Founder — BizDev Labs. Progressive outbound BDR agency (45+ SaaS clients 🚀)

In 2020, I quit my job with $3K in the bank and no plan. 

Two years later, I built a multi-million-dollar BDR agency and earned a spot on Forbes 30U30.

Today I am redefining the role of a BDR. I believe the BDR role is a lifelong career. And my goal is to build the most tenured BDR team on the market.

—-

Outside of work, I was an Olympic-class windsurfer. 

I lived in Israel, Canada, USA, England, Mexico, Hong Kong, Portugal and Aruba.

Roy Itzhaki

R.I.P. Old-School BDR Roles 🪦
The traditional BDR model is broken, and 2025 just buried it for good.

Here’s what’s failing fast:
• Hire 10 junior reps straight out of college
• Pay them $65K to “prove themselves”
• Give them cookie-cutter scripts
• Tell them “it’s a numbers game”
• Use static lists you bulk downloaded from ZoomInfo 
• Watch the best ones leave for AE roles within a year
• Start the same cycle all over again

Here’s how the new model works 👇
→ Smaller teams of elite BDRs (not 10 rookies, just 3 pros)
→ Dedicated pod: call, email, LI, strategy, GTM engineer
→ Deep collaboration with marketing and AEs
→ Flat structure, no “junior” titles
→ High pay, high ownership, high involvement 
→ Built to last, not just to hit this months quota

The results?
• Fewer reps, higher output
• Consistent pipeline month after month
• Institutional knowledge that stays
• Zero ramp when someone leaves (because no one does)

This is the end of “entry-level sales.”
The new era is BDR as a career, not a stepping stone.

Want to see how we built this model?
I’m giving away the full breakdown:
• The Outbound Pod structure we use for 120+ SaaS clients
• Compensation & role design examples

Like this post, comment "2026 model" below, and I’ll DM you the full Blueprint.
The same model that replaced the old BDR model.
[make sure we're connected!]

#SaaS #BDR #Sales #marketing

---

PS. This is the new way for BDRs. Ask me anything...

Roy Itzhaki 

Founder @ BizDev Labs | Forbes 30 Under 30

🚀 SDR/BDR Jobs in the US, January 2026 Edition!

Here’s a curated list of 34 Software & SaaS companies actively hiring for SDR/BDR roles right now:

1. Giga (65 employees) – Sales Rep – San Francisco, CA
2. Prelude (39 employees) – Sales Rep – Remote, US
3. Phamily (178 employees) – Sales Specialist – New York, NY
4. Hypersonix Inc. (103 employees) – Sales Rep – California
5. Wander (215 employees) – Sales Rep – Remote, US
6. PlanHub (183 employees) – Sales Rep – Remote, US
7. Marigold (1,051 employees) – Sales Rep – Remote, US
8. Sigga Technologies (217 employees) – Sales Rep – Remote, US
9. Elaniin (110 employees) – IT Sales Rep – Remote, US
10. Adobe (41,731 employees) – Sales Rep – San Francisco, CA
11. SchooLinks (2,869 employees) – Sales Rep – Austin, TX
12. Precisely (2,948 employees) – Sales Rep – Remote, US
13. Odoo (7,915 employees) – Sales Rep – SF Bay Area
14. Nexthink (1,140 employees) – Sales Rep – Boston, MA
15. Braincube (172 employees) – Sales Rep – Remote, US
16. Securly (331 employees) – Sales Rep – Remote, US
17. WorkOS (108 employees) – Sales Rep – New York, NY
18. Ressio Software (38 employees) – Sales Rep – Washington, DC
19. EngageTech (172 employees) – Sales Manager – Charlotte, NC
20. Oscilar (135 employees) – Sales Rep – New York, NY
21. TORSH (21 employees) – Sales Rep – Remote, US
22. Novatech (746 employees) – Sales Rep – Nashville Area
23. Nimble (135 employees) – Sales Rep – New York, NY
24. Arrow (94 employees) – Sales Rep – Remote, US
25. Thera (23 employees) – Sales Rep – New York, NY
26. Inworld AI (90 employees) – Sales Rep – Mountain View, CA
27. HubStar (55 employees) – Sales Rep – Remote, US
28. AirGarage (107 employees) – Sales Rep – Las Vegas Area
29. SafetyKit (18 employees) – Sales Rep – San Francisco, CA
30. Selector (149 employees) – Sales Rep – Remote, US
31. DNSFilter (171 employees) – Sales Rep – Tampa, FL
32. Timus (48 employees) – Sales Rep – Tampa, FL
33. Loman AI (19 employees) – Sales Rep – Texas, US
34. Intermedia Intelligent Communications (1,301 employees) – Sales Rep – Denver, CO

🔥 Save this. Share it with someone job hunting.

