AI for ABM & LinkedIn Ads

By Yashasvi Saxena

LinkedIn ABM reporting goes wrong in a way that is hard to see because the reports look fine. For a long stretch of our program, I was reporting on ads and calling it ABM reporting. Impressions, CTR, cost per click, best-performing creative – all accurate and well presented. Then someone asked how many of our... Continue reading An AI Workflow for LinkedIn ABM Reporting: Four Loops, One Source of Truth

By Yashasvi Saxena

AI ABM segmentation is marketed as an algorithm that finds hidden clusters in your account list, and that promise is where most programs waste their first quarter. In reality, the segments that predict a meeting are not always the ones you can draw from a firmographic spreadsheet. Moreover, oversegmentation is a problem too. So the... Continue reading AI ABM Segmentation: Clustering Accounts at Scale

By Yashasvi Saxena

AI intent data sounds like it should solve account prioritization for you. Feed a model every signal you have, get back a ranked list, and work the top of it. That is the pitch, and it fails in a specific way that I want to name early. AI does not fix bad intent data. It... Continue reading Using AI with Intent Data for ABM Account Prioritization

By Yashasvi Saxena

AI account scoring for ABM only works when it tells your team which accounts to act on today, and it fails the moment it produces a confident number nobody trusts enough to chase. I run account-based programs on LinkedIn, and the scoring I have watched waste the most time is the black-box kind: a vendor... Continue reading AI Account Scoring for ABM: Models and The LinkedIn Signals Worth Scoring

By Yashasvi Saxena

ChatGPT for ABM usually means asking it to write ad copy, and that is the smallest thing it can do. When we ran our first LinkedIn ABM program, every step was manual: sizing the market, building the account list, researching accounts, auditing the account, reporting to execs, routing hot accounts to sales, and more. Most... Continue reading ChatGPT for ABM: Prompts and Workflows for Account-Based Teams in 2026

By Yashasvi Saxena

AI ABM reporting is not about buying another AI wrapper that gives generic advice, or an automation that drops report PDFs into Slack. All of that is too basic for 2026. For me, it is about having an AI-native system where I can throw a set of prompts at my own account data and get... Continue reading AI for ABM Reporting: How to Automate Account-Based Marketing Reporting with Claude

By Yashasvi Saxena

When we ran our LinkedIn ABM program, every step was manual: sizing the market, building the account list, researching accounts, writing ads, auditing the account, reporting to execs, and routing hot accounts to sales. Well, now we have AI agents for ABM to automate most of these tasks so you focus on judgement, not grunt... Continue reading AI Agents for ABM: Automating Account-Based Workflows

By Yashasvi Saxena

If you are a traditional demand-gen team shifting to account-based marketing, it’s going to be easier for you than it was for most teams when AI didn’t exist or wasn’t good enough. I’m talking about the operational tax: researching accounts one by one, checking engagement account by account, rebuilding the same report every Monday, writing... Continue reading AI-powered ABM: How AI is Changing Account-Based Marketing in 2026

By Emilia Korczynska

A pipeline-level ABM audit in Claude Code: pipeline per dollar, stage movement, the campaigns touching open and closed-won deals, and the intent that predicts deals.

By Yashasvi Saxena

Using Claude Code for ABM is not a theoretical concept anymore. It is already reshaping how B2B marketers run account-based marketing from their terminals. With 78.7% of companies now incorporating AI into their ABM programs (State of ABM 2025), and the shift from “AI-assisted” to “AI-orchestrated” marketing in full swing, Claude Code has emerged as... Continue reading Claude Code for ABM: Use Cases, Free Plugins and Public Repos for Account-Based Marketing (LinkedIn-first)

By Yashasvi Saxena

Claude is the new cool kid of the block, and context engineering using Claude code is now the differentiator between actually successful AI-powered LinkedIn account-based marketing (ABM) campaigns and bogus ones. So, if you’re up to ABM, but new to Claude (or have been using Claude without utilising its context engineering capability), this article is... Continue reading Context Engineering for LinkedIn ABM Using Claude Code: A Beginner’s Guide

By Yashasvi Saxena

Practical AI ABM workflows from the ZenABM Bootcamp 2026 – ICP scoring at scale, derived signal detection, closed-won analysis, and the tool stack for running it without enterprise budgets.