I tested 14 AI SDR tools so you don’t have to. 👀
And honestly…
Most of them promise “AI-powered outbound” but only a few actually move pipeline.
Here’s the breakdown after testing them for prospecting, personalization, follow-ups, lead qualification, and meeting booking:
1. Agent Frank
Best for: Adaptive AI outreach
→ Learns your sales style over time
→ Smart tone shifts & follow-ups
→ Gets better with more usage
Not ideal for teams wanting plug-and-play workflows.
2. Knock AI
Best for: Converting inbound leads fast
→ Works across Slack, LinkedIn, WhatsApp & email
→ Qualifies and books in real time
→ Great at speed-to-lead automation
Needs inbound traffic to really shine.
3. AiSDR
Best for: Full outbound automation
→ Multi-channel outreach built in
→ Dynamic messaging scripts
→ Lead scoring enrichment included
Powerful, but setup can feel heavy.
4. MeetChase AI
Best for: Booking qualified meetings
→ Smart calendar routing
→ Handles timezones automatically
→ Focused on meeting quality over volume
Weak for top-of-funnel prospecting.
5. Artisan AI (Ava)
Best for: Personalization at scale
→ Huge contact database
→ Strong sequence builder
→ Deep multi-channel outreach
Can overwhelm smaller teams.
6. 11x ai (Alice)
Best for: Enterprise SDR automation
→ Tracks buyer intent in real time
→ Personalized outreach using company data
→ Handles objections automatically
Very expensive, but extremely powerful.
7. Piper by Qualified
Best for: Website lead qualification
→ Engages visitors instantly
→ Feels like a real SDR
→ Books meetings automatically
Only works well with strong site traffic.
8. Jason AI by Reply
Best for: Automated outbound workflows
→ Combines prospecting analytics
→ Easy to customize
→ Good automation/control balance
Personalization still feels templated sometimes.
9. Salesforce Einstein / Agentforce
Best for: Salesforce-native teams
→ Smart lead routing
→ Deep CRM integration
→ Learns from deal data over time
Overkill for smaller teams.
10. Saleshandy
Best for: Lightweight cold email outreach
→ Fast setup
→ Simple sequences
→ Great for founders & solo operators
Limited beyond email.
11. Apollo
Best for: Lead sourcing enrichment
→ Massive contact database
→ Built-in sequencing
→ Strong prospecting workflows
UI can feel cluttered fast.
12. Waalaxy
Best for: LinkedIn automation
→ Easy connection workflows
→ Great for LinkedIn outreach
→ Beginner-friendly setup
Not a full outbound system.
13. Dialpad
Best for: AI sales calls
→ Call intelligence & tracking
→ Conversation insights
→ Useful for coaching reps
More call-focused than SDR-focused.
14. Seamless ai
Best for: Finding lead data fast
→ Reliable contact discovery
→ Good enrichment engine
→ Easy integrations
Data quality depends on the niche.
My biggest takeaway?
The best AI SDR tool is NOT the one with the most features.
It’s the one that:
→ finds qualified leads faster
→ creates natural conversations
→ and saves your team hours every week
Right now, Agent Frank Knock AI impressed me the most.
But honestly AI SDRs are quickly replacing repetitive outbound work.
Soon, manually prospecting every lead will feel outdated.
Which AI SDR tool are you using right now? 👇
Welcome Salesforce Headless 360: No Browser Required! Our API is the UI. Entire Salesforce & Agentforce & Slack platforms are now exposed as APIs, MCP, & CLI. All AI agents can access data, workflows, and tasks directly in Slack, Voice, or anywhere else with Salesforce Headless 360. Faster builds, agentic everything. 🚀
#Salesforce#Agentforce#AIsalesforce.com/headless/
6/9~6/10にザ・プリンス パークタワー東京で開催された「Agentforce World Tour Tokyo(AWTT)」に登壇された、ゆうせい(@dx_yusei )、Megumiさん(@megono_tableau )がゲストとのこと🎤
#AWTT 参加できなかったのでありがたい!!!🙌🙌🙌
#たぶラジ
As organisations accelerate adoption of AI, cloud, and emerging technologies, the conversation today is no longer just about transformation - it is about enabling trusted and responsible transformation at scale.
Organisations need to implement secure-by-design practices, continuous monitoring, automation for rapid response action as well as technology-enablement of governance for machine speed decisioning to confidently innovate with greater speed, resilience, and trust. In a cloud environment, one additionally requires a clear understanding of shared responsibility model, deploying right security controls and crucially maintaining unified view of the entire environment: @kpande2, @KPMGIndia.
