Marketing to AI Buyers: Understanding the New Decision-Makers
Authors
Chris Salazar
Innovative marketing executive with a 18+ year proven track record of driving market expansion, increasing revenue, and elevating market awareness for Fortune 500 companies.
In this article
Introduction
Marketing to AI buyers isn’t about retrofitting your existing B2B strategy, it requires a complete rethink of how enterprise technology is evaluated, purchased, and scaled. As AI adoption surges across industries, the decision-making power has shifted from isolated CTOs to complex buying committees that span data science, marketing, legal, procurement, and finance. These aren’t passive stakeholders, they’re active gatekeepers with distinct metrics, risk concerns, and success definitions.
In this new environment, understanding the AI purchase process means navigating cross-functional politics, aligning with evolving regulatory expectations, and providing business value from day zero. If you’re selling AI solutions in 2025, it’s not enough to speak the language of tech, you need to speak to the AI decision makers shaping strategy, compliance, and transformation across the enterprise.
Welcome to the new reality of marketing to AI buyers. Let’s break down what it takes to win.
The Changing Face of the AI Buyer in 2025
From CTOs to Cross-Functional Coalitions
The AI buying process has expanded well beyond the traditional scope of IT leadership. Decisions that were once solely the responsibility of the CTO are now made by cross-functional teams that include stakeholders from data science, product, operations, legal, and procurement
Many companies are creating formal AI steering committees to guide these decisions, and these groups play a central role in evaluating solutions, selecting vendors, and ensuring readiness for deployment.
Each function contributes a specific perspective to the table. Data science assesses how well the AI models perform. Whereas product teams look at how solutions will fit into existing systems. Legal teams focus on compliance and regulatory concerns. Meanwhile, procurement reviews financial and contractual risks. Because of this broad involvement, a successful AI product marketing strategy must speak to a wide range of business needs and expectations, not just technical requirements.
A practical AI buyer toolkit can help align these groups more effectively:
- Data teams need benchmark datasets to validate performance.
- Product teams look for integration roadmaps to assess fit with existing systems.
- Legal teams require regulatory risk maps to flag potential compliance gaps.
- Procurement teams need TCO (Total Cost of Ownership) calculators to evaluate financial impact.
The CMO’s Expanding Role in AI Adoption
CMOs are becoming key decision-makers in enterprise AI investments, especially in areas like customer experience, marketing automation, and revenue operations. Today, CMOs are actively involved in leading and sponsoring AI projects across the organization.
Whether it’s using generative AI to speed up content creation or applying predictive analytics to improve account-based marketing, CMOs are helping shape how AI supports business growth. This shift means that marketing to AI buyers must include messaging that speaks directly to marketing leaders.
AI should be positioned not only as a technical solution, but also as a way to improve brand value, customer relationships, and overall business outcomes.
In this new role, the CMO acts both as a strategic influencer and an internal buyer which makes it essential for AI vendors to tailor their approach accordingly.
Buyer Personas in Flux
AI buying decisions now involve a range of stakeholders, each with different goals and responsibilities. To succeed, vendors must understand and address the needs of these key buyer personas:
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Technical Evaluators: These include AI/ML leads, data engineers, and solution architects. They assess the technical fit, looking at integration, scalability, latency, and how well the solution works with existing systems. Their focus is on performance, architecture, and future flexibility. -
Strategic Buyers: This group includes Chief Data Officers and Chief Innovation Officers. They look at how the solution supports long-term goals, such as building new capabilities or improving data strategy. They care about roadmap alignment, product vision, and the ability to innovate together.
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Economic Buyers: CFOs and finance leaders focus on return on investment. They evaluate total cost, expected savings, and how fast the solution delivers value. They need clear proof of business impact and strong risk management.
Technical Evaluators: These include AI/ML leads, data engineers, and solution architects. They assess the technical fit, looking at integration, scalability, latency, and how well the solution works with existing systems. Their focus is on performance, architecture, and future flexibility.
Strategic Buyers: This group includes Chief Data Officers and Chief Innovation Officers. They look at how the solution supports long-term goals, such as building new capabilities or improving data strategy. They care about roadmap alignment, product vision, and the ability to innovate together.
Economic Buyers: CFOs and finance leaders focus on return on investment. They evaluate total cost, expected savings, and how fast the solution delivers value. They need clear proof of business impact and strong risk management.How AI Buyers Discover and Evaluate Vendors
Today’s enterprise AI buyers do most of their research before they ever speak to a vendor. AI-enhanced search, peer reviews, and social signals now shape buying decisions long before outreach happens.
Google’s AI Overviews and generative search have changed the game. Instead of scrolling through endless links, buyers get AI-curated summaries, often pulling from top content, reviews, and expert voices. If your brand isn’t part of that mix, you’re invisible at the moment that matters.
Peer reviews on platforms like G2 and TrustRadius carry more weight than ever. And on LinkedIn, buyers look for relevant content and trusted recommendations, not sales pitches.
How We Stay Visible at UnboundB2B
Our GTM strategy meets buyers where they research:
- Optimized content for AI-powered search and AI Overviews
- Strong LinkedIn presence with relevant, credible insights
- Programmatic ads and influencer partnerships to drive trusted visibility
The takeaway? If your brand isn’t showing up where AI buyers look: AI search, peer reviews, social feeds, you’re not even in the race.
