If you’re confused by the sales tech landscape right now, you are not alone. If it feels like someone threw an explosive into an already noisy category and made it even messier, that's because there was an explosion - it’s called AI, and it has blown up the tech stack.
A few years ago, NVIDIA CEO Jensen Huang described this sudden uptick in AI as a “Cambrian explosion,” likening it to the period in human history where we went from simple single-cell organisms to more complex, genetically diverse life.
This comparison feels especially true for sales tech where AI offers an opportunity to leapfrog from simple tools to real intelligent systems.
To help demystify the shifts, consolidations, and upheavals we’ve put together this market map with the help of a few industry experts.
Market maps are not the easiest to build when everything is changing so quickly… and let’s be honest, all market maps are biased by the writer’s point of view. VCs have their perspective, analyst firms like Gartner and G2 will have theirs.
Our landscape is vendor-led and heavily vetted by our community, analysts, and VCs for a complete perspective on AI’s impact on the sales tech market map. We recommend reading how AI reshapes sales ‘jobs-to-be-done’ as well to understand the primary use cases for AI in sales.
Accelerated death of GTM 1.0 tools
AI is the driving force behind much of the big changes we’re seeing in the sales tech landscape but it’s not the only force. Some themes that have been at play for the last few years have simply been accelerated by the explosion of AI. Let’s dive into each of these:
Salesforce losing ground as the primary system of engagement
There’s been a lot of discussion about the “death of Salesforce” for years. There was a time when every workflow a rep executed happened in Salesforce. But now, Salesforce is simply a database with critical work happening in other tools. As the CRM lost influence, other tools gained ground.
In our own experience working with PLG companies (like Asana, ClickUp, Canva) whose sales motions did not perfectly fit the rigid Salesforce architecture, the data warehouse was the primary system of record and most of sellers’ workflows happened in Pocus’ platform.
Push for consolidation of disjointed tooling
The sales tech stack has become unsustainably complex. Sales leaders are wrestling with 15+ point solutions, creating a fragmented experience for reps and a nightmare for operations teams. This complexity has real costs associated with the number of tools, but also hidden costs of managing the integration between data silos, maintenance of these tools, the management overhead of training and ultimately the loss of rep productivity.
One of the biggest complaints we hear from sales leaders and reps is the “toggle tax” where reps are jumping between multiple tools to complete their prospecting workflow. Insights are siloed from actions, tools offer incomplete data, and reps are forced to dig for their own data.
Changing buying behaviors need new tools and tactics
Even before the big AI explosion we were already seeing a big shift in buying behavior. Engaging a prospect used to take 7-8 touches, but now takes over 30. Channels like email are being flooded, resulting in buyers tuning out. Unfortunately, the AI content writing tools made this problem worse, especially in email, making barely personalized spray & pray easy as a few clicks.
Buyers are now forced to find new tactics and tools to break-through the noise. Reps are embracing research to make outreach more strategic, reaching out to fewer but more targeted contacts, and exploring new channels.
All of these trends, plus the megatrend of AI, is ushering in a new stack optimized for a future where accessing insights and orchestrating workflows will be more seamless, integrated, and simpler for reps (and the ops/enablement teams that support them).
Rise of the AI-first sales tech stack
Gone are the days when every system required a rep to stare at a blank search bar and figure out their own workflows. The new stack operates to guide or entirely replace reps’ work. The AI-first sales tech stack can be divided into two new modes of working.
- Autopilot systems that seek to complete end-to-end work that replaces a rep (mostly junior roles like SDR or support) in that job.
- Copilot systems that seek to complete work on behalf of reps, augment them, or otherwise assist them in their job.
While autopilots are powered by autonomous agents who complete tasks without human intervention (referred to below as AI Digital Workers), copilots are agents who may complete tasks on behalf of reps, but allow for human oversight and guidance at key steps. In the AI-first sales stack, it’s now possible to have one agent complete a workflow that may have previously spanned 5+ tools.
For years, incumbents have promised to consolidate and offer complete solutions for core sales workflows. AI-first sales tools finally unlock that promise. What holds many incumbents back is their heavy existing architecture that is not nimble enough for this new world. Younger startups have the advantage of not being constrained by what already exists.
AI Digital Workers
This is a new category that wasn't possible before recent LLM breakthroughs. Think of AI Digital Workers as the sales tech equivalent of Waymo, autonomously driving specific workflows. The most popular workflows today are SDRs and Support Reps, both high-turnover jobs that require manual, tedious work.
In the world of AI SDRs there are tools like 11x and Unify trained for outbound prospecting, while Warmly and Qualified tackle inbound demand. Support reps have been long on the chopping block - Intercom released AI chatbots to manage support before the more recent developments in LLMs. We now also have avatars like 1mind or HeyGen, going beyond automating email or chat but actually replacing reps on calls.
There is a lot to consider when it comes to replacing humans with AI Digital Workers. What we’ve seen is that the safest place to apply digital workforces today is within lower ACV, higher volume segments of your market. We’ve already been working for years to automate high-volume (often low ACV) segments, AI workers offer the next iteration of that effort. We’d also recommend focusing on workflows where you already have clean data, a strong playbook, and very little creativity required. AI Digital Workers are only as good as the data and guardrails provided, so tread carefully.
For more in depth analysis of AI SDRs in particular, check out:
AI Sales Intelligence
Sales intelligence is getting a major facelift. The old guard (think ZoomInfo) started by building glorified contact databases. Sure, they added buying intent data over time, but let's be real - there wasn't much "intelligence" happening. The data was often wrong or outdated, and sales reps had to do tons of manual work to actually make it useful.
