I typed one sentence into Claude this week.

We booked 8 meetings 👇

And it's only Wednesday.

Low-volume, super targeted campaigns.

( while most founders would be like, "we booked 133 meetings in 12h" )

Not us.

We wanted to "test" a segment.

Since Apollo.io got connected to Claude - our workflow changed a LOT.

So I typed:

"Find Head and Director of Sales at logistics companies in the US, UK and Australia, 51–200 employees, verify emails and add the qualified ones to my sequence."

That's it.

Apollo.io pulled from 230M contacts.

Enriched and verified.

In my sequence.

No Clay. ( iykyk )

One prompt inside Claude.

Before this → that workflow used to take us 45 minutes minimum for every segment / ICP.

This is what Apollo "anywhere" actually unlocked:

→ Apollo inside Claude
→ prospect, enrich, sequence in plain English
→ Apollo inside Chrome
→ verified emails on any LinkedIn profile without leaving the page
→ Apollo inside HubSpot
→ intent signals on accounts you already own

Your data doesn't wait for you to go find it anymore. It travels with you.

11,000+ teams connected Apollo.io to their AI tools in 11 weeks.

6 million data requests through Claude and ChatGPT alone.

Numbers look crazy; and this is the new sales game.

One sentence.
Eight meetings.
Wednesday.

Struggling at the moment landing new meetings?

Smarter prospecting is the solution.

Try Apollo Anywhere 👇

https://lnkd.in/eYff6DKM

Apollo.io #ApolloPartner #ApolloAnywhere

I think I speak for everyone working in sales when I say this…

I’m sick of building prospect lists manually.

Early in my career, I’d spend 2–3 hours digging through LinkedIn and copying contacts into spreadsheets.

Now it takes me about 5 minutes.

Here’s what I do now:

1. Open Apollo.io
2. Use the AI Assistant (basically ChatGPT for sales)
3. Tell it exactly who I want to target
4. Layer in buying intent to find companies actively looking for a solution

That buying intent is the game changer.

Instead of calling random people, I’m reaching out to prospects who are already showing signs they’re in-market.

And just like that, I have a targeted list of qualified prospects ready to call.

If you’re still building prospect lists manually, stop.

Ai isnt here to replace us, its here to help us.

#Sales #TechSales #Prospecting #ColdCalling #Apollopartner #ad

2 months ago, if you had asked me how long I spend researching prospects and writing personalized outbound emails…

I would’ve said:
“Too long.”

20 to 25 minutes per prospect.

Reading LinkedIn profiles.
Understanding company context.
Finding triggers.
Writing relevant emails.
Figuring out who else in the org to target.
Planning follow-ups.

It was effective.
But painfully manual.

So I built something for myself.

Today, I can generate:

• ICP-calibrated prospect intelligence
• personalized outreach angles
• 5-email sequences
• LinkedIn messaging strategies
• stakeholder mapping
• follow-up paths if someone ghosts
• qualification + scoring

…in under 5 minutes per account.

What used to take me hours now happens before my coffee cools down ☕

And the outcome?

Q1: 146% achieved.

That’s when I realized this wasn’t just a personal workflow hack.

It could help other sales teams too.

So I turned it into:
Impact Prospect Intelligence

An AI-powered prospect scoring + outbound intelligence system calibrated to your exact ICP.

If you want access to the trial version, comment:
IMPACT

.
.
.

#Sales #B2BSales #OutboundSales #Prospecting #AI #SalesAutomation #RevenueOperations #ImpactProspectIntelligence

The best “AI SDR” ideas aren't about replacing SDRs ‼️ They're about giving great reps better systems. Here are 5 takeaways from Clay's livestream yesterday on the AI-native SDR:

1. Start the day from signals, not a task list.
Johnny DeFazio from Notion talked about reps beginning in an internal app that surfaces account-level signals: product usage, engagement, warmth, and activity. That becomes the source of truth for what to action.

2. Build your own signal layer if you don’t have PLG data.
Nicole McKelvey from Decagon shared how her team hunts for signals like AI initiatives, leadership changes, competitor mentions, website visitors, new hires, champions, and warm paths. The goal is to give reps a real reason to reach out.

3. Replace hundreds of sequences with one smart shell.
Rob Cook from Clay described moving from maintaining endless persona/use-case sequences to one shell sequence where AI stacks signals, selects proof points, surfaces pain, and personalizes the message. Humans still review, but the system does the heavy lifting.

4. Teach the workflow manually before automating it.
Rob also said Clay has reps do the job manually before leaning on automation. That one stuck with me. If you can’t do the job well yourself, you probably can’t prompt or automate it well either.

