Everyone is Counting the Work that Disappears

Across a podcast, an interview, and a ServiceNow webinar, Signal Labs CEO Rajeev Ronanki traces one failure. Enterprises keep measuring AI by the work it removes, while the decision that sets returns is what the organization agrees to pay attention to.

"Clearance rack."

That was Chad Sowash's tongue-in-cheek comment on the recent acquisition of BrassRing, nine minutes into a high-energy Chad & Cheese interview with Signal Labs founder and CEO Rajeev Ronanki. The show’s co-host Joel Cheesman asked: how does $25 million buy the ATS and talent acquisition platform that runs recruiting for many enterprises, including plenty of Fortune 500 organizations?

In the video below, Raj corrected the premise, and the correction turns out to be the better story.

Watch the turnstile

The $25M wasn’t the price of the acquisition, but early total contract value already attached to Signal Labs, when it launched in April 2026. The company is backed by Lightspeed Venture Partners as well as private investors, and a handful of marquee customers signed on as design partners when Signal Labs came out of stealth. As Raj described it, partners such as Infinite Computer Systems "made a bet on Signal Labs," on the view that an asset they had treated as one worry among many could be front and center somewhere else.

Joel went for the deal terms. No dice, as Raj shared smiling, "Well, terms weren't disclosed for a reason.”

What the deal actually buys is a turnstile. For more than a quarter century ATS and talent platform leader BrassRing processed more than 350 million applicants, recording two things that matter most about a workforce over time: who came in, and how long they stayed. When Chad pressed Raj on data quality, Raj remarked on the limited value of resumes and disposition codes. Instead, what interests Raj is "the singular act of the signal of you're hiring someone and the number of people you're hiring," and then the outcome on the other side: how many left, how long they lasted, how effective they were.

This turnstile becomes useful the moment enterprises stop focusing on headcount and start accounting for capacity. Raj traced this idea back to a customer conversation, where Signal Labs clients kept naming BrassRing as the system they used for hiring. So, he asked them what happens when they put AI agents next to people, with physical AI & robotics arriving later, where all of it should managed on one ledger?

Raj heard crickets. That came from many different HR leaders.

Nobody should read that as HR failing to keep up. It matches Raj's description of the function being handed "an order to go do something" instead of shaping the workforce as a thought partner. A turnstile only earns its keep if somebody in the building has decided what the building is for, which is the subject of the second conversation.

Pilot zombie land: why enterprise AI pilots stall

This clip tells a story, crucial for company leaders. Annual AI token budgets burned through in months, where one well-known enteprise spent $500 million in just 30 days. So many organizations have no AI spend caps, and roughly 90% of companies are not seeing ROI on their spend.

Recent numbers put that story in company. Gartner reported on September 1 that only 22% of the 1,303 companies it surveyed have scaled AI across multiple business units, while 85% of functional leaders plan to spend more in 2026. The math is moving the wrong way, as a single task routed to an agentic reasoning model costs at least five times a basic chatbot request. The consulting leader expects cost per agentic workflow to rise more than fivefold through 2028.

Raj sees two related problems:

1. You take on an AI project and cannot find the ROI in it, or
2. you get stuck in what he calls pilot zombie land, running lots of interesting proof of concepts and pilots that never scale: a hangar full of aircraft, fueled and staffed, none of them cleared for takeoff.

Both come from "trying to shoehorn AI into the existing structures of an enterprise." These are structures that are older than many of the people who work in them. Departments, processes and functions date back to the 1980s, when computers arrived to automate paper-based workflows.

Raj reaches for Mad Men, making it physical: work shuffled between floors in big brown envelopes until computers made that shuffling obsolete. The envelopes are gone, and still the floors they traveled between remain standing. While tools have automated the same routes faster, AI is being pointed at the same job.

When AI takes over more of the doing, the coordination or ‘talking’ between departments drops off, and the context goes with it. Every data silo gets deeper, with better tools and worse hearing.

"So inadvertently AI is leading to a level of incoherence because of the way that companies are organized. The work itself is organized incorrectly to put AI into it."

Raj Ronanki, CEO, Signal Labs

Emily Binder, Signal Labs CMO, and the inteview host, recalls the early 2000s and the scramble to mobile optimize websites. Taping a desktop layout onto a phone screen gave companies a meaningful lesson that the interaction itself had changed.

Raj refuses the framing that treats AI as the newest tech upgrade. Instead, he takes a bold step further, saying "It's fundamentally a new form of intelligence," and giving each department AI with the wrong framing wastes it.

Next: fresh questions about customer service and a tie-in to operating model and organizational reimagination.

Seventy percent of the calls

The third conversation shows what happens after a company does automation well. Raj is with Milind Shah, Head of Payer at ServiceNow, on healthcare AI transformation. The two worked together at Elevance.

In 2021 and 2022, the large payer set a goal of automating roughly 70% of inbound calls. This was before generative AI, and they pulled it off. Today, Raj shares, “I think the question is what level of automation should be the target.”

For a health plan, the contact center is close to the primary signal on how members think and feel about their care. Raj believes it’s time for customer service to evolve into a sensing mechanism, reading leading indicators on Stars and quality. Measured against that, the deflection target looks small: "we get twenty million calls a year, how do we get rid of ninety percent of them... I think that's somewhat of a short-sighted goal if we did it that way."

What he proposes is human plus AI serving the customer together, and the headcount conclusion runs the other way from the usual one. "Perhaps you need to expand the number of agents versus reducing that."

Milind pushed on the clinical side. The same team looked at triaging for primary care and at extending the approach to Medicare members, and those proved harder than the administrative ones. From a technologist's chair the application looks like common sense, he said, then it meets the operating reality of a company the size of Elevance and comes back as something you would design differently.

The through line

Three chairs, one failure.

A recruiting ledger has to account for people and agents on the same page, and almost nobody has told HR that yet. AI Pilots stall because the org chart they run on was drawn for paper, and no amount of model quality fixes that. Sure, a contact center can be automated to seventy percent; at the same time, the question of what the automation serves goes unanswered.

Each case comes down to the same missing decision. An enterprise that has never settled what deserves its attention ends up counting the work that disappears, because that’s the only number on the table when the review meeting starts. Signal Labs has created and introduced a new category of software built to answer that challenge: Systems of Attention.

Which brings the argument back to the twenty million calls. Deflecting ninety percent of them dismantles the plan's listening post to save on the electricity. Plenty of enterprises are signing off on that trade this quarter, and very few have written down what they agreed to stop knowing.