B2B businesses are increasingly adopting agentic AI. According to research firm McKinsey, nearly 80% of enterprises report using agentic AI in at least one business function, including B2B sales.
This rapid uptake is largely driven by the evolution of AI capabilities. What began as basic demographic tagging has evolved to the development of real-time relevance engines that can predict and act on B2B buyer intent. In other words, agentic AI tools are now capable of orchestrating and delivering hyper-personalized journeys across the entire sales funnel – if teams can deploy and apply them effectively. Here’s how.
Top Challenges in Applying B2B AI at Scale
Deploying AI is a first step; it doesn’t guarantee sales success. Common challenges in applying agentic tools at scale include:
Moving From Pilots to Production
As noted by Boston Consulting Group (BCG), B2B companies can potentially reduce manual workloads by up to 60% with agentic AI – if they can successfully navigate the transition from pilot to production.
Consider an AI solution designed to analyze B2B buyer patterns and create personalized, post-purchase messaging. While pilot projects may perform well, scaling them often reveals issues with data accuracy and reliability. Here, slow and steady wins the race, but in the competitive world of B2B conversions, speed is a tempting solution.
Creating Secure Data Environments
Secure data environments protect both B2B businesses and their buyers. The multi-source nature of agentic AI data use, however, makes governance and visibility difficult. While tools may still perform as intended, businesses may find themselves at risk of regulatory non-compliance.
Disrupting the Buyer Journey
Although B2B buyers represent businesses, they are still human. To make the sale, businesses need to walk the line between operational and individual personalization. Failure to do both effectively can lead to lost sales.
Here’s an example: Bob’s Office Supply Store is negotiating with a prospective buyer and using agentic AI to streamline the process. From an operational perspective, Bob must consider the buyer’s current On the individual side, the company needs to build B2B sales decks that align with the preferences of business decision-makers.
Put simply, personalization is a two-part effort.
Three Ways to Boost B2B Personalization
Three best practices can help B2B sales teams boost personalization and drive more conversions.
1. Prioritize first-party and zero-party data.
Personalized AI outputs are driven by data. The closer this data is to the source, the better.
Consider third-party data, which is gathered from multiple sources and may be available publicly or for purchase through a data collection company. This data offers scope and scale for broad trend analysis, but because it is also available to competitors, it provides little in the way of personalization.
First-party data, meanwhile, is information collected by your company from your buyers. It may include behavioral data from websites, social media platforms, and mobile applications, along with preference data from surveys, customer service transcripts, and purchase histories. This data enhances what you know about B2B buyers and why they make specific decisions.
Zero-party data is information provided voluntarily by customers. This type of data is the most effective for personalization because buyers are actively and directly telling teams exactly what they want. Combining first and zero-party data helps AI tools build targeted B2B sales efforts.
2. Identify your ideal AI use case.
Agentic AI for sales is not a monolithic solution. As noted by BCG, it takes three common forms:
- Augmented selling – Uses AI to enhance seller efforts with talking points and recommendations.
- Assisted selling – Sees AI agents listening in on sales calls, offering real-time suggestions and creating follow-ups.
- Autonomous selling – Has AI interacting directly with customers to nurture leads and facilitate handoffs.
There’s no right answer here. Instead, consider the what, when, why and who of your sales efforts to find the ideal use cases.
3. Meet buyers where they are.
B2B decisions depend on buyers – the people behind the processes and purchase orders of prospective clients.
For AI-driven sales strategies to be effective, they must meet these buyers where they are. This is what separates general from granular personalization. Consider a business decision-maker who prefers social media interactions to those via email or mobile applications. Ads need to appear on the feeds they follow and the social platforms they visit, otherwise, they’re not truly personal.
Even the most compelling, custom-built sales copy won’t get the job done if your target audience doesn’t see it.
When it comes to B2B personalization, the technology moat is widening. Technology itself is no longer the dividing factor, but rather the ability of an organization to supply AI systems with high-fidelity, first-party data and combine human expertise with agentic AI operations. The result is a more consistent pipeline of qualified leads and higher B2B conversion rates.
