◈ X-Research

Buyer / Operator Map

The operators Seva writes for: roles at companies spending $1M+/month on ads and AI sales-led hyperscalers. What they care about, how they talk, what they read.

Category: AI sales-led GTM · Revenue operator · Performance marketing · Growth

Shared Operator Dynamics

Cross-role tensions
What makes content land with ALL of them
Vocabulary that signals credibility
MER (Marketing Efficiency Ratio) — signals post-iOS-14 awareness Incrementality / geo-holdout — signals measurement sophistication Dark funnel — signals demand gen awareness NRR + GRR — signals you understand B2B retention math MEDDIC/MEDDPICC — signals enterprise sales process knowledge ROAS window (7-day, 28-day) — signals platform-specific knowledge

Head of Performance Marketing

VP Performance Marketing Director of Paid Media Head of Paid Acquisition Director Performance Marketing
Typical companies

Scale-stage SaaS (Notion, ClickUp, Monday.com, HubSpot) · Consumer-facing AI companies (Perplexity, Character.ai, Midjourney) · DTC/e-commerce at scale (Duolingo, Calm, Headspace) · Fintech growth companies (Ramp, Brex, Carta)

What they care about
  • ROAS (Return on Ad Spend) across all paid channels
  • Efficient CAC (Customer Acquisition Cost) at scale without blowing payback periods
  • Creative performance — which ad creatives convert and why
  • Cross-channel attribution — knowing what's actually driving acquisition
  • Incrementality testing — separating real lift from measurement noise
  • Platform concentration risk — too much spend on one channel
  • Keeping CPMs manageable as competition on major platforms increases
Key metrics (what they're measured on)
ROAS (channel and blended) CAC and CAC payback period MER (Marketing Efficiency Ratio) Creative thumbstop rate / hook rate (video) Conversion rate by channel CPM trends vs. competition New customer acquisition rate vs. retargeting mix
Language / vocabulary
ROAS / blended ROAS / channel ROAS CAC / CAC payback / LTV:CAC MER (Marketing Efficiency Ratio) — a post-iOS-14 blended measurement Incrementality / geo-holdout / lift studies Creative refresh rate / creative fatigue Attribution window / last-click vs. data-driven CPM / CPC / CPA / CPL Creative velocity — how fast new creatives are produced Hooks / thumbstop rate / 3-second views (video-specific) Signal quality / MMM (Marketing Mix Modeling)
Current anxieties (2026)
  • iOS 14 / signal loss still unresolved for attribution; many still flying blind
  • TikTok ban uncertainty — major creative investment in a channel that might disappear
  • AI-generated creative: does it actually perform or just reduce costs?
  • Google PMax: opaque, hard to control, sometimes cannibalizes branded
  • Platform CPMs rising as AI companies flood into paid (OpenAI, Anthropic Super Bowl ads as symptoms)
  • Budget under pressure while leadership wants efficiency AND growth
  • Incrementality testing is hard to operationalize; most companies fake it
Relationship with AI tools (honest 2026 view)

Cautiously excited. Most have experimented with AI for creative copy, ad variation generation, and creative brief automation. Few have seen dramatic ROAS improvements. Main use cases: creative scaling (more variants from fewer source assets), copywriting speed, audience brief generation, and reporting automation. The big skepticism: 'AI creative vs. human creative performance' — most find AI creative underperforms on initial testing but is useful for volume and variation. Emerging: AI-native measurement and attribution tools that promise better incrementality modeling.

Content that resonates with this role:
Specific before/after creative performance data. Real incrementality test results with methodology. Honest takes on 'AI creative that actually worked vs. AI creative that flopped.' Platform-specific playbooks with numbers. When someone says 'our CPM dropped 30% after X' — that stops the scroll. Generic 'AI will transform performance marketing' claims do not.
Key influencers / accounts they follow
Andrew Faris @andrewjfaris — Highly practical DTC/performance marketing operator; trusted for specific ROAS/CAC frameworks
Taylor Holiday @taylorholiday — CEO of Common Thread Collective; data-driven DTC performance marketing; known for MMM advocacy
Evan Kimbrell @evankimbrell — Ad creative testing and velocity; practical playbooks
Cody Plofker @codyplofker — Head of Marketing at Jones Road Beauty; known for blended ROAS transparency
Nick Shackelford @iamnickshack — Direct response performance marketing; Meta/TikTok playbooks
Media diet

Head of User Acquisition

Director of UA VP User Acquisition Head of Growth Marketing Head of Mobile Marketing
Typical companies

