Why Integration Data is Critical for Powering SaaS Platforms’ AI Features

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As SaaS companies look to differentiate and improve their platforms, they’re increasingly tapping into AI for help. Case in point: Prominent SaaS vendors like HubSpot, ClickUp, Zendesk, and GitHub have all recently-launched AI features in their product.

And, there’s no sign that the growth of AI features will slow any time soon. According to Omdia, AI will be featured in almost every new software product and service by 2026.

Of course, not all AI should be treated equal – and SaaS companies should consider how to optimize these features to best serve clients and drive business performance. 

While there are a variety of ways that organizations can build and enhance their SaaS products’ AI capabilities over time, collecting and leveraging their clients’ integration data ethically and securely should be a priority. By seamlessly integrating with their clients’ tech stacks – which may include a CRM for client data or an HRIS for employee data – SaaS companies can garner critical data that can improve the performance of their AI features and their businesses at large.

Integration data enables SaaS organizations to arm their newfangled AI features with the capabilities needed to separate themselves from the competition. Most notably, it allows organizations to collect a high volume of data over time that can lead to more personalized customer experiences and process optimization.

Integration data allows SaaS organizations to collect a high volume of data over time, securely

AI features need data to train or use as inputs for their underlying models. And the more data feeding these models over time, the better the AI features will perform. Integrations meet this requirement to a tee, as many of the systems they connect with will often store a high volume and diverse set of data.

To help illustrate this point, let’s use an example.

Consider a scenario in which a SaaS provider is striving to provide a feature that uses AI to deliver clients relevant recommendations on accounts they should target for sales purposes. Let’s also assume that the provider offers secure integrations with their clients’ CRM systems so that any lead the product recommends can automatically get added to a client’s CRM solution. 

Aside from the utility of syncing leads between the platform and the clients’ CRM systems, the integrations allow for the gathering of information on all client leads in an ethical manner. This includes key insights into secure data, such as the leads that closed (and those that didn’t), the industries they’re in, their locations and more. 

Integration data enables SaaS organizations to deliver personalized customer experiences

AI features are only valuable for clients when they cater to the clients’ unique situations.  

Embracing product integrations unfolds a strategic advantage to cater to clients’ unique situations – the ability to not just amass a wealth of data but to probe unique client data, particularly those who have adopted the product integrations. This allows companies to fine-tune AI models with a personalized touch at the individual client level, positioning integrations as a pivotal asset.

Let’s use the previous example to illustrate this point. 

Consider the hypothetical AI feature for recommending leads isn’t as valuable if it’s using aggregated data across clients to provide recommendations. Each client has had unique successes and failures in their sales motion and failing to account for these differences, however nuanced, can degrade the recommendations’ efficacy.

The ability to gather lead data from each integrated client and link the received data to its respective client enables the delivery of recommendations that effectively cater to the specific needs of clients.

Integration data lets SaaS organizations constantly optimize their AI features 

AI features should remain valuable for each client no matter how their business changes.

As integrations enable the collection of up-to-date (even real-time) data on clients’ systems, the AI features are able to evolve accordingly.  

Let’s go back to the example one more time to illustrate this point. 

Imagine that a client has recently seen success in selling to enterprise accounts and they want to double down on targeting this type of account. Since many of the latest closed-won leads in their CRM system include enterprise organizations, the AI model is trained to place greater weight on enterprise accounts when deciding which lead to recommend.

Product integrations play an invaluable role in powering SaaS organizations’ AI features, as they can help collect a high volume of client-specific, up-to-date, secure data over time. By feeding their AI models with robust, real-time data, SaaS companies can deliver optimal value to their clients ethically and establish themselves as a critical instrument for B2B brands. This, in turn, leads to closing more deals, especially when competitors offer fewer integrations; retaining more clients by allowing products to deliver additional value; and expanding to new markets by offering integrations with the applications target markets care about.

Taking all of this together, the question, clearly, isn’t whether SaaS organizations should offer product integrations; it’s how they should go about building them.

About the Author

Gil Feig is the co-founder and CTO of Merge, which is a single platform to add an entire category of integrations to your product. Merge makes secure data access easy by offering unified APIs across key software categories, such as HRIS, accounting, CRM, and file storage. Merge also handles the full integrations lifecycle—from an easy initial build to providing integration management tools to ensure customer delight, to fully owning the maintenance integrations. Prior to Merge, Gil was the Head of Engineering at Untapped and worked as a software engineer at Wealthfront and LinkedIn. A graduate of Columbia University, he lives and works in New York City.

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