The Army of AI Agents That Turn Your Sales Conversations into Action
Attention x The Signal
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Hey, y’all 👋
I recently caught up with Anis Bennaceur, Co-founder & CEO of Attention, and I came away genuinely impressed by what they're building. In an era where everyone claims to have 'AI agents for sales,' Attention is actually building something far more robust — an end-to-end solution that's redefining how sales teams operate across the entire cycle.
I have talked about a new crop of GTM tech products that are building AI-first - I would put Attention in this bucket. Plus, Anis is building their company in person in New York City, which is always a good sign of a company and their potential trajectory (a contrarian bet that I think will pay off in the long run).
Last month, I published an article explaining Jeff Bezos’ analogy of how AI is not like the gold rush, but is more like electricity - it’s a horizontal layer that is going to touch everything. Bezos said, “there is not going to be a web application that AI doesn’t touch that isn’t made better as a result.”
Attention is a great example of a product that's helping leverage the power of AI in a bunch of little ways that help a company do a lot more with fewer people (helping increase ARR per rep). They’re truly innovating here. So I'm excited for today's deep dive post.
Here's what we'll cover in today's post:
Building an Army of AI Sales Agents
Moving Beyond Note-Taking to Task Execution
AI Agents for Bottom-of-Funnel
The Bet on AI to Help Drive EBITDA Improvements
Real-World Use Cases Transforming Sales Teams
Let’s dive in.
The Army of AI Agents That Turn Your Sales Conversations into Action
Most people building go-to-market tooling are obsessing over third-party data. But, there is a goldmine of information in every company’s systems already: customer conversations, emails, and meeting transcripts.
The problem is - this data sits dormant (becoming less useful over time as it collects dust in the corners of the CRM or CDW or otherwise), and it is distributed across disparate systems and “objects.” Attention is activating this data. Mining for the interesting nuggets and then operationalizing them, in real-time. That’s the vision they’re realizing, by building a system of AI agents that don't just capture sales conversations—they automate the work traditionally done by the best enablement analysts, RevOps specialists, and top performers.
The goal? Help GTM orgs achieve 10x results with just 10% of the workforce.
Building an Army of AI Sales Agents
Incumbent call recording platforms are great note-takers. They capture conversations and provide summaries, but they're limited in what they can do with that information. Attention sees conversation data as just one of many inputs that can be orchestrated to perform actual work.
They’re creating an entire army of AI agents that are each good at doing one or a few things. These agents aren't just dumb automation tools—they're intelligent systems that can independently reason and execute tasks with minimal human guidance.
Imagine asking your AI agent: "Give me a report of competitors mentioned in our deals last quarter, how they're being perceived, their positioning, strengths and weaknesses, and how often they appear in conversations." Within seconds, you'd have a comprehensive competitive analysis that would have previously taken hours, if not days, to put together manually (I know, I’ve done it). This is magic.
Moving Beyond Note-Taking to Task Execution
Old world: typing my demo notes into Salesforce at 9pm while eating dinner and sipping a beer.
Slightly better world: call summaries are emailed or put into the Salesforce fields automatically, while eating dinner and sipping a (NA) beer.
The world Attention has built: action items from my calls kick off AI workflows and/or AI agents that do the tasks on my behalf, while eating dinner and… sipping bone broth (or some other aspirationally healthy drink).
This future world is being built by companies like Attention.
These agents are orchestrated by what Anis calls a "system of cognition"—an intelligent layer that coordinates their activities and enables them to collaborate. The power comes not just from individual agents, but from how they work together to solve complex problems. This is a wild world we’re going into, and Attention is on the cutting edge here.
Here are a couple of examples:
After a discovery call, an agent can automatically create a tailored sales deck incorporating the prospect's specific pain points, business goals, and objections mentioned during the conversation. It can pull from a library of templates and update them with relevant information, even generating custom charts and visuals that address the prospect's unique situation. (What I would have done to have this when I was personally doing 5+ demos a day, many years ago.)
When a customer mentions a pain point related to a different product in your suite—one that's sold by a separate team. In the old world (pre-AI), this information would probably be lost if the sales rep is focused solely on their own product (they may not have any incentive to dig into the pain point mentioned because they know they can’t sell the product to solve it). Attention can automatically identify this cross-selling opportunity and pass that intelligence to the appropriate team, saying, "Here's a related pain point for a product you're selling—consider reaching out to this person/company."