We're pleased to collaborate with @Salesforce on a co-branded discussion paper launched at the 𝗔𝗴𝗲𝗻𝘁𝗳𝗼𝗿𝗰𝗲 𝗪𝗼𝗿𝗹𝗱 𝗧𝗼𝘂𝗿 𝗠𝘂𝗺𝗯𝗮𝗶 on '𝗗𝗿𝗶𝘃𝗶𝗻𝗴 𝘁𝗿𝘂𝘀𝘁 𝗮𝗻𝗱 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝗰𝗲 𝗶𝗻 𝗱𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻: 𝗔 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 𝗳𝗼𝗿 𝗜𝗻𝗱𝗶𝗮.'
Arundhati Bhattacharya
#SFDC#Salesforce#agentforce#AWT2026#FinancialServices#AI#SecureByDesign
Meet your new guide to Tableau Public. 👋
Powered by Agentforce, the Help Agent answers your questions, surfaces inspiring vizzes, and helps you explore everything Tableau Public has to offer—so you can start creating faster. tabsoft.co/49WBslm
$CRM Salesforce isn’t collapsing—it’s being tested.
The stock fell nearly 9% this week, but the bigger story is AI.
Investors are questioning whether Salesforce can grow fast enough as OpenAI, Anthropic, and other AI platforms reshape enterprise software.
At the same time, Salesforce is doubling down:
• Agentforce AI ARR has surpassed $1.2B
• AI and Slack usage continue to grow rapidly
• The company is restructuring and shifting resources toward AI
• It also owns a valuable stake in Anthropic
The market is worried about slowing growth in legacy products like Tableau, Marketing Cloud, and Commerce Cloud, not about the company’s survival.
For long-term investors, the key question is simple:
Can Salesforce evolve from a traditional CRM leader into an AI-first enterprise platform?
If Agentforce and Data Cloud continue gaining adoption, today’s weakness could look like a period of transition rather than a permanent decline.
If you hold $CRM or any legacy SaaS, Whale Rock's Q1 short thesis is worth understanding before your next earnings call.
On @InvestLikeBest, Alex Sacerdote listed the four compounding headwinds: AI tokens eating IT budget faster than renewals, seat shrinkage from hiring freezes, an inability to raise prices under competitive pressure, and AI-native startups rebuilding verticals without technical debt.
"The old way of software is like a horse and buggy. The new way is a jet engine."
Sacerdote explicitly says Salesforce's $40B revenue base with $500-700M in AI ARR is the template for why the offset isn't working yet.
Four headwinds and what a potential cover signal looks like: podcastalpha.substack.com/p/…
Source: Invest Like The Best - youtube.com/watch?v=DZt1DDmM…
Salesforce sold Agentforce as the enterprise AI product. That product was not ready.
Dan Nathan raised Salesforce on @RiskReversal June 10 as the cautionary case for enterprise AI that ships a brand before it ships a working tool. The risk: customers buy the narrative, run a pilot, and when the pilot underdelivers, the credibility of the broader enterprise AI wave takes a hit.
This matters beyond $CRM. Every enterprise software company is repricing on AI potential. When a flagship AI product from a marquee vendor disappoints at scale, it raises the bar of proof for every vendor behind it.
Ives named Copilot and Agentforce in the same breath - both products that needed third-party intervention or replacement to function as advertised.
What this implies for how to read enterprise AI product claims in Q3 earnings: podcastalpha.substack.com/p/…
Source: RiskReversal Media - youtube.com/watch?v=uaMpnRch…
¿Puede un CRM funcionar en el agro? 🐷🐔
Italcol demostró en el Agentforce World Tour Bogotá que la transformación digital no es exclusiva de la banca o el retail.
Con una estrategia diseñada para operar offline y un proceso de codiseño junto al personal de campo, la compañía alcanzó un 95 % de adopción digital en sus operaciones.
Conoce las claves de este caso de transformación aquí: zurl.co/tlsIe#CasosDeÉxito#TransformaciónDigital
Is this bullish SaaS/companies that productize AI? Like if Agentforce / ServiceNow get guaranteed access to the best models then you know it’ll work for your enterprise use case, but if you DIY you’re stuck with a increasingly less capable open weight model, or risky frontier API that can get pulled at any moment.