Ninety-nine percent of BDRs are now using AI. Yet average quota attainment is statistically unchanged from last year.
When we released our 2026 State of the BDR Report, the finding that surprised me most wasn’t about AI tools themselves, even though there’s a lot of eye-opening data there. What struck me is what we saw organizations doing with the tools they’ve deployed. For most of them, it’s not much. And that’s where the real story is.
Having the Tools Is Not the Same as Using Them
Ninety percent of organizations in our survey use tools designed to identify accounts in an active buying process.
But somehow, only 2% of BDRs say those signals determine which accounts they work. Most reps still receive their assignments through territory coverage, sales leadership, or marketing campaigns. In other words, they’re operating on the same structures that existed before any of this technology was available.
The signals do shape how BDRs engage once they’re already working an account (83% say buying stage influences their outreach strategy), but they rarely determine which accounts get worked in the first place.
That’s a big miss. Organizations have access to the intelligence they need to prioritize the right accounts. They even deploy the right tools. But then territories get built the same way they always did.
The data puts a number on what it costs to revert to old habits rather than targeting accounts based on what signals and AI surface:
- Among organizations with no account prioritization tools, 78% of BDRs cite wrong timing as a top reason prospects say no.
- Deploy the tools without changing how targets are set, and that number falls to 65%.
- Deploy the tools and set differentiated performance targets for in-market accounts, and it falls to 44%.
That’s a 34-point difference in successful timing between having a capability and building your process around it.
What BDRs Are Using AI For
AI adoption among BDR teams is steadily growing:
- 53% in 2024
- 62% in 2025
- 99% in 2026
But while AI is now nearly ubiquitous among BDRs, there’s a lot of opportunity to improve how it’s being used.
The most common way BDRs use AI is for producing messages and content (74%). That’s a good use case for quality control and efficiency, but unfortunately, it’s not yielding better quota performance.
Automated outreach, the third most common use, is effective at driving volume: BDRs using AI-powered dialing tools deliver about seven more touches per contact than those who don’t. The average BDR now delivers about 34 touches per contact, up from 17 just two years ago. But again, while this use case has doubled outreach volume, it hasn’t had any measurable effect on attainment.
One AI application that does predict better performance? Reviewing and analyzing conversations: call simulation, role-play, and conversation intelligence. That’s second in adoption at 62%. Using AI to get better at the actual work of the conversation produces results. Using it to send more messages at scale does not.
AI for account identification and prioritization sits at only 35% adoption, even though it shows a directional advantage in attainment. It’s the application most directly connected to the operationalization problem in the first section: using AI to determine which accounts actually get worked, not just how quickly the outreach goes out once the territory list arrives.
Content generation and outreach automation have real value. They reduce friction and save time, but they only help teams do more of what they were already doing. The applications of AI that change who gets prioritized and how reps prepare for calls are where the performance return is. And they’re still underdeployed relative to their impact.
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The Handoff Is Getting Worse, Not Better
There’s a structural shift in this year’s data that I think is being underread. BDR reporting has swung back toward Marketing. At the same time, the share of BDRs passing prospects to sales as fully qualified opportunities dropped from 73% last year to 61%, with a growing share now going to sales as leads.
Read those two things together and you get an organization reconsidering where qualification should happen, and, implicitly, who owns it. That’s not a bad thing on its own. But it creates a real problem when marketing and sales aren’t operating against the same definition of what ‘ready’ looks like. If marketing is passing leads earlier and sales doesn’t know why, that’s a handoff argument waiting to happen.
The BDRs who complete the full handoff sequence (qualifying the prospect, providing notes to sales, booking the meeting, and attending it themselves) report higher quota attainment than those who don’t. Only 36% of BDRs do all of that. Most stop well short.
This is where alignment actually lives: not in the kickoff deck or the QBR slide, but in whether the person passing the ball and the person catching it have agreed on what a good pass looks like. Shared ICP, shared pipeline targets, and shared accountability for what happens after the handoff … and enough trust between the two teams that when something isn’t working, someone says it out loud before it shows up in the numbers.
My number one goal as a CMO has always been for sales to love marketing. Not to tolerate it, not to use what we create occasionally, but to love it. That means a brand they’re proud of, content they’re eager to share, high-quality pipeline from campaigns, tools embedded in their workflow, and a BDR motion that makes their job easier rather than creating more cleanup. When that’s working, the handoff is just the next step in a motion both teams own.
This Finding Has Held for Five Years. It’s Worth Taking Seriously.
I saved this for last because I think it’s the most important thing in the report, and I didn’t want it to get lost in the data.
For the fifth consecutive year, the single strongest predictor of BDR performance is perceived job support, meaning whether BDRs feel equipped, valued, and set up to succeed. Not quota level. Not AI adoption. Not outreach volume.
On its own, this factor explains nearly 15% of the variation in quota attainment. BDRs who feel clearly supported average around 100% of quota. BDRs who don’t average around 77%. That 23-point spread widened from last year.
Fifty-three percent of organizations raised quotas in 2026, the highest rate in five years. BDRs with increased quotas do show higher attainment on the surface. But when you control for perceived job support, the effect of raising quotas nearly disappears. The lift isn’t coming from the higher bar. It’s coming from the fact that the same organizations raising quotas tend to be the ones where BDRs feel supported.
The main factors that generate that sense of support:
- Closing the feedback loop on why prospects say no and visibly acting on it
- Compensating BDRs for the behaviors you actually ask them to perform
- Giving reps autonomy over outreach timing and messaging
- Investing in training hours, which have been declining for three straight years and are now down to about 45 annually
BDRs know whether their organization is keeping pace with what the job actually requires. Leaving people to find their own AI tools, setting targets without aligned incentives, collecting rejection data without acting on it … all of it registers. And it shows up in attainment.
None of this is new information. The data has been saying the same thing for five years. What changes outcomes isn’t knowing it. It’s doing it.
What Efficiency Actually Looks Like
There’s a version of the AI efficiency story that’s mostly about speed: more emails drafted faster, more dials in less time. That’s real, and it’s not nothing. But it doesn’t explain why some organizations are seeing meaningfully stronger outcomes from the same tools.
Stronger outcomes happen when organizations deploy tools that actually shape how the team works. Here’s what that looks like:
- Signals determining which accounts get prioritized, not just how an email gets personalized after the territory assignment arrives.
- AI making reps sharper going into conversations, not just more prolific on the way to them.
- Sales and marketing operating against the same pipeline number, with the same ICP, reviewing the same data, and having the honest conversations when the dashboard looks fine but something is clearly off.
That’s alignment. And alignment isn’t a workshop or a quarterly sync. It’s shared ICP, shared metrics, shared accountability, and the discipline to change what isn’t working before it shows up in the numbers.
AI supports all of that. It doesn’t replace any of it.
The performance return right now isn’t in the next tool. It’s in using the ones you already have to change how your team works, not just how fast.
About the Author of this Article
Kelly Hopping is CMO at 6sense
About 6sense
6sense is on a mission to revolutionize the way B2B organizations create revenue by predicting customers most likely to buy and recommending the best course of action to engage anonymous buying teams. 6sense Revenue AI is the only sales and marketing platform to unlock the ability to create, manage and convert high-quality pipeline to revenue.
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