Mobile-first consumer apps (gaming, fintech, health, productivity) · Consumer AI apps at scale (ChatGPT, Claude, Perplexity) · Subscription consumer products (Calm, Duolingo, Spotify) · B2C SaaS with self-serve motion (Notion, Canva, Figma)

What they care about
  • New install / signup volume at target CAC
  • D1/D7/D30 retention rates (quality of acquired users)
  • App store optimization (ASO) for organic + algorithmic lift
  • Mobile attribution and SKAdNetwork complexity
  • Paywall conversion rate and subscription LTV
  • Creative testing velocity — shipping 20+ variants per week
  • Channel mix: where is the next scalable channel after Meta saturates?
Key metrics (what they're measured on)
CPI (Cost per Install) CPM/CPC on install campaigns D7 ROAS / D30 ROAS Subscription conversion rate from trial LTV vs. CAC ratio at cohort level Creative performance: hook rate, completion rate, CTR
Language / vocabulary
D1/D7/D30 retention Install CPR / CPI (Cost per Install) Paywall CVR (conversion rate) LTV / predicted LTV at Day 7 ROAS Day 30 / Day 60 / Day 180 SKAdNetwork / privacy-safe attribution ASO (App Store Optimization) Creative hooks / video hook rate / first 3 seconds Lookalike audiences / seeding
Current anxieties (2026)
  • SKAdNetwork and iOS privacy — still limiting targeting and measurement
  • Meta CPMs increasing; CPI rising across categories
  • AI-generated creatives saturating feeds — declining ad novelty
  • Consumer AI apps (ChatGPT, Claude) now in direct competition for mobile UA budgets/attention
  • TikTok regulatory uncertainty — can we build a real UA channel there?
Relationship with AI tools (honest 2026 view)

Highly active users of AI for creative. AI-generated UGC-style videos (avatar creators, synthetic spokespersons) are an emerging channel — some seeing strong performance, others seeing fast fatigue. Also using AI for audience research, creative briefing, and voiceover production. The biggest 2026 question: can AI generate creatives that match human UGC performance at 1/10th the cost?

Content that resonates with this role:
Creative testing results with specific metrics. 'Here's the creative + here's the D7 ROAS.' UA channel discoveries ('this channel worked for us at $X CPI'). iOS/privacy measurement solutions that actually work. Real subscription pricing experiments with results.
Key influencers / accounts they follow
Eric Seufert @eric_seufert — Mobile marketing / UA authority; mobiledevmemo.com; known for iOS 14 impact analysis
Matej Lancaric @matejlancaric — Mobile UA specialist, publishes detailed breakdowns of UA strategy at scale
Lior Eldan @lioreldan — Mobile growth operator; known for practical App Store and paid UA guidance
Media diet

Head of GTM

VP of Go-To-Market Head of Revenue Programs VP Commercial Head of GTM Strategy
Typical companies

AI-native B2B companies at Series A-D · Sales-led AI companies (Gong, Glean, Writer, Decagon, Moveworks) · Hypergrowth B2B SaaS at $10M–$100M ARR · PLG companies expanding into enterprise (Notion, Linear, Figma)

What they care about
  • Sales cycle efficiency: pipeline velocity, conversion rates at each stage
  • ICP (Ideal Customer Profile) definition and qualification
  • Demand gen engine: is marketing generating qualified pipeline?
  • Expansion revenue: land and expand mechanics
  • Pricing strategy: per seat, consumption, outcomes-based
  • Competitive positioning: how do we win vs. incumbent and next-gen competitors?
  • Sales team structure: AE/SDR ratio, territories, compensation
  • Enablement: can reps actually sell what we built?
Key metrics (what they're measured on)
ARR and MRR growth Pipeline coverage (3-4x quota) Stage conversion rates Sales cycle length Win rate vs. competitors ACV (Average Contract Value) NRR (Net Revenue Retention) CAC payback (sales-led)
Language / vocabulary
ICP / Ideal Customer Profile Pipeline velocity / stage conversion rates TAM / SAM / SOM Land and expand / expansion motion Consumption-based pricing / outcomes-based pricing Wedge / entry point / land motion Champion / economic buyer / mobilizer Proof of value (POV) / pilot NRR (Net Revenue Retention) Sales-led vs. PLG vs. hybrid motion
Current anxieties (2026)
  • AI SDRs / outbound automation — if everyone automates, does outbound work at all?
  • Pricing pressure: customers pushing back on per-seat pricing; want consumption or value-based
  • ICP drift as AI-native buyers have different buying behavior than legacy enterprise
  • Sales motion clarity: should we go PLG or stay sales-led as our product improves?
  • How to get founder-dependent sales to transition to repeatable AE-driven motion
  • Competition from OpenAI/Anthropic as platform players in every vertical
Relationship with AI tools (honest 2026 view)

