Each Monday, a short list of the sharpest sales + GTM thinking from the past week. Not summaries — read the originals, they're better. Just what's worth your time.
Four pieces this week.
1. Sam Senior — "The shortlist has shrunk to one or two, and you're probably not on it"
Sam Senior (Founder & CEO, TestBox) joined Sophie Buonassisi on The GTMnow Podcast (Ep. 186) to break down how the B2B buying process has structurally changed. His core argument: buyers are doing so much research via LLMs before reaching out that by the time they talk to you, they've already formed 70-80% of their decision. The shortlist used to be 3-4 vendors. Now it's 1-2. If you're not on the day-one shortlist, you've already lost — and you won't know it until the deal is dead. The mid-funnel, counterintuitively, is getting longer: buyers arrive with high expectations because they've simulated your use case in ChatGPT or Claude and gotten 70-80% of the result in 30 seconds. Bridging that last 20% gap — proving real-world value beyond the AI simulation — takes more time and trust-building than the old demo-to-close motion.
→ GTMnow Podcast Ep. 186 is available on all major platforms.
2. Maja Voje — "Context engineering is the GTM discipline that separates the 24% from the 53%"
Maja Voje (GTM Strategist, author of GTM Strategist) has been doing some of the most concrete work I've seen on why most GTM teams get nothing from AI. Her GTM Guide to AI Context Engineering puts the number plainly: 53% of GTM leaders report little to no impact from AI. Only 24% are seeing real returns. The difference, she argues, is structural — not the AI tool, not the prompt, but the context architecture. The 53% are treating AI like a chatbot: one-off conversations, no memory, re-briefing every session. The 24% have built what she calls a Context System — a living document the AI reads at session start so it already knows who you are, who you serve, how you communicate, and what your encoded playbooks are for repeatable tasks. The returns compound: week one you eliminate re-briefing; month three you're accumulating campaign learnings; month six you have a living knowledge base that doesn't degrade.
→ Her GTM Strategist Substack has been one of the better practitioner-grade AI-for-GTM resources I've tracked this year.
3. ICONIQ Growth — "AI-enabled GTM teams are running leaner at every revenue band — and the gap is widening"
ICONIQ Growth's State of Go-to-Market 2026 (published March 2026, based on a survey of high-growth private companies) is the most data-dense GTM benchmark report I've seen this cycle. The headline findings that are most operationally relevant: AI-enabled teams run leaner at every revenue band, with AI-influenced pipeline generation driving lead-to-MQL conversion up 11% and MQL-to-SQL up 8%. Average sales cycles have shortened by approximately six weeks — but contract terms are getting shorter too, driven by buyers demanding flexibility in a market where multi-year AI bets feel like a gamble. Free trial and POC motions are converting at 50% to paid, up 14 percentage points year over year. High-growth companies are generating 60-80% of pipeline from sales and channel sources versus 15-20% from marketing — a significant inversion of the inbound-heavy playbook that dominated 2020-2023.
→ SaaStr's coverage of the ICONIQ report has a good compressed read if you want the findings without the full PDF.
4. Revenue Operations Alliance — "The three AI ROI bottlenecks that 35 CROs named — and data quality is only part of it"
The Revenue Operations Alliance's CRO Insights Report 2026 (produced in partnership with Gong, Conga, and Backstory) pulls from real conversations with 35 revenue leaders globally. The three pressure points CROs named most consistently as AI ROI barriers: (1) inability to tie AI deployment to hard revenue metrics — conversion rate, deal velocity, NRR — before scaling; (2) time and internal expertise constraints, with most organizations lacking bandwidth to go from small pilot to enterprise-wide deployment; (3) data quality at 26%, because AI amplifies the assumptions already baked into your systems and scales bad data faster than good data. The report also names the three biggest revenue cycle bottlenecks where AI could help most: lead-to-opportunity conversion, forecasting accuracy, and quote-to-cash speed. NRR was named the primary north star metric by more than a quarter of respondents — notably outpacing new ARR as the headline number.
→ The full ROA CRO Insights Report is worth a read for any operator thinking about how to build the AI ROI case internally.
Common thread this week. All four converge on what's worth calling the context problem: the buyers have it (they've already researched you), the sellers lack it (they're briefing from scratch each call), and the AI tools meant to solve it mostly don't persist context in a way that compounds. Voje names it explicitly. Senior shows what it looks like from the buyer side. ICONIQ shows what it looks like in the data when teams solve it. The ROA report shows what happens when you don't. The shared lesson: context only helps if it's durable, structured, and flows back to the system of record.
Next Monday: a new four.
If you've read something this week worth flagging in next week's list — hello@mallin.io.