Meet The AI Agents Redefining B2B GTM Strategies And Approaches At B2B Summit EMEA

Goal-oriented, self-improving, sometimes autonomous AI agents purpose-built for B2B buying and selling will soon impact every interaction during the purchasing journey and postsale path to value. Investment is on the rise, with 74% of B2B and B2B2C organizations adopting AI agents and 14% planning to adopt them, according to Forrester’s State Of Customer Obsession Survey, 2025. While most are “agentish” or agentic-adjacent (for now), AI agents are already augmenting the B2B go-to-market (GTM) workforce and workflows at a rapid pace, interacting directly with internal and external audiences and impacting both the customer and employee experience. Unlike a conversational interface, general-purpose chatbot, or personal productivity assistant, GTM AI agents are specialized by focus area and role, with more distinctly agentic capabilities as the technology continues to evolve. These AI agents will autonomously research, identify, and engage prospects to qualify opportunities, negotiate, and even execute complex deals. But for an AI agent to be successful on the job, its work must align to how buyers buy and how customers engage, based on demand type, purchase complexity, audience personas, opportunity types, the makeup of decision-making groups, and how a product is utilized postsale.

Agent Casting Call: Select, Align, And Integrate B2B AI Agents To Fit Your GTM Objectives

Plan for a mixed group of specialized AI agents to support B2B marketing and sales processes. Match B2B AI agents to use cases based on their focus area, scope of work, technical capabilities, and collaboration requirements based on the GTM function supported. Start by breaking down sales and marketing workflows into steps and tasks (or have an agentic AI agent do that for you), then determine which are best performed by human, AI agent, or hybrid human-AI approach. AI agents purpose-built for B2B GTM broadly align to seven archetypes, each tailored to specific business contexts, audience needs, interaction scenarios, and training data and tooling requirements:

• Rule-Follower: Automates repetitive tasks within predefined workflows.
• Producer: Generates custom content and messaging at scale for diverse audience segments and personas.
• Savant: Analyzes buying signals and offers actionable insights for optimizing buying interactions.
• Influencer: Crafts buying group experiences that accelerate decision-making.
• Choreographer: Ensures ongoing collaboration among AI agents and human stakeholders.
• Planner: (Implied role for strategic coordination and scheduling.)
• Innovator: (Implied role for creative problem-solving and new approaches.)
Use these archetypes to create a well-rounded cast of AI agents connected to key B2B GTM initiatives and the work to be done based on use case, specialization, capabilities, constraints and limitations, interaction style, and target business outcomes. In an ideal state, these AI agents could work together to deliver personalized B2B buying experiences by blending domain expertise and adaptability with creativity and process optimization. As autonomy continues to increase, so too does the need for AI agent alignment, accountability, governance, and human-first change management. Join Anthony McPartlin and Jessie Johnson at Forrester’s B2B Summit EMEA in London this October to learn more about AI agents and what they mean for GTM strategies and teams in the breakout session, “AI Agents: Hype Vs. Reality And Implications For B2B.”

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Originally published at Forrester on August 6, 2025.

Frequently Asked Questions (FAQ)

What are AI Agents in the context of B2B GTM?

AI agents in B2B Go-To-Market (GTM) strategies are specialized, often autonomous, AI systems designed to assist or execute tasks across the entire customer lifecycle, from prospect identification and engagement to deal negotiation and post-sale value realization. They differ from general chatbots by having specific roles and more advanced, agentic capabilities.

How do AI agents differ from chatbots or personal productivity assistants?

Unlike conversational interfaces or general chatbots, B2B GTM AI agents are purpose-built for specific business functions. They possess more sophisticated capabilities, such as autonomous research, prospect identification, qualification, negotiation, and even deal execution, aligning their actions with buyer behavior and customer engagement models.

What are the seven archetypes of B2B GTM AI agents mentioned in the article?

The article outlines seven archetypes for B2B GTM AI agents: Rule-Follower (automates repetitive tasks), Producer (generates content), Savant (analyzes signals for insights), Influencer (accelerates decision-making), Choreographer (manages agent and human collaboration), Planner (strategic coordination), and Innovator (creative problem-solving).

What is the key to successful AI agent implementation in B2B GTM?

For AI agents to be successful, their operations must be closely aligned with how buyers actually buy. This includes considering factors like demand type, purchase complexity, audience personas, opportunity types, decision-making group composition, and post-sale product utilization.

Crypto Market AI's Take

The increasing adoption and specialization of AI agents in B2B Go-To-Market (GTM) strategies highlight a broader trend of AI integration across industries. At Crypto Market AI, we see parallels in how AI is revolutionizing financial markets, particularly in cryptocurrency. Our platform leverages advanced AI agents for sophisticated market analysis, automated trading, and personalized financial insights. These AI agents, much like the ones discussed for B2B GTM, are designed to identify patterns, predict trends, and execute strategies with precision. We believe that the development of specialized AI agents, as seen in the B2B space, will continue to drive innovation and efficiency in the crypto world, offering powerful tools for traders and investors. You can explore our suite of AI-powered trading bots to see how these concepts are being applied in the financial markets. Additionally, our insights into AI data analytics for strategic crypto portfolios showcase how AI is being used to optimize investment decisions.

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