Actively evaluating AI tools for sales productivity: Gong, Chorus, Clari, AI-SDR tools (Artisan, 11x, Clay). Significant skepticism about fully autonomous outbound. Most valuable current use: call recording + coaching (Gong, Chorus), territory planning, and CRM hygiene automation. Emerging: AI for competitive intelligence and proposal generation. The big question: can AI actually close deals, or just automate the boring parts?

Content that resonates with this role:
'Here is our actual pipeline conversion data before/after this change.' Specific sales cycle improvements with methodology. Honest takes on AI SDR ROI. ICP refinement stories with numbers. Win/loss insights that reveal something non-obvious about the market.
Key influencers / accounts they follow
Kyle Poyar @kylepoyar — Partner at OpenView; PLG pricing expert; widely cited for SaaS growth benchmarks
Elena Verna @elenaverna — PLG/sales-led growth expert; former Head of Growth at Amplitude, Surveymonkey
Jason Lemkin @jasonlk — SaaStr founder; B2B SaaS GTM authority; most widely followed SaaS CEO/GTM voice
Sam Jacobs @samfjacobs — Pavilion CEO; GTM community builder; RevOps and sales leadership discourse
Jacco van der Kooij @jaccovdk — Winning by Design; revenue architecture / recurring revenue motion
Media diet

Chief Revenue Officer / VP Revenue

CRO VP Sales Head of Revenue VP of Revenue
Typical companies

Post-Series B B2B SaaS ($20M–$200M ARR) · AI-native companies with enterprise motion · Sales-led hyperscalers (Gong, Glean, Moveworks, Writer, Harvey)

What they care about
  • Revenue attainment vs. plan
  • Sales team productivity: quota attainment distribution
  • Forecasting accuracy: predictable revenue
  • Hiring and ramping AEs quickly
  • Comp plan design: balancing growth incentives with profitability
  • Board relationship: delivering the number every quarter
  • Market share vs. category-defining incumbent
  • How to deploy AI to make reps more efficient without destroying sales culture
Key metrics (what they're measured on)
ARR attainment vs. plan Quota attainment distribution (% of reps at/above quota) ARR per AE Sales cycle length Win rate NRR Pipeline coverage ratio Ramp time to first deal
Language / vocabulary
Revenue attainment / % of plan Quota capacity vs. quota attainment Top of funnel / qualified pipeline Sales productivity (ARR per AE) Ramp time for new AEs Forecast commit / best case / pipeline MEDDIC / MEDDPICC (qualification methodology) Executive sponsorship / champion Gross margin on new business vs. expansion
Current anxieties (2026)
  • SDR ROI is collapsing as AI outbound floods inboxes — rethinking the model
  • Enterprise customers slowing decisions due to AI governance/security reviews
  • Defending ACV against commoditization pressure as AI makes basics cheap
  • Talent war for AEs who can navigate consultative AI sales conversations
  • Board pressure to grow efficiently while capital markets tighten
  • Platform risk: what happens to pipeline if OpenAI launches a competing feature?
Relationship with AI tools (honest 2026 view)

Primary buyer of revenue AI tools. Gong/Chorus for call recording and deal intelligence, Clari/Salesforce Revenue Cloud for forecasting, Clay/Apollo/Artisan for SDR workflow. Growing interest in: AI for deal coaching, competitive battle cards, executive briefing automation. Fundamental skepticism: can AI actually help AEs close? Most believe AI helps the mediocre rep, not the top rep. The top reps already close through relationship.

