Unlocking the Power of AI Agents with Data-Driven Insights!

As Salesforce unveils its latest AI innovation—#AgentForce—the future of sales, marketing, and service is being rewritten by autonomous agents. These AI-powered agents hold the promise of transforming how work is done, making it more efficient, scalable, and personalized.

However, without a solid foundation of high-quality data, this transformation may fall short of expectations, leaving businesses grappling with inefficiencies, subpar outcomes, and misaligned strategies.

In this post, we’ll dive into why data is the backbone of AgentForce and what organizations need to consider as they prepare for this seismic shift.

Data: The Fuel for AI Agents

Autonomous AI agents rely heavily on data—both historical and real-time—to operate effectively. They use data to understand context, make decisions, and execute tasks autonomously. However, the quality, completeness, and accuracy of this data will determine whether these agents are a game-changer or a missed opportunity.

For years, companies have been collecting sales and customer data, but much of it sits in silos, is outdated, or is riddled with gaps. With AgentForce, the challenge isn’t just collecting more data—it’s about transforming existing data into a usable, AI-ready format. Vectorizing data and structuring it in knowledge models is key to enabling agents to function autonomously with high accuracy.

Imagine an AI agent trying to manage customer relationships using incomplete or outdated CRM data. Key decision-makers have left the company, or product usage data hasn’t been updated. Without comprehensive, real-time data, AI agents may not only fail to augment human efforts—they may actively work against them, undermining business success.

Overcoming Data & Tech Debt

One of the most pressing issues for organizations adopting AgentForce is reckoning with decades of data and tech debt. Large enterprises, in particular, face complex, fragmented data ecosystems spread across multiple systems and departments. Leaders will need to address how to integrate this disparate data into a cohesive, AI-friendly structure.

But this isn’t just about cleaning up databases. Enterprises need to rethink how their teams manage data. "Getting the data right will require getting the talent and team design right," as one sales leader at Dreamforce put it. This means building cross-functional teams that include data architects, data engineers, and revenue operations specialists to ensure data governance and manage the pipeline feeding AgentForce.

AI-Ready Data: A New Kind of Preparation

AgentForce isn’t the first AI-driven technology to hit the market, but preparing data for Generative AI agents is a different ballgame compared to traditional AI models. Most companies are accustomed to working with predictive models that rely on clean, structured data. But for AgentForce to succeed, it needs contextually rich, real-time data to make accurate decisions.

This shift in data preparation requires more than just neat tables. Generative AI requires metadata, activity capture, and dynamic knowledge models to ensure agents deliver relevant, timely outcomes. For sales and marketing teams, this means moving away from static customer profiles and focusing on continuously feeding AI agents real-time data that adapts to shifting customer behaviors and market conditions.

Automated data capture from emails, meetings, and calls—paired with advanced tools like Nektar.ai that enrich this data—can help companies fill in the gaps and ensure AI agents always have access to the most complete and actionable data.

Trust in AI Agents: Building Confidence Through Data

A critical element in making AgentForce a success is building trust—both internally and externally. For sales teams, the idea of deploying AI agents might evoke skepticism. Will the agents make the right decisions? Will they represent the brand’s voice accurately? These doubts often stem from a lack of trust in the data that powers these agents.

Trusting the data is essential. If companies want their sales agents to embrace AI, they need to ensure the data feeding AgentForce is accurate, real-time, and contextually relevant. Providing sales reps with visibility into the data behind AI-driven decisions can increase confidence. Dashboards that show how data is being used and allow teams to validate AI-generated insights can help alleviate concerns.

What Happens When Data Is Incomplete?

The risks of poor data governance are real, especially when it comes to AI. Agents trained on incomplete or inaccurate data are likely to make poor decisions—leading to missed opportunities or even damaged customer relationships.

Imagine an AI agent sending an incorrect pricing quote to a high-value prospect or triggering a poorly targeted outreach message. In these cases, the AI isn’t augmenting the sales team; it’s hindering their efforts.

Data integrity is the key to unlocking AgentForce’s full potential. Businesses must prioritize capturing contextually relevant signals from customer interactions and maintaining strong governance over the data they use to train and operate AI agents.

Data-Driven Accountability for AI Agents

A common question raised at Dreamforce is: if AgentForce doesn’t perform as expected, who is responsible? Traditional sales teams have performance metrics and quotas that hold them accountable, but the rise of AI agents in the sales process means businesses need to rethink how they evaluate success.

Organizations will need to develop new metrics to assess the performance of AI agents. These metrics might include data accuracy, the quality of AI-generated recommendations, customer satisfaction, and overall business impact. Sales leaders will need to ensure that data feeds are continuously updated to reflect real-world changes and drive better outcomes.

Redefining the Buying Experience: The Role of Data

AgentForce will not only revolutionize internal sales operations but will also have a profound impact on the buying experience. Buyers today are cautiously optimistic about engaging with AI agents, but their confidence will depend on the quality of interactions.

AgentForce can streamline simple, transactional buying experiences by anticipating buyer needs based on behavioral data. However, for complex sales processes, buyers will likely expect a hybrid approach—where AI agents assist, but human expertise remains essential.

Delivering personalized, timely, and relevant interactions hinges on structuring the data that powers these agents. Without this foundation, companies risk undermining buyer trust and missing out on potential revenue.

The Road Ahead: Data as the Competitive Advantage

Data is the backbone of AgentForce, and companies that successfully tackle data integrity, governance, and infrastructure will gain a competitive edge. By contrast, those that fail to adapt will find themselves left behind as competitors deploy AI agents that drive growth and operational efficiency.

To prepare for the future of sales automation, businesses should begin investing in their data infrastructure today. This includes cleaning up existing data, implementing robust data governance practices, and ensuring teams have the skills to manage and enrich data for AI readiness.

Conclusion

AgentForce represents a monumental shift in how companies can scale their sales, marketing, and customer service efforts. But data will be the real foundation for its success. To stay ahead of the curve, companies must prioritize data quality, preparation, and governance, ensuring their AI agents can deliver on their promise of transforming customer engagement. In this new world, data isn’t just an asset—it’s the backbone of a revolutionary shift in how businesses operate.

Is your data ready for AgentForce?

Contact Nektar.ai today to learn how you can transform your data and prepare your business for the AI revolution.