Who else should I add?

EDIT:

35. Sardine is hiring a BDR please DM Eric Zimmerman 🐟 
36. Climate X is hiring SDRs in NYC DM Tom Giles
37.  Hexmodal is hiring SDRs in NYC DM Benjamin Nordwick
 
#saas #salesjobs #techsales #startups

Here’s a list of 20 SaaS companies hiring for BDR / SDR roles in Canada:

1. Club Caddie (50) is hiring a BDR in CA
2. tofu (11) is hiring a BDR in Toronto, ON
3. Basetwo AI (33) is hiring a BDR in Toronto
4. Aragon.ai (14) is hiring a BDR in CA
5. DataFramer (2) is hiring an SDR in CA
6. Folks (68) is hiring a BDR in ON
7. Traild (181) is hiring an SDR in CA
8. CyberQP (41) is hiring an SDR in Vancouver
9. Faronics Corporation (116) is hiring a BDR in BC
10. Great Question (71) is hiring a BDR in Van
11. Swapcard (182) is hiring an SDR in CA
12. Spotwork (39) is hiring an SDR in Toronto
13. Quadient (3) is hiring an SDR in Markham
14. Besty AI (14) is hiring an SDR in GTA
15. TechPOS (33) is hiring an SDR in Vancouver
16. D3 Security (171) is hiring an SDR in CA
17. Mycroft (28) is hiring a BDR in Toronto
18. Huzzle.com (78) is hiring a BDR in Toronto
19. DBSync (112) is hiring an SDR in ON
20. CoLab (177) is hiring a BDR in Toronto, ON

Who else is hiring? Comment below and I’ll add them.

EDIT (added from comments):

21. ServiceTitan DM Tai S. for ENT BD in GTA
22. Spellbook DM Lara Bragan 
23. Ordesa AI

#hiring #bdr #sdr #saas #sales #canada

Two BDRs.
One makes $70K.
The other? $120K+.

Same product.
Same tech stack.
Same sequences.

Why?

It’s not luck.
It’s not the territory.
And it’s not effort.

It’s the STRATEGY behind the motion.

The top rep didn’t start the day guessing.

They started with:

✅ Picking the right channel
✅ Running actual campaigns not static lists
✅ Prioritizing intent signals
✅ Building cold plays with real triggers
✅ Layering in value touch points
✅ Tracking everything weekly as tests

The average rep?

They open Salesforce and just started calling.

📉 Random activity = random results
📈 Strategic activity = predictable pipeline

If you’re trying to build consistent pipeline...

💥 I am sharing our Strategic Outbound OS Framework.
Built from working with 120+ SaaS companies.

Want access? 
Write "Outbound OS" in the comments.

[and make sure we're connected!]

#BDR #Outbound #LeadGeneration #Sales

I didn’t accidentally become a lifelong BDR.
I chose it.

Early in my career, I was told the same thing everyone hears:
“BDR is just a stepping stone.”

Do your time.
Make your calls.
Get promoted.
Move on.

But once I was actually inside the role, 
I noticed something most people ignored.

I was the first person talking to buyers every day.
The first hearing why they weren’t interested.
The first seeing which messages landed.
The first hearing how people describe their problems.

And yet… none of that insight was valued.

BDRs were treated like executors, not thinkers.
Volume mattered more than signal.
Activity mattered more than learning.

That never sat right with me.

So instead of rushing to “escape” the role, I leaned into it:

• I tracked patterns, not just outcomes
• I fed objections back into positioning weekly
• I tested ICPs in real time, not anually
• I optimized order of operations, not scripts
• I treated outbound like a system, not a grind

That’s how you book more meetings.
That’s how you create real momentum.
That’s how you build a repeatable system.

