The AI transition redefines growth
Artificial intelligence continues to drive a fundamental shift in demand generation. As AI passes the peak of its hype cycle, GTM teams have become increasingly cautious with respect to its adoption, as evidenced by the influx of new roles and departments throughout 2024. 71% of leaders now prioritize AI skills over experience, with 66% stating that they would not hire candidates without these skills (Microsoft 2024 Work Trend Index Report).
GTM teams are under immense pressure to move away from ad-hoc applications to activate AI in a strategic manner to drive continuous innovation and revenue attainment—all while reducing costs.
However, organizations impatient to drive rapid returns from AI applications risk impacting their growth. According to Forrester, 49% of US generative AI decision makers shared expectations for ROI from AI investments within one to three years, and 44% indicated a timeline of three to five years (Predictions 2025, Forrester).
Despite these forecasts, uncertainty plagues AI adoption and ingenuity. Indeed, Microsoft’s 2024 Work Trend Index Annual Report found that 60% of leaders worry that their organization lacks a congruent plan and vision for implementing AI—with a further 59% expressing concerns about quantifying the impact of AI on productivity.
Therefore, to successfully leverage AI, organizations must now focus on building unified AI strategies that drive innovation through efficiency, rather than growth at any cost.
2025 will be a transitional year for AI-augmented demand generation, with more solutions and startups built around the technology entering the market. Those who succeed will do more than simply eliminate mundane tasks—they will drive innovation and encourage businesses to rethink their GTM and demand strategies.
AI is now integral to the buyer’s journey. Its strength lies in interpreting complex data to forecast buyer intent and enable precise targeting and personalization, making it a powerful solution for scaling demand generation and ABM. However, the future of AI is less about automation and more about intelligent augmentation, allowing revenue teams to devote more time to strategic work.
The goal is to augment the human intellect, not replace it.
This article explores the fundamental ways organizations can align AI strategies across all teams to meet objectives and enhance the buyer experience.
55.4% of marketing teams are investing in AI for automating & analyzing data
“Organizations need to explore how AI can enhance demand performance, and that requires breaking down silos to leverage AI for transformative thinking and data-driven decision making.”
INFUSE
1. Building a sustainable AI strategy for demand generation
Marketers face increasing pressure to deliver results, despite limited resources and budgets. AI is often touted as a resource for enhancing marketing efficiency, yet the influx of AI-driven tools hinders the ability to identify solutions that best align with their unique goals. This challenge is further complicating B2B buying decisions, with a notable increase in buyer’s journeys that last longer than 12 months (Voice of the Buyer 2025).
As a result, GTM teams struggle to address the complexities of AI adoption as they balance this challenge with developing strategies.
Our research suggests that demand generation teams are often expected to use AI to drive organizational transformation with little understanding of how to integrate it strategically throughout the buyer’s journey. To succeed, they need clear guidance, realistic expectations, and well-defined outcomes. Only then can teams align AI adoption with organization-wide go-to-market (GTM) strategies that break down the silos that inevitably result from ad-hoc implementations.
The first step in building a sustainable AI strategy is forming a specialized team that takes ownership of AI adoption for the entire organization. This involves upskilling and reskilling team members in various use cases for AI in demand generation. GTM teams can then build a structured approach for AI adoption—one characterized by robust governance and standardization.
The importance of governance and ethical standards cannot be understated. These two aspects are much more challenging to enforce without an organization-wide strategy.
Below are the fundamental characteristics of a sustainable AI demand strategy:
- Privacy and transparency by design: Ensure personalization is based on consent data and clearly communicated privacy policies. AI usage must also comply with new standards and legislation as they evolve, such as the EU AI Act
- Clear alignment with buyer needs: Anchor AI practices with buyer needs to fine-tune your targeting, messaging, and campaign optimization. Brand perception and the quality of the buyer experience depend on this alignment
- Accountability and governance: Establish a governance framework for ethical AI use. This must include the regular review of AI models and applications to mitigate bias and ensure fair and inclusive targeting and personalization
AI is integral to the optimization of the buyer’s journey, but its adoption must be buyer-centric
- Alignment with buyer motivationsAlign strategies with buyer needs and pain points to deliver genuine value
- AI-augmented personalizationLeverage AI for demand intelligence to build tailored experiences for each buyer persona
- Foundational principlesMaintain trust by ensuring that AI enhances buyer confidence, rather than undermining it
“Many AI implementations have centered on cutting costs or saving time when we really should be exploring its potential to boost transformative outcomes. Without supporting AI adoption with the correct strategy and organizational alignment, it’s likely that AI will simply amplify existing problems and limitations in your demand generation processes.”
INFUSE
2. Improving targeting with AI‑augmented demand intelligence
The rise and fall of the AI hype cycle has led to increased scrutiny surrounding the utility and validity of AI-powered solutions. This is not only true of software companies offering new AI tools, but also those who are incorporating AI into their current technology stacks. As buyer awareness of AI rises, brand perception has become heavily dependent on strategic implementations that do not overpromise outcomes.
