Boosting Your Sales with AI

Inflexion’s latest Commercial Exchange focused on enhancing efficiency and productivity through AI in go-to-market (GTM) strategies, showcasing practical approaches that clearly connect to business results.

In the past year, GTM teams have experienced a surge in AI products, including feature releases and indispensable point solutions.

“With the plethora of AI options available, it is crucial to establish a robust GTM foundation before introducing any tools. AI acts as an accelerant – it allows you to move faster – but if your underlying GTM framework is weak, you risk propelling your efforts in the wrong direction,” emphasizes Ada Pham, Inflexion Assistant Director focusing on commercial strategy.

To delve into this topic, Inflexion invited its portfolio to engage with Vanessa Goolsby, author of “The $100M Push” and a GTM and AI growth advisor collaborating with PE-backed businesses. She shared key insights from her experience to guide companies in leveraging AI for enhanced productivity.

1) Start with Pain Points and Outcomes, Not Tools

“We’re actually not here to play around with AI,” Vanessa underscores. The market is brimming with options, with research indicating thousands of new AI startups projected for 2025 alone, and major platforms rapidly integrating AI functionalities. This environment leads teams to experiment, but potentially at the expense of effective progress.

It is vital to begin with a clear understanding of what needs improvement commercially. Where are the bottlenecks: pipeline creation, conversion, deal velocity, onboarding, forecasting accuracy, or customer responsiveness? What does “good” look like in measurable terms — improved conversion rates, shorter cycle times, fewer manual transitions, faster integration for new hires, or enhanced data integrity?

Subsequently, tools should be seen as means to an end rather than the end itself. Vanessa outlined four guiding principles to keep teams focused:

• Don’t chase tools. Identify pain points, use cases, and desired outcomes first.
• Utilize your existing tech stack. Begin with low friction solutions: low or no code.
• Impact precedes scale. Develop MVPs, pilot, and demonstrate value before expanding.
• Design AI with the buyer in mind. GTM decisions shape the buyer experience; fragmented tools can lead to a disjointed customer journey.

Furthermore, decisions about tools made in silos can inadvertently complicate communication with customers—such as through automated outreach, sales emails, and customer success nudges—leading to an overwhelming buyer experience. Viewing the entire buyer journey as a unified system helps avoid “AI-enabled spam” and preserves brand trust.

2) Establish Robust RevOps Foundations and Clear Ownership

AI can amplify both strengths and weaknesses, making data cleanliness paramount. If data is poor, definitions are inconsistent, or handovers are unclear, AI may lead to greater confusion.

This underscores the importance of solid RevOps foundations: pristine CRM data, well-defined lifecycle stages, consistent activity logging, and clear accountability. It is essential to establish ownership, ensuring shared responsibility doesn’t devolve into a lack of accountability.

One effective strategy is to create small AI subgroups or champions who can translate needs into actionable pilots and manage ongoing optimization. Once a solution is implemented, accountability should be maintained by these champions rather than reverting to a central “innovation” team.

3) Approach This as a Cultural and People Shift: Prioritize Adoption Over Deployment

Integrating AI into GTM represents a transformation in how teams operate, learn, and make decisions, making leadership vital. Effective communication is key. Teams tend to respond more positively when AI is positioned as “AI-enabled human efficiency” rather than a threat to jobs.

Adoption is often the most challenging aspect: even the best tools can stagnate at partial utilization if employees must actively choose to engage with them. Jan Beitner, Director for Data & AI at Inflexion, highlighted an important distinction: “pull” models, where individuals must remember to use AI, often result in limited adoption. In contrast, “push” models, which seamlessly integrate AI into workflows and automatically reveal insights, are more likely to see consistent usage. Orchestration tools can facilitate a balance between off-the-shelf solutions and custom builds, enabling automation that doesn’t demand extensive engineering efforts.