Investments in AI have reached unprecedented levels.
According to a World Economic Forum report, the global AI infrastructure market was valued at $35.42 billion in 2023, and projected to reach $223.45 billion by 2030.
Our own Q4 2024 market research indicates that 55.4% of B2B marketing teams are investing in AI to automate and analyze data to produce actionable buyer and buying group engagement and accelerate conversions.
However, this rapid adoption has revealed a key challenge: the lack of cohesive strategies for these unprecedented investments.
Microsoft’s 2024 Work Trend Index Annual Report echoes this, revealing that 60% of leaders are concerned over their organization’s AI strategy, while 59% are uncertain about AI’s impact on productivity.
For B2B marketers, the real opportunity lies in leveraging AI-optimized data analysis, combined with AI agents, to enable buyers more effectively in a market defined by complex buying journeys and large, defensive buying groups.
The strategic use of AI allows marketers to harness vast pools of data to develop buyer-centric strategies that address these complexities and empower decision-making.
In addition, AI can be leveraged to enhance and append market and marketing insights to demonstrate the tangible value of these efforts.
In this article, I will share key tactics for leveraging AI to enable buyers – and increase conversions by delivering a rich experience.
Why Is Buyer Enablement So Important?
We have seen a significant trend of buyers conducting their own independent research, with almost 70% of the buying journey spent collaborating with other members of the buying group, according to a recent report by 6sense.
To engage these buyers effectively, marketers must shift their focus toward enhancing brand awareness, creating brand preference, and delivering relevant content that supports these buyers in their research and decision-making.
In essence, marketers must create a more enriched buyer experience that aligns seamlessly with the preferences and behaviors of entire buying groups, accounting for the unique needs of each stakeholder.
AI is uniquely positioned to support these buyer-centric strategies by augmenting and optimizing marketing data.
This enables marketers to develop highly tailored and effective strategies to engage buyers at every stage of their journey.
4 Tactics For Leveraging AI To Power Buyer-Centric Strategies
1. Improve Personalization And Targeting With AI-Augmented Intelligence
Demand intelligence, derived from first-party data sources such as analytics, client relationship management (CRM) data, campaign metrics, and client feedback, is essential for delivering personalized outreach that drives qualified engagement.
AI can enhance this personalization by analyzing in bulk and enriching first-party data with firmographic, technographic, and location insights to build detailed buyer personas and detect prospect behavior and intent.
This not only improves targeting but also enables precise mapping of buyer journeys, offering the insights needed to craft highly personalized messaging that resonates deeply with each buying group member.
Additionally, AI can be leveraged to generate conversational content aligned with user behavior and preferences – obviously depending on organizational policies regarding generative content.
This ensures messaging is both relevant and engaging, further driving demand success.
2. ABX Enablement
Personalization at scale is a cornerstone of successful Account Based Experience (ABX) strategies, but achieving it can be both complex and resource-intensive.
AI offers a tactical solution by streamlining critical tasks such as segmentation and data analysis across large sets of accounts.
It can be leveraged to identify pain and friction points in the buyer’s journey, enabling marketers to craft and optimize omnichannel experiences tailored to target accounts.
AI also excels at account prioritization, leveraging dynamic scoring and intent data to pinpoint accounts and buyers with the highest likelihood of conversion.
This ensures that resources are directed toward the most promising opportunities, driving efficiency and maximizing the impact of ABX initiatives.
3. Sophisticated Automated Nurturing Sequences
One of the more exciting use cases of AI is the creation of automated omnichannel nurturing strategies that deliver targeted, cohesive experiences across channels such as email, social media, paid media, and content networks.
By leveraging data analysis, behavioral insights, and machine learning, AI can tailor messaging, timing, and delivery to individual prospect preferences.
How AI optimization can be utilized across marketing channels:
- Email: Personalized content based on user engagement and behavior.
- Social media: Social listening and sentiment analysis.
- Paid media: Large-scale A/B testing and optimized messaging in real-time.
- Content activation: The curation and distribution of content to niche platforms based on audience preference.
4. Performance Insights For Greater Optimization
AI has the potential to play a critical role in optimizing performance measurement by providing deeper insights and enabling smarter resource allocation in a timely and cost-effective manner.
Below are only a few ways AI can unlock demand performance:
- Multi-touch attribution analysis: Identifying channels, content, and touchpoints that contribute most to conversions, as well as tracking content consumption patterns and trends.
- Conversion rate insights: Uncovering key factors influencing conversion rates across account segments, sales stages, or campaigns to inform future outreach.
- Engagement trend detection: Detecting shifts in how key accounts engage with different content types or formats to determine content priorities.
- Centralized performance hub: Consolidating campaign metrics and results, enabling real-time monitoring and analysis of buying group behavior.
- Resource optimization: Identifying underperforming tactics or channels to allow resource allocation to higher-impact activities.
The Importance Of A Unified Strategy
The promise of AI lies in driving innovation through efficiency over pursuing growth at any cost. For this reason, sophisticated strategic planning and data analysis should take precedence over ad-hoc content creation tasks.
With 55% of buyers using AI to automate and analyze data, and 45% focusing on streamlining and optimizing systems and processes, organizations need clear guidance, realistic expectations, and well-defined outcomes to succeed (findings from our Q4 2024 market research).
To achieve this, it is essential to upskill teams in AI and provide a suitable framework for its adoption, including clear guidelines for AI usage, privacy protections, and safeguards against cyber threats.
By doing so, Go-To-Market (GTM) teams can develop a structured approach to AI adoption, characterized by robust governance, standardization, and a focus on sustainable, value-driven implementation.
Key Takeaways
- The world is experiencing an AI investment surge: Global investment in AI has reached unprecedented levels. However, many organizations struggle with a lack of cohesive AI strategies and measuring its impact on productivity.
- Buyer-centric strategies: The increasing complexity of buying journeys, with large, defensive buying groups, presents a significant opportunity for B2B marketers to leverage generative and agentic AI for more effective engagement.
- Ensure strong alignment with buyer needs: Centering AI practices around buyers and buying groups refines your targeting, messaging, and campaign optimization. This alignment directly influences brand perception and the overall quality of the buyer experience.
More Resources:
Featured Image: Golden Sikorka/Shutterstock