5. Fix data before obsessing over messaging.
Nicole McKelvey put it well: garbage in, garbage out. Better targeting and cleaner data make every downstream workflow better.

The big takeaway: The AI-native SDR isn’t a bot.

It’s a rep with better context, better signal prioritization, better prep, and tighter feedback loops with GTM engineering and ops.

Thank you Davide Grieco for hosting a great session 🔥

#GTMEngineering #SalesDevelopment #AI #OutboundSales

Really impressed with the new plugins for Copilot Cowork, especially Dynamics 365 Sales.

I've tried a few of the AI sales tools across Dynamics 365 and M365 Copilot recently, and even built a custom agent to update Dynamics 365 Sales records. However, the tools felt disjointed and not in line with how I work.

This changed with the latest Copilot Cowork release. Microsoft has added plugins, including Dynamics 365 Sales, that lets Cowork read and write directly to my CRM.

It is now part of how I work my pipeline.

The built-in Sales AI agents in Dynamics 365 & M365 Copilot have clear roles. Sales Qualification Agent triages leads. Sales Research Agent prepares meeting briefs. Sales Copilot lives in Teams and Outlook. Each one is designed for a specific moment in a defined sales process. And for a sales team running that process at scale, they make sense.

My situation is different in that I'm one person managing a small pipeline end to end.

Here are some examples.

In a Copilot Cowork session with the new Sales plugin, I sent it a rough, vague prompt:

"Can you review my latest emails from [client name] and suggest updates to the opportunity?"

Copilot Cowork then:
• found all relevant emails
• found the correct Opportunity record in Dynamics 365
• suggested updates to the Opportunity record with clear reasoning
• highlighted real risks to the Opportunity
• after I confirmed the suggestions, it updated the Opportunity

Other things I got it to do were:
• create multiple new Dynamics 365 records (Account, Contact, Opportunity) all in one go from emails or Teams conversations
• research an Account before populating its details
• add notes and follow-up tasks
• close completed tasks as we work through them together
• create a Sales SKILL with my preferences for managing sales in Dynamics 365

The built-in Sales AI agents in Dynamics 365 & M365 Copilot are designed for teams with defined processes across multiple roles. For the right setup, they work well.

There is no single approach that works for every sales team. Whether you are evaluating which AI tools to roll out across a team or managing your own pipeline, finding what fits how you and your team actually work matters more than using the most capable tool. If the prescribed agent flows feel tight, the new Sales plugin for Copilot Cowork is worth a look.

Prospecting Agent is live! I'll be honest, this one hits different. I've spent the past couple years bringing several Agentforce Sales agents to market, and every launch teaches you something new. But Prospecting Agent? This feels like the one we've been building toward. Here's why I'm so fired up about it: outbound has always been brutal. And in today's world of AI-generated noise, sellers are burning hours just trying to find and reach the right buyers. That time should be spent on actual selling. Things like building relationships, closing deals, doing the work that actually matters. Prospecting Agent fixes that. I've seen firsthand how much that upfront grind weighs on sales teams. Now they have an agent that handles it for them. And the customer reaction? It's real. I just got back from Agentforce World Tour New York and the energy around Prospecting Agent was electric. Talking to customers face to face and hearing how eager they are to get their hands on this... that's the kind of validation that makes all the hard work worth it. None of this happens alone. Rohan Punamia brought something special to this. As the former CEO and Co-founder of Bluebirds, he's lived this problem from every angle, and having that founder-level obsession driving the product made all the difference. It was great to partner with Cordelia Hsiao , Will Mehall , and so many others who showed up every single day (and are still delivering on) to make this real. We're just getting started. And I genuinely believe the best is yet to come. Check out the demo! #Salesforce #Agentforce #AI #SalesInnovation #OutboundSales #FutureOfSelling

Check This => Prospecting Agent is now GA.

One of our launch customers saw these results:
- 60% of SDR-generated North America pipeline built by Prospecting Agent
- 3x increase in call volumes | 2x email reply rate
- Deployed in 2 weeks — booked meetings that very first week

I killed 4 hours/day of manual outreach with one Claude SDR workflow.

Here’s the 6-step setup I run.

Most teams still treat Claude like a smart chat box.

I treat it like an SDR.
With a job description.
With workflows.
With clear targets.

Step 1: Give it a role

Not: “Write me a LinkedIn message.”

I tell Claude:
“You are my AI SDR. Your job is to find warm signals, score leads from 0 to 100, and write personal DMs that show clear buying intent.”

One clear role.
Zero guesswork.

Step 2: Build workflows, not one-offs

I do not re-explain things every day.

