Why AI Meeting Assistant Products Need Integrations To Win

In the past year, I’ve seen an explosion of AI meeting assistants and notetakers enter the market, and the market has certainly been growing. Nowadays almost every other call I’m on has a meeting notetaker (or sometimes multiple). But beyond that, one key indicator has been the pace of growth for our customers that operate in this space (such as TL;DV).

However, companies like Recall.ai have made it so easy for companies to add meeting notetaker capabilities to their products that the space become super crowded and undifferentiated, resulting in a race to the bottom on pricing for many (or making it free in the case of a consolidation play).

So how do you compete and win? From talking to our customers as well as observing the space more closely, I've noticed a common thread amongst all the successful AI meeting assistant products - they all go beyond generic transcription and summarization. The inflection point for the fastest growing AI meeting assistant products has been the addition of context-specific meeting summaries, and post-meeting agentic automation, and this is where integrations play a critical role.

Let me elaborate on why this is crucial in today's landscape.

TL;DR

AI meeting assistants lack sufficient context: Most AI notetakers today are given minimal context about the meeting participants, the history of the relationship, or the specific goals of the meeting. This results in generic summaries that miss critical nuances and fail to capture the true value of the conversation.

Post-meeting tasks remain manual: Despite having AI-generated summaries, teams still need to manually update CRMs, create follow-up tasks, or send recap messages. This severely limits the value of AI Meeting Assistant products, making customers pick the cheapest options.

If you're building an AI meeting assistant product, staying ahead of your competitors will hinge on your ability to seamlessly integrate with a wide array of tools and platforms. So let's break down these points in more detail.

Context-enriched meeting agendas and notes

AI meeting assistants can only be as good as the context they have access to. Generic summaries based solely on transcription are quickly becoming commoditized, and the real value lies in personalized, context-rich insights for each of your customers. Just to better illustrate, here are some examples of external context that can dramatically improve the quality of your AI-generated meeting summaries, which enables you to add more value to specific personas:

For customer-facing teams (sales & CS)

By ingesting context from your customers’ CRMs, their email, and other communications with a given prospect/customer, your AI meeting assistants can provide much more tailored insights in its summaries/notes, by referencing past interactions, deal history, and account-specific details.

For internal meetings:

Having access to your customers’ Jira, Asana, or other project/task management apps will enables your AI meeting assistant to summarize meetings all within the context of existing projects, such as understanding who the task owners are, the progress of specific tasks, and what deadlines exist.

For recruiters and HR teams

Data from your customers’ Applicant Tracking Systems (ATS) can enable context-rich summaries of interviews by referencing candidates’ responses in relation to the job requirements.

These are just a few examples of how integrating with these systems will unlock more tailored meeting notes and summaries for the specific participants based on the broader context of the relationship or project.

However, getting the context is just the first step (albeit an important one). The most tedious part about meetings is all the action items that come out of it. Someone still has to make sure everything that was discussed is logged in the appropriate places, be it Asana or Salesforce, and ensure everyone knows what their action items are. This brings me to the next point and the biggest opportunity - automating post-meeting tasks.

Automating Post-Meeting Tasks

The ability to automatically execute post-meeting tasks across various tools is what will truly set apart the next generation of AI meeting assistants. I’ve already seen how this has created inflection points in growth for some of our customers, and thankfully it goes hand in hand with much of the context

1. Update CRM fields after a sales call: Automatically populate or update fields like next steps, deal stage, or key stakeholders based on the meeting discussion.

2. Create tasks in task management apps after an internal meeting: Generate and assign action items in tools like Asana, Trello, or Monday.com based on the meeting summary.

3. Update Applicant Tracking System after an interview: Automatically log feedback, update candidate status, or schedule next steps in systems like Greenhouse or Lever.

4. Update or create tickets in Jira after an engineering standup: Create new tickets or update existing ones based on the discussion of bugs or feature requests.

5. Send summary of call to Slack/Teams: Automatically share key points, action items, and next steps to relevant channels or direct messages.

6. Draft a sales follow-up email after a sales call: Generate a personalized follow-up email draft based on the meeting discussion and action items.

By implementing these automated actions, AI meeting assistants can offer a level of efficiency that goes far beyond simple transcription and summarization. This not only improves the accuracy and completeness of data across systems but also frees up team members to focus on high-value activities that truly require human expertise.

We actually built a demo of a few common post-meeting AI actions - you can check out this video below. For more details on this demo and how it was implemented, click here.

Ingesting context and building agentic actions at scale

As you have probably noticed, the context needed to generate truly valuable meeting insights lives across dozens of your customers' 3rd party applications, and similarly, the post-meeting actions that need to be automated take place across various tools in the tech stack as well.

But it’ll cost months engineering resources to build those connections and the robust ingestion pipelines to process millions of records and files of all sizes, not to mention the ongoing monitoring required to ensure reliability.

That's why the engineering teams at multiple AI meeting assistants, including TL;DV, Update.ai, and Broadcast, all rely on Paragon as their products’ integration infrastructure. This allows them to focus on their core product and AI capabilities, while still shipping integrations at lightning speed.

See how you can ingest users' data from 3rd party apps like Google Drive and Notion in this tutorial here.

Winning the AI Meeting Assistant Market

To close out - the market for AI meeting assistants has becoming increasingly crowded, and it will only get more crowded. Even general sales enablement platforms are trying to bundle meeting transcription and summarization features into their platform, further commoditizing the meeting summary functionality. To truly stand out and deliver value, especially to larger organizations, your AI meeting assistant needs to be context-aware and action-oriented.

By investing in building these features, you can transform your AI meeting assistant from a nice-to-have tool into an indispensable part of your customers' daily workflows, which will be critical for driving long-term adoption and growth.

If you want to get ahead of your competition, see how Paragon can help you accelerate your AI meeting assistant product’s external data ingestion and agentic action roadmap by booking a demo with us today.