The obstacle to success with agentic AI

As competition intensifies, many organisations are being held hostage by their own disorganised and siloed data, with 58 per cent of C-suite and data leaders describing their organisation’s collection and management of customer data as ‘chaos.’ 

 

This chaos isn’t just slowing decision making; it’s preventing companies from deploying the autonomous, self-directed AI systems that are defining the next era of digital transformation and greatly boosting customer loyalty.

 

When the majority of leaders say data is the single biggest driver of customer experience, with seven out of ten admitting they will struggle to retain customers without the right data stack, data chaos becomes not just a technical inconvenience, but a strategic risk. Until data is trusted, connected, and available in real-time, agentic AI cannot operate safely or effectively.

 

 

Data Silos Are Killing Agility

Most organisations are still operating with siloed data, a system that remains one of the biggest barriers to innovation, as it prevents a single, shared understanding of customers, operations, and risk. Businesses are left blind, slow, and reactive, which can put them behind their competitors and risk losing customer loyalty. Without unified insights, teams cannot act accordingly as data and customer expectations change at a rapid pace.

 

While many companies understand the urgency to update their data systems to be ready to keep up with the speed of today’s market demands, and are prioritising the upgrades in their budgets and operations, many still lack any formal data governance at all, making speed, accountability, and autonomous decision-making fundamentally unattainable. These governance gaps worsen the problem as it leaves teams unsure of what data they can trust or use. These gaps can lead to inaccurate data that can compound over months to even years, increasing the amount of work needed to fix these issues.

 

Agentic AI systems require a unified organisation to fully operate across all functions, so without a company-wide data culture that supports good data governance to ensure data is clean and reliable, these systems are unable to act intelligently. Without breaking down these silos, businesses remain stuck reactively responding to events rather than anticipating them.

 

 

No Real-Time Data, No Competitive Edge

While companies try to implement agentic AI as quickly as possible, these efforts will be lost if they rely solely on batch reporting, as agentic AI thrives on continuous feedback loops to learn, decide, and act. Without real-time data, businesses are left behind on customer insights as delayed reports will be outdated by the time they can act.

 

Moreover, most businesses lack the infrastructure needed to process and act on data as events unfold, which means these companies are increasingly being outpaced by their competitors who have these capabilities. With the lack of the insights required to compete against large enterprise programmes that have the budget to upgrade their systems, it is critical for companies to adapt to real-time data systems.

 

As fast, dynamic decision-making becomes the new normal, companies begin to cut ties with their legacy systems that cannot keep up and rush to implement AI with their current data, even though they know that the data may be incomplete or inaccurate, which can introduce operational and reputational risks.

 

Companies need to ensure that the right data infrastructures are in place so that the data is clean, reliable, and ready to be used in real-time to receive the most accurate and up-to-date information to respond to customer demands. Real-time data is no longer a performance upgrade, but the baseline for survival in this AI-driven economy.

 

 

The AI Divide Is Growing

As AI adoption accelerates, three-quarters of leaders now see loyalty as a data-driven arms race that many organisations feel they may not be able to win against a widening AI and data access gap structurally.

 

Companies investing in advanced data infrastructure and AI are accelerating ahead, leaving data-poor organisations to struggle to scale AI beyond pilots or specific use cases. This divide shows up most clearly in customer experience, loyalty, and operational efficiency, where small advantages in data quality, speed, budget, and insights add up quickly, allowing companies to personalise more effectively with each interaction.

 

Without a major AI investment, it would be hard for mid-level companies to compete with enterprise players, as the gap increasingly affects data-driven decision-making speed, personalization capability, and the ability to scale operational digital services autonomously. However, just like company size does not guarantee competitiveness, neither does huge spending on technology without fixing the underlying data.

 

The real differentiator is whether companies treat data as a strategic asset rather than an afterthought. Implementing AI does not become the equalizer on its own, as it magnifies existing strengths and weaknesses, so without the right data foundations, AI will fail, no matter the company’s size.

 

 

Real-Time Data is the Top Tech Priority

Leaders know that real-time data is the foundation of digital competitiveness and that solving the data problem is step one to becoming truly digital-first and enabling agentic AI. The race is on to build the infrastructure that allows AI agents to operate safely, responsibly, and at scale, and yet most organisations are still investing in AI before repairing the data that it depends on.

 

Until this contradiction of knowing the problem and acting inconsistently is resolved, agentic AI will remain an expensive distraction rather than a value driver, as it is susceptible to fragile systems, limited ROI, and growing risk exposure.

 

Agentic AI depends on trusted, governed, real-time data to operate autonomously as companies want to streamline operations and create a strong, consistent customer experience to lead to customer loyalty. When the foundation AI is built on is weak due to the data itself, as well as a lack of data structure, AI becomes inconsistent and unreliable to implement on all fronts, so its role is limited to narrow, low-impact use cases or becomes obsolete.

 

 

Agentic AI starts with fixing the data

Solving the data problem is not optional, but the required step for any organisation, whether enterprise or mid-level, to be able to deploy AI at scale and enhance their customer loyalty programs.

 

Agentic AI has become a fundamental shift from tools that assist to a system that takes on action, but this shift raises the stakes for data quality, governance, and trust to ensure the information is clean, accurate, and ready to be used so that AI can work in the way it was intended to be used.

 

Those who invest in unified, real-time data foundations will have an AI system that performs exceptionally well with faster decisions, smarter automation, and sustainable competitiveness, while creating consistent experiences for both employees and customers. When AI is reliable and usable, the return on investment shifts from risky experimentation to measurable business impact.

 

The race for agentic AI will not be won by who adopts first or how quickly they adopt it, but by the organisations that fix their data the fastest.

 

---

 

Richard Bovey, Chief of Data at AND Digital

 

Main image courtesy of iStockPhoto.com and MF3d