Nearly every GTM team has adopted AI agents at this point. 87% of sales organizations use AI for prospecting, lead scoring, and drafting emails, and 88% of marketers use it daily. The interesting question is no longer "should we deploy agents." It's the one I hear on almost every customer call once the first campaigns have proven out: "this is working, so what now?"
I spend most of my time with teams after deployment, and the gap between the teams that plateau and the teams that compound is not the tool. It's what they feed it and how they push it. The best deployments I've seen keep improving along four vectors: volume, complexity, consistency, and the performance of the agents themselves. Here's what each one looks like in practice.
Vector 1: Volume.
You're allowed to target more than you think.
Before AI, most GTM teams could only run cold lead gen against a fraction of their addressable market. Building and enriching new lists and standing up new campaigns took days or weeks, so teams triaged. They picked a slice of their ICP and left the rest untouched.
Once your first campaigns are proven, expanding coverage takes minutes or hours, not weeks. That means you can go after the rest of your ICP list, then keep going: new industries, new regions and languages, new company sizes, secondary markets that were never worth the manual effort, and the supporting stakeholders around your primary buyer who influence the deal but never got a touch.
Most teams don't think this way because they never felt like they were allowed to. They were stretched thin covering a percentage of their lists and couldn't imagine testing outside their ICP. That constraint is gone.
Ask yourself: which companies on our ICP list are we still not touching? Which industries, regions, or company sizes within our ICP are uncovered? What near-ICP segments could we test as secondary markets? And within accounts we already target, which personas and supporting stakeholders are we ignoring?
Cofenster ran 12-15 campaigns in parallel to 86,000+ prospects with a single operator. That's roughly 40 BDRs worth of effort, and it started with this exact exercise.
Vector 2: Complexity.
Intent and personalization are where the multiples are.
A lot of teams are still running the same traditional plays that convert a little worse every year. They know personalized, intent-driven campaigns perform better. They just never had the bandwidth to run them.
The bandwidth problem is solved. A highly personalized email takes seconds instead of ten-plus minutes, which means sophistication finally scales. We usually recommend starting with a personalized three-message sequence per primary persona, but once that's proven, this is the vector with the biggest payoff I see in the data.
Our customers who feed intent signals into their campaigns convert replies to pipeline at three to four times the rate of broad outreach. One customer saw a 580% increase in response rates after switching to intent-based campaigns. Another hit 91% open rates, the best of any campaign in their company. The pattern is consistent: precision beats volume on a per-message basis, and now you can have both.
Ask yourself: what channels beyond email can we activate, like social, consented voice, or SMS/WhatsApp? How many more touches can we run, both initially and in follow-up? How do we personalize past role, title, company, and industry into the person's actual context? And what signals tell us the moment a target is most open to hearing from us?
Checkr used intent-based campaigns to test signals before scaling, ran more complex sequences, and got a 3.2x increase in reply rates.
Vector 3: Consistency.
Every lead gets the same treatment, every time.
Many GTM teams still operate in firefighting mode. Campaigns go out when someone has time. Follow-ups happen when a deal feels important enough. SLAs technically exist, and some get met at the last minute.
Agents end that. Every lead gets the same campaign quality, the same number of touches, the same speed of response, whether it's the first lead of the quarter or the five hundredth. Most teams first feel this on inbound, where an agent responds in seconds and qualifies and routes automatically. But the same principle applies across the whole lifecycle, and the gaps are usually bigger than teams realize.
Ask yourself: does every prospect get the same nurture sequence before a call? Does every lead get the same number of touches before it's a deal or a clear closed/lost? Does every cold, stalled, or closed/lost deal get a reactivation campaign at the same level of care? Does every current customer or PLG user hear from us when there's more to offer, or when they're ready to start paying?
Canibuild cut speed-to-lead by 99%, automated customer expansion and payment motions, and standardized outbound. Those agents alone now generate 20% of pipeline.
Vector 4: Performance
The agent is only as good as what you feed it.
This is the one I push hardest on, because it's where teams most often misunderstand what AI does. An agent executes and scales what you give it. Hand it a broken motion and it does the wrong things faster, easier, and at much greater scale.
Improving agent performance is a loop with two halves. First, monitor what's running, find the winning campaigns, and shift more of your activity onto them. Second, generate new variations to challenge the winners. If you're running five campaigns to roughly the same personas, put 80% of your volume behind the best one and use the remaining 20% to test three or four challengers. Then repeat.
The customers who win at this share two habits I see over and over in our call data. They track three to five conversion points across the funnel (positive replies, meetings, opportunities, pipeline) instead of a single vanity metric, and they had baselines before they deployed. And they think in improvement trajectories rather than expecting instant perfection. The teams that show up to check-ins asking "what should we test next" are the ones whose numbers keep climbing.
Ask yourself: is our campaign performance tracked in one place, or scattered across five tools? Across everything running, what's working and what has a ready replacement? For each campaign, what new messaging, materials, case studies, or offers can we test, and if we have nothing new, how do we reposition what we have? And if campaigns take minutes to create, how many variations can we test in the next two weeks instead of the next two months?
Questex ran more experiments, with more variations, to more segments, and generated $1M in pipeline in their first three months while saving 2,000 hours, with reply rates 6x above industry averages.
Take the next step
Sit down for 20-30 minutes and work through these questions honestly. You'll walk away with a concrete list of ways to expand what your agents are doing and improve what they're producing. This is the same exercise we run with our own customers, and it's usually the difference between a deployment that's doing well and one that's doing great.
If you'd rather work through it with one of our experts, we're happy to.
