Editorial
The ability of agentic AI to operate autonomously, mirroring human-like functionality, represents a major step forward for the development of AI.
Traditional generative AI (GenAI) relies on explicit human directives and lacks the ability to independently navigate complex reasoning or collaborative tasks. Agentic AI, however, pulls from a network of intelligent AI agents that learn, adapt, collaborate and take action to independently solve problems or achieve goals. These agents emulate the real human cognitive process.
So, what does this look like?
What Makes Agentic AI Unique
Imagine AI cybersecurity agents that manage threats autonomously in real time, or AI workflow agents that can identify organizational bottlenecks and suggest actionable improvements in minutes.
The bottom line is that agentic AI is a true paradigm shift. But for organizations ready to harness agentic AI, they must understand the essential do's, don'ts and must-haves of agentic AI to take advantage of its potential effectively and ethically.
The Benefits and Risks of Agentic AI
Agentic AI offers the tantalizing potential to revolutionize how work is done, automating routine tasks and providing new insights into data that might otherwise be missed.
However, like any new technology, AI agents that are not properly governed can introduce new challenges related to the characteristics of the underlying AI technology, such as hallucinations and ethical concerns. For example, if an agentic AI system were to be compromised, the autonomous decisions it makes could lead to consequences ranging from minor disruptions to far-reaching effects.
Despite these challenges, the benefits of agentic AI are too significant to ignore. As enterprise pilots and deployments mature and progress, their sophistication will have a dramatic impact, improving day-to-day experiences for both customers and employees.
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How Businesses Can Plan for Agentic AI at Scale
Businesses across every industry are already investing in agentic AI. Gartner predicted that by 2028, at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from 0% in 2024.
However, before organizations can turn these investments into real, tangible impacts, they must overcome several hurdles.
This includes having well-defined workflows and services in place to ensure agentic AI deployments are thoroughly integrated into existing IT infrastructures. For example, an agentic AI system designed for inventory management will need to be integrated with existing systems of record to enable those hyper-personalized queries.
Additionally, many deployments will need to be custom-tailored, further necessitating the need for careful integration. Depending on the specific tasks at hand, some will be better suited for specialized AI agents, while others might be best served by more standard generative AI tools.
Challenges of Scaling Agentic AI
At the same time, organizations must tackle tough questions such as determining where accountability falls if an AI agent malfunctions. This means not only establishing mechanisms to detect and correct errors but also defining a clear chain of accountability — whether it rests with AI developers, operators, the company or, in some cases, the AI system itself.
Essentially, there’s a readiness factor that must be addressed before escalating the use of AI beyond straightforward applications.
By focusing on these questions from the beginning, companies can align the growth of their AI systems with long-term objectives. This will make sure that their investments not only meet current needs but also adapt to future demands and harness the transformative potential of agentic AI to create meaningful change within the organization.
The Future of Agentic AI and Governance
As agentic AI continues to evolve, governance will emerge as a focus point for companies, regulators and broader society.
Effective governance is more than a box to check — it’s a necessary requirement to ensure that these systems operate within an ethical and regulatory framework. Such a framework should clearly define the deployment procedures, ongoing management and oversight mechanisms to uphold ethical standards and meet regulatory demands.
The autonomous capabilities of agentic AI, while a major draw, only increase the need for robust guardrails. For instance, companies might consider training their AI models on proprietary data so they can improve the relevance and accuracy of AI decisions while also mitigating risks associated with data privacy and security.
Balancing Autonomy and Human Oversight
In the early stages of agentic AI, maintaining “human above the loop” is crucial. This means that AI will augment human capabilities without removing human judgment and accountability.
While many will be quick to embrace agentic AI for its autonomous capabilities, the reality is that employees will still need to play a proactive role in reviewing and validating outputs, as well as scrutinizing the underlying processes and data sources that inform these outputs. The extent of human involvement may vary as the technology matures, but in the early days, agentic AI deployments must be regularly monitored and learned from so that these interactions can improve and better align within the ethical practices and standards set out by the organization. That's why it's essential to invest in tools that help customers monitor agentic AI usage, making it easier to manage offensive content, security risks and sensitive information.
Furthermore, it can be advantageous for companies to establish an ethics committee to assess both potential and existing AI applications to ensure decisions are made prudently to safeguard against potential biases and maintain proper data security.
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Organizations that hesitate to define a comprehensive governance strategy in the agentic AI era will risk inadvertently undermining their overall AI strategy. That’s why the establishment of these cross-functional controls and risk management protocols is essential for ensuring compliance with both internal policies and external regulatory demands. And, relying on fragmented point solutions and systems can complicate these implementations as they expand throughout the enterprise. Therefore, to effectively navigate these challenges, companies should seek integrated solutions that unify AI strategy, technology, security and compliance across a single digital environment.
Getting Agentic AI Deployment Right
Ultimately, the successful deployment of agentic AI hinges on finding the balance between its autonomous operations and human oversight. By carefully designing systems — both technically and organizationally — with a focus on these elements, businesses can better unlock the potential of the next phase of AI.
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