Navigating the AI-Driven Buyer: A New Era for Sales Engagement

As technology continues to evolve, the landscape of B2B buying is undergoing a significant transformation. With digital tools advancing rapidly, buyers increasingly have access to advanced capabilities, leveraging machine learning and AI, that enable them to partially or completely delegate purchase activities and decisions to technology.

Gartner refers to these technologies as complex machine customers. Sales leaders are now facing a new challenge: Preparing their organizations to meet the data-driven requirements and expectations of AI-driven buyers.

By 2028, Gartner predicts that at least 15% of day-to-day business decisions will be made autonomously through agentic AI machine customers for rule-based tasks, up from 0% in 2024. Furthermore, Gartner also predicts that, over the same period, a machine customer will earn Customer of the Year at a global top 500 company.

The Emerging Role of AI-Enabled Technology in B2B Buying Groups

Gartner has found that 94% of B2B buyers are utilizing dedicated digital tools to support their purchase journeys. This goes beyond using common online channels like Google; it involves intentional delegation to sophisticated technology to aid in specific buying decisions and outcomes, such as the three use cases listed below:

1. Finding and assessing potential suppliers using applications such as supplier risk management, supplier diversity solutions or supplier discovery solutions.
2. Evaluating suppliers and awarding supplier contracts via tools such as strategic sourcing and RFx platforms.
3. Managing the bidding and negotiating process through contract lifecycle management solutions and advanced contract analytics solutions.

Outside of dedicated technologies, buyers are also leveraging large-language model-powered GenAI solutions, such as ChatGPT and Google Gemini during their purchases. Although less commonly used today, the reported level of adoption from buyers, and its association with delivering high-quality deals, illustrates GenAI’s rapid emergence and potential for future growth.

As technology continues to develop, buying groups are expected to delegate more tasks to these complex machine customers throughout the purchase journey. For example:

• During the problem identification stage, supplier discovery solutions leverage AI and natural language processing to ask questions of buying groups to understand project objectives and purchase intent.
• As buying groups explore potential solutions, supplier data platforms use machine learning and AI to constantly update supplier profiles and evaluate potential vendors against buyers’ business needs.
• RFQ processes, such as form creation, vendor follow-ups and vendor evaluations, are automated AI agents deployed by emerging procurement/sourcing applications.
• Contract negotiations are handled by AI-based contract negotiation applications that automatically evaluate agreement terms and offer unbiased resolutions to deliver a fair deal for both parties.

How Should Sales Leaders Respond?

Sales leaders must adapt their sales organizations to cater to both human and machine customers. This involves several initiatives for updating their sales engagement strategies:

1. Educate and Mobilize a Cross-Functional Team: Establish a cross-functional team to align on the machine customer concept and define scenarios where they are most likely to emerge.
2. Evaluate the Emergence of Machine Customers: Assess customers’ perception of spend with the organization, focusing on identifying accounts that consider your solution to be a nonstrategic purchase. Use market intelligence tools to assess technographic data and determine the purchase intent of customers across complex machine customer technologies and vendors.
3. Prepare Digital Channels and Sellers: Enhance the availability, accuracy and completeness of public-facing online company and product data which fuels the algorithms of complex machine customer technology. Educate sellers on engaging with new buying group stakeholders responsible for managing machine customer technology and interpreting their outputs.

The expanding role of complex machine customers necessitates a radical new approach to sales engagement. Sales leaders who fail to adapt risk losing revenue to competitors who effectively provide high-quality data to fuel algorithms and influence human buyers collaborating with these technologies.

By prioritizing the actions outlined above, leaders can ensure their sales organizations are equipped to navigate the complexities of AI-driven buyers and maintain a competitive edge in this evolving landscape.

Luke Tipping and Daniel Hawkyard are Director Analysts in the Gartner Sales Practice, focused on various sales engagement and strategy topics.