B2B Tech Vendors Must Rethink AI Strategies for Future Growth

The rapid evolution of artificial intelligence (AI) has left B2B tech vendors at a critical juncture in their go-to-market (GTM) strategies. While Generative AI (GenAI) has captured significant attention for its promise to boost productivity, many organizations remain stuck in short-term efficiency models, overlooking the broader transformative potential of AI. According to Gartner research, a majority of Chief Marketing Officers (CMOs) prioritize productivity and cost-saving applications for AI, relegating customer experience enhancements to a secondary focus. While this approach may deliver immediate wins, it risks commoditizing AI capabilities and stalling innovation—a potential pitfall no forward-thinking organization can afford.

The Productivity Paradox in AI Adoption

The “productivity paradox” encapsulates a widespread trend: businesses are adopting AI to eliminate repetitive tasks but failing to explore its deeper possibilities. Gartner’s data reveals a troubling gap between AI ambitions and executions—while 88% of marketers require internal guidance on AI use, only 7% of organizations provide comprehensive strategies addressing key areas like workflow optimization and technology integration. This lack of applied AI guidance often reduces productivity gains to unfocused activities, further marginalizing the strategic benefits AI can offer.

Moreover, this disconnect between marketing’s productivity-driven AI initiatives and the C-suite’s broader growth objectives underlines the importance of reframing AI’s role. As Gartner suggests, forward-thinking marketers must shift their narrative from “doing more with less” to “unlocking unprecedented opportunities.” This shift has the potential to transition AI from a simple tool to a game-changing catalyst for growth and differentiation.

Three Phases of AI Evolution in B2B Marketing

To bridge the gap between current AI practices and its future potential, Gartner outlines three distinct phases of AI evolution: AI as a Tool, AI as an Agent, and AI as an Influencer. These phases chart a roadmap for how B2B marketers can fully unlock AI’s capabilities over time.

In the first phase, AI remains a basic tool focused on internal efficiency—replacing mundane tasks and streamlining operations. However, this stage often lacks customer-centric strategies, limiting its impact. Moving into phase two, AI transitions into an agent, capable of semi-autonomous operations that prioritize customer-centricity. Here, AI starts to engage directly with customers, providing tailored, differentiated experiences based on deeper logic and reasoning.

Finally, we enter the ‘AI as an Influencer’ stage, where AI becomes an embedded decision-maker. This futuristic paradigm introduces the concept of “machine customers,” where AI-driven systems, connected to over 15 billion devices, autonomously order supplies, evaluate providers, and even leave feedback. By 2030, some CEOs predict up to 20% of their revenue could come from these machine-operated customers. This transformation redefines customer-vendor interactions, demanding new strategies to cater separately to human and machine decision-makers.

What CMOs Should Do to Stay Ahead

To capitalize on this transformation, CMOs must take immediate action. First, redefine productivity metrics—shift focus from efficiency gains to business outcomes that include addressing untapped markets or unfulfilled customer needs. This realignment positions marketing as a vital growth driver.

Second, invest in data-driven customer archetypes and segmentation tools. The upcoming AI-driven market will depend on precise, well-researched personas that inform AI learning models, ensuring businesses can deliver value to both traditional human customers and emerging machine-based ones.

Finally, organizations should start experimenting with machine customer scenarios today. Low-complexity, autonomous AI-driven transactions offer an excellent starting point to glean valuable insights for future strategies. Pilot programs in this space will enable businesses to adapt swiftly when machine customers become mainstream players in the market.

Ultimately, the organizations that succeed in this shifting landscape will balance today’s productivity-focused AI applications with strategic groundwork for the machine-led future. As AI reshapes the rules of engagement, marketers must combine human emotional appeals with logical optimizations tailored for machine decision-makers. The future of B2B marketing demands adaptability, foresight, and a proactive embrace of AI’s transformational potential.