As the sales landscape continues to evolve at a remarkable pace, PreSales teams find themselves at the forefront of a critical shift—one driven by the transformative power of artificial intelligence (AI). No longer a futuristic concept, advances in Generative AI are redefining how PreSales professionals operate, innovate, and deliver strategic value to their organizations.

The State of PreSales: AI Edition uncovers how AI is driving this transformation, offering deep insights into the challenges and opportunities facing the industry based on survey results from over 500 Sales Engineering leaders. 

For PreSales leaders, this shift presents both the opportunity to leverage AI for enhanced performance and efficiency, and the necessity of mastering AI to remain competitive in an increasingly data-driven world.

AI is doing more than automating processes—it’s reshaping customer engagement, refining decision-making, and helping Sales Engineers achieve new levels and means of strategic impact.

In this blog, we’ll explore the four most critical lessons from the report, providing actionable insights that PreSales professionals can use to adapt to this AI-driven landscape. These lessons offer a roadmap for integrating AI into Sales Engineering strategies, ensuring that teams remain agile, data-driven, and poised to excel in the future of B2B SaaS.

Lesson 1: AI Drives Scalability without Sacrificing Outcomes

One of the most significant insights from the report is how AI enhances efficiency. By automating repetitive tasks such as data entry or generating demo environments, PreSales teams can focus more on strategic activities, such as interactions with customers, or strategy for tapping into new addressable markets.

78% of PreSales teams that implemented AI solutions reported improved productivity, with fewer resources required to achieve the same or better outcomes. This increase in efficiency allows SE teams to handle larger deal volumes or support larger AE:SE ratios without sacrificing quality.

Additionally, the knowledge centralization and transfer enabled by AI tools allow for faster ramping and enablement, traditionally very time-intensive efforts.

We plan on fully adopting AI tools in the next 6 months. We expect to transform our workflows and boost efficiency.

Respondent #177

Lesson 2: AI as a Customer Insight Generator

Sales Engineering teams want AI tools that help them better understand customer and market needs, through analytics.

By analyzing customer data and tracking deal progress, AI systems can quickly identify trends, potential bottlenecks, and optimal strategies to pursue new markets. These insights are especially crucial for sales engineers working in complex B2B environments where understanding the customer’s needs at a granular level can make or break a deal.

To dive deeper into AI-driven insights for sales engineering, check out our AI resource center.

Lesson 3: AI Elevates Customer Engagement

AI tools have a profound impact on customer engagement by enabling more personalized and relevant interactions. Chatbots, predictive analytics, and intelligent content recommendations allow PreSales professionals to deliver the right message at the right time, tailoring their pitches to the specific needs and pain points of each customer.

50% of PreSales leaders are using AI to personalize customer touchpoints and reduce friction in handoffs from Sales to PreSales or PreSales to Post-Sales.

AI can ensure that both buyers and internal teams receive the precise information they need, when they need it, improving engagement and reducing friction in the sales process.

Explore more ways AI is reshaping customer engagement in 5 AI Trends Shaping the Future of Sales.

Lesson 4: AI Scales Technical Expertise Across Go-To-Market Teams

AI’s strongest suit won’t be the decks it creates or even the insights it produces— it will be the cross-functional alignment it drives across the entire GTM function.

Historically, there has been a disconnect between Sales and PreSales teams, often due to misaligned goals or siloed workflows. However, AI is bridging this gap by providing both teams with shared data, insights, and automation tools. This fosters collaboration, ensuring that PreSales efforts are more closely aligned with the overarching sales strategy.

Moreover, by facilitating knowledge transfer, AI can provide AEs with more independence. Once sales engineers equip AI with insider knowledge, sales reps and customer success managers can use AI for themselves, becoming more self-sufficient in the process. They’ll be able to get instant answers to sales engineering questions, straight from an AI agent.

Learn more about how AI is shaping the future of sales collaboration.

AI Challenges and Opportunities for Sales Engineering

With its focus on AI’s role in transforming the Technical Sales role and process, this research provides actionable insights and practical strategies for improving efficiency, customer engagement, and GTM collaboration.

The next challenge is to ensure strategic applications of AI tools to avoid a complete oversaturation. Tools not only need to be easy to use but also need to be leveraged correctly to provide the greatest lift. Otherwise, teams will face AI tech stacks that balloon out of control, becoming difficult to manage and potentially creating security risks.

Our biggest challenge with AI tool adoption is ensuring that the tools are user-friendly and seamlessly integrate into our existing workflows without disrupting our team’s productivity.

Respondent #453

We’ve been juggling a multitude of tools for our sales engineers, and it’s becoming increasingly difficult to keep everything aligned and efficient.

Respondent #288

If you’re ready to dive deeper into how AI is shaping the future of sales engineering, download the full State of PreSales report here.