This is also why I stayed a BDR, by choice.

Because mastery compounds.

--

Now let me ask you:

If you’re the one closest to the market...
Why treat your role as temporary?

#BDR #Outbound #Pipeline #GTM #SaaS

Outbound isn’t hard, it’s just built wrong...

Most teams fail because they expect one BDR to do everything,
and then wonder why pipeline is random.

I wish someone had shown me a clear system earlier.
So I built one.

📌 The Outbound Pod System™ Cheat Sheet
Who does what.
Who owns what.
How pay actually works.

All on ONE page.

Save it.
Use it.
Stop guessing.

If you want to see how we run this with real B2B SaaS teams,
it’s all here →

https://lnkd.in/gZVTJ5vA

This photo was taken a few weeks after I quit my job.

COVID had just started.
Everything was shutting down.
And I had no real plan beyond betting on myself.

No runway.
No safety net.
No clear idea how this would work out.

Very few people believed in the move.
Some days, I didn’t either.

If it failed, I genuinely didn’t know how I’d recover.

But I did have one thing most people overlook:

Outbound.

No industry credibility.
No big-name network.
No unfair advantage.

Just the ability to reach out, start conversations, and get real responses.

When everything around me was unstable, that skill stayed predictable.

Here’s the lesson people miss:

If you know outbound, you’re never actually stuck.

You can create surface area on demand.
You can open doors without waiting for permission.
You can build momentum when there is none.

Practically, that looks like this:

* Selling a product with no brand
* Finding your next role without referrals
* Getting time with people “out of your league”
* Asking for capital, partnerships, or sponsorship

Most people wait for opportunities to appear.

Outbound people create them.

That’s why this skill compounds under pressure.

I didn’t survive that period because I was confident.
I survived because I could generate conversations on demand.

Now to you:

If you already know outbound or lead gen, don’t downplay it.
You’re holding leverage most people never bother to build.

#outbound #leadgeneration #bdr #sales #gtm

Everyone calls BDR “entry level.”

It’s not.

It’s just treated like one.

Here’s the reality most people miss:

-> BDRs are the first ones to feel the market.
-> They hear objections before leadership does.
-> They test messaging before marketing knows what works
-> They see which ICP actually leans in and which one looks good on a slide but never buys.

That’s not entry-level work.
That’s front-line strategy.

The reason it looks junior is simple:

• Little context
• No ownership
• No feedback loop
• Paid to “execute,” not to think

So most BDRs are stuck dialing lists they didn’t choose,
pushing messaging they didn’t shape,
for a pipeline they don’t control.

But when you flip that?

When a BDR owns:
• ICP learning
• Signal detection
• Messaging iteration
• Channel strategy

They stop being a stepping stone.
And become a growth lever instead.

The best BDRs I know aren’t trying to “graduate” out of the role.

They’re building unfair advantages inside it.

#BDR #Sales #Outbound #SaaS

If BDR is “entry level”… why is it the hardest role to replace when someone great leaves?

Every company I worked with had the same setup.

• Marketing planned campaigns in theory
• Sales closed whatever showed up
• BDRs sat in the middle, expected to “execute”

But from the inside, I kept seeing something different.

I was the first one hearing real objections.
The first one testing messaging in live conversations.
The first one feeling friction in the market.
The first one seeing which ICP actually leaned in.

And almost none of that intelligence was being used.

So I leaned into it.

Instead of treating the BDR role as a middleman,
I treated it like a market research engine.

Someone who:
• Tests messaging daily
• Listens for patterns, not opinions
• Iterates within the same week
• Feeds real, unfiltered feedback back to leadership

Over time, that does one thing extremely well:
It shows you the path of least resistance to pipeline.

Here’s what I’ve noticed since:

• Most companies don’t have a lead problem
• They have a feedback problem
• They’re slow to learn
• The people closest to the market aren’t empowered to think
• And none of this is even valued as a KPI

That gap is one of the reasons why I started BizDev Labs.