I built 3 base workflows:
→ Signal capture: find warm leads from posts, comments, job changes
→ Lead scoring: give each lead a 0–100 score
→ Message writing: write DMs based on the exact signal

Same steps.
Same logic.
Different accounts.

Step 3: Give it memory

Claude needs context.

I store a simple file with:
→ ICP and segments
→ Value prop and offers
→ Tone and style
→ Past winning messages
→ Current campaigns

Claude reads this file before it works.

Step 4: Let it handle low-value work

I did a one-week audit of my time.

Tasks that did not need me:
→ Finding profiles
→ Researching signals
→ Drafting first messages
→ Scoring leads

All of that went to Claude.
I only handle review and replies.

Step 5: Refine weekly

Every Friday, I review:
→ What messages missed the mark
→ What signal logic felt off
→ What small prompt tweak would fix it

The model stays the same.
The system gets sharper.

Step 6: Scale what works

One solid workflow saved 2 hours/week.

So I added:
→ Reply handling
→ Meeting qualification
→ Simple follow-up rules

Now the stack runs most of top-of-funnel for me.

Before: 4 hours/day of manual LinkedIn prospecting.
After: 30 minutes/week reviewing Claude’s work.

Want the exact setup?

Comment SETUP and I will send you:
→ The full 6-step Claude SDR system
→ 5 copy-paste prompts for signals, scoring, and DMs
→ Context file template
→ Weekly refinement checklist

P.S. If you ship this inside your GTM, your outbound operation will not look the same in 30 days.

There are 7 places you can use #AI in your sales motion.

At some deal complexities, AI accelerates everything.
At others, it destroys the one thing that closes deals:
buyer trust.

Here's the full breakdown.

I've been studying how #perception works across B2B buyer interactions, and AI has introduced a pattern I haven't seen before.

For the first time, the tool that saves time is also the tool that can silently kill credibility.

And nobody has mapped where the line is.

So I did.

Save this.
The AI-in-sales debate ends here.

The confusion exists because people argue in absolutes.

"AI SDRs are the future."
"AI emails are spam."
"Use AI for everything."
"Never let AI touch your sales."

None of these are right.

Because AI effectiveness isn't binary.
It shifts based on one variable:

How complex is the deal?

Complexity isn't just ACV.

It's a combination of:
→ Number of stakeholders involved
→ Length of evaluation cycle
→ Depth of customization required
→ Risk the buyer takes by choosing you

A $5K self-serve SaaS with one buyer and a 7-day cycle is low complexity.

A $150K platform deal with 6 stakeholders, a 120-day cycle, and a security review is high complexity.

Same "B2B SaaS."
Completely different AI rules.

Here's what I've mapped across 7 sales activities:

→ Prospecting & list building
→ First outreach
→ Research & personalization
→ Discovery preparation
→ Follow-up sequences
→ Proposal & asset creation
→ Relationship management

At each complexity tier, the right AI usage shifts.

Low complexity:
AI does 80% of the work.
The human reviews and sends.

Medium complexity:
AI does 50% of the work.
The human customizes and adds context.

High complexity:
AI does 20% of the work.
The human leads everything.
AI handles the invisible backend.

The pattern I keep seeing:

Founders at low complexity UNDER-use AI.
They're manually writing emails for $3K deals.

Founders at high complexity OVER-use AI.
They're sending AI-generated outreach to VP-level enterprise buyers who can smell it in the first sentence.

Both are leaving money on the table.

The cheat sheet below maps all 7 activities across 5 complexity tiers.

Plus:
→ Where AI creates leverage vs. where it destroys trust
→ The tradeoff between efficiency and authenticity
→ Specific guidance per tier

This isn't an opinion on whether AI is good or bad.

It's a map of where it works based on what I've observed studying how buyers form trust.

Because trust is structural.
And AI interacts with that structure differently depending on how much is at stake.

Save this.
Audit your current AI usage against it.

You're probably over-using it somewhere and under-using it somewhere else.

I’m not anti-AI. We use it. But not the way most teams do. There are places where it’s incredibly effective, especially around targeting and tightening messaging.

There are other places where it creates more problems than it solves.

The biggest issue I’m seeing right now isn’t the tools themselves. It’s how they’re being used.
Layered on top of each other. Unchecked.
Disconnected from any real system.

After nine years in this space, I’ve yet to see a company using tools like Sales Navigator with true precision across their pipeline.

Not because they don’t want to. But because the system around it isn’t fully built. AI doesn’t fix that. It just makes it easier to hide for a while. Until it shows up in the numbers.

I broke this down in more detail in this week’s article if you want to take a look:

https://lnkd.in/dea4TqPB