AI Search: A Better Way of Data Retrieval

Realistic image of a person’s hand resting on a laptop keyboard with one finger of another hand approaching a search bar with the words “AI Search.”

Data is a double-edged sword—an extremely powerful asset and an overwhelming flood. As organizations and the world swell with information, the not-so-easy task of finding the right data at the right time and in the right context becomes critical. AI search (sometimes known as cognitive search) stands out as a transformative force amidst the data deluge, promising to cut down the time and effort spent rummaging through information to find what you need. This AI-based search paradigm is redefining the efficiency of workplaces, rescuing employees from the quicksand of unproductive search habits.

What is AI Search?

AI search is a type of search technology that uses artificial intelligence (AI) to understand and anticipate the user’s needs and intentions. It can deliver relevant and contextual information to the user by analyzing the patterns and relationships that exist within the data.

Conventional Search Is Inefficient

Question: Do you know how much time your organization wastes searching for information? Not even getting to do anything with that information—simply looking for and gathering the information needed to do their jobs?

Answer: A ridiculous amount of time is wasted searching for information. According to a study by IDC back in 2001, employees spent an average of 2.5 hours per day (12.5 hours per week) on this task alone! Fast forward to 2012, and McKinsey found that knowledge workers still spent roughly 19% of their time on this non-productive activity. In 2018, another IDC study revealed that data professionals lost 50% of their time every week, with 30% spent searching for, governing, and preparing data and another 20% duplicating work.

Approximately 328.77 million terabytes of data are newly created each and every day across the world. Traditional search must be revised due to the increasing variety and sheer quantity of data within organizations. Those searches typically return irrelevant or incomplete results, forcing users to spend valuable time sorting through to find the necessary information.

Traditional Search Has Way Too Many Problems

The reliance on traditional search methodologies often encounters several significant pain points. These challenges do not just affect overall productivity, they are productivity killers:

  • Unstructured data: This refers to information that either does not have a predefined data model or is not organized in a pre-defined manner. Unstructured data is challenging to process and analyze because it comes in various formats, such as text, images, and videos, making it difficult for traditional search tools to interpret and retrieve relevant information efficiently.
  • The dreaded silo issue: Information often gets trapped in isolated pockets within an organization. This fragmentation leads to difficulty accessing relevant data, causing redundant efforts across different departments. The silo problem stifles collaboration and knowledge sharing, ultimately impacting decision-making processes and operational efficiency.
  • Poor search queries: The effectiveness of a search largely depends on how well the question is phrased. Unfortunately, many users struggle to formulate queries that accurately reflect their search intent. This mismatch often results in the retrieval of data that is not applicable or relevant, leading to frustration and inefficiency in the search process.
  • Limited human bandwidth: While humans are capable of remarkable achievements, rapidly processing large volumes of data is not one of them. This limitation becomes evident in environments where quick data retrieval is essential. Human beings cannot match the speed and efficiency of computers in sifting through and analyzing vast amounts of data, leading to bottlenecks in information processing and decision-making.

AI Search = The Smarter Way to Search

AI search is an incremental improvement and a radical shift in how we find information. Imagine a tool that not only understands your query but the context behind it. A tool that improves with each search, tailoring itself to your patterns and needs.

This is AI search—a system that:

  • Enhances query relevance, thereby boosting productivity.
  • Provides precise results by understanding natural language and context.
  • Personalizes outcomes through machine learning, fine-tuning its accuracy and speed over time.

These features address the core limitations of traditional search systems, which often return a barrage of irrelevant or incomplete results. By contrast, cognitive search sifts through the complexity of varying data types and volumes, delivering precisely what you need.

The Future of Search Is Generative AI and RAG

Generative AI search, exemplified by Retrieval Augmented Generation (RAG), is the next step of cognitive search. It is a concept that merges the intelligence of AI search tools with the creativeness of generative models (like ChatGPT). This powerful combination promises a seamless and intuitive search experience that answers queries and provides precise references. Imagine querying your company's HR documents about employee health plans and receiving an intelligible answer and a citation link to the specific document in question. RAG makes this possible, leading to a more efficient and informed workforce.

OpenAI: AI Search That Remembers … and Is Multilingual

Integrating generative AI search with models like ChatGPT marks a significant leap forward. ChatGPT, a generative AI search tool, remembers past queries, ensuring that the conversation progresses without the need to backtrack and repeat questions—conversations flow naturally.

This AI-based search model can converse in multiple languages, even if the data it searches through is not in those languages, highlighting its versatility in a globalized business environment.

Ensuring Data Sovereignty with AI Search

Employing AI search tools like Microsoft’s Azure AI Search does not compromise data integrity. With AI Search, your sensitive information remains within your organization's control, ensuring that your cognitive search capabilities are secure and proprietary. Data stored within Azure AI Search is fully encrypted in transit and at rest, using industry-standard encryption methods. Microsoft Azure also offers a range of identity and access management tools, enabling organizations to control who can access and modify their search data.

Importantly, Azure AI Search does not inherently expose your organization's data to the public or anyone outside your designated users. Access to your search indexes and data is controlled through Azure's authentication and authorization mechanisms, which you can configure to limit access to authorized users and applications. Additionally, organizations can implement data access policies, firewall rules, and role-based access control to protect their data further. Organizations need to adopt proper access controls and data governance practices within Azure AI Search to ensure the confidentiality and security of their data, irrespective of the underlying AI framework.

Better Data Retrieval Is Possible—Let HBS Help

As we navigate the era of information overload, the need for efficient and intelligent data retrieval is more pressing than ever. With its AI-driven capabilities, cognitive search is transforming how we interact with and benefit from the vast ocean of data. Whether it is enhancing productivity, ensuring data sovereignty, or enabling more informed decision-making, the benefits of AI search are clear. It is time to embrace this change and unlock the full potential of your organization's data.

Don't let your data be a burden; let it be the key to your success. Reach out to HBS today and take the first step towards revolutionizing your data retrieval process with AI search.

author avatar
Nate Freidhoff