Not just to “book meetings.”
But to help companies learn faster than their competitors.

---

The fastest way to scale revenue isn’t more activity,
it’s faster truth.

#BDR #Outbound #Sales #GoToMarket

What people think successful outbound requires:

• A clean launch where execution works immediately
• Messaging that’s mostly “done” after kickoff
• Targeting that doesn’t need much refinement
• Meetings booked quickly to justify the motion
• KPIs that prove ROI before learning is complete
• Low tolerance for mistakes or missed handoffs
• Systems that run smoothly without senior input
• Confidence that scale comes before clarity

What successful outbound actually requires:

• Senior context early (founder, exec, or product expert)
• Clear ownership of ICP, pain, and buying trigger
• Time to learn what doesn’t work before optimizing
• Fast feedback loops between market and message
• Signal quality prioritized over raw activity
• Space to move from noise → patterns → consistency
• Pressure applied only after repeatability exists
• Trust built through progress, not instant outcomes

Outbound fails when it’s judged like a finished product.
It wins when it’s treated like a system under construction.

If you’ve owned outbound before,
which assumption took you the longest to unlearn?

#outbound #bdr #gtm #sales #leadership #founders

Most outbound problems aren’t execution problems.

They’re thinking problems.

BDRs jump straight to:
emails, calls, sequences, tools.

Before answering the harder questions:

* Who exactly can we sell to?
* How big is the real market?
* Which segments deserve focus first?
* How much runway do we actually have?

Without those answers, outbound becomes random.
Busy, but random.

After working with 120+ teams, the pattern is always the same:
the reps who perform best aren’t “working harder”,
they’re operating inside a clear system.

They know:

* which campaign they’re on
* why it exists
* how much is left
* and whether it’s working

That clarity compounds.

I wrote down the framework we use internally to create that clarity.
Not as a playbook, as a mental model.

If you want a copy of the Strategic Outbound OS Framework,
comment Outbound OS and I’ll share it.

[and make sure we're connected].

#BDR #Outbound #Sales #GTM

Christ First | AE @Airtable | Host - MSP Revenue Secrets

Rhondre Giscombe

I've been studying GTM engineering workflows in my free time.

Across the stack, I'm seeing three layers:

And across the stack it’s Three Layers.

Layer 1: Data
Your CRM. Data warehouse. Product usage data.
The foundation every software company has.
Mostly accessed by Data, Engineering, and some GTMEs.

Layer 2: Intelligence
Where orchestration happens.
Accounts get enriched, scored, routed.
Messaging gets prepped. Emails drafted.
This is where RevOps, Marketing Ops, Sales Ops, and GTMEs live.

Layer 3: Engagement
Where reps actually work.
Most salestech is fighting to own this layer.

I use five tools daily: Airtable, LinkedIn, Outreach, Claude, Salesforce.
Trying to get it down to three. Ideally one.

Here's the thing: the better my intelligence layer works, the less time I waste figuring out who to talk to and what to say.

Intelligence feeds me the signal. I send smarter outreach. That becomes pipeline. That closes.

Am I thinking about this right? What am I missing?

Would love to hear from folks building or operating in this space.

brendan short Brandon Smith Chris Balestras Joe Rhew

Disciple of Christ | GTM @Airtable | Building apps & agents for Operators

I've been trying to understand how Claude Code impacts GTM work. Still early in my research, but one thing keeps standing out.

The compounding intelligence piece.

From what I'm seeing, it's not just about build speed. It's about how context accumulates over time.

Feed it call transcripts and it updates your ICP definition. Feed it more data and it refines what you thought was true. It challenges your assumptions and suggests new angles you might have missed. The system doesn't just store information. It iterates on what you know.

A few other initial observations:

1️⃣ The build speed is real. Clay tables, n8n workflows, JSON configs all ship noticeably faster.

2️⃣ Every Claude Code project pulls context from markdown files. ICP definition, messaging framework, competitor intel. Modularizing your GTM knowledge this way should make outputs more accurate and consistent across projects.

It's designed to compound intelligence, not just automate tasks. The system learns your business over time and gets smarter with every input you feed it.

