Agentic AI is the next big leap on the AI frontier, and it’s dominating the minds of technologists and organizations alike, with Gartner listing it as a top technology trend for 2025. It’s clear why: Agentic AI goes beyond responding to prompts and requests to making decisions and acting as specialized teams addressing specific business challenges. Multiple AI “agents” can be trained and fine-tuned for specific domains to do much more, empowered to act on behalf of people, make autonomous decisions, and work collectively to complete tasks.
Using AI to power employee productivity is one thing; deploying multi-agent AI systems that never sleep, continuously learn, and can predict, diagnose, or plan with insights from vast amounts of data will be transformative for organizations of every size and in every industry. The combination of the two – using AI to support individual work tasks and deploying AI agents to support the business where it’s needed most – create an even more compelling ROI for organizations looking to invest. Recent AI research found that over the next 12 months, 60% will make AI a top IT priority and 53% expect to increase budgets for generative AI by up to 25%. More than 69% of leaders cite productivity and operational improvements as the dominant value drivers. Agentic AI can help deliver on these goals while accelerating a return on investment.
It’s difficult not to get excited about the possibilities of agentic AI: Frontline workers will be able to rely on AI agents to protect them in high-risk environments by alerting humans of machinery malfunctions and troubleshooting issues. Healthcare providers can use them to offer round-the-clock patient assistance and quickly analyze large volumes of complex medical data in real-time. AI agents can help write software code and automate routine tasks so that developers focus on creative problem solving and strategic oversight. The potential for agentic AI is already being made real, so focus naturally turns to how it can be implemented responsibly.
The Path to Agentic AI in the Enterprise Starts with a Responsible Approach
Agentic AI will fundamentally change what work looks like between humans and technology. From defining data access and risk tolerance to determining when human oversight is necessary, here are three key considerations for building a responsible agentic AI approach:
Responsible use of agentic AI requires a responsible framework.
Research shows that just 39% of organizations have a complete set of responsible AI guidelines in place – yet these frameworks are crucial for success. Whether an organization is just beginning its AI journey or already deploying AI agents at scale, governance, privacy, security, and ethical considerations must be addressed upfront.
Before deploying agentic AI, leadership must establish clear guidelines for deploying AI responsibly, including governance, privacy, security and ethical considerations. Planning in advance helps to assess tolerance for risk, identify thresholds for human intervention, and to ensure AI agents are working responsibly and ethically. This foundational work is essential for ensuring AI agents operate responsibly and transparently.
Build a business case for agentic AI that’s centered on trust.
Only 33% of employees say they are confident their leadership can reliably differentiate between AI and human-generated work – and only 26% fully trust the results that AI generates. This trust gap underscores the need for greater transparency and engagement.
Deploying agentic AI starts with identifying the most pressing business challenges where the use of agents can help make a difference. Will an AI agent that automates the creation of an RFP, for example, give a salesperson more time to spend building a trusting relationship with a client? Can a marketer use agentic AI to find the most relevant research for a white paper?
Understanding where the pain points exist – and how agentic AI can help address them – starts with engaging employees from the very beginning. This inclusion builds both meaningful business cases and trust in the technology from the ground up.
Responsible agentic AI starts with good data.
Employee and customer experience is only as good as the data that serves as the foundation for those initiatives. Copilot and agentic AI share the same data foundation in large language models. For both, it’s crucial to have a robust data strategy that defines how data is responsibly collected, stored, managed, analyzed and used. Standardizing data approaches not only ensures that AI agents are armed with the right information and using it responsibly to perform their tasks, but it also speeds up the time it takes to create and scale agentic AI across the organization. A strong data strategy accelerates AI deployment and scalability.
Entering a New Era of AI
The adoption of agentic AI is still in its early stages, but it marks a pivotal shift in how AI empowers organizations. To realize full potential, responsible AI must be more than a requirement but rather a foundational component of AI strategy. Organizations that prioritize ethical AI practices not only mitigate risk but also build trust, drive innovation, and create lasting business value. As the landscape evolves beyond agentic AI, responsibility becomes less of a limitation and more of a competitive advantage.
