The next phase of generative AI presents a golden opportunity for businesses
The gap between organizations that can effectively harness their troves of data and the vast majority that can’t will only widen. And quickly. Fortunately for the latter, generative AI may also pose the solution—if you’re able to connect the dots of your data. Here’s how.
Twenty-five years ago, Hewlett-Packard CEO Lew Platt famously lamented, “If only HP knew what HP knows, we would be three times more productive.” By that, he meant management had lost touch with both the data hiding on its servers and the tacit knowledge stuck in workers’ heads. Unlocking these “unknown knowns” has been the goal of knowledge management for decades—to no avail. In fact, the problem has only gotten worse, as organizations stockpile data under the correct assumption that connecting the dots of their data is their competitive edge—if only they knew how.
Three areas where the combination of search and generative AI can help organizations discover their unknown knowns are operational resilience, cybersecurity, and customer experience. In each case, the struggle is to envision a more complete picture of operations, threats, and opportunities neither human eyes nor current systems can spot on their own.
Besides “shining a light on” data that organizations haven’t been able to take advantage of in the past, generative AI also has a critical role to play in restoring a measure of surprise and delight to customer experiences, where traditional search experiences can fall flat when users can’t get relevant, contextual results. “E-commerce is an obvious one, because in that world, people know relevance equals revenue,” says Steve Mayzak, senior director of solutions architecture at Elastic. Generative AI promises to sharpen the signal-to-noise ratio using conversational language and multimodal search, (e.g., Find a product that looks like this) to deliver personalized answers rather than page-after-page of irrelevant results.
In a recent survey of more than 3,000 IT professionals worldwide commissioned by Elastic, a leading search AI company, nearly everyone reported struggling to unearth insights from data buried across their organizations. This challenge has grown more acute during the past 18 months as companies race to harness generative AI. The breakneck pace of AI development has favored those able to holistically bring all their data into the AI equation. Which means the gap between the handful of organizations that can do so effectively and the vast majority that can’t will only widen, and quickly. Fortunately for the latter, generative AI may also pose the solution.
“Surfacing actionable insights is hard and only getting harder,” says Vinay Chandrasekhar, director of product management for observability at Elastic. “That’s because data volumes, application complexity, and the pace of change are increasing exponentially to a point where it’s next to impossible to use archaic technologies to get work done.” This is why firms must invest in search solutions capable of pulling results in real time from data across scattered silos—because before you can ask generative AI to connect the dots in ways you hadn’t thought of, you must first collect the dots.
AUTONOMOUS OBSERVATION . . . AUTOMATIC REMEDIATION
For example, rather than wading through a blizzard of alerts, the future of observability might just be a conversation with an AI assistant that’s flagging anomalies and resolving them as it goes. Of course, the technical complexity would be staggering—autonomous observation coupled with automatically generated scripts for remediation—but given the capabilities of current large language models (LLMs), is totally conceivable. More significant is how roles and functions would evolve given the human-machine pairing. “Expert users can focus on the bigger picture, solving those problems and leaving routine, mundane activities to an AI assistant,” Chandrasekhar explains. Instead of struggling to see, they’ll have more time to think.
This is especially important in industries such as financial services, where security threats are outnumbered only by the internal rules and external regulations for monitoring and securing systems. Not only will LLMs absorb these rules, but they will begin to suggest and write their own—along with the appropriate response for each—and then act on them while attacks are still in progress, before the evidence is visible in security operations centers (SOCs), mitigating security risk.
Once again, this would represent a profound change in organizational posture, from passive analysis to instant reaction. “Going from alert views—which is today’s analytical SOC—to attack views—which is tomorrow’s insightful SOC—is one of the most enjoyable, breathtaking, innovative things coming our way,” says Santosh Krishnan, general manager of security at Elastic.
Once again, this would represent a profound change in organizational posture, from passive analysis to instant reaction. “Going from alert views—which is today’s analytical SOC—to attack views—which is tomorrow’s insightful SOC—is one of the most enjoyable, breathtaking, innovative things coming our way,” says Santosh Krishnan, general manager of security at Elastic.
“RELEVANCE EQUALS REVENUE”
Number of surveyed IT professionals who report difficulty unearthing insights from
data buried across their organizations.
+
Vinay Chandrasekhar, director of product management for observability at Elastic
“Expert users can focus on the bigger picture, solving those problems and leaving the mundane activities to an AI assistant.”
Santosh Krishnan, general manager for security at Elastic
“Going from alert views—which is today’s analytical SOC—to attack views—which is tomorrow’s insightful SOC—is one of the most enjoyable, breathtaking, innovative things coming our way.”
Commissioned by
Created BY
Source: Elastic Generative AI Report
AUTONOMOUS OBSERVATION . . . AUTOMATIC REMEDIATION
+
Number of surveyed IT professionals who report difficulty unearthing insights from
data buried across their organizations.
In each of these instances and industries, semantic search underpins generative AI’s ability to monitor, analyze, and act in bringing buried insights to the surface. Organizations anxious about keeping pace with their peers when it comes to seizing generative AI’s potential should invest in a solution that can correlate real-time, proprietary data in any format from across environments before the leaders break away from the pack.
Besides “shining a light on” data that organ-izations haven’t been able to take advantage of in the past, generative AI also has a critical role to play in restoring a measure of surprise and delight to customer experiences, where traditional search experiences can fall flat when users can’t get relevant, contextual results. “E-commerce is an obvious one, because in that world, people know relevance equals revenue,” says Steve Mayzak, senior director of solutions architecture at Elastic. Generative AI promises to sharpen the signal-to-noise ratio using conversational language and multimodal search, (e.g., Find a product that looks like this) to deliver personalized answers rather than page-after-page of irrelevant results.
In each of these instances and industries, semantic search underpins generative AI’s ability to monitor, analyze, and act in bringing buried insights to the surface. Organizations anxious about keeping pace with their peers when it comes to seizing generative AI’s potential should invest in a solution that can correlate real-time, proprietary data in any format from across environments before the leaders break away from the pack.
Humans and machines working together to connect the dots has been a winning strategy since the days of computer chess, let alone ChatGPT. Putting our collective heads together, through search and silicon, will be key to delivering better experiences for workers and customers alike.
Humans and machines working together to connect the dots has been a winning strategy since the days of computer chess, let alone ChatGPT. Putting our collective heads together, through search and silicon, will be key to delivering better experiences for workers and customers alike.
In each of these instances and industries, semantic search underpins generative AI’s ability to monitor, analyze, and act in bringing buried insights to the surface. Organizations anxious about keeping pace with their peers when it comes to seizing generative AI’s potential should invest in a solution that can correlate real-time, proprietary data in any format from across environments before the leaders break away from the pack.
Humans and machines working together to connect the dots has been a winning strategy since the days of computer chess, let alone ChatGPT. Putting our collective heads together, through search and silicon, will be key to delivering better experiences for workers and customers alike.
Click here to learn more about creating generative AI apps at your organization.
Click here to learn more about creating generative AI apps at your organization.
06-24-24