What Outreach is saying in “Prospecting 2025” (and what matters)

We have been tracking what we are seeing across GTM teams adopting AI in sales development, and we want to ground that perspective in what Outreach reported in its “Prospecting 2025” research. Wolfpack has also been conducting extensive experiments across a wide array of companies we support and we'll use this forum to further elaborate on the transition the market is seeing. Sales is one of the oldest known professions. The fundamental expectations of a seller’s role. building trust-based relationships, finding opportunities to create value for customers, and creating experiences that minimize friction. have remained constant over time. What’s new are the tools available to help sellers become more productive, especially with the rapid emergence of generative AI (gen AI) in recent years.

Outreach’s report is clear on two things. First, outbound is not disappearing, it is being restructured. Most teams are running a hybrid inbound and outbound model inside a single function (43%), with another 37% splitting inbound and outbound into dedicated teams, and a notable 23% of SDR orgs reporting directly to the CEO. Second, attention is getting harder to earn. Outreach reports 4.81 touches are now required on average to get a response regardless of lead temperature, and the top constraint is not writing copy, it is identifying quality leads (about 54%), getting prospects to answer (51%), and keeping them engaged once they do (44%).

The operational takeaway is that teams are leaning into tech to stay competitive. Roughly 74% of sales teams are using technology to personalize and automate emails at scale, while SDR workload remains high, with 51% juggling two to three AEs and 74% of orgs reporting 21 or more SDRs. Despite investments in AI, there's a longer 5 year trend that remains near unchanged. These is one constant truth about success in B2B sales. The “rule of thirds” rules all. At any given stage of the buying journey, one-third of customers hope for in-person interactions, one-third want remote communications, and one-third prefer digital self-serve options. The rule of thirds holds true across all geographies, industries, and company sizes, all types of buying occasions from new to repeat purchases, and across high- and low-value purchases.

Our insights, and what we see in the field

AI adoption is happening fast, but the winning strategy is not labor replacement

McKinsey’s 2024 global survey work shows gen AI adoption spiking, with the biggest increase from 2023 found in marketing and sales, and 65% of respondents saying their organizations were regularly using gen AI in at least one function. At the same time, McKinsey’s B2B sales research points to a more grounded reality: only about one fifth of surveyed commercial leaders reported enterprise wide enablement of gen AI for B2B buying and selling, with many still in pilots. That is consistent with what we see. The durable outcome is a hybrid operating model where AI compresses low value work and improves decision quality, while humans remain accountable for the trust layer and the micro decisions that determine whether outreach is perceived as signal or noise.

The main economic win is reclaimed selling time, not automated messaging volume

Sellers spend only about 25% of their time actually selling, and AI’s near-term value is taking on the surrounding work so sellers can spend more time with customers. Early successes with AI are showing 30% or better improvement in win rates, with meaningful gains coming from reimagining sales processes rather than automating existing ones. Outreach confirms what we're seeing with the “4.81 touches per response” statistic. This matters. If touches are inflating, simply generating more outbound with AI is not a strategy. It is a faster way to discover that targeting and relevance are not strong enough. The executive move is to use AI to increase the quality of who you pursue and why now, not to increase message throughput.

The biggest constraint is still lead quality/context, which is exactly where AI should be applied

We continue to hear/see/experience the quality of lead identification as the top challenge for SDRs and AEs. B2B growth through gen AI work maps directly to this, emphasizing “next best opportunity” and consolidated battlecards that synthesize disparate data sources to prioritize accounts, map stakeholders, and accelerate research. This is a critical point for CMOs and CROs. The highest leverage use cases are upstream: prioritization, trigger detection, persona mapping, stakeholder identification, and next action recommendations. Drafting emails is helpful, but it is not the unlock.

The value is large, but executives should plan for process, data, and governance work

McKinsey estimates gen AI could unlock an incremental $0.8T to $1.2T in productivity across sales and marketing, and separately estimates sales productivity impact of roughly 3% to 5% of current global sales expenditures. Our view aligns with this: one off use cases rarely move the needle because seller work is fragmented across dozens of tasks, and scaling requires end to end journey mapping, data cleanup, and governance decisions. This is where many “AI SDR” deployments fail. They add tools without shutting down old workflows, they do not clean data, they do not standardize definitions, and they cannot measure downstream impact beyond activity metrics.

Agentic AI increases upside and risk, so governance becomes a revenue competency

As organizations shift from copilots to agents that can take actions, risk is no longer theoretical. Wolfpack's work on agentic AI highlights the need for an operating layer that provides observability, authentication and authorization, evaluations, feedback management, and compliance guardrails to prevent autonomy drift and lack of traceability. This is especially true as most organizations aren't considering the cybersecurity mess they may find themselves in if these 'agents' are not governed properly. Evidence of this can be find in a McKinsey’s 2024 survey where they also note many organizations have experienced negative consequences from gen AI use, and that governance practices are not yet widespread.

For a CMO or CRO, this translates into practical requirements: clear allowed actions, human approvals for irreversible steps, auditable logs, and strict controls on what data agents can access and transmit. If you cannot explain why an automated action happened, you cannot defend it to a customer, an auditor, or your own board.

What we would want a CMO or CRO to do next

1. Treat “AI SDR” as an operating model redesign, not a tooling decision. Our point about reimagining processes is the difference between micro productivity and measurable revenue impact.

2. Shift success metrics from activity to conversion quality. Overwhelming data makes clear the battle is engagement and lead quality, not message generation.

3. Invest in governance early, especially as you move toward agentic workflows or make sure that you're using a company (like Wolfpack) that is.

If you take one thing from any of the reports being generated, there's a common theme. data: the winners will not be the teams that automate outreach the fastest. They will be the teams that redesign the SDR operating system to improve decision quality, protect trust, and scale responsibly. AI is already compressing the cost of activity. What does not get cheaper is credibility. In a market where touches are rising and lead quality is the top constraint, the advantage shifts to teams that can prioritize correctly, message with legitimacy, and measure outcomes that hold through the funnel.

If you are a CMO or CRO evaluating your 2026 SDR motion, we will run a short diagnostic with you or we can talk about providing the most efficient SDR/BDR program at the lowest price you'll find anywhere globally. Let's create your success together. Proven operators leveraging proven processes.