Content that resonates with this role:
Real quota attainment data before/after AI tool deployment. Specific AI-assisted deal stories with named customer. Honest CRO perspectives on whether AI SDRs replace or augment. Pipeline forecasting accuracy improvements with methodology. Board deck-worthy revenue growth cases that involve AI-enabled efficiency.
Key influencers / accounts they follow
Jason Lemkin @jasonlk — SaaStr; revenue leadership discourse for B2B SaaS at scale
Mark Roberge @markroberge — Former HubSpot CRO; Revenue Collective; sales science and process
Keenan Smith @keenansmith — Gap Selling author; B2B sales methodology
David Brock @davidabrock — Sales management and leadership; Partners in Excellence
Media diet

Head of Growth

VP Growth Head of Product Growth Growth Lead Head of Growth Engineering
Typical companies

PLG companies (Notion, Figma, Loom, Linear, Webflow) · Consumer AI at scale (Perplexity, Character.ai, ChatGPT) · Hypergrowth SaaS with self-serve motion · B2C subscription apps

What they care about
  • Activation rate: do new users reach value fast enough?
  • Retention curves: D7/D30/D90 retention and what bends them
  • North Star Metric (NSM): the one number the whole company optimizes for
  • Viral / referral loops: does the product grow itself?
  • Monetization: when and how to convert free to paid
  • Experimentation velocity: how many A/B tests can we run per week?
  • Growth model: what are the levers and coefficients?
Key metrics (what they're measured on)
Activation rate (reach value-defining event) D7/D30/D90 retention Virality coefficient (K-factor) Conversion rate (free to paid) North Star Metric Feature adoption rate Experiment velocity (tests per sprint)
Language / vocabulary
North Star Metric / NSM Activation event Aha moment Retention cohorts Referral / virality coefficient Paywall / conversion rate A/B test / multivariate test Growth loops Friction points PLG (Product-Led Growth)
Current anxieties (2026)
  • AI products have non-obvious retention curves — users churn after novelty wears off
  • How to build virality into AI products when the core interaction is private (chat)
  • Usage-based pricing creates revenue volatility that's hard to model
  • Personalization with AI raises privacy concerns that slow adoption
  • Growth team relevance: as AI does more optimization, what's the human growth role?
  • Attribution for AI-native marketing (hard to separate brand vs. performance)
Relationship with AI tools (honest 2026 view)

Most experimentally advanced operators with AI. Use AI for: feature flagging analysis, experiment result interpretation, cohort analysis automation, onboarding personalization, and A/B test design. Many using AI to generate experiment hypotheses. The frontier: AI that monitors retention and automatically identifies drop-off points without analyst time. Skepticism: can AI really generate good hypotheses, or just faster bad ones?

Content that resonates with this role:
Specific growth loop designs with metrics. 'We changed X in onboarding, retention went from Y to Z.' AI-specific growth patterns that are different from traditional SaaS. Real experiment results with methodology. North Star Metric choices and why — especially for AI products.
Key influencers / accounts they follow
Lenny Rachitsky @lennysan — Lenny's Newsletter + Podcast — most read growth content for PMs and growth operators
Brian Balfour @bbalfour — Reforge CEO; growth loops and systems thinking; trusted by serious growth practitioners
Elena Verna @elenaverna — PLG/growth expert; former Head of Growth at Amplitude, Miro, Surveymonkey
Andrew Chen @andrewchen — a16z partner; the definitive author on growth loops, cold start problem, viral mechanics
Casey Winters @onecaseman — Former CPO Eventbrite, Chief Product Officer at Airbnb; growth practitioner to advisor
Media diet

Head of Demand Gen / Growth Marketing

Head of Demand Generation Director of Demand Gen VP Marketing Operations Head of Revenue Marketing
Typical companies

B2B SaaS ($5M–$100M ARR) · AI-native enterprise companies · Sales-led companies that also have an inbound motion

What they care about
  • MQLs (Marketing Qualified Leads) and SQL (Sales Qualified Lead) conversion rate
  • Pipeline contribution from marketing
  • Content and SEO as compounding acquisition channels
  • Webinar, event, and field marketing ROI
  • ABM (Account-Based Marketing) for enterprise segment
  • Marketing automation and nurture sequences
  • Attribution: what marketing actually drives pipeline
Key metrics (what they're measured on)
MQL volume and quality Pipeline coverage from marketing Marketing-sourced vs. marketing-influenced ARR Email open / click / conversion rates Content organic traffic and conversion CPL (Cost per Lead) by channel
Language / vocabulary
MQL / SQL / PQL Pipeline influenced vs. pipeline sourced ABM (Account-Based Marketing) Intent signals / first-party data Nurture sequence / drip campaign Conversion rate optimization Marketing automation (Marketo, HubSpot) ICP targeting Dark funnel (demand that doesn't show up in attribution)
Current anxieties (2026)
  • AI flooding every content channel — SEO being disrupted by AI search (Perplexity, Google AI Overviews)
  • MQL quality deteriorating as forms get easier to fill with AI assistance
  • Dark funnel attribution: marketing creates demand but can't prove it
  • Budget pressure: leadership wants more pipeline with less marketing spend
  • Webinar and virtual event ROI collapsing as fatigue peaks
Relationship with AI tools (honest 2026 view)

Heavy users of AI for content production, email copy, landing page optimization, and SEO brief generation. Mixed results on quality; many running human review layers over AI-generated drafts. Actively experimenting with AI for: intent signal analysis, personalized nurture sequences, and webinar follow-up automation. The SEO disruption is the biggest anxiety: if Google AI Overviews and Perplexity answer queries directly, organic content strategy needs a full rethink.

