Context Meets Code: The New Rules of B2B Marketing in an AI-First World (Part 1)

AI of today is seen as an engine for growth and transformation – seemingly a silver bullet to redefine business in a gamut of areas including financial performance, cost reduction, customer satisfaction, operational efficiency, employee engagement and development.

Yet the question remains, where can AI really move the needle where businesses can see revenue growth, customer retention and increased productivity?

While AI has much hype, the real deal comes only when it can be an enabler and optimizer so that businesses can do more with less time and effort.

There are two trends emerging which we will cover in this 2-part series, one in the area of contextual AI and secondly in changing buyer dynamics due to generative AI and agentic AI. In this article, let us explore how focusing on context and changing buyer behavior can help B2B marketers apply AI better across various applications.

Making AI Work Where It Matters Most

In the B2B marketing world, there is an increased demand for contextual AI, which merges insights from three key areas: customer data, the context – which includes the industry nuances – and meaningful connections through MarTech.

I view industry-driven data in AI marketing as a best practice approach to the use of AI as it integrates public data with your unique first-party data. When you are training your large language models, you need good data. While public large language models (LLMs) use massive amounts of data, they don’t get you the specific insights into your business that you need.

Meanwhile, your business has a wealth of data specific to your unique environment. By bringing your unique data to your AI models via API integration and combining it with public data, you can uncover valuable insights, create tailored offerings, and refine processes that provide a competitive edge.

Connected Data as a Strategic Asset

For brands to take full advantage of AI, there needs to be a proper data strategy. The human element of crafting that right data strategy is foundational and cannot be so easily replaced by the machine. Humans help set parameters, guide training of AI models, eliminate biases, and provide governance. This allows for the ability to harness data well so that quality data feeds into your AI models, critical to improve productivity. With the right data strategy in place to handle AI complexity, brands can then focus on productivity and profitability. True power is released when you’re able to use data combined with the right AI applications and tools to transform your business end-to-end.

With this in mind, AI needs to be built on the right data and platform strategy in order for brands to extract real value. B2B marketing is becoming highly intent-driven and data-driven, and brands should use these intent data to effectively identify and act on customer pain points, buying behaviours, and intent signals.

Marketers of tomorrow need to know how to interpret data and make data-driven decisions by being savvy with the relevant AI applications and tools they can use in their specific industry context. 

Changing buyer dynamics due to the rise of AI

The second trend is in the changing dynamics of B2B buyers. Besides an increased use of Generative AI tools to support buyer research, the types of buyers who influence a purchase decision are evolving. These influencers are not just human, they include machines and AI agents who have been trained to make purchase decisions.

Increased use of Generative AI tools to support buyer research

The Forrester’s Buyers’ Journey Survey, 2024, shows that 68% of Asia Pacific (APAC) purchase influencers are also using GenAI to gather information about vendors.

Specifically in the APAC region, purchasing power has shifted to a younger generation of buyers. In fact, 71% of purchase influencers in APAC are under 44 years old. They primarily seek information about vendors through interactions with a mix of human and AI-driven influencers – including subject matter experts such as analysts, partners, consultants, and industry influencers, as well as insights generated by Gen AI tools.

Owing to these, marketing is now required to have intelligent, purposeful digital engagements for both human and AI.

With this reliance on large and diverse buying committees for B2B buyers, marketing and the wider product, analyst relations, consulting, and sales teams need to work cohesively to facilitate connections between the influencers, partners, and customer advocates.

Companies should also reevaluate their digital and content strategies to optimize for AI search. It's essential to consider how AI tools identify user intent. Marketing has a significant opportunity to shape a brand’s narrative in the AI landscape by creating high-quality, clear, structured, and differentiated content.

Additionally, companies need to remain vigilant even in the early stages of the buyer cycle by monitoring buying intent signals. This proactive approach is crucial for capturing mindshare and building trust during the discovery, evaluation, and consideration stages of the buyer journey.

A powerful approach involves developing insight-driven campaigns that seamlessly integrate a digital sales layer with traditional sales strategies. Utilizing AI as an enabler, analyzing buying intent signals, and making precise adjustments to ensure consistency between online and offline experiences are non-negotiable strategies for marketing.

In Part 2 of this series, I dive deeper into the second trend which explores further on the adoption of agentic AI and how it is rewiring B2B lead funnels.