The GTM AI gold rush will fail if companies forget the BORING stuff. AI use cases need a well-architected foundation & data. 3 examples: (1) Want to set up an AI SDR to do personalized outreach? > You need to collect, standardize, and surface data on the buyer and their signals the right way. Otherwise the messaging will be crap. (2) Want to use AI to ask analytics-related questions (like what channel is performing the best)? > You need to collect the channel engagement data, tie the buyers to opportunities the right way, etc. Otherwise, the "insights" will lead you astray. (3) Want to set up a predictive AI account score? > You need to have a quality account database in CRM, set up integrations with enrichment tools & scoring tools, set up a method to highlight the top accounts for sales to work, and train your sales team. Otherwise, the score will be wrong and not trusted by sales. The requirement to not have a shitty foundation has never been greater in this AI era. And right now, AI can't do the job of fixing your data and architecting your foundation. That is up to your Ops team. So, by extension, your Ops team has never been as important as they are right now (if you don't want to be left behind by your competitors leveraging AI).
For the companies that want to use AI, they first need to organize and fix their data management
I wrote a counter-take to this last week (mostly targeted at CDOs and data people). What I like about your approach, vs the traditional data audience, is that you actually have defined use cases, you know what data you need, how to get it, and where it comes from. I speak to way too many people who go all-in on data management, but don't know what they should actually do with it after they spend 6-9 months on a monolithic data project. https://www.linkedin.com/posts/jonathan-hansing_too-many-data-leaders-and-cdos-have-their-activity-7248049426886205440-_kPE?utm_source=share&utm_medium=member_desktop
Wait... so you mean AI doesn't solve the garbage in -> garbage out problem and we still have to do the same boring data governance and CRM taxonomy stuff that has plagued 'digital transformation' in enterprises and RevOps in startups? Isn't there an AI for that? [<--- Joke - I have to clarify because irony often falls flat in LinkedIn comments] On the real though, one thing I do think ops teams should be excited about is using LLMs for what they are very good at: natural language processing. Standardizing, summarizing, and categorizing data with LLMs can be a huge input lift to any of these use cases. You have to be careful but there is reason for optimism. Notice I said optimism, not unmitigated hype 😉
Wait for the HockeyStack AI launch
Your output is only as good as your input - this applies to your data as well.
Agreed there is no way AI will be able to sort through the poorly structured, inconsistent, incomplete CRM data and find accurate important insights or take intelligent automated actions
Basically get your ducks in a row 🐤
Charlie Saunders Agreed. AI is a data game and most companies data are a pile of isht, especially when it comes down to sales or field activities
Amen
CRO at CS2 | GTM Operations For B2B SaaS
5moalso worth mentioning I don't actually think this stuff is boring, it's what I have worked on my whole career in Ops. But I see lots of companies trying to do the "fun/exciting" stuff with AI and ignoring the important foundational work in the process