3️⃣ It's not as "non-technical" as advertised. You still need to understand APIs, JSON structure, and how to set up proper context. There's a learning curve if you haven't touched these concepts before.

Would love feedback if I'm heading in the right direction here.

Matteo Tittarelli ⚡️ Jordan Crawford Jacob Dietle Joe Rhew Taimoor Tariq Javeria Shah

Over the next 4 weeks, I'm building an AI-powered call brief system in Airtable.

Not because I need another project. Because I used to spend 15 minutes before every call scrambling to remember what matters.

Here's what my prep used to look like:

Open the CRM to check deal history. Fire up Perplexity for recent company news. Review Gong history for previous calls. Check product usage data. Skim LinkedIn for the contact's background. Then tie together the core questions I should ask.

Every. Single. Time.

I built a system that does this for me. One hour before any scheduled call, an automation kicks off. It pulls everything I need, synthesizes it with AI, and drops a call brief in Slack with a link to an Airtable interface.

I already use a version of this, but I'm rebuilding it in public so you can see how this actually works in Airtable.

Quick note on scope: This leans into what Airtable does well: centralizing data from different sources. Company news, CRM deal history, Gong call summaries, product usage signals, all synthesized through AI. If you need third-party intent signals (job changes, website visits, LinkedIn engagement), other platforms handle that better.

Before I touch a single automation or field, I need to answer three questions. Skip this, and you'll build something you don't actually use.

⚫️ What's the desired outcome?
✔️ A call brief one hour before every first discovery call. Not raw data. A synthesized view: What's happening at this company? What's our history? How are they using the product? What should I focus on in this conversation?

⚫️ What's required to make this work?
✔️Company news (AI field agents or Perplexity) 
✔️ Deal history (Salesforce) 
✔️ Gong call summaries 
✔️ Product usage data 
✔️ Contact context (LinkedIn) 
✔️ Core discovery questions 
✔️ AI synthesis on what to focus on

⚫️ What does the workflow look like?
✔️ Trigger: Calendar event detected
✔️ Process: Automation pulls data, AI synthesizes the brief
✔️ Output: Slack notification with link to Airtable interface

I open the interface, scan the brief, and show up prepared.

Once I have clarity on those three, then I get into the fun stuff: table structure, automations, integrations, and the interface layer.

Next week: Starting the build. I'll show you the data model and first automation.

Most Airtable users don't know you can enrich LinkedIn profiles natively.

Here's how:

Use Airtable Field Agents to pull LinkedIn profiles as long as you have their Full Name and Company Name.

Back in November, one of my customers was manually finding LinkedIn URLs for 3,000 contacts. Every time a new prospect was logged into their system, they'd pull the profile by hand.

We built with an Airtable Field Agent and ran it across all 3,000 records in 5 minutes.

Coverage landed around 60-70%. Accuracy on matches was about 80%.

Not perfect. But good enough for their use case.

Now every new contact logged into Airtable automatically pulls their LinkedIn profile.

I'll drop the exact prompt in the comments.

Call sentiment alone doesn't tell you if a customer is at risk. 

You need the full picture. 

Airtable integrates with Gong so RevOps teams can cross-reference call transcripts with product usage, CRM health scores, and support ticket trends in one place. 

→ Spot sentiment shifts in calls 
→ Layer in product adoption signals 
→ Surface real risk before it shows up in churn reports 

Complete customer intelligence in one system. 

Check it out today.

I'm going through HubSpot's RevOps Academy in my free time.

As I'm learning more about GTM engineering, I realized I needed deeper RevOps knowledge to understand how revenue operations actually work across the business.

Here's what's landing so far:

1. RevOps makes growth exponential by improving operational efficiency across the business

It's not just sales efficiency. It's marketing, customer success, product, everything that touches revenue.

2. Flywheel model beats funnel model

Customers don't disappear after they buy. They fuel growth if your systems are set up right.

3. Force and friction benefit the organization, even when it doesn't feel like it

The right friction in the right place keeps bad data and bad process from spreading.

4. You need the right number of leads as you scale, not just more
Volume without qualification breaks everything downstream.

5. The buyer's journey: Awareness (problem aware), Consideration (solution aware), Decision (provider aware)

Targeting certification by late Jan / mid Feb.

That said, what else should I be paying attention to? Any core lessons or principles I need to add?

Brandon Smith Jeff Ignacio Haris Odobasic Stefan Mersch 💭 Sebastien van Heyningen

I've been building AI-native apps on Airtable, Lovable, and Replit.

Here are 9 lessons that changed how I approach rapid app development:

1️⃣ Build in layers, not one-shot builds
Nail one component, then move to the next. It's easier to fine-tune and catch issues early.

2️⃣ Clarity of outcome is essential
AI platforms need clear targets. Vague inputs get vague outputs.

3️⃣ Edge cases require knowledge, not just prompts
AI handles standard paths well. Edge cases need platform expertise to close the gap.

4️⃣ Platform flexibility is the real unlock
Airtable lets you reshape your data model mid-build. That flexibility is the foundation of rapid app development.

5️⃣ AI is core to how these platforms work
It's not tacked on, it's foundational to what's possible and how fast you can move.

6️⃣ Advanced functions require platform familiarity
Formulas, automations, and integrations aren't plug-and-play. Expertise accelerates shipping.

7️⃣ Building happens in minutes. Production-grade takes weeks.
The first version is fast. Making it reliable and scalable is where the real work is.

8️⃣ Operators can build without waiting on IT
Quick wins in weeks vs waiting quarters for engineering to pick up your ticket.

9️⃣ The best builds are simple
The apps that stick solve one problem really well.

Rapid app development still requires expertise; it just shifted where it matters. But platforms like Airtable, Lovable, and Replit are unlocking what operators can build on their own.

Build with Airtable today. Link in the comments.

I asked AI to build a competitor to SpaceX.

It came back with a full competitive analysis deck in 20 minutes.

We just launched Superagent, a whole new Airtable product.

I gave it one prompt: "Create a competitive analysis deck of the commercial space market and opportunities for differentiation."

Multiple specialized AI agents deployed. Each tackled a different piece: market analysis, competitive positioning, timeline synthesis, regulatory requirements, technology comparisons.

20 minutes later: rich visuals, interactive charts, full citations. Ready to present.
This isn't another AI summary tool. It's a research and design team working in tandem.

If your team is blocked on competitive analysis or market research that requires real depth, give Superagent a try: bit.ly/3ZC0m40

Most AI roadmaps in 2025 started with the wrong question.

They asked "What can AI do?" instead of "Where are my teams spending time on work that doesn't require a human?"

With AI adoption moving past experimentation in 2026, companies are deploying actual use cases and getting tremendous gains.

So let's look at where AI is getting deployed:

1️⃣ Summarization & Synthesis 
Product teams are condensing product feedback from support tickets, sales calls, and user interviews into actionable themes that inform roadmap prioritization.

2️⃣ Classification & Prioritization 
Product Operations teams are ranking feature requests by revenue association so teams build what impacts the bottom line first.

3️⃣ Personalization at Scale 
Marketing teams at retail companies are generating email copy drafts across multiple campaigns, each one tailored to customer segments and purchase history.

4️⃣ Workflow Automation 
Marketing Operations teams are turning campaign performance data from static reports into automated budget reallocation recommendations for next quarter while there's still time to act on it.

5️⃣ Data Enrichment 
Revenue Operations teams are finding LinkedIn URLs and contact data for prospect lists in minutes instead of hours of manual research.

6️⃣ Generation & Content Creation 
Marketing teams are creating localized campaign content across 12 regions while maintaining brand voice and compliance standards.

If you're building an AI agenda in 2026, start with the workflows you already have. The best AI implementations don't require new systems. They make your existing ones smarter.

Harvard studied 100,000 AI agent users. The pattern is clear: AI that's siloed in chat gets abandoned. AI that's integrated into actual workflows gets stickier.

The reason?

Context engineering.

Most companies buy AI tools that can't touch their CRM, their Google Drive, or their project tracker. Then they wonder why adoption stalls.

Here's the thing: AI's real value isn't answering questions. It's completing tasks. But to complete tasks well, it needs the right information at the right time. When AI is disconnected from your tools, you're getting 10% of its potential. You're stuck with a Q&A bot, not a system that can actually execute.