Content that resonates with this role:
'Our content production volume went from X/month to Y/month with AI, and here's the quality impact.' Real pipeline attribution data from non-obvious channels. Dark funnel / community strategies that generated actual deals. ABM case studies with specific deal outcomes.
Key influencers / accounts they follow
Chris Walker @chriswalker171 — Refine Labs CEO; dark funnel / demand creation vs. demand capture; B2B demand gen
Gaetano DiNardi @gaetano_nyc — B2B growth + demand gen; SEO + content for SaaS; formerly HubSpot/Nextiva
Brendan Hufford @brendanhufford — B2B SaaS content and demand gen practitioner; known for transparent playbooks
Media diet

Creative Ops / Creative Strategy Lead

Head of Creative Creative Director (performance-focused) Head of Creative Ops Creative Strategy Lead
Typical companies

Companies spending $500K+/month on paid social · Consumer brands with heavy creative testing programs · DTC brands at scale · Consumer AI apps investing in paid UA

What they care about
  • Creative production volume: enough variants to test properly
  • Creative quality threshold: what separates performing from non-performing ads
  • Creative briefing: translating performance insights into creative direction
  • Iteration speed: from insight to creative in <48 hours
  • UGC (User Generated Content) sourcing and scaling
  • Video production: how to produce video ads at scale without breaking budgets
  • AI creative tools: promise vs. reality for actual performance
Key metrics (what they're measured on)
Creative CTR (Click-through rate) Hook rate / thumbstop rate (3-second views) CPA by creative Creative fatigue rate Number of creative iterations per week Winner rate (what % of tested creatives beat control)
Language / vocabulary
Hook / opening hook / first 3 seconds Thumbstop rate Creative fatigue UGC / authentic content Creative brief / creative concept A/B winner / control vs. variant Static vs. video vs. carousel Creative velocity Performance creative (creative optimized for conversion, not brand)
Current anxieties (2026)
  • AI-generated creatives are becoming ubiquitous — audience recognition and ad fatigue accelerating
  • UGC saturation: the 'authentic' format is now faked by most brands
  • Video production costs still high even with AI assistance
  • Brand vs. performance tension: creative that converts often looks cheap
  • Quality control at volume: AI generates faster than humans can QA
Relationship with AI tools (honest 2026 view)

The most directly AI-affected operator role in this map. Already using: AI image generation for static ad creation (mixed results on performance), AI video tools for product demo variations, AI voiceover for video ad variants, AI-generated scripts and hooks, and AI tools for creative analysis (which hooks worked, why). The key insight from 2025-2026: AI accelerates creative production dramatically, but performance creative still requires human insight about what emotionally resonates. The job is shifting from 'make the creative' to 'direct the AI creative production.'

Content that resonates with this role:
'Here's an AI-generated vs. human-generated ad performance comparison with real numbers.' Creative testing frameworks with specific winner/loser examples. AI video tool reviews with actual ad performance data (not just output quality). UGC automation results. What made a specific hook work — with the creative attached if possible.
Key influencers / accounts they follow
Ash Melwani @ashmelwani — Obvi CMO; known for DTC creative strategy + performance creative approaches
Katya Allison @katyaallison — Head of Creator at Marcom; creative strategy for performance
Barry Hott @barryhott — Performance creative consultant; clear frameworks for what makes ads convert
Media diet

Lifecycle / CRM / Marketing Automation Lead

Head of Lifecycle Marketing Head of CRM Email Marketing Lead Head of Retention Marketing Head of Marketing Automation
Typical companies

Subscription businesses (SaaS, consumer apps) · E-commerce with repeat purchase dynamic · B2C AI products focused on retention

