5 Reasons Organizations Are Replacing GenAI With Agentic AI

Businesses have been making investments in AI for years. In fact, nearly eight in 10 companies report that they’re already using generative AI, according to McKinsey industry research. As its name implies, generative AI—or gen AI for short—refers to AI that generates new content based on patterns in existing data.

Businesses rely on generative AI to synthesize information, generate context, and communicate with computers using conversational human language (think: ChatGPT). Generative AI, however, isn’t the original incarnation of AI for the business world. Generative AI is actually the spiritual successor to analytical AI, a simple form of AI that supercharges basic data analyses and makes predictions. Generative AI is different from analytical AI in that it adds a powerful self-generative component.

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But generative AI has a spiritual successor of its own now: agentic AI. Unlike gen AI, agentic AI doesn’t require constant human input. Agentic AI can think, learn, and act on its own. That core distinction is driving the next generation of AI’s use in business. In fact, agentic AI could be key to AI finally being able to deliver the bottom-line value that businesses have been seeking when they originally began investing in AI. The reality is that even generative AI has not proven out its value.

Although McKinsey research indicates that nearly 80% of companies have made investments in generative AI, an approximately equal portion of companies say generative AI has not yet delivered bottom-line value. In other words, businesses need agentic AI to pick up where generative AI leaves off. 

Let’s explore the key reasons that are driving businesses to replace generative AI with agentic AI—and how agentic AI is poised to finally deliver on AI’s promise:

Agentic AI acts as a virtual personal assistant

Employees devote large chunks of their day to manually sorting through emails and doing routine tasks, including generating reports and reviewing status of work and projects. These laborious activities take away employees’ time from higher-value, strategic tasks. To some degree, generative AI has helped lighten this burden: With adequate coaching and setup, generative AI can take care of some of these time-consuming tasks. But the employee still must play a big role in supervising and guiding this work. 

Agentic AI provides a monumental leap forward, serving as a virtual personal assistant that learns from employees and adapts based on the employee being served. For example, agentic AI can review emails, identify and prioritize tasks, and generate assignments for others on the employee’s behalf—without the employee needing to oversee or guide these activities. Agentic AI also connects dots for employees, drawing from multiple sources of information to surface insights that help teams collaborate more effectively and move in a more coordinated fashion toward common goals. Meanwhile, just like a human executive assistant, agentic AI learns from the personal habits and preferences of each employee, then tailors and fine-tunes its own ability to deliver support and more relevant, insightful intelligence. 

Agentic AI optimizes personalized experiences for customers

One of the most common reasons that businesses invest in AI is to improve the customer experience. Their strategy is to point AI at their customer data, with the goal that they can get AI to analyze huge volumes of customer data and then serve up personalized recommendations and tailored marketing campaigns. To some degree, other AI tools already have been doing this work. But these capabilities are limited in terms of the types and degree of customer intelligence they can unlock. 

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Agentic AI represents a monumental leap forward for the customer experience. Agentic AI cuts through the mountain of siloed channels, data streams, and data analysis tools that businesses use, and generates truly fully integrated, comprehensive customer insights. Significantly, agentic AI can generate these insights with minimal human guidance—in real time and at scale. Agentic AI also can intelligently pivot and adapt its analysis and decision-making capabilities on its own, without human intervention, in response to its own analyses of customer data. 

Agentic AI has powerful predictive capabilities

A key goal of any AI tool is to predict changes in demand, customer sentiments, and buying behaviors before they happen. Indeed, simple analytical AI tools have been making these predictions for more than a decade. But agentic AI takes predictions to a new level. Instead of humans creating templates and structures that AI then uses as its frameworks for analyzing data and developing predictions, agentic AI figures out on its own what the frameworks should consist of and how to analyze data within these frameworks. Moreover, as agentic AI gains experience, it gets better over time at providing predictions in real time.

Agentic AI offers support agents

AI-powered support isn’t a novel concept. For years, businesses have been deploying chatbots to help users get answers to commonly asked questions. Chatbots, which are often used for customer service, use pattern matching and keyword recognition to generate answers that are responsive to the question being asked. That makes chatbots good at answering simple questions, but bad at understanding complex questions and adapting to new information.

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Agentic AI takes a very different approach to providing support, in the form of AI support agents. These systems are designed to mine data from multiple sources, then integrate and process the data in real time to understand context and nuance. AI agents use machine learning and natural language processing. Moreover, AI support agents learn over time, which means they get better at answering questions and solving issues that are unique to each individual business.

Agentic AI bolsters cybersecurity

Security teams are constantly being hit with new and different types of threats. They need all the help they can get to fend off attacks and minimize risks. Already, AI has demonstrated its ability to help with routine anomaly monitoring, triggering automatic incident response workflows, and analyzing security data. But the real value of AI for cybersecurity will come with agentic AI.

Agentic AI isn’t dependent on a human to supervise and guide its work. Without human prompting, agentic AI can continuously monitor and trigger its own incident response workflows. Likewise, agentic AI does not depend on humans to initiate running drills and vulnerability assessments; agentic AI can take care of these activities end to end—and simply share its findings and the follow-up actions it took with human security teams.

Final thoughts

Agentic AI combines the best elements of human ingenuity—autonomy, planning, memory, and integration—to help businesses extract bottom-line value from AI. Unlike predecessor AI technologies, agentic AI can serve employees as a highly skilled virtual personal assistant, create truly optimized personalized experiences for customers, generate relevant and insightful predictions with minimal human involvement, provide intelligent support agents that far surpass the abilities of chatbots, and help cybersecurity teams maintain the upper hand. In the process, agentic AI will help shift AI from a resource that humans must carefully guide and manage, to a self-sufficient, resourceful partner on par with the strongest human teams.

To learn more about how to integrate agentic AI into your organization’s business ecosystem, please reach out to the agentic AI experts at Fullcast. We’ll be glad to help you strategize how, when, and where to invest in agentic AI—especially if you’ve struggled to fully realize the value of your prior AI investments.