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Authentic8: The CRO Who Benchmarks Before He Deploys

Nabeel Ahmadieh was expecting 80%. That was his internal bar when he decided to take a first pass at an RFP response himself, using AI trained on Authentic8‘s product roadmap and sales enablement material built over the last two years. He understood the competitive landscape well enough to want to lead from the front before handing it to the specialists.

The first draft came back at 97%.

“I was so used to the last 12 to 18 months when things would come out at a 70% accuracy or 80%, you still had to manually really work through things,” he said. “When it came out with that much, that cleanly and with only really like I need little 3% tinker, I was pleasantly surprised.”

That moment crystallized something Nabeel had been working toward as CRO: not just using AI, but finding the right places to deploy it. In a revenue org of 42 people spanning AEs, SDRs, renewals, customer success, sales engineering, and professional services, that distinction matters more than most leaders acknowledge.

In a recent episode of Unicorn Builders, Nabeel shared the framework he uses to direct AI investment — and the failure that sharpened his thinking about where AI breaks down.

Benchmark the Funnel Before Deploying Anywhere

Most revenue leaders approach AI adoption directionally: find something that sounds useful, try it, see what happens. Nabeel approaches it structurally.

His process starts with the funnel. He maps every conversion stage from first marketing touchpoint to closed-won deal, compares each stage against enterprise cybersecurity industry benchmarks, and finds where Authentic8’s numbers diverge most from the standard. That gap becomes the first target for AI investment.

“I’m looking at our entire funnel from the marketing side all the way to a close won deal,” he said. “I’m looking at conversions along the process… I’m comparing everything to industry standards and I’m trying to pinpoint stuff that is broken. That is the first place I’m looking to improve from an AI perspective.”

Currently, that gap is page-view-to-lead conversion. Traffic is coming in. Conversion isn’t following. So that’s where Authentic8 is actively testing — AI-assisted calls to action, faster response sequences, and potentially a bot to engage inbound visitors.

The discipline matters as much as the process. Nabeel is explicit that he wants tools that move a measurable number, not tools that generate activity. “None of that vaporware crap,” he said. “I want tools that are actually going to help move the needle.”

What the AI SDR Pilot Exposed

Authentic8 ran an outbound AI SDR pilot on LinkedIn with a specific mandate: cover market segments the human team wasn’t reaching. The results were disappointing.

“It just did not sound original. It sounded like it was AI,” Nabeel said. “There’s a lot more hunting and art to it that is really hard to replicate if you just feed it something based on their LinkedIn page.”

The failure pointed to a specific gap in how the pilot was built. Authentic8’s best SDRs don’t build outreach from LinkedIn profiles. They check job boards to identify what tools target companies are actively hiring for, map those signals against ICP fit, and construct outreach from live market data. That signal-sourcing process — research layered with judgment — is what makes their messaging feel relevant. An LLM fed static profile data produces static output.

The lesson isn’t that AI SDRs don’t work. It’s that output quality is bounded by input signal quality. If the underlying data doesn’t reflect what a prospect is dealing with right now, the personalization gap isn’t a prompt engineering problem.

AI Adoption Inside the Org Is a Distribution Problem

Alongside the SDR pilot, Nabeel identified a different problem inside his own team.

Individual contributors had found effective ways to use AI in their day-to-day work. But they were doing it quietly, without sharing what they had figured out with anyone else.

“We have a number of pockets in the organization that are doing really great things to speed up their day to day work,” he said. “But they’re not sharing that with the broader organization. They’re almost operating in different silos.”

His fix was structural. He created a dedicated cross-GTM channel for AI use cases, carved out recurring time blocks in his own week for experimentation, and shifted contribution from voluntary to assigned — team members are asked to present what they’ve built, not just post it if they feel like it. He also described “volunteering people” to share directly, rather than waiting for organic participation.

The underlying point: the efficiency gains from AI already exist inside most revenue orgs. The compounding doesn’t happen until there’s infrastructure to distribute them.

The Hiring Composition Shift

Nabeel’s AI framework connects directly to how he’s thinking about headcount. His bet is that AI absorbs the administrative and operational layer of revenue work — renewals, process management, documentation — which means future investment should concentrate on roles that require what AI can’t do.

“I see a world where we invest more in account executives, enterprise salespeople and less in more administrative operational roles, like renewals, for example,” he said. “I want the team less on the keyboard and more in front of customers.”

This is a composition argument, not a headcount reduction argument. The context matters: Authentic8 sells into organizations where procurement is structurally slow regardless of sector. Nabeel came up through government sales and assumed commercial would be faster. It wasn’t.

“When you work with enterprise clients, whether it’s the big tech companies, the big banks, their procurement cycles can be just as long as the government, which was sort of mind blowing to me,” he said.

In long-cycle, multi-stakeholder deals, the enterprise AE who can navigate procurement and build credibility over time is the scarce asset. As AI compresses the operational work around that AE, the return on the relationship work goes up. That’s the ratio Nabeel is building toward.

The Operating Principle

Nabeel’s approach to AI reduces to one discipline: find where you’re underperforming against a known standard, then direct investment there. It applies to funnel benchmarking, to building the internal sharing channel, and to cutting the SDR pilot when it failed to produce original output.

Most AI adoption inside revenue orgs is driven by enthusiasm and vendor pressure. Benchmarking first, deploying second, and cutting what doesn’t move a measurable number is harder — and the only version that compounds.