The number of AI tools marketed to sales and marketing teams has grown faster than most teams can realistically evaluate, which makes it easy to end up with a tech stack full of overlapping tools that nobody fully uses.
1. Start with Real Problems, Not Popular Tools
The most useful starting point isn't a list of popular tools, it's a clear list of specific, recurring problems your team actually has.
A tool that promises to "supercharge your outreach" means very little without a concrete problem attached, like reps spending too long writing follow-up emails, or leads sitting unqualified for days before anyone reviews them.
2. Choose Tools That Fit Your Existing Workflow
Once the problem is clear, it's worth being skeptical of tools that require a complete workflow overhaul to show value.
The tools that tend to stick around are the ones that fit into an existing process — a CRM, an email platform, or a call recording tool — rather than ones that ask a team to abandon what already works in exchange for a new system.
3. Fix Data Quality Before Adding AI
Data quality deserves more attention than it usually gets. An AI tool is only as useful as the data it's working from, and teams with messy CRM data or inconsistent lead information often find that AI features amplify existing problems rather than fixing them.
It's worth cleaning up core data before adding another layer of automation on top of it.
4. Take AI Tool Trials Seriously
Trial periods are worth using seriously rather than as a formality. Running a new tool with a small group for a few weeks, with a specific metric in mind, gives a far clearer picture than a polished sales demo ever will.
5. Regularly Review Your Existing Tech Stack
Finally, it's worth building in a regular review of the tools already in place, not just new ones being considered.
Tools that made sense for a five-person team can become redundant, or actively unhelpful, once a team doubles in size. It's easy to keep paying for something out of habit rather than because it's still solving a real problem.
Use Fewer Tools, More Effectively
A smaller stack of tools that's genuinely used well beats a large one that's mostly there for show.