Agentic AI is the current buzzword on everyone’s lips – for good reason though. We’ve seen what improvements and benefits generative AI (GenAI) brought, but Agentic AI goes one step further, switching from passive information delivery to active, autonomous decision-making. Think of it as AI that doesn’t just answer questions. Instead, Agentic AI gets things done, by learning your company’s processes and rules so it can independently execute tasks and make decisions autonomously.
Gartner has predicted that by 2028 33% of enterprise software applications will include Agentic AI, which is up from less than 1% last year (2024). And it’s easy to see why, as the analyst house also recently forecasted that Agentic AI will autonomously resolve 80% of common customer service issues without any human intervention by 2029.
Agentic AI can also boost digital employee experiences so your teams are as happy as your customers. Agentic AI promised to automate complex workflows, like procurement or IT support, by handling the process end-to-end. The only question is whether your data pipelines are robust enough to support such a level of autonomy, because put simply Agentic AI is only as good as the data you feed it.
Agentic AI relies on high-quality, real-time data
We’re talking about autonomous systems making critical decisions, often without a human safety net, which is why the “human-in-the-loop” discussion is a practical element in assessing the risks of such emerging AI technologies. Complete automation may be the dream, but without a solid data foundation, you’re building on a sand, not stone foundation. You always need a human eye somewhere in the process, whether it’s checking the code, validating the data or reviewing the outcomes.
The core issue is that AI doesn’t magically know good data from bad data. It’s a simple input-output equation. So, if you’re allowing your AI to access any poor quality and potentially incorrect or outdated data then your results are going to be just as wrong and out of date. That means you need to ensure your organisation has high-quality data hygiene. You need to make sure your data is “clean” by checking records for accuracy and removing errors. However, getting to “good data” is a real challenge, and it’s the biggest hurdle businesses face when deploying AI.
Truly high-quality, good data requires visibility, to see all the data you need, frequency, so you know it is in real-time and not stale or old, and structure, where it’s been organised in a way that the Agentic AI can use. Additionally, you need to have people who actually understand how to manage and interpret the data, which could mean upskilling your team.
And here’s the kicker: Agentic AI amplifies all of these data challenges since it is more complex and making more autonomous decisions, so there is greater potential for error. This means you can’t just slap a data governance policy on it. You must first have a strong, thorough and proactive strategy in place, from Day One.
How organisations should approach data for Agentic AI success
The data strategy isn’t about fixing typos and removing duplicates. It requires an underlying shift in how organisations approach data. Think of it as building a data infrastructure that’s not just functional, but intelligent. During this shift, accuracy and wholeness are essential since bad data will only lead to bad decisions.
Real-time visibility is also important. Agentic AI thrives on real-time data so if your data pipelines are slow or outdated, the AI’s decisions will be based on stale information, leading to disorganisation, inefficiencies and errors. Hence your organisation should prioritise building real-time, high-fidelity data streams which include more than just numbers and text. AI has to understand the relationships between data points so it can make informed decisions. For this, context is needed and can be done by adding metadata and semantic information to enrich the data available.
AI systems can pick up biases from data that they are training with. This can result in prejudiced outcomes. It’s important to find and fix these biases to make AI tools and technology fair, and ethical. This means carefully choosing data and regularly checking how the AI performs. Also, when we have more control over data, we need better security, so good data protection rules and security steps help keep sensitive information safe and ensure compliance regulations are followed and met.
The importance of a data-first culture company-wide
Deploying Agentic AI isn’t just about technology; it’s also very much centred on your organisation fostering a data-first culture where data quality is prioritised from the ground up. Quicker and more effective AI installation comes off the back of investing in data literacy, and training employees to understand and interpret data, to ensure data quality. All businesses must have clear data governance policies and procedures so they can guarantee data quality, security and compliance.
Data quality is an ongoing process, so monitoring for improvements to be as and when they are needed is critical. Data quality is also a shared responsibility. Encouraging collaboration between different departments and creating an environment where data flows freely, outwith data silos, can ensure consistency and accuracy.
Agentic AI will dramatically change how businesses operate. However, its success relies on a solid foundation made up of high-quality, real-time data, which is why building a data-first culture is such a priority. Through such a company-wide base, organisations can unlock the full potential of autonomous decision-making systems, while mitigating risks and ensuring sustainable success.
