In 2025, the line between sales and marketing has blurred. Once seen as separate entities, they must now operate as a unified force, especially for Emerging Technology Services (ETS) firms. While that alignment deserves its own deep dive, today we’re focusing on how sales and marketing teams execute Account-Based Marketing (ABM) strategies with AI-powered tactics.
For ETS firms, traditional sales and marketing demanded countless hours of building content, running outreach campaigns, and nurturing leads across a wide funnel. Time and resources are often limited, making this model inefficient.
But AI has completely changed the game. It doesn’t just accelerate ABM, but it redefines what’s possible.
What once took hours of manual research and customization can now happen in seconds. From personalized content generation to smarter distribution and full-cycle automation, AI empowers small teams to operate like enterprise giants.
One of the key components of ABM is content personalization to the target accounts. This has traditionally been extremely human-intensive. Usually, this involved creating more materials, rewriting existing materials with different goals and outlooks, organizing and managing the materials, and creating a hierarchy based on the customer’s anticipated needs.
AI has changed the script on this.
“Create a case study based on project details and customer conversations that show how we solved the customer problem.”
“Suggest 5 blog posts based on the customer’s project details and outcomes.”
“Draft a blog post detailing the challenges in implementing <solution>.”
“Can you modify this article to showcase the work in compliance?”
“Can you showcase how this case study was used in the manufacturing space and how the problems faced were critical to manufacturing?”
“I would like to send a cold outreach to the <targeted account> of <targeted company> for our <solution name> solution. What material/tactics do you suggest I use?"
“What existing content would you suggest I share with <targeted account> to get an engagement from them?”
“Can you create custom content relevant to <targeted account>?”
“Generate relevant case studies relevant to <targeted account>.”
One of the most overlooked aspects of ABM is when, where, and how content is distributed. You can have the most personalized asset, but it falls flat if it reaches the wrong stakeholder or hits their inbox at a poor time. Traditionally, distribution has relied heavily on scheduling tools, gut instinct, or broad assumptions based on buyer personas.
AI tackles these areas:
“Which of our targeted accounts will engage with us with our <solution name> based on their interests?”
“Show me the top-performing content our target accounts are searching for online.”
“Out of our current blogs and SEO, which ones will our targeted accounts engage with the most”?
“Assess the current high-impact whitepapers, case-studies, and E-books, which ones would our targeted accounts likely engage with?”
“Which target accounts have been engaging with our case studies recently?”
“How many targeted accounts have registered this year for our <title> webinar compared to last year's <title> webinar?”
“Categorize how many targeted accounts are engaging with what tier of pricing model for our <solution>.”
“Which content touchpoints contributed most to high-scoring accounts in the last quarter?”
“Rank these 25 accounts by intent and engagement level to prioritize outbound.”
“Based on behavioral data, which 5 accounts should we prioritize this week for outreach?”
“Assign a conversion score (1-10) to each account using data from <your company>'s CRM, website analytics, and email interactions.”
ABM requires a lot of effort and resources to create customized outreach, track engagement, and qualify leads. Traditionally, this meant manually building workflows, monitoring CRMs, scheduling meetings, and flagging warm leads using big teams. But AI-driven process automation has single-handedly redefined that workflow, and here's how:
“Pause paid ads for accounts that haven’t opened the last two nurture emails.”
“Automatically move a lead to sales-qualified status if they watch a full product webinar and download a whitepaper.”
“Book a follow-up demo with any target account that visits the ‘Security’ landing page twice in 48 hours.”
“Auto-schedule a call with any user who visits our demo page and fits our top priority ICP.”
“Score this list of leads using company size, industry fit, and whether they’ve interacted with our compliance content.”
“Generate a ranked list of accounts with the highest lead scores based on website visits and email opens.”
“Based on historical data and webinar attendance, which leads are closest to sales-ready?”
“Trigger an alert when someone views our ‘Case Studies’ page and spends more than 2 minutes.”
"Two executives from <targeted account> signed up for your newsletter within the same week."
By integrating AI into ABM, ETS firms can reclaim time, sharpen focus, and close deals faster. What once required multiple full-time roles can now be streamlined using smarter AI tools.
At Vixul, our agentic AI, Vixie, powers our ABM strategy by identifying customer pain points and shaping more meaningful engagement. With Vixie’s insights, we’ve crafted tailored case studies that speak directly to what our accounts care about. And we’re not the only ones, our portfolio companies also use Vixie to get insights and so much more.
If you're ready to reform your ABM strategy with Vixie, apply to join the next cohort here:
Read more of our ABM articles here: