At SaaStr AI 2026, Aurasell co-founder and CEO Jason Eubanks skipped the AI futurism. He put the exact go-to-market stack he ran at his last company on screen, what it cost to run, and why he thinks the whole model is about to come apart.
It is probably your stack too.
Sellers Just Don’t Sell Enough: 24-30%
The average B2B seller spends just 24-30% of their time in front of prospects and customers, whether face to face or over Zoom.
Eubanks’ challenge to the room: if you cannot say exactly what that percentage is for each of your reps, go measure it. He treats it as a top-line KPI, not an HR metric.
The other 70-plus percent goes to context switching, manual account research, prep, follow-up, and internal busywork like deal reviews and QBR prep. None of that is selling. All of it is overhead you are paying full quota-carrying salaries to perform.
You cannot maximize the productivity of a go-to-market org when the people you hired to sell are selling a quarter of the time.
What the Legacy Stack Actually Costs
Eubanks didn’t theorize about tool sprawl. He put his old numbers on the screen.
At Harness, his go-to-market stack ran:
- 22 products
- $3M+ per year in software fees
- 11 ops team members just to keep it standing
Those 11 people were not driving revenue. They were stitching together integrations, patching fragile workflow layers that stepped on each other, and reconciling data across multiple databases. The one thing they actually wanted, a single view of the customer journey, stayed out of reach.
He found the rest of the damage through an internal audit he called Project X-Ray. Mid-COVID, his board asked him to cut burn and accept slower growth. So he logged every activity, every tool, and every overlap across the org. The finding that stuck: reps were working inside 10 to 12 products a day to do their job, bleeding time to context switching and manual work the entire way.
Why “Just Add Agents” Makes It Worse
This is the part most legacy vendors are getting wrong right now, and Eubanks named it directly.
Every niche tool you buy brings its own siloed database. That silo might sync with your CRM at the field level, which looks fine on a data map. But the context, the actual conversations, activities, and signals, stays trapped inside each silo.
That context is what an agent needs to act intelligently. Without it, the agent is guessing.
So when legacy vendors bolt agents onto fragmented data, you get agents running on a fraction of the metadata, blind to the full picture. Eubanks calls the result “agentic thrash”: low-quality automation at best, and at worst agents that act autonomously and step over each other. Adding more agents to a fragmented stack does not fix sprawl. It compounds it, and it drives your costs up while it does.
The Bet: One Data Layer, Then the Agents
Aurasell’s architecture starts from the data, not the agents. Three layers:
- A unified data foundation. Structured and unstructured data in one place. The platform ships with 900M contacts and 85M accounts, auto-enriched, with room to extend through custom enrichment.
- A conversational context layer. Every conversation, every channel, every signal, all the time, feeding one context graph instead of a dozen silos.
- An automation layer on top. Some agents come prebuilt and autonomous. Others you build yourself in natural language. Aurasell describes the coverage as contact to contract, the full sales process for both your team and the buyer.
The deployment choice is the smart part for adoption. You can run Aurasell as your AI-native CRM and auto-migrate off your existing tools. Or you can lay it on top of Salesforce or HubSpot with a bidirectional integration and move AI-native workloads over at your own pace. Rip and replace is optional, not required.
The Proof Point: Nearly $3M in 41 Days
Aurasell closed the session with a live product walkthrough, which is rarer than it should be at a conference full of slideware.
The framing was a new rep’s first 41 days, ending in a $2.7M closed deal. On day one she logged in to a territory the platform had already built and prioritized by ICP. No spreadsheets, no other tools. From there:
- AI columns ran custom research at scale, surfacing things like which accounts hired a new CRO in the past year, pulled from reputable sources off a single prompt.
- Contacts were pulled and ranked by propensity to engage, then auto-enriched with email and phone for direct dialing inside the platform.
- Sequences were built by prompt, with every account and persona getting a unique message off the custom research, even in mass outreach.
- Cold call blocks surfaced as tasks with the context already attached: recent events the prospect attended, discovery questions, likely objections, rapport notes.
- An AI block on the opportunity scored both deal completion and quality against the sales methodology, then coached on the gaps, including tying metrics back to revenue to strengthen the business case.
Everything fed one timeline, every touch from SDR, marketing, and rep, all writing back to the same context graph that the agents run on.
Top Takeaways
- Reps sell just 24-30% of the time. The rest is research, prep, follow-up, and internal busywork. If you cannot say what that number is per rep, that is where to start.
- Tool sprawl has a hard price tag. Eubanks ran 22 products, $3M+ a year in fees, and 11 ops people just to hold the stack together, and still never got a single view of the customer.
- Bolting agents onto fragmented data backfires. Agents are only as good as the context they can see. Pointed at siloed databases, they produce “agentic thrash” and drive costs higher, not lower.
- Fix the data layer before the agents. Consolidation is a data problem first. The win goes to whoever’s agents can see everything, not whoever has the most agents.
- The model can work. A new rep closed $2.7M in her first 41 days on a territory the platform built and prioritized for her on day one. No spreadsheets, no other tools.
