Sales Channel Technology Adoption

AI is transforming productivity across industries but sales is still a frontier waiting to be unlocked. Bain & Company’s research shows that while generative and agentic AI are already freeing up hours of work in marketing and operations, adoption in sales is lagging behind. That’s surprising, because sales is one of the most time-intensive, high-impact functions and even small conversion gains can deliver outsized business results. For CMOs, CROs, and GTM leaders, the opportunity is clear: use AI to give sellers back time, improve decision-making, and boost win rates. And here’s what often gets overlooked: buyer-group expansion and engagement are two of the most powerful drivers of those win rates. The more effectively teams can identify, engage, and influence the full buying committee- the CIO, the CFO, the head of engineering, security, procurement- the greater the likelihood of advancing and winning deals. AI can now do this at a scale and speed that simply wasn’t possible before. AI in sales isn’t about replacing people, it’s about equipping them with better tools. The organisations that act early will be the ones to capture the biggest gains. At Thoughtworks, we’ve been actively working toward this vision. Our award-winning PerformanceAI agent removes the need for sales and marketing teams to click through endless dashboards and instead delivers insights in plain English, on demand. Less time on analysis and more time on insight and action. We’re also reducing manual work through automation, from data entry to intelligence orchestration. We’ve invested in tooling that mines sellers’ conversations across voice, email, and calendar to extract key signals, map the buying group, and match insights to the right accounts and opportunities. And in the AI era, adoption in sales has become much simpler. The technology runs quietly in the background rather than becoming another system sellers need to feed. When human input is needed, we’re moving toward a voice-first experience so no navigating complex CRM interfaces, and sellers can make updates on the go right after a client meeting. How is your team approaching AI in sales today? Let me know in the comments.

GENAI + B2B = Five Key Lessons for Deploying Gen AI in B2B Sales 1. Start with the Problem, Not the Technology The decision to adopt #GenAI should be driven by specific business challenges, not by the allure of the technology itself. #B2B leaders must identify areas where Gen AI can drive significant, profitable #growth — such as #lead generation, account management, or service optimization. In some cases, simple automation might be more appropriate, especially where processes are still manual or error tolerance is low. The key is understanding the core business need before choosing the best technology to address it. 2. Keep the Seller at the Center Successful #GenAI #tools are designed around the needs of the sales team. Organizations should assess current workflows and look for ways Gen AI can free up sellers’ time or deliver valuable insights. Solutions should be: a) Impactful b) Clear c) Understandable d) Prescriptive e) Reliable If a #solution fails any of these criteria, it likely needs redesign. The more aligned the solution is with seller workflows and needs, the higher the likelihood of #adoption. 3. Buy the Easy Stuff, Build for Competitive Advantage Most companies use a “buy-plus-build” approach to #GenAI. Off-the-shelf tools can be deployed for basic functions (e.g., #meeting summaries), while high-impact, differentiating use cases (e.g., personalized offers) benefit from customized solutions. The key is knowing when to buy vs. when to invest in building for strategic #advantage. 4. Balance Quick Wins with Long-Term Capabilities A clear #AIstrategy and scalable architecture are critical. Leading companies start with minimum viable products (#MVPs), align their AI efforts across the business, and build foundational capabilities like strong data infrastructure and skilled talent. The goal is to deliver near-term impact while ensuring long-term sustainability and #scalability. 5. Invest in Seller Adoption from Day One Technology alone isn’t enough—seller adoption determines impact. Organizations must prioritize change management, continuous #feedback loops, training, and communication. Involving sellers early, recognizing their successes, and encouraging experimentation can accelerate adoption. AI Centers of Excellence can help drive scale and responsible use across the organization. With these five lessons in mind, B2B sales leaders can turn Gen AI from a promising #concept into a transformative force for growth, #productivity, and competitive advantage - with Thiago F Silva - Inteligência Artificial e Gamificação e Herick Ferreira:

