At TrailblazerDX (TDX) 2025, the Digital Labor: Lessons from Early Adopters of Agents panel, moderated by Jayesh Govindarajan, gave press, analysts, and influencers a clear look at how businesses are using artificial intelligence (AI)-powered agents. The discussion focused on what’s working, where challenges remain, and how companies can scale AI across their operations—including in commerce and the buying journey.

Panelists included:

• Srini Tallapragada, President and Chief Engineering Officer, Salesforce
• George Pokorny, Senior Vice President of Global Customer Success, OpenTabl
• Gloria Ramchandani, Senior Vice President of Product, Copado
• Shane Smyth, Chief Technology Officer, Saltbox Mgmt
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Scaling AI: From One Agent to Many

Companies adopting AI agents often start small, automating repetitive tasks. Once they see results, they want more. But growth brings hurdles. Businesses struggle to balance speed, personalization, and accuracy, especially as AI interacts with more complex workflows. Scaling AI isn’t just about adding more agents—it’s about integrating them into existing systems while keeping data secure and reliable.

This is especially true in commerce, where AI agents are redefining how businesses support buyers at every stage. Whether it’s automating order management, personalizing product recommendations, or speeding up approvals in B2B transactions, AI-powered agents can help companies streamline the buying experience. But, as the panel pointed out, success depends on high-quality data and strong governance.

Data Is the Biggest Challenge

AI is only as good as the data it can access. A major discussion point was how businesses ensure their AI-powered agents pull the right information from the right sources. Many companies store data across different systems, including data lakes, application programming interfaces (APIs), and third-party platforms. The challenge is making sure AI can securely and accurately use this data without creating compliance risks.

For commerce, this means ensuring AI has real-time access to inventory, pricing, customer history, and order details. Buyers—whether in B2B or direct-to-consumer (D2C)—expect seamless experiences, but those depend on AI pulling the right insights from across ERP, CRM, and commerce platforms. If the data isn't clean or connected, AI can create friction instead of removing it—one of the biggest risks companies face as they scale digital labor.

Best Practices for AI Adoption

The panelists agreed: Start simple. The most successful companies begin by automating small, low-risk tasks. Once that works, they expand into more complex, reactive and proactive AI-driven workflows.

For commerce, this could mean starting with automating order status updates or inventory checks, then expanding into more advanced use cases like AI-powered product recommendations or personalized promotions. A phased approach ensures that AI improves the buying experience rather than adding complexity.

AI Is Becoming Digital Labor

The discussion made one thing clear: AI-powered agents are more than automation tools. They are evolving into digital team members that assist with real-time decision-making and continuously improve.

For commerce, this means AI can take on buyer-facing roles, guiding customers through complex product catalogs, handling pricing negotiations in B2B, or even ensuring that returns and refunds happen smoothly. AI isn’t just about removing manual work—it’s about elevating the entire buying experience.

The past six months have seen a major shift. AI-powered agents are no longer experiments—they’re becoming essential. Businesses that figure out how to scale AI while ensuring data security, compliance, and integration will lead the way in this new era of digital labor.

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