But now we're seeing something totally different with AI-first platforms in this space like (shameless plug) Pocus. AI Sales Intelligence is a consolidation of several other categories. In our opinion, these tools don’t make sense in silos and need to live together in one cohesive solution that brings together prioritization, research, list building, and enrichment.
We’re building the AI-first prospecting platform for reps. This is not a database that requires reps to search for intelligence. Instead, agents are actively working for you - crawling the web, learning from your internal data, and piecing together the full picture of what's happening with your prospects.
Look out for a separate deep dive on the evolution of sales intelligence.
AI Enablement & Content Generation
We’re starting to see a convergence between content generation, content personalization, and enablement in sales. The current content situation in sales is broken. Marketing and enablement teams hate when sales reps don’t use the carefully built enablement collateral. Sales reps hate being stuck with generic content that doesn't speak to their prospects and are always asking marketing for “custom” landing pages or one-pagers.
There are 3 types of companies tackling this space:
- Content generation first: Copy.ai, Lavender
- Content personalization at scale: Mutiny, TofuHQ
- Content orchestration & enablement: Spekit, Sifthub, Naro, Hyperbound
AI Sales Engagement
While incumbent platforms like Gong, Clari, and Outreach expand into full revenue orchestration platforms, a new breed of AI-focused tools is taking a more targeted approach. Rather than building all-in-one solutions, these new players focus on mastering specific, high-value workflows in the sales process. Companies like Attention are zeroing in on call recording while Nooks and Orum attack the legacy power dialler category. All are focused on providing better conversation intelligence, real-time coaching, and more efficient outreach. Traditional email automation is naturally shifting toward AI SDR platforms, where it fits better as part of their autonomous prospecting workflows.
Navigating the new landscape
If you're a sales leader trying to make sense of all this change, here's our suggestion: don't rush to rip and replace your entire stack. Instead, start experimenting with AI-first tools in areas where you're feeling the most pain - whether that's prospecting, content creation, or disconnected workflows. While the landscape will keep evolving, the future is clearly more consolidated and autonomous, with tools that either run complete workflows (autopilot) or make your reps dramatically more effective (copilot). Start by identifying where your team wastes the most time toggling between tools or doing manual work. These are your opportunities to test AI solutions.
Remember, the goal isn't AI for AI's sake - it's about enabling better selling motions and buying experiences.
Tactical tips for getting started
We’ve gotten great advice from leaders who have already been in the trenches experimenting with new AI tools for GTM. Here are 3 ways you can make sure you’re placing bets effectively on tools that fit your needs.
Think use-case first, tool second
Not new advice but even more important as we’re all inundated with new AI tools. The use case and your need should always be where you start, not the tool. Go deep into defining all the jobs to be done within that use case, how things are done today, the outcomes you’re trying to achieve, and what’s blocking them today. Especially if you’re buying a tool for your sales team, don’t skip surveying your reps. So much tooling turns into shelfware when reps are not bought in, so loop them into your use case definition process.
The approach we are taking when introducing AI tooling is from a use case first perspective. We identify use cases that require lots of human effort to drive and/or there is a low change cost (e.g., process/workflow does not exist or is currently not being utilized) and a measurable impact.
- Matt Piotrowski, VP Operations Anaconda
Check out part 1 and 2 of this series for a map of all the best sales uses for AI and our top examples.
Avoid tool bloat (and the same problems) with ROI rigor
Build on your use case exercise by doing an ROI assessment. If you were to purchase an AI tool to replace an existing process (or headcount) how would that impact your business? In the 2020 tool craze we lost this rigor, but it’s very much a must-have now.
AI is also ushering in new pricing models like pay per outcome which actually make the ROI exercise a lot easier, you only pay for the outcomes you unlock (this is a whole other blog we’ll explore later).
ROI/business cases are back in full force. For years CFOs couldn't or weren't pushing this. Now, tool owners are being required to justify ROI at each renewal. The disjointedness of tools is painful, but I think an even bigger driver of consolidation is that no company actually needed all the tools we all purchased in 21-22.
- Angela Winegar, VP Marketing, Invisible
Do both top-down and bottom-up experimentation
Nothing will get done if you don’t have strong internal processes for how you place bets and how those bets get executed up and down the organization. Our friends at Hubspot have really nailed this. Kieran Flanagan shared his framework for running AI experiments with us on a recent episode of the 10x GTM Podcast.
It’s called “Do and Deliberate.” Step 1 is to ‘deliberate’ this is where you set top down strategy for areas of focus (use cases), hypotheses you want to test, owners, and accountability rituals. Step 2 is to ‘do,’ empowering your teams to start testing and documenting their learnings. Learn more by listening to his episode here or read our summary of all the best GTM x AI insights from this season here.
If you need guidance, our team is always happy to help or join our 10x GTM community to connect with peers who’ve already implemented tools and can share their advice.
Staying up to date
Things change with the GTM landscape faster than ever, stay on top of the latest on AI x GTM by joining the 10x GTM community. Our next cohort is accepting application until January 29. Apply to join here.
Curious about how Pocus can help you unlock more pipeline with AI prospecting tools? Talk to our team.
Acknowledgments
Thanks to everyone who contributed and reviewed this piece: Andy Mowat Angela Winegar Blue Bowen Brenden Short Caryn Marooney David Yockelson James Melcer Kirra Greye Matt Piotrowski Meka Asonye Scott Williamson.
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