AI & Machine Learning

The AI-powered buyer journey: How B2B marketers must adapt to thrive

Why B2B marketers must rethink data, content, and visibility to succeed in AI-driven buyer discovery and decision-making

What you need to know

  • AI is reshaping how B2B buyers discover, evaluate, and choose solutions.
  • Marketers must ensure their brand is AI-visible, consistent, and trustworthy.
  • Unified data, identity resolution, and content aligned to buyer questions are critical.
  • Success requires discipline across data, content, and systems, plus continuous testing in AI tools.
  • Measuring AI-influenced discovery, recommendations, and pipeline impact is essential to adapt and lead.

Generative AI has transformed how buyers discover, evaluate, and choose solutions, creating a new competitive arena where your brand must earn visibility inside AI-generated recommendations as much as in traditional search. New research from Croud and ImpactSense shows the shift is already here: 39% of AI users discovered their favorite brand through tools like ChatGPT and Gemini, and 42% switched brands based on AI recommendations. While the study focuses on consumers, the implications for B2B are immediate. Decision-makers now rely on AI assistants to shortlist vendors, compare options, and inform purchases worth millions.

AI transforms brand discovery and trust

According to the Croud and ImpactSense study, AI-assisted shoppers spend roughly twice as much per transaction, not because they have higher budgets, but because AI gives them confidence through clear comparisons and relevant recommendations. Confidence compounds into trust, and trust drives action. B2B buyers carry these expectations into work: instant clarity, personalized guidance, and credible proof.

Visibility in AI results will soon matter as much as search rankings. The catch: AI can only recommend what it can understand. Fragmented data, siloed insights, and inconsistent messaging create blind spots that prevent AI from grasping your full value proposition. Without a complete, consistent, and machine-readable brand footprint, you risk invisibility in an AI-mediated market.

B2B decision-makers are already using AI

Across the buying journey, business professionals now lean on AI to accelerate due diligence and reduce risk. They use AI to:

  • Compare software and service providers
  • Draft RFPs and shortlist vendors
  • Evaluate brand reputation and performance data
  • Summarize reviews, case studies, and analyst reports

This changes the game. Your website, content, and sales enablement may never be seen if AI cannot parse and connect them to buyer needs. Winning in this environment requires more than better SEO. It demands an information architecture and data discipline that makes your brand intelligible to machines and indispensable to buyers.

Understanding the complete customer identity

Modern buyers don’t compartmentalize their lives. The same person researching ERP platforms at 2 PM may be shopping for running shoes at 7 PM, often using the same AI assistants and expecting the same quality of recommendations. Data Axle’s recent research underscores this: 72% of consumers feel more connected to brands that recognize their complete identity, yet fewer than 40% of marketers integrate psychographic or cross-contextual data into targeting.

As Andy Frawley, CEO of Data Axle, notes:

Andy Frawley, CEO, Data Axle “True customer insight comes from connecting the dots between who someone is as a consumer and who they are professionally. That connection helps brands synchronize their data, messaging, and activation across both identities.”

For B2B marketers, this means moving beyond job titles and firmographics. A CFO evaluating financial software brings consumer-grade expectations for speed, relevance, and personalization. Meeting them requires a unified view across channels, contexts, and moments.

Building your AI-ready foundation

Creating an AI-discoverable, AI-trustworthy brand requires disciplined execution across five areas:

1. Structure your data for discovery

  • Standardize product descriptions, features, and pricing terminology
  • Implement schema markup and maintain accurate knowledge panels
  • Ensure company, product, and people data are consistent across all profiles and directories

2. Create content that answers real buyer questions

  • Map top tasks and queries by role, industry, and use case
  • Publish explainers, comparisons, benchmarks, and decision guides in clear, unambiguous language
  • Align vocabulary to how buyers describe problems and outcomes

3. Connect your systems to eliminate data silos

  • Unify CRM, MAP, CDP, web analytics, support, and product usage data
  • Establish governance for identity resolution, consent, and data freshness
  • Maintain a single, accessible source of truth for AI and humans alike

4. Build trust through proof and transparency

  • Surface customer outcomes, case studies, and third-party validations
  • Clarify data practices and model governance where relevant
  • Keep documentation, SLAs, and security attestations current and easy to find

5. Continuously test how AI represents your brand

  • Audit your presence in leading AI assistants and vertical AI tools
  • Identify gaps, inaccuracies, and content ambiguities; iterate quickly
  • Treat AI visibility as a managed channel, not a black box

Implementing unified data strategies

Forward-thinking organizations are investing in unified data platforms to power this shift. Data Axle’s ProfileFuse™, for example, connects business and consumer profiles through advanced identity resolution to give marketers a more complete view of customers and prospects.

The benefits extend beyond personalization. Unified data enables:

  • Predictive analytics to anticipate needs and timing
  • Smarter recommendations aligned to intent and context
  • Seamless experiences across channels, devices, and roles
  • Higher fidelity signals for AI systems to interpret and trust

Execution requires both technology and operating model changes. Start with a data audit to pinpoint quality issues and duplication. Map journeys across B2B and B2C contexts to reveal behavioral patterns. Invest in enrichment and identity resolution to connect the dots. And align teams, marketing, sales, success, product around a shared customer view with clear data ownership and governance.

Measuring success in AI-driven markets

Traditional metrics still matter, but they’re not sufficient. Expand your scorecard to include:

  • Share of AI recommendations: Frequency and quality of brand mentions across AI assistants and vertical tools
  • Representation accuracy: How consistently AI systems describe your value proposition, differentiators, and pricing
  • Digital footprint completeness: Coverage and consistency of your structured data, profiles, and third-party listings
  • AI-influenced pipeline: Leads, opportunities, and revenue attributable to AI-mediated discovery and research

Listen closely to how customers say they found you, which questions they asked, and which comparisons they made. Monitor how AI tools portray competitors and where gaps exist that you can exploit. Build adaptable measurement frameworks and analytics that can ingest and interpret AI-driven interactions as platforms evolve.

Preparing for continuous evolution

We are early in the AI transformation of B2B buying. Assistants will grow more context-aware, more specialized by function and industry, and more capable of handling complex purchasing with minimal human intervention. To prepare, get the fundamentals right now, and cultivate the capabilities to adapt.

  • Foundations: Clean, organize, and connect your data. Publish content that reduces buyer effort. Document proof of outcomes and trust signals.
  • Capabilities: Upskill teams on AI tools and data literacy. Foster a test-and-learn culture with short feedback loops. Prioritize interoperability to avoid lock-in.
  • Investments: Close critical data gaps today while building platforms that scale tomorrow. Focus on capabilities such as identity, governance, enrichment, and orchestration, over one-off tools.

Transform your B2B marketing for the AI era

AI has permanently altered how buyers discover and choose B2B solutions. Thriving in this landscape requires more than incremental tweaks; it demands a fundamental shift in how you collect, connect, and activate customer data, and how you present your value in machine-readable, buyer-centered ways.

The brands that win will see customers completely, engage them intelligently, and iterate continuously as AI evolves. Your path to AI readiness starts with a decision: lead this transformation, or follow competitors who move faster. Ready to get started? Reach out.

Natasia Langfelder
Content Marketing Manager

As Content Marketing Manager, Natasia is responsible for helping strategize, produce and execute Data Axle's content. With a passion for writing and an enthusiasm for data management and technology, Natasia creates content that is designed to deliver nuggets of wisdom to help brands and individuals elevate their data governance policies. A native New Yorker, when Natasia is not at work she can be found enjoying New York’s food scene, at one of NYC’s many museums, or at one of the city’s many parks with her two teacup yorkies.