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Forbes
12-08-2025
- Business
- Forbes
Why RAG Alone Isn't Enough To Achieve Real ROI In The Agentic AI Era
Alon Goren, CEO and Cofounder of AnswerRocket, transforming your analytics with AI. If you looked under the hood of generative AI (GenAI) technologies over the last year or so, you probably came across the concept of retrieval augmented generation (RAG). RAG has gained a lot of buzz, celebrated as a helpful innovation that enables AI to deliver more accurate, powerful insights from data. But there's a problem: RAG isn't the AI panacea that many organizations have envisioned. Although RAG-based solutions showed early promise in simple AI applications, they can fall short of board-level expectations for transformative enterprise value and ROI. RAG Use Cases As NVIDIA explained, 'RAG is a technique for enhancing the accuracy and reliability of generative AI models with information fetched from specific and relevant data sources.' It works in three steps: • Retrieval: A user queries a model, which searches its knowledge base for relevant context. • Augmentation: The retrieved data is added as context to the prompt. • Generation: A generative AI model produces a response to the user based on both the input query and the retrieved context. Essentially, RAG can produce more accurate AI responses by incorporating additional context. It's valuable for many basic applications, like finding information buried deep in lengthy static documents (like spreadsheets or Word documents). But it struggles when users try to push it beyond its limits, such as depending on RAG alone for hardcore data analytics or highly complex statistical computations. Basically, RAG tends to fall short for hefty, data-driven analytics workflows that involve many different steps. And it's those types of use cases that organizations are imagining when they think of AI as a true business differentiator. A good example is the use of RAG to generate SQL queries. Although RAG-created SQL queries may look right for general business analysts, they could be asking the wrong questions and ultimately getting incorrect answers. And that's one of the key dangers of AI—getting bad 'insights' with high confidence. When people leverage RAG for use cases, it's not suited to—such as analytics applications with dynamic content, custom business logic or complex math—they're liable to get error-prone and out-of-context results, possibly without knowing it. AI Agents: An Alternative To RAG RAG is valuable for some use cases. But for organizations to get the broad ROI they're looking for from artificial intelligence, AI agents are often the better option. Agentic AI refers to systems with genuine autonomy. They can make decisions and take action independently. They don't just follow pre-defined rulesets, and they don't require input from humans. What makes AI agents so special is the freedom they offer. With agentic AI, models have real agency. For example, if a user comes to an agent and asks, 'Why are my sales declining? What's going on?' The model can essentially tell itself, 'I'm not really sure. I need to consult some other sources.' Then, it can analyze sources like the internet to learn about policy driving consumer behavior, databases for trends in product evolution, syndicated data to look at the overall market and other relevant information. AI agents have the freedom to leverage additional tools (including RAG solutions) and do more research so they can provide accurate, deep insights and answers to complicated questions. RAG models, on the other hand, have been architected to force a potentially incorrect or incomplete response based on limited data. Is This Agentic AI Really Agentic? The agentic AI space is heating up quickly. More and more vendors are rolling out AI agents or incorporating agentic AI technology into their platforms. Organizations should look hard at these technologies. Early in the process, they should be careful to make sure that the agentic AI they're evaluating is truly agentic. Like every major new enterprise tech innovation (especially anything involving AI), there's a lot of marketing hype surrounding AI agents, and the label is often slapped onto solutions that really don't fit the bill. Here's a quick checklist that will tell you if AI is really agentic. Agentic AI should: • Make decisions on its own. • Be capable of handling complex tasks. • Leverage external resources to get the job done better. • Clearly explain how it arrived at its conclusions or why it took action. • Identify its own errors or inaccuracies and course-correct in real time to improve results. • Use verifiers to ensure it stays within proper guardrails. Transformative AI Requires More Than Basic Capabilities The effective use of AI begins with understanding what a model can really do and not leaning on it for much beyond that. You wouldn't ask your company's internal HR chatbot to analyze and provide insights about the business' sales data. So, you also shouldn't lean on RAG for more challenging data analysis or to try and uncover multifaceted insights. Corporate boards and the C-suite are looking at AI to transform their organizations, delivering incredible new insights and recommendations for their businesses. RAG alone can't provide that. But AI agents can. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
24-06-2025
- Business
- Forbes
How AI Agents Can Take Your Business Analytics To Another Level
Alon Goren, CEO and Cofounder of AnswerRocket, transforming your analytics with AI. In my last FTC piece, I provided a primer on the capabilities of agentic AI, the value of the tech and how it can be tested and tweaked to improve accuracy. AI agents can make a major impact in many ways, such as cybersecurity, robotic process automation and customer support. But there's one use case where I've seen them really shine: business analytics. Back in November 2023, I discussed how generative AI is accelerating enterprise analytics. LLMs allow normal business users to tap into their organization's data and uncover critical new insights. Agentic AI is taking things to an exciting new level here, and I believe they will eventually make LLMs obsolete when it comes to driving value from business data. Agentic AI Analytics: Fulfilling The Role Of An Expert Analyst Traditional GenAI analytics is powerful, making enterprise data more accessible and yielding far more insights versus plain business intelligence (BI) analytics. It works within clearly defined guardrails, learns as you interact with it and provides precise answers. LLM analytics ultimately fulfills the role of a junior analyst for organizations. Agentic AI plays the role of a manager or expert analyst. It teaches itself new things, researches things on its own and delivers insights autonomously. The key differentiator for agentic AI analytics is its proactive nature—it delivers valuable insights without needing explicit requests or prompts. For instance, consider a consumer goods company specializing in beverages. An AI agent could proactively alert business users that sales of a seasonal product line, such as flavored seltzers, are projected to decline significantly over the next quarter due to shifting consumer preferences. At the same time, AI could highlight emerging trends, such as rising interest in non-alcoholic spirits, recommending that the company explore opportunities in this growing market segment within the upcoming year. As is always the case with AI and analytics, the important thing is that insights support meaningful actions. In the first example, the liquor company might want to consider pivoting away early from the declining category before sales tank. In the second example, they would want to think about launching a new product to get ahead of their competitors. Here are the features that define early-stage generative AI analytics solutions: • Rule-Based: Performs only the tasks it's explicitly programmed to do • Opaque: Offers answers without explaining how it reached them • Tool-Limited: Can only operate within a fixed set of preloaded tools • Inflexible: Needs manual corrections or instructions to adapt • Requires Oversight: Relies heavily on expert oversight to function properly Here's how agentic AI analytics contrasts in the same categories: • Autonomous Decision Making: Weighs options and makes choices independently • Explainable: Clearly shows how it reached its conclusions • Tool-Agnostic: Can choose and use tools on its own as needed • Self-Adaptive: Adjusts behavior in real time without external input • Self-Monitoring: Performs built-in checks to stay compliant and accurate Don't Fall For Regular GenAI Posing As Agentic AI The AI market is evolving rapidly. It can be difficult for enterprises to make heads or tails of all the various moving parts. Complicating things further—and this is always the case with the rise of significant new technologies—there are a lot of vendors that cling to buzzwords even when they don't fit their offerings. Organizations looking to leverage agentic AI to accelerate their analytics efforts need to be careful not to fall for plain generative AI that rebrands itself as agentic. This will become less of a problem as the agentic AI market matures and winners and losers emerge within the next two to three years. In the near term, organizations will just have to do a little research. The best place to start is with this checklist, reflecting the points I hit above. Agentic AI analytics should: 1. Make decisions independently. 2. Explain reasoning. 3. Use tools autonomously. 4. Self-correct and adapt on its own. 5. Be overseen by verifiers to ensure optimal accuracy. A Step Further: Multi-Agent Networks Looking even further ahead, agentic AI gets even more groundbreaking. Eventually, singular AI agents will evolve into multi-agent networks. Here, several AI agents will connect into a network with broader access to enterprise datasets, tools, models and domain context. These agents will be highly goal-driven and capable of completing more complex tasks that span multiple systems within a business. AI Agents: Transforming Enterprise Analytics In 2026 And Beyond AI continues to develop at a breakneck pace. It wasn't long ago that LLMs were a brand new, cutting-edge way to support analytics. It should be repeated that traditional GenAI is still a fantastic, powerful method to improve analytics workflows and uncover more insights. However, AI agents are going to raise the bar. The tech is still in its nascent stage, though the market will start to take shape in a year or so, delivering insights that will prove transformative for organizations across the spectrum. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
