Meenakshi Ganesh discusses the maturation of AI into Agentic AI
The landscape of enterprise work is undergoing a significant shift. As content volumes explode and tasks become increasingly complex, the need for intelligent automation has never been greater.
In parallel, the AI landscape has also been rapidly evolving, and one can liken it to a human's journey, from nascent to adulthood.
Infant AI: AI's initial stages were all about rules-based systems, limited capabilities and functionalities. Because of this, the applications of "infant AI" could only be on narrowly trained uses, like recognizing simple patterns, or playing chess.
AI in its teens: A significant leap with the advent of large language models (LLMs) like GPT-3 demonstrated that AI had progressed greatly into its "teens," being capable of generating content, including text, images, videos, advanced reasoning, and even understanding intent.
Post-grad AI: AI now has advanced more to reason across multiple modals, including image, audio, and even performing calculations on spreadsheets. This enables AI to understand much more complex asks, situations, and sophisticated reasonings.
Where does AI go post-graduation? Into the workforce of course, as AI Agents.
AI agents are the next technological evolution, acting as virtual assistants that augment human capabilities and transform the way we work.
AI agents are intelligent software programs that are capable of understanding, learning, and acting within an organization's specific context. Much like humans, they take actions to achieve specific goals, and even learn from experience, often operating alongside a human with minimal need for input. Imagine a digital assistant that is seamlessly integrated into your existing enterprise systems, with all the security and privacy controls built in. AI agents can access and analyze vast amounts of data, automate repetitive tasks, and make informed decisions to optimize routines.
To understand where AI agents fit into an organization, let's take an insurance claims department and see how AI agents at each level of the organization can have an impact, from the employee, such as the one who processes claims, to the organization-level, and finally working across multiple organizations.
To start, a picture of a car damaged in an accident is submitted to an insurance claims company. The claims employee would need to research and understand the damage in the submitted photo, submit the right claim form, obtain end-to-end approval, and trigger repair work.
AI agents can help the employee skip some of these preliminary steps in their routine. First, they most likely want to know and assess the damage based on the submitted photo. Without an AI agent, the employee would have to do this manually, taking time to carefully review the submitted photo. But with AI's capability to analyze images, this employee can ask the "AI Claims Agent" to "describe the damage to this car, and guess the cost of repairs.
The AI Claims Agent can provide a ballpark estimate for the employee and reduce the guesswork, or search through similar past incidents to predict the cost of repairs.
The benefits extend to the organizational level as well. With AI agents integrated into business routines, organizations can scale productivity across departments. Commonly referred to as agentic routines, they can not only automate repetitive processes, but also improve decision-making, by understanding enterprise content using its metadata.
Let's again take the example of the insurance claims department, but take a step back, and view the entire insurance company as a whole. The "AI Claims Agent" can not only analyze images, but also create new claims forms, fill out forms based on the metadata and analysis of an image, and generate a draft report that the employee can then move forward. This agentic routine eliminates the number of steps and time taken to generate a claims report.
Extending productivity outside of the organization, advanced AI agents can also effectively coordinate and collaborate with other agents across different organizations, facilitating seamless information sharing and workflow integration. Imagine multiple agents, such as the "AI Claims Agent" and the "Mechanic Agent"--they can coordinate together, align on the more accurate estimate of cost of repairs, and provide a schedule for it.
Content is at the heart of enabling this kind of automation. Enterprises can unleash AI agents to streamline content lifecycle, unlock deeper insights from their content (90% of which is unstructured), and help enhance security and compliance.
There is a goldmine of valuable information in unstructured content, but extracting insights often requires manual analysis, a time-consuming and resource-intensive task. AI agents can unlock this potential by:
Automated content summarization: AI agents can analyze large volumes of text and generate concise summaries, allowing users to grasp the key points of documents quickly.
Sentiment analysis: AI agents can analyze text to identify the overall sentiment expressed, helping businesses understand customer feedback or gauge employee morale within internal documents.
Trend identification: AI agents can scan large datasets of content to identify emerging trends and patterns. This allows businesses to make data-driven decisions and capitalize on new opportunities.
Content security and compliance are critical concerns for organizations of all sizes. AI agents can actually help address these concerns in a number of ways.
Automated Data Loss Prevention (DLP): AI agents can scan content for sensitive information and automatically apply security measures to prevent unauthorized access or leakage.
Compliance automation: AI agents can be used to analyze content to assist customers to adhere to specific regulations like HIPAA or GDPR. This reduces the risk of non-compliance and associated penalties.
Risk assessment and anomaly detection: AI agents can identify unusual access patterns or suspicious activity related to content, allowing for timely intervention and safeguarding sensitive information.
As AI agents become more pervasive, it is crucial to address security and permissioning concerns. AI agents leveraging secure RAG ensure that AI only access content that the user has access to, generating relevant answers, while honoring content and user permissions.
AI agents are not here to replace entire departments of employees. Instead, they act as intelligent and proactive helpers, automating tedious tasks and uncovering hidden insights. They empower employees to focus on building relationships with their customers and end users and other higher-order tasks that require creativity, judgment, and strategic thinking. This employee-AI collaboration will usher in a new era of intelligent content management, where content is not just stored, but actively leveraged to drive business success.
The future of content management is intelligent. As AI continues to evolve, from infant stages, to now having the potential to work autonomously in an organization, executing on routines, we can expect even more transformative advancements that will revolutionize the way businesses interact with their content.
We've featured the best AI chatbot for business.
