SALES TECH

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Sales teams can field a high number of calls each day, and relying on AI summaries has become increasingly important.

Matt Oess, CEO of sales intelligence platform Revenue Growth Agent, penned an article this month titled “Why B2B sales teams need more than AI call summaries to improve deal decisions,” stressing that sales teams need to hold these summaries to a higher standard to get the most out of them.

“Sales teams have largely solved the call-capture problem, but they have not necessarily solved the decision problem,” Oess said in a statement. “A polished summary can tell a sales rep what was discussed without revealing whether the opportunity is actually qualified or whether the seller is relying on assumptions the buyer never confirmed.”

Sum it up: Oess says clean copy and accurate transcripts should be the bare minimum for AI call summaries. Instead, sales teams should use the recordings to determine if there are any risks, opportunities, and what next steps need to be taken.

Oess said deal intelligence can analyze buyer concerns, questions, and other sentiments and flag what is important and requires a rep to take action.

“Capturing what the buyer said is the foundation,” Oess said. “Deal intelligence comes from determining what those statements reveal about business value, qualification, risk, and the actions required to move the opportunity forward.”

He stressed that this can also address discrepancies between what a customer said and how a rep interpreted the conversation.

“Sales teams need AI that challenges assumptions instead of reinforcing them,” said Oess. “If the buyer did not confirm the urgency, quantify the impact, or explain how a decision will be made, the seller needs to know that before treating the opportunity as qualified.”

Read more on the AI tools available to sellers.—LI

      

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REVENUE MARKETING

Noah Marbach

Building an AI platform might not be every high school student’s extracurricular activity, but it was just the right move for 19-year-old Noah Marbach. After being sidelined with a sports injury, he launched employee scheduling and workforce management platform XShift AI during his senior year of high school last November.

The idea for the platform came from his experience working as a lifeguard and in restaurants where he saw pain points in employee scheduling and management.

Revenue Brew sat down with Marbach to talk about building XShift AI and his goal of becoming one of the world’s youngest billionaires.

This interview has been edited and condensed for clarity.

How did you come up with the idea for XShift AI?

I saw firsthand how brutal employee scheduling is for managers, and not just employee scheduling, but building schedules, handling the callouts, enforcing budgets, communicating with team members—it’s all still manual. I know there’s obviously competition on the market when we started. There’s multiple billion-dollar companies competing in the shift-based industry, but they all still force managers to build the schedules manually. They all still force managers to validate all these constraints manually. A good example is when you’re building a schedule—typically it varies based on the industry—but you’re validating all sorts of constraints when you’re building a schedule: employees’ availability, reliability, preferences, budgets, overtime, PTO, and various other custom policies. You’re validating all these constraints, and that’s very time-consuming for companies.

Read about the company’s growth since launch.—LI

      

CYBERSECURITY

Illustration: Morning Brew Design, Photos: Adobe Stock

Before a business like a consultancy or consumer mortgage lender rolls out a new agentic system, they might turn to Scale AI to try to break it.

The startup, which has tried to remake itself after a Meta quasi-acquihire last year, keeps a stable of human red teamers who simulate the intentional or unwitting antagonists an AI agent might face in the wild—casual users, trolls, hackers, or misaligned agents.

“Are [users] encouraging an agent, and is the agent simulating access it shouldn’t have? Is it claiming that it’s a human when it’s not? Is it granting a loan on terms that would be illegal in an enterprise context?” Patrick Oathout, Scale’s red team and safety lead, told us of scenarios they simulate.

Exercises like these are even more important in the agentic era, when companies may not start with a comprehensive picture of all the ways networks of autonomous bots can go awry, according to Oathout. Case in point is a headline-grabbing string of incidents in which sophisticated AI agents went rogue, slipping out of test environments and conducting complex attacks, which caught even their leading-edge makers off guard.

A recent survey from observability vendor WanAware found that nine in 10 IT leaders said they could root out rogue agents, but only 26% thought they could measure their impact within minutes. Meanwhile, a CrowdStrike report said the amount of risky behavior detected from agents is growing at 2.5 times the rate of that from humans.

Keep reading for more on guardrails and auditing.—PK

      

active pipeline

Stat: $87,000. That’s how much bitcoin jumped past, marking the highest value since late January. (Bloomberg)

Quote: “AI will quickly spin out of human control and could kill us all if we allow AI safety and protocol...to be controlled by private self-serving agreements between the world’s most powerful ‘for-profit’ technology companies.”—Nick Rowley, lead attorney for the plaintiffs in a lawsuit against Anthropic, OpenAI, SpaceXAI, and Google (Associated Press)

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Forget sifting through endless listings. Revenue Brew and CollabWORK highlight only a handful of roles each week, chosen for your interests and industry. Click here to view the full job board.