Artificial intelligence continues to change fundamentally the way we do business, and in the last year, a new innovation has entered the center of attention. AI agents are being adopted at record speed between organizations, from marketing to data management and customer service, with the promise to optimize decisions, involve customers and increase productivity so that companies boost commercial value.
We have seen launches from AI agents from companies of all sizes and industries. In May, Google announced that it would incorporate AI agents into their searches, while Microsoft also announced a plan to use AI agents to help its users search the web. The use of AI agents is increasing in all industries, from finance and medical care to car dealers.
In fact, Boston Consulting Group predicts that the market for AI agents will grow to a 45% TCAC in the next five years. Gartner has also estimated that 80% of common customer service consultations will be resolved by AI agents in less than five years.
But here is the capture: the agents are as good as the data they run.
Derek leads product equipment, engineering, operations and amusement information.
Why the data still rise to AI
No matter the avant -garde nature of the AI tool or its very high promises, a constant remains when it comes to the data in which they are operating: garbage, garbage.
Companies that compete against competitors to implement AI agents without step back to evaluate the sources that are operating face an important risk: if these agents trust fragmented or inaccurate data, they will not work as expected. Even the most capable AI systems cannot deliver results if they are based on bad information.
According to MIT Technology Review Insights, 78% of global companies are not ready to implement AI and LLM agents. What stops them? Your data is not prepared to support AI. In the nucleus of the success of the AI, the data of unified, precise and real -time customers.
When AI agents work with bad and disjointed data, the consequences can be expensive. Last year, Air Canada was forced to reimburse a client when his chatbot promised a discount that did not exist. And, in April, a technological company suffered consequences after the error of a customer service agent resulted in a wave of canceled subscriptions.
These types of mishaps can threaten customer loyalty and result in rotation. IA agents are as intelligent and useful as the data on which they are built. To trust your AI agent, you must trust your database.
Identity resolution, reinvented for agents
The most essential piece, and more overlooked, to do a job of the agent is the identity resolution. Without a clear and precise vision of whom the client is historically disconnected and fragmented, the agents fly blindly.
That is changing. IA agents can now assume identity resolution as part of their function, coincide the records in real time, continuously refine the connections and operate without systems based on fragile rules. Instead of relying on the static profiles of single size, agent identity resolution creates a living image of the client, improving with each interaction and promoting greater productivity and precision.
This means less errors, a manual preparation less time that takes a long time and faster view time for each subsequent system.
Get the correct database
Before AI agents can operate effectively, underlying data should be:
Unified: Data from each contact point, ranging from electronic commerce and CRM to customer service, must be sewn in a unique and accessible layer that can be used for both marketing and engineering equipment.
Accurate: The identity resolution must reconcile inconsistencies or duplicates in multiple channels and contact points to build a reliable profile.
Contextual: Different use cases need different views. Marketing may need probabilistic profiles for broad orientation, while support needs deterministic precision of a single session.
Governed: Access controls, human supervision, feedback loops and consent monitoring are table bets for the AI in accordance and reliable, especially following evolutionary privacy regulations.
A modern Lakehouse architecture, combined with Native AI tools for identity resolution and customer profile construction, can drastically reduce the required manual effort and make decisions with real energy with AI are viable.
Data as a competitive differentiator
Often, the quality of the data is treated as plumbing, which is necessary but invisible. But in the era of AI agents, it becomes a competitive asset.
Data ready for the high quality agent allow a better customization, faster experimentation and safer automation. It allows AI to act with confidence, knowing who is interacting with, what they want and how to respond better efficiently and effectively.
When they are done well, the data not only admits AI, but also elevates it.
What follows?
AI based on agents is already remodeling the expectations of response capacity, customization and automation. But the real advance is not in the models, it is in the data.
The companies that invest in a high quality database will now be the ones that make the AI useful, reliable and transformative not only for their operations, but also for the final experience of the client. That is the difference between a striking interface or a first level algorithm and a shocking and scalable solution.
Before building your next agent, create the database you need.
We list the best customer experience tool (CX).
This article was produced as part of the Techradarpro Insights Expert Channel, where we present the best and most brilliant minds in the technology industry today. The opinions expressed here are those of the author and are not necessarily those of Techradarpro or Future PLC. If you are interested in contributing, get more information here:
