Editorial
Agentic AI is the shiny new coin getting everyone’s attention — as it should. But if you’re not fueling your AI agents with high-quality data, you’ll never realize their full potential.
What Is Agentic AI, and Why Is Everyone So Excited About It?
So far, most of the hype around AI has been focused on generative AI, or genAI for short. GenAI is pretty good (and getting better) at generating responses to questions.
Agentic AI is different because, as the name suggests, it has agency. Agentic AI can be trained on your organization’s processes and rules, then make decisions and complete complex tasks. That's why it's capturing the most interest and attention amid emerging AI technology, according to Deloitte.
Here’s how AI agents work: suppose someone on your sales team submits a ticket requesting a new laptop. An AI agent can assess how the employee uses their existing computer based on historical data, then reference its knowledge of your procurement rules to order the best laptop for the employee, notify them and create a ticket for IT to set the new laptop up when it arrives.
This autonomous efficiency is one reason why 92% of companies plan to increase AI spending over the next three years.
Related Article: How Will AI Agents Redefine the Workplace?
Pros and Cons of Removing the Human in the Loop
Agentic AI eliminates the “human in the loop,” or at least has the potential to do so.
Some companies that are more cautious about adopting AI may want to keep a human in the loop — for example, letting the AI agent suggest a new laptop, but having a human review the specs and confirm the order. Other organizations may be fine with letting AI agents do everything and simply having an audit trail that a human can review if needed.
But think about the pros and cons of having AI agents handle tasks with little human supervision, if any. On the positive side, when you don’t need an IT person to review someone’s request for a new laptop, you free that person up to focus on more strategic tasks that bring more value to your organization. But without a human in the loop, you run the risk of increased errors and unintended consequences.
As one of my colleagues wrote, “There should always be a human involved somewhere in the process, whether they’re writing the code, inspecting the data or validating the results.”
The Increasing Importance of High-Quality Data
With fewer humans in the loop to catch mistakes, you need to pay even more attention to the data that’s fueling these agents.
“AI doesn’t know good data from bad,” an Enterprise Strategy Group report noted. “It just knows data, and whatever data is being used to train a model will ultimately become the model.”
If you don’t have good data, you can’t achieve trust — which means you won’t reach your adoption goals within your organization.
According to that same report, gaining access to quality data is the number-one challenge for organizations implementing AI. Getting high-quality data requires IT staff to consider the accuracy, visibility, frequency and structure of the data, as well as understand the need to upskill employees.
The obstacles to adopting AI don’t go away with agentic AI. If anything, addressing data deficiencies and other factors is arguably even more important, according to that same Deloitte report. And, due to the complexity of agentic AI systems, it's more challenging.
As organizations implement independent AI agents, investing in data quality must be a top priority. From collection and pre-processing to validation, access to high-quality data unlocks the full potential of AI agents deployed at scale.
Related Article: The AI Agent Explosion: Unexpected Challenges Just Over the Horizon
How Data Quality Improves Agentic AI
Here are a few ways data quality improves agentic AI:
Decision-Making and Accuracy
Data that’s inaccurate or incomplete is more likely to lead to poor decisions, whether you’re trying to predict system failures, mitigate enterprise-wide risks or optimize your resource allocation.
For example, if an AI agent is tasked with ordering a new laptop for an employee but doesn’t have accurate data regarding that employee’s current software and hardware usage, the AI agent may end up purchasing a device that’s under-powered (frustrating the employee) or over-powered (wasting resources).
Real-Time Adaptation and Context
Like a conscientious employee, agentic AI can adapt on-the-fly to changing conditions. Maximizing the potential of agentic AI requires access to real-time, high-fidelity data that accurately reflects your current situation. If you don’t have 24/7 visibility, your AI agents will be limited in how they can respond to evolving circumstances.
Security and Privacy
As some experts have noted, security and data privacy will be a major obstacle standing in the way of AI adoption. A data management plan can mitigate your risk — and help you move forward — by outlining ways to address data governance, IP rights issues and the use of your organization’s sensitive information by agentic AI.
Addressing Bias
Here’s a simple example of bias: if employees in different time zones submit fewer help desk tickets, an AI might interpret that data to mean that these employees need less IT support — when the reality may be that they’re trying to solve issues themselves when IT staff aren’t available.
Look for biases in your data that can distort decision-making and result in unintended consequences for AI agents.
Enabling Proactive IT
With accurate, real-time telemetry data, agentic IT can identify patterns and predict hardware failures, perform software updates and much more. But if you want AI agents to do more to reduce the burden on your help desk and prevent disruptions before they occur, you need high-quality endpoint data from devices throughout your organization.
Webinar
Sep
25
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Register
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
Webinar
On demand
From Hype to High-Impact CX Strategies That Actually Scale
Turn buzzworthy AI and outsourcing trends into measurable CX wins with fresh 2025 data.
Watch Now
Webinar
On demand
Insights to Action Rethinking the Contact Center for Real Business Impact
Join our exclusive webinar to hear CX executives share their innovative strategies for transforming service delivery.
Watch Now
Webinar
On demand
Accelerating Healthcare Ops with AI
Sign up to see how your peers are putting AI to work — and making it stick.
Watch Now
Data is the foundation for your AI agents. Start with a strong foundation now, and for years to come, you can deploy agentic AI that builds trust and exceeds expectations throughout your organization.
Learn how you can join our contributor community.
