Why AI SDRs Need Integrations To Move Upmarket

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There has been a wave of startups and incumbents building AI SDRs in the last year, and many have seen good growth in the SMB segment. However, most of these solutions have failed to deliver on their promise to more mature organizations. In fact, I’ve seen many B2B sales and marketing leaders B2B (myself included) share that they’ve been ‘burned’ by AISDRs.

So this got me thinking, what will it take for AISDR products to achieve true product-market fit and successfully move upmarket? While there is no singular solution, one significant gap in AI SDR products today is the lack of integrations, and I’ll elaborate on why this is in this article.

TL;DR

1. AI SDRs don't have enough context: This is true across both knowledge and context about your customers’ businesses and products, and also their activity. AISDRs today are generally given a few paragraphs describing the company's product and ICP, which is never comprehensive enough, and most are not sufficiently integrated with their customers' CRMs, meaning they have no context on prior communications with a prospect. Unfortunately, this context lives across many apps - emails, internal/customer-facing Slack messages, Gong calls, Intercom chats, and more. Missing any piece of context can result in terrible results

2. AI SDRs can't do administrative tasks: They’re not be able to send calendar invites, generate notes in Salesforce, notify teammates of new leads in Slack, or upload calls to Gong. This is a nightmare for RevOps leaders who need to distribute accounts, analyze touchpoints, and maintain an accurate log of activities in their system of record (aka CRM).

3. Most AI SDRs are limited by one, maybe two channels: Some AI SDRs can only send emails, others can only interact via chat, but multi-channel outreach is the predominant strategy in today’s GTM.

If you’re building an AI SDR product/company, achieving product-market fit will hinge on your ability to seamlessly integrate with a wide array of tools and platforms. Now let me break each of these points down into more detail.

1. Access to Context

AI SDRs are only as good as the context they have - in fact, missing any piece of context can lead to very terrible customer interactions (imagine sending a cold email to someone who is actively in a deal). That’s why coverage is critical, and some of the most popular contextual data sources we’ve seen among AISDR companies include:

• CRM data: All touchpoints and notes on a prospect

• File storage: Product knowledge, sales training, and other documents that contain context

• Communication platforms: Slack/Teams threads, emails from Gmail/Outlook, Intercom chatbot interactions, and call recordings from Gong

• Marketing automation: Marketing emails that have been sent

By integrating with these systems, AI SDRs will have access to the same knowledge as real SDRs, except with infinite memory, which will enable them to be even better at copywriting and personalization than a real rep.

Additionally, by building real-time ingestion pipelines, they will always be up-to-date with every piece of new information or training that is created, making them the fastest learners in your customers’ sales teams. Just to make it more tangible, one example could be that whenever a new case study is built, the AISDR can access it and begin to use it in their outreach immediately.

2. Agentic Actions for Administrative & Collaborative Tasks

You’ve likely built out your own email/calling engines for your AISDR product, but you need all that activity to be logged in the existing tools that your customers’ use. Mature companies rely on tools like Salesforce as their system of record and their revenue operations teams will not adopt your platform if your AISDR’s activities are not synced to their CRM.

Additionally, if you think about all the things a human-SDR does (send calendar invites, share notes with Account Executives on Slack, etc.), a lot of their work occurs their sales stack.

That’s where cross-app agentic actions come in, and here are a few examples:

• Check calendar availability and send invites: If the AISDR needs to set up demo calls, they need be able to check for their Account Executives’ availabilities (ie. via Google Calendar), and create the meeting invite.

• Share notes with teammates: When a meeting is booked or a reply is received, the AI SDR may need to notify its colleagues in Slack/Teams with the necessary context.

• Log tasks/notes in the CRM: After a call is completed, or an email is sent, or a reply is received, your AI SDR needs to be able to log all of those activities in your customers’ CRMs

By implementing agentic actions, AI SDR solutions can offer a level of automation and efficiency that goes far beyond simple data syncing. This not only improves the accuracy and completeness of data across systems but also frees up human sales reps to focus on high-value activities that truly require their expertise.

3. Multi-Channel Engagement

We all know that it often takes multiple touchpoints across different channels to get prospects to book a demo. However most AI SDRs are isolated to one channel today - email and chat being the most common ones given that they’re the easiest to build.

In order to get closer to becoming as good as an average SDR, AISDRs need to expand into multiple channels, which can include:

• Website/in-app chatbots

• Calling (given how good Voice APIs have become)

• LinkedIn messaging

• Texting via Whatsapp

I left this point last because the first two pieces are much more important - once you have the context and the actions pieces built out, you should be able to effectively layer on additional channels relatively easily.

Ingesting context and building agentic actions at scale

As mentioned earlier, context lives across dozens of your customers’ 3rd party applications, and the work that needs to be automated also takes place in those external apps.

Building the integrations and ingestion pipelines to pull all of the contextual data (both structured and unstructured) is costly and will take months of engineering, and building out actions for your AISDRs will take even longer.

That’s why the engineering teams at AI sales companies are using Paragon’s embedded integration infrastructure, to offload all the connections, infrastructure, and monitoring for these integrations and ingestion pipelines.

Winning the AI SDR market

It’s pretty incredible to see how many companies are building AI SDRs, but once the hype cycle dies down and year-one renewals hit, there may be a bit of a reckoning. That said, people will be willing to pay a lot of money (think about the salary of an entry-level SDR) if you can get it right. If you invest in building integrations thoughtfully for your AI SDR product, you will likely grow faster than most other AI SaaS products that exist today.

If you want to get ahead of your competition, see how Paragon can help you accelerate your AI SDR’s integration and agentic action roadmap by booking a demo with us today.