Can AI Help Sales Engineers Work More Effectively?
We asked Sales Engineering leaders from top SE teams at Snowflake, AT&T, and more about their uses for generative AI. This article condenses our findings to present three realistic use cases for AI that Sales Engineers are considering right now.
Sales Engineers (SEs) manage tons of information - ranging from the details of customer deployment to the technical inner workings of their products. They're an invaluable source of truth for other internal teams and their customers.
We spoke to 12 SEs from leading teams across the industry - they commonly described spending too much time on administrative tasks, leaving them too little time and energy to drive positive outcomes with customers. Thanks to Large Language Models (LLMs), SEs are rethinking the various types of "busy work" they spend time on. Can AI help?
Organizing information into an easy-to-navigate knowledge base is essential to the smooth functioning of any Sales Engineering team. A knowledge base helps onboard teammates faster, allows you to deflect questions from account reps and support teams, and broadly ensures your team is on the same page.
Knowledge bases for Sales Engineering teams commonly include onboarding and demo flows, technical documentation, system diagrams, sales collateral, product security information, and more.
As teams grow, these knowledge bases become outdated, difficult to navigate, and tedious to maintain. We frequently heard about the broken search experience in these tools. You need to know specific jargon and keywords to find what you need.
Popular Tools: Notion, Drive, Confluence, and Sharepoint.
AI Search can turn your knowledge base into an interactive and intuitive search engine. You can not only find the information you need without knowing the exact keywords to search for - but you can also guide the formatting of the answer so that it's immediately useable.
Let's take the example of an SE named James, who wants to show a potential customer in the telecom industry examples of his success working with similar customers.
LLMs with the proper retrieval infrastructure can allow you to quickly search through vast amounts of information and synthesize the findings into usable answers.
RFPs, RFIs, and security questionnaires can waste days of SE's time (and they're no fun to work on). While they're necessary for many sales processes - they're full of questions you've answered before and are usually a long, tiring game of search and copy-paste. Leveraging AI to automate this process can save Sales Engineers tons of time while removing a source of stress and boredom.
Existing RFx tools rely on building an extensive Question-Answer Bank separate from your internal knowledge base. Your answer bank will frequently need to be updated. Any automation relies on exact matches between questions in the RFx and those in your bank, usually failing with even slight variations of the question. Finally, the answers lack personalization.
Popular Tools: Loopio, RFPIO, HyperComply
LLMs are great at reading large documents, sifting through tons of data, and finding the answer regardless of the question's phrasing. They can also provide deep personalization of the response to tailor content to each opportunity.
Let's see our hypothetical SE James respond to a security questionnaire.
Conversational Intelligence tools help SEs understand their team's discussions with specific customers, enabling them to propose customized solutions. This data is often used to write proposals, prepare for meetings, and customize deal room documents. LLMs are a powerful method for integrating the knowledge gained from meetings into sales workflows and automating these workflows.
The workflows powered by conversational tools like Gong are built for sales reps - they can help write a better email or perform better in their next discovery call. Sales Engineers tend to use the data in these tools as guidance while manually completing the workflows mentioned above.
AI can use data in these platforms to inform the customization of proposals, making them relevant and compelling with little human intervention. AI can also emphasize specific phrases or value propositions that resonate with the customer, which can be integrated into the proposal to enhance effectiveness.
AI can also help in preparing for follow-up meetings or presentations. It can provide a comprehensive analysis of previous interactions with the customer, highlighting the main topics of interest or concerns. This preparedness can make the proposal defense more successful and further improve the chances of closing the deal. AI's ability to automate these tasks lets sales engineers focus more on building stronger customer relationships and perfecting their sales strategy.
While AI has the potential to revolutionize Sales Engineering workflows, it's essential to understand its limitations and implement realistic solutions that speed up your processes instead of slowing you down. With effective use of large language models, Sales Engineers can automate time-consuming tasks, provide personalized content, and increase the utility of knowledge bases. By doing so, they can work more efficiently and effectively, bringing in more revenue for their organizations.
If you're interested in learning more, check out www.dealpage.ai. Our mission is to make Sales Engineering easier through the power of AI and intelligent automation.