Ozan D.

one thing worth considering early on — most CRM decisions get made based on feature lists, but the real differentiator is how well the AI features actually work for YOUR specific workflow vs just looking good in a demo. I'd suggest doing a 2-week parallel trial with your top 2 picks using real prospect data before committing. the gap between "has AI features" and "AI features that actually save time" is bigger than most vendors let on

one signal that helped us was tracking how many new stakeholders appeared after proposal. if the buying committee grows post-proposal, the deal is alive but getting more complex. if it stays the same size, it's either a done deal or it's dead and nobody wants to tell you

this resonates. the thing that finally clicked for us was that distribution isn't a separate phase from building — it's a feedback loop. the conversations you have trying to get people to care end up reshaping the product itself. if distribution feels impossible, it might mean the framing is off more than the product is wrong

the one that actually gets me to respond is when someone references something specific I shipped or wrote — not 'I noticed your impressive background' but like 'saw your post about X, had a question about Y.' shows they actually looked instead of running a mail merge.

the data quality point is so real. even with clean data in the CRM you still need someone who actually understands the business context to sanity check AI outputs. ive seen AI pull technically correct pipeline numbers that told a completely misleading story because it didnt understand seasonal patterns or deal stage definitions unique to that org

honestly attribution tells you what happened but not why it worked. we stopped trying to get a single source of truth and just started asking "would we miss this channel if we turned it off" — way more useful for budget decisions than last-touch vs first-touch debates

one thing worth considering — if you go the MCP route you get tighter integration but its harder to debug when something breaks. n8n (like Areeb mentioned) gives you visibility into each step which is nice when youre still figuring out the workflow. id probably start with n8n to nail down the logic then move to direct MCP once its stable

Practically I'd start with providers for volume and layer in a custom AI workflow for the high-value accounts where you want differentiated signals. That hybrid approach tends to get better results than going all-in on either side.

Building your own with Claude or GPT is better for catching signals that are specific to your ICP — things like monitoring job postings for certain titles, tracking tech stack changes, or parsing earnings calls for keywords that suggest budget shifts. The upside is the signals are unique to you. The downside is you need to maintain the data pipelines and the model needs clear criteria for what counts as a signal vs noise.