Editor’s Note: This is Part 2 of our 2026 predictions. You can read Part 1 here.
The Association of National Advertisers (ANA) chose “agentic AI” and “authenticity” as its marketing words of the year. The dual nod – its first-ever in its 12th year of bestowing the honor – reflects a marketing landscape where, the ANA says, “success will come from navigating advanced AI capabilities without losing the trust, truth, and transparency that define strong brands.”
According to Mordor Intelligence, the agentic AI market is expected to grow from $7 billion to $43 billion by 2030, representing a CAGR of 44%. This growth is expected to be driven by a shift toward autonomous systems, breakthroughs in LLM reasoning and the maturation of multi-agent orchestration frameworks.
In Part 1 of our 2026 predictions, we explored the reasons why data readiness is rapidly becoming essential for producing the best CX and AI outcomes – aligning with the need for hyper-personalized digital experiences. The second part of our predictions shifts the focus to agentic AI and how it impacts decisions about an organization’s underlying data foundation.
- Agentic AI Will Demand Innovative Data Readiness Solutions to Solve Persistent Data Quality Issues
According to Gartner, it is estimated that by 2028, 40 percent of agentic AI projects developed to support customer experience will fail due to issues with the quality and consistency of the data being used.
For agentic AI to play a key role in managing the relationships a brand builds with its customers, it must be supported by clean data fit for the intended use case. Data readiness is a crucial element for providing the foundation of good data to fuel agents.
But AI agents should not have to fix their own data. Ideally, the customer data will be ready before they need it – i.e. it will be right and fit-for-purpose – as we explored in Part 1 of our Predictions. Absent a good foundation, AI agents can now use key data services that already exist to get customer data ready for high-value autonomous use cases in CX, marketing and data products. These data services include identity resolution, data quality, matching, and data hygiene. MCP servers can provide best-in-class options, ensuring data is ready (by drawing on a complete, real time customer profile) allowing autonomous agents to operate accurately and efficiently in complex customer data processes.
A channel activation agent, for instance, queried by a marketing user to understand an audience, to visualize segments, or to analyze product affinities, becomes a key marketing and CX asset by virtue of being underpinned by data readiness. The same holds true for a DataOps agent managing data observability and data quality, handling questions from data engineers that might include how ready a particular dataset is to support a specific use case or how source data quality is trending.
The use of agentic AI will take off in the coming year as recognition builds that AI agents and MCP services can be used to resolve data quality issues. There will be widespread use of agents that continuously check for problems as data is created or updated, spot unusual activity, identify duplicate records, and automatically apply data quality rules without human intervention.
By leveraging the capabilities of agentic AI, organizations will significantly enhance their approach to customer data management, ultimately leading to improved data quality and operational efficiency.
- Proof Point: According to Martech for 2026, more than half (56 percent) of data teams say the biggest issue teams have with integrated AI systems is poor data quality. “AI,” the report says, “is the epitome of garbage in, garbage out.”
- ‘Customer Agents’ and ‘Brand Agents’ will Collaborate in Breakthrough Ways
The Gartner five-year personalization outlook posits that a digital twin of the customer strategy (DToC) will become a staple of the personalization landscape. As such, there will be an even greater need to understand customer intent, expectations, and needs. Agentic AI expands the concept of a digital twin in CX, in which brands build AI agents representative of a customer that autonomously perform tasks on behalf of a customer, such as scheduling an appointment, initiating a return, booking a hotel room, etc. The more a brand knows about a customer, the better the CX that will be delivered through the agent as a proxy.
Agentic AI has the potential to redefine customer engagement as autonomous agents manage complex tasks and anticipate customer needs – underpinned by real-time, high-quality customer data. As these complex tasks become more routine, we will see the concept expanded to where customer agents interact directly with brand agents. Customers will create agents to interact with their favored brands, providing the agent with the information needed to operate on their behalf. The more trusted the brand, the more information the customer will provide the agent – knowing the tradeoff will be a more relevant CX.
One breakthrough CX use case may be for customers to begin using agents to start a customer journey. Instead of a product search on a website, for example, a customer might set rules for its agent and, using GenAI tools, empower it to negotiate with one or more brand agents to find the best deal.
The continued integration of AI into customer engagement frameworks will revolutionize how customer agents and brand agents interact. By fostering autonomy, enhancing data integration, and promoting collaboration through advanced tools, organizations will see significant improvements in engagement strategies and customer service.
- Proof Point: According to Gartner, AI agent machine customers will replace 20 percent of the interactions at human-readable digital storefronts by 2028. Agentic AI will eliminate the need to interact with websites and applications. Why bother when your AI agent can do it for you?
- Proof Point: According to McKinsey, considering the growing availability and adoption of AI-powered discovery tools, along with moderate assumptions about merchant readiness for agentic commerce, by 2030, the US B2C retail market alone could represent an opportunity to orchestrate revenue in the range of $900 billion to $1 trillion.
- Data Management Leaders Will Bring Apps & AI to the Data (vs. Data to the Apps)
The rise of agentic AI places great importance on having all the data accessible and visible in one place, to avoid having the agent miss data or incur huge costs pulling the data in from multiple places. Particularly when a real-time, contextual understanding of a customer is needed.
Enterprise companies can minimize this risk with a data-in-place strategy that not only limits unnecessary data exposure, but also performs needed data quality processes in a single platform to create a more timely, trusted, and accurate single view.
By keeping customer data in a centralized data lake, such as Snowflake or Databricks, with tools and applications processing the data where it resides, the enterprise maximizes the value of its AI investments. Essentially this brings ‘applications to the data,’ vs. bringing ‘data to the applications,” making it more cost-effective and faster to power insights.
Such an environment also allows for greater flexibility and agility, with organizations able to integrate best-in-class components that suit their specific data readiness needs – components that might include data ingestion tools, data quality, identity resolution, segmentation and activation systems, and real-time interaction modules. This environment allows for pieces to be added, swapped out, or customized as requirements and business strategies evolve.
- Proof Point: According to a 2025 report by CData, only 6 percent of enterprise AI leaders say their data infrastructure is fully ready for AI.
Conclusion
The role of data readiness in supporting effective CX and AI use cases – particularly to support AI initiatives with AI-ready data – will only become more pronounced with the continued rise of agentic AI. A key trend will be the embrace of agentic AI by the enterprise as a key CX tool for autonomously completing more and more complex tasks, for solving customer data problems, and for allowing customers and brands to interact in innovative new ways. In addition, with a goal of extracting more value from AI, more companies will transition to a data-in-place architecture that allows for more agility and quicker time-to-value in the delivery of relevant, real-time experiences.