What they care about
  • Onboarding completion rate and time-to-activation
  • Churn prevention: identifying at-risk users before they cancel
  • Email deliverability and engagement rates (open/click)
  • Personalization: right message at right moment in lifecycle
  • Upgrade/upsell flows: converting free or low-tier to higher-tier
  • Win-back campaigns for churned users
  • Push notification opt-in rates and engagement
Key metrics (what they're measured on)
Email open rate (adjusted for Apple MPP) Click-to-open rate (CTOR) Activation rate at Day 7 Churn rate (monthly / annual) Expansion revenue from lifecycle programs Win-back conversion rate
Language / vocabulary
Lifecycle stage (acquisition → activation → engagement → retention → expansion) Churn prediction / churn risk score Email open rate / click rate / unsubscribe rate Deliverability / sender reputation Segmentation / dynamic segments Behavioral trigger (send email when user does X) NPS / CSAT LTV prediction
Current anxieties (2026)
  • Apple Mail Privacy Protection broke open rates — can't measure without click
  • Inbox zero behavior means emails compete for attention like never before
  • AI-generated email content saturating inboxes — personalization loses signal
  • Churn is accelerating in AI products as novelty wears off
  • Building retention for AI products that users love but don't need daily
Relationship with AI tools (honest 2026 view)

Heavy users of AI for email copy generation, subject line A/B testing, and personalization token population. Using AI for churn prediction models and behavioral segmentation. The most valuable use: AI that identifies which users are about to churn 14 days earlier than rule-based systems. The skepticism: AI-generated emails feel generic even when 'personalized' — open rates haven't moved.

Content that resonates with this role:
'We added this AI trigger to our churn prevention sequence and reduced monthly churn by X%.' Real lifecycle flow designs with specific metrics. Email deliverability solutions that work. Personalization approaches that actually moved CTR vs. placebo. Apple MPP workarounds that give real engagement signals.
Key influencers / accounts they follow
Nikita Zhitkevich @nikitazhitkevich — Email / lifecycle marketing practitioner with detailed playbooks
Matthew Holman @matholman — Retention and LTV frameworks for subscription businesses
Media diet

RevOps / Revenue Operations Lead

Head of Revenue Operations VP Revenue Operations Head of Sales Operations Director of GTM Operations
Typical companies

Post-Series B B2B companies · Sales-led companies with multi-AE sales org · AI-native companies building a sales motion at scale

What they care about
  • CRM hygiene: is Salesforce/HubSpot data accurate?
  • Pipeline visibility: can leadership trust the forecast?
  • Sales process standardization: is every rep following the process?
  • Quota setting and territory planning
  • Compensation plan administration
  • Tech stack management: 40+ tool sprawl
  • Reporting and dashboards: one source of truth
  • Handoff process: marketing → SDR → AE → CS
Key metrics (what they're measured on)
Forecast accuracy (% of committed deals that close) CRM data completeness score SDR to AE handoff time Pipeline data freshness Sales velocity (deals × win rate × ACV / cycle length)
Language / vocabulary
CRM hygiene Forecast accuracy Stage progression Territory carving Quota capacity vs. attainment MQL → SQL → SAL → Opportunity → Closed Won funnel Tech stack / martech stack Data enrichment Handoff SLAs
Current anxieties (2026)
  • AI adds more data but doesn't solve the data quality problem — garbage in, garbage out
  • Tool sprawl getting worse as every vendor adds 'AI features'
  • AI forecasting tools (Clari, etc.) still require clean underlying data to work
  • Finance wants one version of truth; sales has six dashboards
  • AI agent adoption in sales workflow creates new process compliance gaps
Relationship with AI tools (honest 2026 view)

Cautiously optimistic. Most valuable AI use: CRM data enrichment (Clay, Apollo), automated meeting scheduling and follow-up logging, and AI-assisted quota modeling. Using Gong/Clari for deal intelligence. Biggest friction: AI tools require data quality that most sales orgs don't have. RevOps is often the person who has to make AI tools work despite messy data. The irony: AI needs RevOps to work, and RevOps needs AI to scale — but the dependency is circular.

Content that resonates with this role:
'Here's how we cut CRM hygiene time by 60% with this AI workflow.' Specific RevOps automation stacks with tool names and costs. Forecast accuracy improvements before/after AI implementation. Process playbooks that actually reduce tool sprawl.
Key influencers / accounts they follow
Rosalyn Santa Elena @rosalyn_SE — RevOps authority and community builder; The RevOps Collective
Brendon Cassidy @brendoncassidy — Revenue operations practitioner; VP Sales at multiple hypergrowth companies
Media diet