March 26, 2024

Comprehensive Guide to Selecting the Right GTM AI Vendor For Your Business

Mariah Petrovic
Comprehensive Guide to Selecting the Right GTM AI Vendor For Your Business

Table of Contents

Today, it seems like every SaaS company is calling itself an artificial intelligence (AI) company. But not all AI tools are created equal and organizations need to be cautious during the purchase process. Forrester analysts predict that 70% of B2B AI buyers will experience buyer's remorse due to low-quality AI outputs in the next couple of years. This is because many tech companies are adding automation capabilities to existing products and calling it AI. Those sub-par products lack access to the data they need to actually produce high-quality, accurate outputs, don’t prioritize data protection, and can be detrimental to GTM business strategy. 

So, how do you navigate this sea of software and find the AI tool that’s right for your sales team? It requires diligent up-front assessment of both the AI tool and the vendor before you make an AI technology purchase decision. In this blog you'll find the top eight questions to ask GTM AI vendors - and the key things to listen for.

We've also created a comprehensive and customizable AI vendor RFP template that can be downloaded by clicking on the banner below. Questions are divided into 10 different capabilities sections ranging from activity capture to generative AI to information security. Each question has a column with the key things to listen for in vendor responses to ensure you're purchasing a high-quality and comprehensive solution for your business.

Data Foundation Questions

The biggest differentiator between a great AI tool and a mediocre one lies in its data foundation. A data foundation is the data the AI workflows have access to in order to produce insights. Any high-quality AI tool will be designed to help your sales organization build a custom data foundation on which to build your AI strategy. It should also have the ability to pull in real-time internal and external data from a variety of different sources. Unfortunately, this capability isn’t always immediately apparent when you’re getting a great demo showing off an AI vendor’s amazing UI.

Here are some data-related questions you can ask to find out if the AI vendor is right for your sales organization. 

Q1: What data sources does your solution use to generate insights? Do you have access to deal data, account data, relationship insights, and engagement data?

What to be listening for: The AI solution is only going to be as useful as the data it has access to. Too much data isn't the answer either. AI needs to have access to relevant data in order to produce high-quality outputs. It should be trained on the most important events of a deal to actually give you accurate next best actions.

Q2: Does the AI tool leverage both public and private large language models (LLMs)? [Specific to genAI tools]

What to be listening for: The best private generative AI (genAI) tools for sales teams start with a public large language model (LLM) foundation and then layer a private LLM on top of it. By using a public model like Azure’s version of OpenAI, a tool gains all the expansive knowledge benefits of the public OpenAI model but doesn’t push data back to the public model. The private LLM layer sits on top of the public one and acts as an extra security layer with minimized risk. The private layer also gets trained on data specialized for its use case, making it far more useful than a public tool.

Q3: Is the AI tool specialized for your industry and use cases? [Specific to genAI tools]

What to be listening for: Public generative AI tools like ChatGPT are like Swiss army knives. They’re useful for a wide variety of things but not great at any one thing. The genAI tool you choose for your organization should specialize in the industry and use cases you’ve identified as top priority. 

Data Processing, Matching, and Enrichment Questions

Most AI vendors will tell you that they can automatically capture your sales reps’ activities (meetings, emails, phone calls,etc). But it’s what happens after the data is captured that matters the most. The way the AI tool processes and matches all that data to fields in your CRM is called activity matching. Most so-called AI tools don’t do this very well, leaving AI workflows an incomplete and inaccurate data foundation to work from. And leaving you with useless or hallucinated insights. 

“The devil’s in the details. It sounds great to capture activities in context but if you don’t do it properly, you end up with a bunch of confusing data that doesn’t make sense. The accuracy of being able to do that with automation was really important to us, and we felt that had the best solution in terms of how it was being approached.” - Esther Friend, VP of Sales Efficiency and Transformation, Five9

Here are questions to ask any vendor selling AI tools for sales teams about their activity matching and data processing capabilities. 

Q4: Can your AI tool automatically create net-new contacts and domains when they are not already in the CRM?

What to be listening for: One of the key jobs of a sales rep is to constantly meet with new contacts. Those contacts need to get added to your CRM. If the AI tool cannot automatically create new contacts or domains in your CRM, data from those activities will be lost or associated with the wrong contacts/accounts/opportunities. 

BONUS: When a tool can create net-new contacts and domains in your CRM, it is actually improving your CRM hygiene. By filling in missing data, it helps itself make better rep activity data matches in the future, leading to better revenue intelligence in the long run.

Q5: What underlying technology do you use to match data about opportunities and accounts generated by my reps (meetings, phone calls, etc) to the correct fields in the CRM?

What to be listening for: You can gauge how sophisticated AI technology is based on how comprehensive this list is. One of the signals to look for in a great AI tool is Natural Language Processing (NLP). NLP is used to extract content from activity text that can be used to find the relevant account or opportunity.

Q6: Is your AI tool able to differentiate between similar CRM records when finding a match?

What to be listening for: When there's an activity that might match to multiple CRM records (e.g. multiple opportunities in an account), can the system use all of the signals to decide between them to find the best one? A rules-based matching system helps the tool select the most specific account or opportunity match based on multiple categories of signals (ideally 10+). This advanced method allows the tool to make accurate matches even when the available data is less than ideal.

Data Privacy and Security Questions

As with all new technologies, a sales AI vendor’s security policy should be one of your top considerations. Your company’s infosec team will make sure of it! There are a couple things you can look for to make sure your new AI technology partner is as serious about security as you are. 

Q7: Who are some of your enterprise-sized customers? 

What to be listening for: Always pick vendors that have large enterprise customers. Chances are those customer’s security requirements are even more stringent than yours. Those vendors will have put security and trust as two of their top AI priorities and will have documented how they’re going to protect your data (ask them for this documentation as well). 

Q8: Is my data going to be used to train public models?

What to be listening for: The only acceptable answer is no. Your proprietary company and customer data should remain in a closed and secure environment at all times. A public generative AI tool can be used by absolutely anyone and it’s important you don’t risk exposure of customer data or proprietary information by feeding it into a public tool.

Learn more about how to develop an AI roadmap for your sales organization.

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