The breakthrough happens when AI can pull context from across your tools, take action based on that context, and learn as it completes more tasks.

This is why platforms that integrate deeply with Google Drive, email, and project management tools see higher stickiness. Users don't just ask questions. They complete workflows. The AI has context, so it can act. And as it acts more, it gets better.

When evaluating AI tools, ask yourself: Can it access the context it needs? Can it take action, or just suggest it?

Context plus action equals 10x the impact of chat alone.

(Read Will Sullivan’s full breakdown of the Harvard study, link in comments)

Week 1 of building the call brief system. If I mess up the data model now, I'll be fighting this thing for months.

And the whole point of this is to stop scrambling before calls, so I need the data to flow cleanly from the start.

I'm building seven core tables:
1️⃣ Calls (the main hub): Tracks every scheduled call. Links to Call Briefs, Contacts, and Accounts.
2️⃣ Call Briefs (the generated output): Stores the synthesized intelligence for each call. One brief per call. This is what I actually read before the call.
3️⃣ Accounts: Company-level data. Links to Deals, Product Usage, and Company Intelligence.
4️⃣ Contacts: Individual people I'm talking to. Linked to Accounts and Calls.
5️⃣ Deals: CRM history. Deal stage, notes, last activity. Linked to Accounts.
6️⃣ Product Usage: For existing customers. Bases, users, activity trends, automations created. Linked to Accounts.
7️⃣ Company Intelligence: AI-pulled company news, strategic priorities, recent developments. Linked to Accounts.

Each table does one thing. Calls are for tracking the event. Call Briefs store what I actually need to know. Accounts are where all the context lives.

Calls and Call Briefs are linked one to one, so every call gets exactly one brief. There's no duplicates or confusion on which brief belongs to which call.

Linked records mean when I update deal history in Salesforce, it flows through to the brief automatically. When product usage data refreshes, the brief reflects it. I'm not copying and pasting data across tables.

The Accounts table is the hub. Everything connects through it. Company Intelligence, Product Usage, Deals, all linked to Accounts. So when I pull a call brief, it knows which account I'm calling and it pulls everything associated with that account.

I tested this with a fake call record. Linked it to a contact and an account. Added some deal history. Data flows where it should.

Next week: Building the first automation. Can I actually pull company news and deal history on a schedule?

If your tables don't talk to each other cleanly, your automations will break. And you'll be right back to scrambling before every call.

Built this entire base structure with Airtable's Omni in 10 minutes. The hard part isn't building the tables anymore. It's knowing how they should connect.

Most teams are deploying AI without asking the right question.

It’s not “can we automate this?” 
It’s “what are we willing to lose?”

I’ve been building with AI for months now, across customers I consult with at Airtable and my own projects. And I keep seeing the same pattern.

AI is incredible for volume and speed. If you need 500 product descriptions written, or you’re generating e-commerce images at scale, it’s a no-brainer. The output is good enough, and you get it fast.

But the moment you need something that reflects your brand, your specific visual identity, your tone, your creative direction, AI falls short.

I’ve tested this with podcast graphics, brand assets, creative campaigns. The output is generic. It doesn’t carry the theme. It doesn’t have taste.

Here’s the tradeoff most people don’t think through. AI gives you efficiency, but it costs you originality. It gives you speed, but it costs you brand equity.

And that’s fine if you’re optimizing for volume. 
But if what you’re building is brand-critical, you can’t afford that loss.

Before you automate something, ask yourself: 
- What am I optimizing for? 
- Speed or originality? 
- What’s the tradeoff?
- What do I gain, and what do I lose?
- Can I live with that loss?

Zoom in on the task. Zoom out on the impact. 

Then decide what you’re willing to sacrifice.

Not everything should be automated. Some things still need a human in the loop.

Prob one of the best decisions I made in 2025. 

Switched from Apple Podcasts to Snipd. 

I listen to 10-15 hours of podcasts a week across faith, mindset, leadership, AI in business, RevOps/GTME. 

Snipd lets me capture insights as I go. Nothing disappears into the ether. 

Check it out if you aren't using it already.

