it's a real-time video roleplay platform where the ai buyer reads the rep's facial expressions and tone live during the session, meaning if the rep sounds uncertain, the simulated buyer pushes harder. Contrary to what most sales training tools do, it trains presence under behavioral pressure rather than script recitation.

For reps who need cold call volume, Hyperbound or Second Nature cover that well. For reps closing on video calls with buyers who read body language, Tavus format produces different results because the pressure is behavioral, not just verbal.

In traditional channels, competitor analysis is relatively straightforward. You can compare rankings, ads, and content performance. But AI visibility adds a new dimension that is harder to track. This raises a powerful question: how can a business tell if its competitors are being recommended more often in AI-generated answers? Without direct visibility into these systems, competitors could be gaining an advantage silently. This makes it difficult to react or adjust strategy in time. So, is competitor analysis evolving into something deeper and less visible than before?

With so many automated tools now available for fundraising, I wonder if human effort is slowly becoming less important. Can automation really handle things like investor matching, email writing, and pitch analysis effectively? Or is fundraising still something that requires personal relationships, trust, and direct communication? Do you think automation is just a support system, or could it eventually take over most of the fundraising process completely?

When you have got spreadsheets, folders, and whiteboard sessions, but things still slip through the cracks. How do you go about it to stay on top of your game and avoid missing stuff?

Every time I read posts about AI outbound agents, it feels like everyone trusts their agents to work alone. People talk about setting up these agents and letting them rip at scale within thirty minutes like there is zero risk involved. For me it's fine to experiment with AI lead generation but for actually sending messages (for both cold outreach and warm leads/replies) maybe I am just more paranoid than most, but I feel like I have to verify the outputs pretty rigorously before I trust an agent to work on its own.

Whenever we plug in a new agent or even just change the prompts on an existing one, we run it in a manual review mode for a full calibration week. The agent writes every message into a review queue and one of us has to read each one before it ever hits a prospect. It sounds slow, but that first week is always where the embarrassing stuff gets caught. Just in the last few days, I flagged several things that would have been a disaster to send. I caught one wrong industry pitch because the data enrichment was stale and the prospect had moved from fintech to healthcare months ago. It also generated an opener that completely misread a German LinkedIn bio and used a tone that was way too familiar for that market. And there was even a duplicate sequence that almost went to the same person twice on the same day because they were sitting in two different campaigns.

Right now I am reviewing the messages and manually piping the good ones into Expandi as custom variables for my LinkedIn sequences. It is a bit more manual work upfront, but I use that review process as a feedback loop to refine the AI. If a draft is off, I adjust the prompts and let the agent try again. I usually do this for a full week until the results start hitting the quality bar I want. 

Only once I am really satisfied do I let the agent start working autonomously, and even then I will still spend the following week reviewing what it sends. In my niche the cost of a bad first impression can be pretty high, so I would rather take some time to set things up than risk sending something that makes us look like a generic spam bot or worse.

Is anyone else actually putting their agents through a proper draft mode like this, or are you guys just letting it loose after a few test sequences or messages? I cant tell if im being too paranoid or people arent really telling us the full story and leaving these details out when they write about this.

Hey everyone,

I currently have 6 micro-SaaS live, bringing in a bit over $20k in MRR.

The crazy part? I barely wrote a single line of code. I used AI to generate everything, from the database to the UI.

It wasn’t magic on day one. I spent hours stuck on broken code before I finally cracked the system:

• Keeping the idea tiny (a true MVP).
• Prompting the AI step-by-step.
• Launching fast to get real traction.

Lately, I see too many non-tech people give up at the first AI bug. It sucks because the technical barrier is basically gone.

So, I’m starting a Skool community.

Full transparency: I will probably charge for the full course down the line. It makes sense given the exact workflows and copy-paste prompts I’ll be sharing.

But the main goal right now is to build together. Building alone is the fastest way to quit.

If you want to join and build your own AI SaaS with us: drop a comment or shoot me a DM, and I’ll send you the invite!