How does combining search with AI solve enterprise data problems?

Organizations are drowning in data — from security event logs and application error messages to IoT metrics and help center FAQs. This wealth of information often resides in isolated silos, presenting a substantial challenge in synthesizing it to elevate customer experiences, improve operational resilience, or mitigate security risks. Businesses increasingly find themselves stuck just storing this data or manually and slowly parsing through it. How can organizations overcome the challenges of disparate data and instead harness that data to gain actionable insights and solve problems?

The answer lies in a paradigm shift: solving the underlying data problems that are hindering your organization's ability to deliver on the business outcomes you're looking for. The intersection of generative AI and search technology epitomizes this shift. While generative AI brings formidable computational intelligence — able to generate insights and predictions from large data sets — it often lacks the context to pinpoint relevance. Conversely, search technology excels in delivering highly relevant and precise information but lacks the capability to compute or infer beyond its programmed algorithms. By bringing together these two technologies, organizations can enable their customers and employees to find answers that matter from all data — in real time and at scale.

Let's take a look at three major challenges shared across modern businesses:

• Serving the right information in real time: Serving the right data to the right people in the right place at the right time is challenging, but it’s important for employees and customers alike. When an organization’s users — both internal and external — spend more time searching than they should have to, it can have costly consequences for the business. 
• Keeping systems up and performing: Developers, admins, and architects require tools to keep these increasingly complex digital ecosystems both available and performant. Downtime and poor performance can be costly. ITIC estimates that for most large organizations, downtime on average can cost between $500,000 to over $5 million per hour.  
• Protecting and securing applications and IT infrastructure: Security analysts require tools that enable them to protect an ever-growing attack surface. A single incident can impact both customer trust and, of course, the bottom line. IBM estimates that a single data breach can, on average, cost an organization $4.45 million.

The key to navigating all of these enterprise data management challenges is to first understand that they are connected. Fundamentally, each need is addressed by getting value out of an ever-growing, ever-evolving mountain of data in real time. So how can combining search with AI help?