Generative AI hype: Debunking 4 myths for IT leaders

There’s no denying it: generative AI (GenAI) is reshaping the future of IT (and possibly the world). According to a 2024 pulse survey from Forrester, 67% of AI decision-makers say their organization plans to increase investment in generative AI in the coming year.¹ So what’s just generative AI hype and what’s worthy of long-term investment? And how can IT and business leaders separate fact from fiction?  

The buzz brings both excitement and skepticism. There are questions about how generative AI collects data, its safety measures, and its impact on IT and business operations. Let’s explore the real capabilities of generative AI, debunk common myths, and show how you can responsibly harness its potential beyond the generative AI hype.

It depends on how you want to use it. And the data the generative AI model is using. Some use cases don’t call for generative AI but rather traditional AI. For example, if your organization goes through quality control checks for products shipped out, you might consider a traditional AI-enabled rule-based system instead of generative AI, as checking products against a set of standards is a straightforward process. In these simpler use cases, generative AI’s ability to improve quality control efficiencies is overhyped.

On the other hand, if generative AI can be used to expand the wealth of knowledge to help someone make better decisions, then it might be worth the investment. Imagine your organization enriching a large language model (LLM) with your proprietary data, like your internal company wiki, HR policies, and corporate presentations. Generative AI can help employees self-serve by asking questions in natural language. This practical use case can enable your teams to recoup time finding information for more strategic tasks and complex problems. It’s already been successful at Elastic — our internal generative AI assistant, ElasticGPT, has saved employees more than five hours per month or 63 hours annually. 

As for the future of generative AI hype, the Gartner hype cycle predicts that by 2026, more than 80% of enterprises will have used generative AI APIs or models and/or deployed GenAI-enabled applications in production environments.² That’s up from less than 5% in 2023. As generative AI use cases continue to expand, you’ll need to see through the hype and focus on use cases that will have the most impact on your business.

Let’s jump into what’s generative AI myth and what’s reality so you can create and refine a strategy that ignores the hype and focuses on long-term success.

Reality: AI assistants can provide actionable insights to employees.

You won’t hear about generative AI hype without hearing of its potential to replace employees. Generative AI technologies augment your team’s expertise and workflows — they don’t replace them. While a generative AI-powered assistant offers insights, automates repetitive tasks, and improves response times, human intuition and problem-solving capabilities remain critical. IT analysts, security teams, and customer support representatives still provide the context, judgment, and nuanced understanding of your organization’s systems, the threat landscape, and business priorities that only human expertise can bring. 

AI assistants can focus on repetitive tasks to free up employees to take on more challenging projects. McKinsey estimates $4.4 trillion in added productivity growth potential³ from corporate use cases. A site reliability engineer (SRE) might use an AI assistant to investigate your network issues to understand the root cause. This AI-assisted workflow can help SRE’s quickly resolve downtime impacts and improve system reliability. Now, the SRE can focus on resolving issues or reclaiming time back to focus on complex IT projects that require human ingenuity.

Reality: You can enhance your generative AI models with your own proprietary data through retrieval augmented generation (RAG).

While public internet data powers many AI applications, generative AI models can securely use your organization’s proprietary data to provide tailored responses and a variety of use cases. This approach — using RAG — combines internal, vetted data with generative capabilities to deliver results that are highly relevant and actionable. This lessens the probability of hallucinations and ensures that responses are unique to your organization, not based on whatever exists on the web. 

In security operations, where teams face hundreds or even thousands of alerts daily, generative AI tools can help cut through the noise. With a generative AI-powered assistant that is enhanced with proprietary data, security analysts only have to investigate filtered alerts by receiving intelligent summaries and access to guided next-best actions that streamline response times. The assistant can go further by keeping context after each query, allowing analysts to build insights iteratively and add findings to a case with ease. This data is then fed back into the model through RAG, continuously improving the system and ensuring analysts are working with the most accurate information.

Sources

1.  Forrester, Generative AI Trends For Business: Why, When, And Where To Begin. 

2.  Gartner, What’s Driving the Hype Cycle for Generative AI, 2024.

3.  McKinsey, Superagency in the workplace: Empowering people to unlock AI’s full potential, 2025.

The release and timing of any features or functionality described in this post remain at Elastic's sole discretion. Any features or functionality not currently available may not be delivered on time or at all.

In this blog post, we may have used or referred to third party generative AI tools, which are owned and operated by their respective owners. Elastic does not have any control over the third party tools and we have no responsibility or liability for their content, operation or use, nor for any loss or damage that may arise from your use of such tools. Please exercise caution when using AI tools with personal, sensitive or confidential information. Any data you submit may be used for AI training or other purposes. There is no guarantee that information you provide will be kept secure or confidential. You should familiarize yourself with the privacy practices and terms of use of any generative AI tools prior to use. 

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