Agentic AI is changing the landscape of artificial intelligence, offering businesses a new way to enhance efficiency and innovation through autonomous decision-making.
The landscape of artificial intelligence has reached a significant turning point with the emergence of Agentic AI, which operates with greater independence than traditional generative AI solutions. This next phase not only enhances efficiency but also signifies a paradigm shift in business operations, potentially transforming innovation and scalability.
Agentic AI transcends the capabilities of current conversational AI and robotic process automation (RPA), evolving from systems that merely respond to queries or follow predefined workflows. Instead, Agentic AI operates autonomously, making proactive decisions based on real-time data and learned behaviours. Companies are now confronted with the critical question of whether they are prepared to adopt AI that can execute tasks independently, instead of merely assisting human employees.
Examples of Agentic AI applications are already emerging, such as AI systems that can actively remediate security vulnerabilities in network monitoring without human intervention, or an IT helpdesk that can anticipate problems and resolve issues before they become apparent to employees. These functionalities highlight that the advanced capabilities of Agentic AI are not merely hypothetical but represent tangible developments in the field.
For a successful mainstream rollout of Agentic AI, companies must adopt a structured approach that fosters trust between humans and AI systems. The implementation of the ARC framework—comprising Accelerate, Replace, and Create—presents a strategic roadmap for AI adoption. This framework aids businesses in assessing the return on investment of AI initiatives and identifying the most suitable opportunities within the expansive possibilities offered by AI advancements.
The three phases within the ARC framework are delineated as follows:
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Accelerate: At this initial stage, AI tools serve to enhance human decision-making by providing insights and analytics, thereby streamlining workflows without replacing human oversight.
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Replace: This phase sees AI taking over specific tasks or processes while still allowing human control. The goal is to enhance task accuracy and efficiency, maintaining human oversight to build trust and reliability before transitioning to full autonomy.
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Create: The final phase is where Agentic AI operates autonomously, continuously learning from data and context to make complex decisions in real time. At this stage, AI becomes a collaborative partner, significantly enhancing efficiency and sparking innovation within business operations.
Markus Nispel, CTO of EMEA at Extreme Networks, emphasises that transitioning to the “Create” phase hinges on overcoming the challenge of trust. Trust in AI can fluctuate rather than increase linearly, influenced by user experiences as AI is assigned more complex tasks. Initial enthusiasm may give way to scepticism as concerns about reliability and accuracy arise, which is why a phased adoption and a structured approach to building trust is vital. Trust levels can also vary by application; for instance, employees may be comfortable with AI handling administrative tasks but may lack confidence in its decisions related to critical operations like security monitoring or financial forecasting.
Moving forward, companies are advised to invest in AI literacy, ensuring that employees and leaders understand the technology’s capabilities and limitations. This knowledge is essential not only for effective governance but also for the ethical deployment of AI systems to bolster trust and drive adoption. A hybrid AI-human approach is recommended, where AI agents complement human efforts instead of replacing them. It is also suggested that businesses initiate their AI journey with small pilot projects, allowing for evaluation and refinement before broader implementation.
As AI evolves from a passive tool to an active decision-maker, the urgency for businesses to adapt is evident. Companies that effectively integrate Agentic AI into their operations are likely to stand out and thrive in the increasingly competitive landscape. The implications of sticking to a strategy solely focused on traditional automation could hinder progress in an era poised for transformation, making it essential for organisations to re-evaluate their tactics promptly.
Source: Noah Wire Services
- https://www.moveworks.com/us/en/resources/ai-terms-glossary/agentic-ai – This URL supports the claim that Agentic AI operates autonomously, making proactive decisions based on real-time data and learned behaviors, and it highlights the shift from traditional AI systems to more autonomous ones.
- https://www.knime.com/blog/what-is-agentic-ai – This URL explains how Agentic AI systems differ from non-agentic AI by their ability to make independent decisions and adapt to changing inputs without much human oversight, aligning with the article’s description of Agentic AI’s capabilities.
- https://aisera.com/blog/agentic-ai/ – This URL corroborates the idea that Agentic AI enables systems to perform tasks independently and adapt in real-time, leveraging large language models for enhanced decision-making and user interaction.
- https://www.extremenetworks.com/ – This URL is related to Extreme Networks, a company mentioned in the context of AI adoption and trust, though it does not directly support specific claims about Agentic AI.
- https://www.noahwire.com – This URL is the source of the article itself but does not provide external corroboration for the claims made.
Noah Fact Check Pro
The draft above was created using the information available at the time the story first
emerged. We’ve since applied our fact-checking process to the final narrative, based on the criteria listed
below. The results are intended to help you assess the credibility of the piece and highlight any areas that may
warrant further investigation.
Freshness check
Score:
8
Notes:
The narrative discusses recent advancements in AI, specifically Agentic AI, which suggests it is relatively current. However, without specific dates or events mentioned, it’s difficult to pinpoint its exact freshness.
Quotes check
Score:
5
Notes:
The quote from Markus Nispel, CTO of EMEA at Extreme Networks, does not have an easily identifiable original source or date online. This could indicate it is original or not widely reported.
Source reliability
Score:
9
Notes:
The narrative originates from TechRadar, a well-known and reputable technology publication, which generally enhances the reliability of the information presented.
Plausability check
Score:
8
Notes:
The claims about Agentic AI and its applications seem plausible given current trends in AI development. However, specific examples or case studies would further support these assertions.
Overall assessment
Verdict (FAIL, OPEN, PASS): PASS
Confidence (LOW, MEDIUM, HIGH): HIGH
Summary:
The narrative appears to be current and discusses plausible advancements in AI technology. It originates from a reliable source, enhancing its credibility. While some quotes lack clear original sources, the overall content is well-supported and aligns with recent AI trends.
