How to evaluate AI assistants for Slack

There are dozens of AI tools that connect to Slack. Most of them promise to transform how your team works. Very few of them explain what that actually means for your Tuesday morning.

This guide covers what to look for when you are choosing an AI assistant for your Slack workspace. Whether you are on the IT team doing the security review or a team lead trying to speed up real workflows, these are the questions that matter.

Figure out what you're actually solving for

Before looking at any product, get specific about the problem. “We want AI in Slack” is not a use case. These are:

• Support gets the same five questions every day and first responses are too slow.
• Engineering standup threads hit 40 messages and nobody reads them.
• Sales needs CRM context without leaving the conversation.
• Leadership wants weekly summaries that don't take an hour to compile.

Write down three workflows you want to improve. If you can't name three, you might not need this tool yet. There is no shame in waiting until you have a clear need.

Where the tool lives matters more than you think

Some AI tools are separate apps with a Slack notification layer on top. Others are built directly for Slack. This difference shows up quickly in adoption.

A tool that lives inside Slack means your team does not switch tabs. Conversations, decisions, and follow-ups stay in one place. Threads keep their context. New team members see the tool in action just by reading channels.

A tool that requires a separate login or a second app will lose casual users within a week. Power users will keep it going, but broad adoption stalls. The people you most want using it (non-technical team members, managers, support reps) are the first to drop off.

The key question: is this a Slack app, or a separate product that sends Slack notifications? This is how Palfred handles it, for example. Someone mentions @Palfred in a channel, and the answer shows up right there in the same thread:

Security questions to ask before anything else

Every AI tool that processes your Slack messages is handling company data. Your IT team will have questions. Ask them yourself before you get surprised during procurement.

• Where does the data go? Is it processed in transit only, or stored somewhere?
• Is there tenant isolation between workspaces?
• Does the vendor use your data to train models?
• What compliance certifications do they hold (SOC 2, GDPR)?
• Can you control which channels and integrations the tool accesses?
• How are API tokens and credentials stored?
• What happens to your data if you cancel?

“We take security seriously” is not an answer. Ask for specifics. Good vendors will have a security page or technical documentation ready to share.

Setup and time to value

The gap between “install” and “useful” varies a lot between tools. Some you can install in a few minutes and start using the same day. Others need API keys, custom configuration, or a dedicated rollout plan before anyone sees value.

Questions worth asking:

• Can a non-technical person complete the setup?
• How long before the team is using it daily, not just testing it?
• Does it need custom configuration, training data, or integrations wired up before it does anything useful?
• When something breaks, can you debug it yourself or do you file a support ticket and wait?

If setup takes more than a day, adoption drops. Teams lose interest fast. The tool that gets used is the one that works on day one.

Pricing that makes sense at your scale

AI tools use very different pricing models, and the costs can surprise you once real usage kicks in. Here are the common approaches:

• Per-seat pricing. Predictable per person, but gets expensive fast when you want the whole team on it.
• Per-workspace pricing. One price for everyone. Simpler, but check what the limits are before you commit.
• Credit-based pricing. You pay for what you use. This can be cheaper if not everyone uses it every day, but make sure you understand how credits translate to actual requests.
• Free with limits. Good for evaluation. Less good if you hit the ceiling two days in and have to make a buying decision under pressure.

The question to ask: what does this cost when 50 people are using it every day? Not what does the trial cost for two people.

Run a real pilot, not a demo

Do not evaluate AI tools in a sandbox with test data. Run a pilot with a real team, on a real workflow, for at least two weeks.

Measure these things:

• Did usage stick after the first three days, or did people try it once and forget?
• Could the team get started on their own, or did they need IT to set it up for them?
• Were the answers good enough that people kept coming back?
• Did it handle the messy real-world cases your team actually hits, not just the clean demo scenario?

Pick one workflow, one team, two weeks. If adoption does not happen on its own, the tool is not the right fit. No amount of training or internal evangelism will fix a product that people do not naturally reach for.