By Karthik Rajaram, Area Vice President & General Manager, Elastic India
During the past year and a half, generative AI (GenAI) has embedded itself in the fabric of everyday life. Instantly disruptive, ChatGPT surpassed more than 100 million users within months of launching. With the initial buzz and early experiments behind us, business leaders are looking for ways to harness GenAI across their entire organisations, rather than as a plug-in for existing software. They’re seeking areas where tools such as large language models (LLMs) can not only add value today but eventually transform how their companies operate. The big question on everyone’s minds: When and how will GenAI transcend being a mere feature or chatbot?
The answer is hiding on the other side of the screen, deep in the software and IT systems underpinning modern organisations. For example, Microsoft’s code completion tool GitHub Copilot has predated ChatGPT by more than a year, and programmers consistently report writing faster, cleaner code because of it. Now, Cognition AI aims to transform the field again with “Devin,” an agent it describes as “the world’s first fully autonomous AI software engineer.” IT professionals are watching the AI revolution unfold in areas such as cybersecurity, system resiliency, and information discovery. Their firsthand experiences highlight what’s possible today, while illuminating the underlying capabilities needed given where the tech is going.
To this end, Elastic, the leading Search AI company, commissioned a survey of more than 3,000 IT professionals globally, asking how GenAI could drive change in their organisations. 63% of the respondents in India saw external opportunities for improving customer experience and engagement, and another 66% saw internal possibilities for increasing operational efficiencies and individual productivity. In both cases, value is derived from a paradigm shift away from simple search results, alerts, and notifications toward receiving exact answers to a problem. For GenAI to serve up answers from data it wasn’t directly trained on, it needs contextual aid. And the key to the best contextual aid? It begins with search.
The right data at the right time
Foundation models such as GPT-4, Llama 2, and Gemini are typically trained on vast training sets of data culled from the open Web. Their ability to learn new information is limited by their context window, which can increasingly range in size from short documents to large codebases or multiple novels. This puts the impetus on organisations to bring together the right data at the right time for GenAI systems to generate context-specific answers. For GenAI to work, organisations need a search engine to bridge the gap between information in the public domain and internal private data that’s changing very rapidly.
For example, asking a GenAI tool to assist you with a customer calling for support might require rapidly pulling your organisation’s proprietary data—the customer’s order history, call logs, and other relevant information—into its context window for analysis. Robust search is essential for maximising the accuracy of answers generated from that proprietary data. With those answers at hand, GenAI helps provide personalised experiences at scale, transforming customer support and similar interactions from a page of search results to a one-to-one conversation.
This is just as true for IT managers and other leaders within the firm striving to understand how operational performance issues might impact business performance. By combining data for both measures within the context window, users can save precious time and resources by asking GenAI directly rather than hunting for correlations themselves.
Immediate benefits for cybersecurity
Not only do these feedback loops help teams become more productive by identifying problems faster and shortening the time spent searching for answers, but they also train the AI over time to connect operational data with business performance data, thus continuously improving its results. Rather than issue alerts and notifications for users to resolve, GenAI tools will learn from and eventually write their own scripts to automatically detect, diagnose, and remediate problems before they escalate to human attention.
Such capabilities will be especially important when it comes to cybersecurity, where the number and severity of threats mount daily while many organisations struggle to fill critical positions. 63% of respondents’ organisations in India surveyed by Elastic anticipate using AI to improve automatic threat detection.
GenAI tools have proven adept at both identifying threats and spotting correlations between intrusions and making it easier for analysts to understand, respond to, and document incidents. This, in turn, frees up senior staff while helping to address the cybersecurity skills gap by making complex tasks more accessible to junior analysts. In this regard, GenAI is delivering immediate value by bolstering defences that would otherwise go unmanned.
Tying all these threads together—key among them, leveraging proprietary data through search—is GenAI’s ability to reduce complexity while learning as it grows, adding new capabilities and functions that will help organisations respond to threats and disruptions faster, more efficiently, and more effectively. That’s why 93% of respondents surveyed in India report they plan to increase their budgets for AI during the next three years. (And they’re not just building chatbots.)