What Makes AI Buyers Different From Traditional Tech Buyers
1. Buying Criteria Beyond Features
When understanding the AI buyer persona, it’s critical to go beyond functionality. For AI solutions, trust, explainability, and regulatory compliance often override raw performance. These buyers scrutinize training data provenance, bias mitigation strategies, and model drift controls.
They prefer interoperable, composable architectures over monolithic platforms. Flexibility to integrate with existing data pipelines and experimentation frameworks is often a deal-breaker.
2. Risk Perception and Pilot Culture
In AI, the purchase process is rarely linear. Buyers demand pilot programs to validate value in real-world conditions, often gated by legal, data privacy, and IT security reviews. This slows down traditional funnels but offers a powerful opportunity to co-create value with your champions.
Creating Messages That Resonate with AI Buyers
1. Build Trust Through Thought Leadership, Not Just Product Features
Marketing to AI buyers means earning credibility. These buyers are not just looking at features, they’re evaluating whether your solution meets complex concerns like algorithmic bias, hallucinations, data privacy, and regulatory requirements.
To build trust, share content that demonstrates real expertise:
- Publish thought leadership grounded in research
- Create co-branded whitepapers that address real AI adoption challenges in enterprises
- Be transparent about your technology’s limitations and safeguards
Superficial marketing won’t work with this audience. You must show depth and understanding.
2. Focus on Outcomes, Not Just Technology
AI buyers want to know how your solution impacts business, not just how it works. Use case studies to show results like:
- Reducing customer churn
- Improving supply chain performance
- Increasing revenue through AI-driven recommendations
Show real numbers and outcomes. This proves your solution delivers value beyond the technical layer.
Crafting Messages That Resonate With AI Decision Makers
1. Communicate for Complex, Non-Linear Journeys
The AI purchase process is not a straight line. Buyers often move back and forth, learning, testing, evaluating, and then repeating. Your value message must support each step, not just the final decision.
Key points to highlight:
- Productivity improvements
- Competitive advantage from proprietary data
- Your willingness to collaborate and innovate together
AI decision makers are looking for long-term partners, not just tools.
2. Use Education as a Core Strategy
The most effective AI marketing strategy for B2B buyers today is education. Help your audience understand your technology and how it solves their problems.
Examples:
- Host webinars with technical experts
- Offer GitHub repos or sandbox environments
- Share practical examples of AI in action
- Run “Ask Me Anything” (AMA) sessions with your team
Even your website copy and pricing pages should reflect AI knowledge, explain how your solution handles performance, infrastructure, and usage costs clearly.
Go-to-Market Strategy for Selling AI Solutions
1. Adapt ABX to Match How AI Buying Decisions Are Made
Account-Based Experience (ABX) should evolve to support modern AI buying decisions. Use tools like intent data and AI maturity scores to guide your outreach.
Coordinate efforts between sales, product marketing, and data teams to provide relevant information to each stakeholder in the buying process.
2. Focus on Influence, Not Just the Funnel
AI buying often happens through networks and internal champions. Map out key roles: AI teams, innovation leads, internal communities. Engage where they spend time, like Hugging Face, arXiv, Discord groups, and enterprise Slack channels.
Influence comes from being present and useful in the right ecosystems.
How to Turn AI Buyers into Strategic Advocates
The customer journey doesn’t end at purchase, especially in the AI space, where enterprise buyers must justify their investment internally and often influence wider adoption. To build lasting relationships and unlock growth opportunities, vendors must shift their focus from onboarding to customer advocacy.
One effective approach is to involve customers in strategic product development, inviting them to participate in roadmap discussions, beta testing programs, and feedback loops. This not only strengthens trust but empowers them to shape the evolution of the solution to better meet their needs.
Additionally, featuring their success stories in case studies or industry panels positions them as thought leaders while reinforcing your product’s credibility. Providing privileged access to product and engineering teams also creates a sense of partnership, rather than just vendor-client interaction. When done right, these efforts transform satisfied users into internal champions who drive wider adoption and influence future buying decisions within their organization.
AI Marketing Metrics that Matters
Traditional metrics like MQLs are inadequate for tracking progress in AI sales cycle. AI sales. Instead, focus on indicators of real engagement such as sandbox activity, participation in technical webinars, GitHub interaction, and pilot-to-production conversion rates.
AI buying is complex and cross-functional, so attribution models must evolve. Use a first-party data strategy to capture influence across multiple departments and touchpoints, giving a more accurate view of what drives purchasing decisions and long-term growth.
Final Thoughts: The CMO’s Role in AI Growth
Marketing to AI buyers means more than just promoting features. CMOs must understand the AI buyer persona, support decision-making across departments, and become trusted advisors during uncertain and evolving buying processes.
To succeed, B2B marketing leaders need to:
- Invest in better content and education
- Adapt AI go-to-market (GTM) strategies to navigate longer, more complex buying cycles
- Engage with the broader AI ecosystem
In short, CMOs must lead both in how AI is used within marketing—and how it’s marketed across the enterprise.
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