Instead of viewing AI tools as ad-hoc shortcuts in demand generation workflows, GTM teams must prioritize their potential to deliver long-lasting success and enhanced buyer experiences. Provided its adoption is anchored in a thorough strategy, AI offers a robust solution for compiling demand intelligence necessary to fuel innovation.
Once you have access to demand intelligence, AI tools can accurately map buyer journeys and enable in-flight optimizations, providing the insight needed to target buyers in a way that truly resonates.
The Role of AI in the buyer’s journey
61% of marketers use intent data to identify and prioritize accounts for targeting
“AI offers huge potential in driving demand and empowering better brand experiences, and the key to supporting that is close alignment around buyer needs and priorities. With an AI-augmented demand intelligence strategy, you can more accurately identify those needs and differentiate your value proposition. This will allow you to deliver valuable experiences rooted in buyer enablement that drive your growth.”
INFUSE
3. Enhancing brand experience with AI-driven personalization
AI-augmented demand intelligence empowers marketing and sales teams to optimize their targeting and personalization thereby giving them a strong foundation upon which to develop tailored strategies. This is essential as B2B buyer’s journeys are becoming increasingly complex, as demonstrated in our Voice of the Buyer 2025 report.
For instance, when purchasing new technology, almost every team is involved in some capacity. 77.7% of buying groups refer to IT professionals for conducting initial research into potential solutions. However, business and security professionals are often involved in signing off on new purchases. Depending on the nature of the purchase (and company size), the buyer’s journey might also cross over into legal, finance, or operations departments.
B2B buying groups are not only bigger—they also span different sources of information throughout every stage of the buyer’s journey. 59.5% of decision makers refer primarily to consultants and subject matter experts (SMEs), while 54.9% refer to technology vendors directly. This variation in preferences is born from differing responsibilities and priorities per buyer persona. For example, business professionals are more likely to be concerned with driving immediate ROI, while security and legal teams will prioritize areas such as governance and regulatory compliance.
Creating demand-ready content that drives conversions remains a key challenge for demand marketers. This is why many continue to explore generative AI’s potential to enhance content creation and build scale. However, such content rarely contributes to building trust and authenticity, or delivering memorable brand experiences—as discerning buyers are overwhelmed with the amount of content being shared and its low quality.
Therefore, marketers must explore AI’s ability to inform an omnichannel demand generation strategy enhanced by rich demand intelligence, beyond uses for content creation.
AI can help to deliver more compelling brand experiences by augmenting these four core pillars:
Understanding the disconnect between buyers and marketers
72% of marketers are still prioritizing AI use for content creation and optimization over buyer experiences and personalization
45% of marketers consider analyst firm reports to be their most effective content format in their demand strategy.
What are the main influencers of purchase decisions?
Peers and colleagues, conferences and events, peer review sites, and AI
Analyst firms, having dropped from 45% to 20.5% from 2024 to 2025
“B2B buying groups are constantly expanding, with stakeholders engaging with a variety of content through different channels at each buyer journey stage. No two buyer’s journeys look the same, making it vital to build a demand generation engine that’s highly adaptable to drive personalization at scale. This is a clear use-case for leveraging AI.”
INFUSE
4. Preparing AI for demand generation: Use cases
The AI demand generation use cases below demonstrate how intelligent augmentation and data-driven insights can drive precision and personalization at scale.
Case #1: AI-augmented ABM
A mid-sized software company provides project management software for the financial services industry. As a highly regulated and risk-averse sector, buying groups consist of stakeholders spanning operations, legal, compliance, and information security.
Case #2: AI-enhanced personalization
A large technology company provides mission-critical Cloud infrastructure to businesses across a broad range of industries. Buying groups are highly varied, making targeting and personalization especially challenging.
“Delivering enhanced and highly personalized buyer experiences must be at the core of AI adoption. Marketers should explore and tailor AI applications that facilitate the buyer’s journey. This will be a critical strategy for buyer enablement and leveraging AI to secure differentiators from competitors.”
INFUSE
Key takeaways
When adopting AI for strategic demand generation, keep in mind the following takeaways to deliver better brand experiences:
- Focus on how AI can enhance brand-to-demand and drive innovation: The true potential of AI is its ability to enhance how brands connect with their target audiences by delivering highly personalized demand generation. AI should also be applied to the execution of campaign tactics to empower teams to focus on high-level strategy and planning
- Prioritize the strategic adoption of AI, instead of siloed, ad-hoc implementations: To realize the full potential of AI for demand generation, it must be integrated across teams and functions to support the entire buyer’s journey and long-term outcomes
- Use data to fuel and continuously improve the role of AI in demand generation: Every AI initiative must be grounded in high-quality, consented, and actionable data, allowing marketers to use demand intelligence to deliver enhanced brand perception and buyer experiences