Over the past year and a half, I’ve had the privilege to work alongside Alex. 

Among other things, three things stand out:

1 - He’s a leader that is in the trenches with you

2 - He’s a leader that challenges growing in your skillset

3 - He’s a leader that cares not just for your performance but, you personally

Joining his team is easily one of the best decisions I’ve made since, moving to ATX.

Apply today 👊🏾

P.S. You’re welcome to hmu too

Commercial Sales Leader @ Airtable | AI + Automation ⚡️

My team is growing! 🚀 I’m looking for a seller with natural curiosity, a strong desire to learn, and the drive to make a real impact. This is a hybrid role for someone who enjoys winning new business and thoughtfully expanding existing accounts over time.

You’ll partner closely with customers as they scale, helping them unlock more value with Airtable through strategic, long-term growth.

Airtable is in a pivotal phase as we build toward IPO, and this role offers real ownership and the chance to help shape what’s next.

📍 Austin (Hybrid)

If this sounds like you (or someone you know), I’d love to connect. Feel free to send me a DM.

Account Executive, Commercial Accounts  

How many internal tools is your team building that you could've bought off the shelf?

And what customer value did you trade for that distraction?

Kyle Norton talked about this on his podcast with Everett from Clay. He broke down a framework on GTM Engineering that clicked for me:

1️⃣ Buy is about 
→ Speed 
→ Broad organizational impact 
→ Resources required to support it

2️⃣ Build is about 
→ Fast execution 
→ Low org impact 
→ Or high enough strategic value to justify dedicated resources

Most things? Buy them. Very few things should you actually build.

If you're a sales tech company building an internal HR tool, you're spending engineering cycles on something Deel or Rippling already solved. That's a distraction, not a strategy.

HubSpot CTO Dharmesh Shah put it clearly: "Spend every calorie possible on creating value for your customers."

Build only when it's so unique to your workflows that it becomes a strategic asset. Something no off-the-shelf tool can do.

Everything else? Buy it and move on.

Stop writing better prompts. Start building better context. 

Most teams think the problem is their prompts. They're tweaking inputs and refining instructions, hoping AI gets smarter. It doesn't work that way. 

AI gets smarter when it has better context to work from. 

The problem is your data lives everywhere. Campaign performance in one tool. Customer intel in another. Product catalog somewhere else. 

When you ask AI to help, it's guessing because it can't see what worked last quarter or connect your product data to your customer data to your performance data. 

Every project starts from scratch and every output feels generic. 

Airtable changes this because it acts as the context layer. When everything lives in one system, AI has the full picture with relational data, linked records, and historical performance tied to real-time inputs. 

I built a demo for a fashion brand that was spending weeks aligning on every creative brief. Past performance was buried, brand guidelines lived in someone's head, and competitor research was scattered across tools. 

We moved everything into Airtable as their single source of truth. 

Now when someone starts a brief, they input the basics and the system pulls the rest: past campaigns, personas, product data, and competitive signals. 
AI drafts using what actually happened before instead of generic best practices.

Performance feeds back after launch, so the next brief is smarter because of it. 

The operators seeing results from AI aren't writing better prompts. They consolidated first and stopped fighting tool sprawl by building on one foundation. 

AI doesn't improve because the model changed. It improves because your context got better, and your context gets better when it lives in one place.

One of the features I've been pushing to every customer I work with.

With the latest improvements Airtable just shipped, there's never been a better time to explore what's possible with custom elements.

Network graphs. 3D models. Custom dashboards. If you can imagine it, you can probably build it.

Check out what builders are creating…

Introducing Built with Omni: a new showcase for builders to share bespoke AI apps 🚀

Get inspired: Browse bespoke elements created by Airtable builders.

Learn: Inspect real code and prompts to uplevel your app building skills.

Contribute your creations: Share your own apps and interfaces for others to learn from.

Plus, we launched a few key improvements to the custom elements experience:
✔️ Code inspection
✔️ Public interface support
✔️ Multi-developer support

Dive into Built with Omni →   https://lnkd.in/gDY2Kdij

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The Battle

Roy Itzhaki

Rhondre Giscombe

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Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Roy Itzhaki's LinkedIn Performance

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