We analyzed the GTM tech stacks of the 63 fastest-growing B2B companies (Stripe, Anthropic, Databricks, Canva, Rippling, Ramp, Deel, OpenAI, and more) across 60 tools and 21 categories. 65% of the fastest-growing private B2B companies use Clay. Zero uses an off-the-shelf AI SDR today. 1/ The "default stack" that shows up in over 50% of these companies: Salesforce + HubSpot + Gong + Outreach + ZoomInfo + Clay + dbt Labs + Snowflake + Zapier That's the baseline (looks similar to what it did 5 years ago). But, there are some new tools starting to also show up in tech stacks of the fastest-growing companies (more on that below). 2/ The companies running the most sophisticated go-to-market motions are the same ones that invested early in data infrastructure: dbt Labs (84%) and Snowflake (79%) are nearly as common as Gong (84%). 3/ Intent & Signals 6sense (38%) and Demandbase (32%) still own the intent category. They’re the two that show up in almost every mature stack. But a new class of signal tools is gaining traction: Common Room (19%), Sumble (11%), and Unify (11%) are showing real adoption at these companies. They’re smaller, lighter, and more tightly integrated into sales workflows than the traditional ABM platforms. 4/ Other findings: → Salesforce: 100%. Uncle Benioff's grip is strong. → HubSpot: 95%. Most run both SFDC (for Sales) and HS (for Marketing). → Gong: 84%. The default operating system for customer conversations. → Clay: 65%. Now the # 2 data tool, ahead of Sales Navigator (ZI is # 1). → Outreach: 71%. Nearly 2x Salesloft's adoption. → 15+ GTM tools per company (on average). ______________________ Data sourced from Sumble and Clay, plus Claude (to pull customer logos from websites and review sites). If you found this Research Report useful, tag someone who geeks out on GTM tech stacks. And if you think we’re missing a tool or a company, comment below (or DM me). This dataset is a living thing that we’ll keep updating. ______________________ Complete analysis (including an interactive dataset and every company's full GTM stack) in the latest issue of The Signal: https://lnkd.in/g4MVJjFF

Sales has one of the highest theoretical AI capabilities of any profession. But actual adoption remains bottom of the barrel. Everyone's talking about AI replacing salespeople. That's not the problem.  The problem is most salespeople aren't even using enough AI to begin with. The average AE is still spending 30-40% of their time on non-revenue generating activities. - Updating Salesforce - Writing follow-up emails - Hunting for the right case study - Prepping for calls by ctrl+F-ing through old notes All of this is theoretically automatable. Conversational intelligence tools can auto-log calls.  AI can draft follow-ups based on meeting context.  LLMs can surface relevant collateral based on deal stage and ICP fit. So the capability exists. But the red line on that chart tells you adoption is microscopic. Why? Because most sales AI is solving the wrong problem. It's optimising for volume. - Mass personalisation at scale. - AI SDRs that send 10,000 emails a day. - Tools that promise to "10x your outreach." But we're already drowning in AI-generated spam. Buyers can smell it a mile away. Adding more noise to an already saturated channel isn't the unlock. The real opportunity is using AI to remove friction - for the seller AND the buyer. → Automate the CRM hygiene so reps aren't spending 45 minutes a day on data entry. → Use AI to surface deal risks based on engagement patterns in your digital sales room. → Let conversational intelligence identify when a champion's losing internal support before they ghost you. → Free sellers up to do what AI fundamentally can't: read the room, build trust, challenge assumptions, create urgency, navigate politics, etc. Office & admin roles show similar theoretical capability but slightly better adoption.  Because the use cases are clearer… Drafting emails, scheduling, summarising documents, low-risk, high-frequency tasks. Sales is harder. The stakes are higher. A bad email kills a deal. An AI-generated follow-up that feels robotic torches a relationship you spent three months building. So reps don't trust it. Most sales AI tools were built by people who've never carried quota.  They optimise for metrics that don't matter - open rates, reply rates, activity logging, instead of what actually moves deals forward. The gap in that chart isn't a tech problem. It's a trust problem. Reps don't believe the tools will work.  Leaders don't want to bet pipeline on unproven automation.  And vendors keep building AI that optimises for the seller's efficiency instead of the buyer's experience. Until we fix that, sales will stay in the bottom quartile of AI adoption. Even though the theoretical capability is sky-high. The capability is there. The adoption isn't. And that gap represents the biggest opportunity in sales tech right now. Not replacing sellers, but enabling them.

70% of your competitors have embedded AI into their Go-To-Market workflows. You're still calling yours “a pilot.” That’s not hypothetical. ICONIQ Growth reports that 70% of companies now have moderate or full AI adoption in GTM. This isn’t early anymore. It’s separation. If you're still running AI as a side experiment while your competitors are running it as operating infrastructure, the gap is compounding. Here’s the 8 shifts CROs need to make: 1/ From Siloed Data to Unified Intelligence Layer → CRM, marketing automation, customer success, and intent signals feeding a centralized decisioning layer → No more “marketing says one thing, sales says another” Organizations treating GTM as a unified revenue discipline are materially outperforming those still running siloed activities. 2/ From Quarterly Planning to Continuous Recalibration → GenAI embedded in forecasting tools enables live strategy adjustment → Static playbooks are now a liability, not a strategy Teams that can reframe messaging and reallocate resources in days are capturing disproportionate share. 3/ From Manual Prospecting to Agentic Outreach → Autonomous AI agents handling lead prioritization, personalized sequencing, and next-best-action recommendations → Your sellers stop being researchers and start being closers AI users in GTM report saving ~12 hours per week by automating top-of-funnel work. 4/ From Gut-Feel Forecasting to Predictive Revenue Intelligence → AI factoring in pipeline momentum, rep activity, deal stage velocity and market signals → Forward-looking and probabilistic, not based on lagging indicators Only ~40% of B2B GTM teams use AI-driven forecasting today. 5/ From Generic Enablement to Adaptive Seller Development → Static LMS replaced by systems that detect skill gaps mid-cycle and intervene in real time → Coaching becomes continuous Teams using AI for sales training are 35% more likely to increase average deal size. 6/ AI Fluency as a Baseline Skill → Every revenue-facing role requires AI workflow competency → This isn’t an IT initiative; it’s a revenue capability Gartner projected 45% of B2B revenue orgs would require prompt engineering for messaging roles. 7/ Moving to an Agentic GTM Architecture → Multi-agent systems coordinating across marketing, sales and customer success → An orchestrated system with governance 79% of organizations report some level of agentic AI adoption. 8/ From Activity Metrics to Outcome-Based Accountability → Measuring revenue contribution, retention, and win rates → Outcome-based pricing models gaining traction as cost-to-serve becomes measurable 29% of B2B orgs still can’t confirm their GTM efforts drive measurable business impact. The real issue isn’t AI adoption. It’s whether your operating model can actually absorb it. Get a hi-res copy of the infographic: https://lnkd.in/gm2Kyf-c Save for future reference.

