Brad Anderson is the President of Products and Engineering at Qualtrics. Brad previously spent 17 years as a key leader at Microsoft.
We stand on the brink of a transformative era in commerce, marked by the advent of new AI agents that can do everything, from automating customer support and personalizing marketing to managing inventory or spotting and fixing customer issues in real time. While many organizations have developed impressive transactional AI agents that excel in generating content and automating routine tasks, this narrow focus limits opportunities for meaningful connections with customers and employees.
Without a deeper understanding of human interactions, these agents fall short of fostering the relationships essential for long-term loyalty and engagement. Experience-focused AI solutions will require businesses to fundamentally rethink how they engage with and serve customers and employees alike.
This deeper connection matters. A new report from Qualtrics and McKinsey shows companies using AI to improve customer experience stand to gain up to $1.3 trillion by using AI to improve the experiences they deliver to customers.
However, the same report shows that a majority of senior executives are reluctant to lead their industry in AI adoption—just 15% of executives aspire to be at the forefront of how AI changes the business landscape. Financial gains aside, the fact that executives are reluctant to dive into this new world headfirst is not surprising.
As AI systems become increasingly autonomous and interconnected, the challenges in execution grow in complexity. The rapid evolution of AI capabilities is revealing significant limitations in many organizations’ technology infrastructures, from disconnected platforms, data silos and rigid architectures to insufficient computing power, all of which prevent them from fully leveraging AI.
The constantly changing AI landscape leaves many leaders questioning where to start. AI’s true potential is realized through coordinated transformation. In this article, I’ll outline three steps, which I’ve found particularly effective in my own work, that every organization can take to prepare for the agentic future.
Build A Future-Ready Technology And Data Foundation With Omnichannel Insights
By collecting and unifying experience data from every customer interaction, from in-store purchases and social media engagement to post-purchase support, mobile apps and email communications, organizations create a complete view of the customer journey. This integration allows AI to understand the full customer journey and generate accurate, real-time insights into experiences at each stage. Teams can use natural language prompts to access these insights, helping them identify ways to improve the customer experience at scale across every channel and touchpoint. This integrated approach is already being used today to improve sales cycle velocity, conversion rates, time to value, customer loyalty and net revenue retention.
Organizations will likely adopt multiple agentic AI platforms, but the key to their success lies in establishing a single, unified data schema for all agents to pull from. Ensuring that data delivered to each service comes from a curated, single source is essential, as this foundational data supply chain is critical for effective AI performance and decision making.
Set Clear Policies For Risk, Ethical Practices And Governance
Organizations need to set clear guidelines for responsible AI use, prioritizing strong protections for sensitive data and addressing potential biases. These foundational principles and controls are essential for building trust in AI adoption and ensuring compliance with changing regulatory standards.
A healthcare organization I worked with recently put this into action to avoid bias in their healthcare AI. They knew that AI models trained solely on their relatively young, healthy state population might produce biased outcomes compared to other regions. This proactive approach to avoid bias has led to successful AI implementations, such as an emergency department stroke-detection system, which automatically alerts the stroke team when it identifies qualifying conditions.
Transitioning from isolated AI pilots to organization-wide strategies requires coordinated oversight and collaboration among customer-facing, business and technology teams. Leading organizations achieve this by establishing clear decision-making processes and accountability for AI projects, often through dedicated teams responsible for evaluation, prioritization and implementation oversight.
Focus On High-Impact Use Cases That Demonstrates Immediate Impact
With the pace of change underway in business with AI, there is a significant first-mover advantage. And today’s leading companies are getting started with small use cases. Those smaller use cases help identify technical or process gaps, build the necessary operational muscles and demonstrate business value. The success of these initial implementations can pave the way for expanded investments.
An effective AI strategy should be led from the top, with leadership defining the organization’s approach to both immediate opportunities and emerging technologies, such as agentic AI. This approach allows customer experience teams to show executives the immediate benefits of their investment while positioning the company for future transformation.
The next generation of commerce will not just be about transactions but about building lasting relationships, anticipating needs and delivering personalized experiences at scale, too.
As executives prepare for an agentic future, they should identify key opportunities for AI implementation, plan necessary organizational changes and assess foundational investments in technology, data, talent and processes to ensure successful integration.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