The level of agency these systems have continues to increase. You can start with more human involvement and gradually remove it as the system learns your preferences and improves its performance. I think there’s a world where agents become digital coworkers in a similar way to human coworkers - eg: do something a few times, then document the process and hire someone to do it going forward. Instead of passing that document to a human worker, we may build/train an agent to pass that work to. Attention is building towards that new world.
This approach resolves one of the biggest challenges in sales tech today: the gap between insight and action. Most tools tell you what happened, but few truly help you do something about it.
AI Agents for Bottom-of-Funnel
While everyone is obsessed with top-of-funnel AI tools, Attention is one of the few companies deeply focused on the bottom of the funnel.
There is an incredible wealth of customer voice data distributed across deals and opportunities, but until now, there's been no effective way to extract, analyze, and distribute this intelligence across the organization.
Traditionally, this process has required humans to review the conversation that the “gen 1” call recorders would highlight. But there is an entire second part to the work - a sales manager or sales enablement specialist has to review conversations, extract insights, and build reports that are outdated almost as soon as they're created. It's slow, inefficient, and misses a ton of valuable information. Attention aims to execute the second half of this workflow, autonomously.
Imagine a sales rep talking to a prospect who mentions they're not interested in Product A but describes a pain point perfectly addressed by Product B (which is sold by another team in your organization). In most companies, this valuable cross-sell opportunity is completely missed unless the rep manually passes this information along—which often doesn't happen due to time constraints, focus on their own product line, or simply lack of incentives.
With Attention, this valuable intelligence is automatically captured and routed to the appropriate team: "Hey, there's this related pain to the product that you're selling; you should reach out to this client immediately."
This isn't just incrementally better—it's a fundamentally different way of thinking about how customer intelligence flows through an organization (think: electricity).
The Bet on AI to Help Drive EBITDA Improvements
There's a larger economic reality driving the adoption of tools like Attention. When I talked with Anis, he called out that many large companies are focusing intensely on EBITDA improvement—not just traditional ARR growth. This mandate typically comes from the very top—CEOs and founders—rather than director or VP levels (who often have incentives tied to the size of their teams). Attention positions itself as the ideal solution for this new economic reality: the same results with a fraction of the workforce.
Anecdotally, I’ve heard this more and more lately (a desire to scale go-to-market through automation rather than headcount). It's not about replacing people entirely, but about making them dramatically more effective with AI augmentation.
Real-World Use Cases Transforming Sales Teams
I don’t envy the product marketers at Attention. Because the product can do so many things!
So, before closing out this post, I want to highlight a small sample of tactical use cases that Attention’s AI agents solve (and, even outside of using Attention, hopefully, this showcases some ways that AI can be an incredibly good ‘partner’ for your GTM org):
One-click sales collateral generation: After a discovery call, automatically create a tailored sales deck that incorporates the prospect's specific pain points, business goals, and objections mentioned during the conversation.
Competitive intelligence automation: Receive weekly reports on competitors mentioned in deals, including how they're perceived, their positioning, and the frequency of mentions—all without manual analysis.
Closed-won/closed-lost analysis: Instead of spending days manually reviewing won and lost deals, get comprehensive insights in minutes on why deals are succeeding or failing.
Automated call scoring: Evaluate rep performance based on best practices without requiring managers to listen to hours of calls.
Cross-selling opportunity identification: Automatically identify and route opportunities mentioned in conversations that might be relevant to other teams or products.
Business case generator: The agent compiles a comprehensive business case document based on all conversations with an account, extracting the specific pain points, quantifying the impact, and building a compelling ROI model.
Content gap analysis: Identify questions from prospects that reps struggle to answer effectively, highlighting needs for new content or training.
Outbound signal detection: Extract compelling events from prospect conversations to inform outbound strategies, like "Company X just lost their growth marketing manager and needs to get pipeline back in order."
Brand perception tracking: Monitor how your positioning against specific competitors evolves over time, with insights drawn directly from customer conversations.
The possibilities are endless.
Attention has been consistently first to market with innovations in this space. They were the first company to auto-fill CRMs back in early 2023, and today they're the first with truly functional AI sales agents. I’m excited to keep seeing Attention push the boundaries in helping make GTM teams more effective over time!
If you’re looking for ways to have AI/agents augment your bottom-of-funnel, Attention is worth checking out.
As always, thank you for your continued attention (pun intended) and trust — I do not take it for granted.
See you next time,
Brendan 🫡