Most teams think AI agents jump straight to full automation Reality in real revenue orgs is very different It's not - Flip a switch. Replace a process. Pray. The trust is earned in tiny steps. I see AI adoption as a ladder of trust with 5 clear gates. Here is how I see AI work best in sales and GTM systems: 1️⃣ Start microscopic You do not start with 500 leads. You start with 5. One workflow, not your whole sales cycle. One rep running point, not the whole floor. 2️⃣ Keep humans in the loop Every action needs human approval. Every email gets a quick review. Goal is not speed yet. Goal is confidence. 3️⃣ Expand in stages 5 leads become 50. One workflow becomes three. One tester becomes a pilot pod. 4️⃣ Remove gates slowly When the agent proves itself, you start dropping checks. One step at a time. One rule at a time. 5️⃣ Scale to full automation Only when every rung feels boring and safe. This is how teams like Relevance AI roll out agents: research → drafts → send with approval → send on its own. Same way you train a new assistant.

When it comes to sales AI, we've got it backwards. At least that’s what is showing from the August 2024 Boston Consulting Group (BCG) report (screenshot below of one of the areas) Many think it's all about the tech. But in reality, 90% of success comes down to change management. I've seen countless teams pour resources into fancy AI tools or any tool for that matter, only to see adoption fall flat. Why? They neglected the human element, or the tool requires a lot of adoption rather than fitting into an already established workflow. Real transformation happens when you: • Get leadership bought in and excited • Engage your sales team from day one • Redefine processes to complement AI • Adapt your culture and metrics • Invest heavily in training and enablement Yes.. BCG says to INVEST HEAVY IN TRAINING AND ENABLEMENT. Which infers that my #enablement crew and I need to be ready to enable AI tech and workflows. Yes, the tech matters. But without change management, even the best AI collects dust. Have you seen AI projects or other tech tools succeed or fail based on the human factors?

𝗣𝗮𝗿𝘁 1: 𝗪𝗵𝘆 𝗠𝗼𝘀𝘁 𝗦𝗮𝗹𝗲𝘀 𝗧𝗼𝗼𝗹𝘀 𝗔𝗿𝗲 𝗤𝘂𝗶𝗲𝘁𝗹𝘆 𝗗𝘆𝗶𝗻𝗴 If your sales tool requires reps to log in, it’s already on life support. The average AE today is juggling 10+ tools across pipeline, research, reporting, and outreach—and most of them don’t make it into the daily workflow. We see it constantly with clients: —One enterprise BD team with $20M+ in pipeline? 30% of opps never even touch the sequencer. —A fast-growing SaaS org trialed a “10x personalization” AI tool. The output was solid—reps just never used it. Too many steps. Too many tabs. —A mid-market team installed a new account research platform. Only 1 in 12 reps ever logged in more than twice. What’s going on? These tools are all built on a broken assumption—that reps will stop what they’re doing, open a new system, learn a new UI, and manually copy/paste the output back into their actual workflow. But reps aren’t users anymore. They’re execution engines operating inside ~6 core systems: —Slack or Teams —E-mail —Salesforce or HubSpot —Outreach or Salesloft —A dialer (Orum 🥇 , Nooks ) —SalesNav or ZoomInfo That’s the footprint. That’s the real estate. Anything that lives outside this stack—or worse, requires switching between tabs, copying info, or manually triggering actions—doesn’t scale. Even with LLMs like Claude, ChatGPT, and Copilot now in every sales org, most tools still die in the gap between “good output” and “actual execution.” Zero-adoption tools are what’s quietly eating the market: —They live inside your existing workflows. —They don’t require reps to think differently. —They skip the training → adoption → behavior-change cycle completely. If your product requires reps to change behavior, it’s already lost. In the next post: how we’re helping teams engineer workflows where reps don’t even know the system has changed—but their pipeline does.