Intelligent Enterprise Search: Bridging the Gap Between Elusive Information & Actionable Insights
Intelligent applications – those that are enhanced with artificial intelligence (AI) and machine learning capabilities — are guaranteed to be a topic of conversation in your company right now. According to Scott Likens, PwC’s Practice Leader of New Services and Emerging Tech, data from 2019 revealed that “80% of U.S. CEOs believe that AI will change the way they do business in the next five years.” So now more than ever, enterprise leaders need to be in on these conversations about AI, machine learning, and Intelligent Enterprise Search.
How does Intelligent Enterprise Search provide you with unique business advantages over your rivals?
Intelligent Enterprise Search can drive effective customer self-service, provide your customer support teams with necessary and timely insights, and unlock crucial competitive intelligence, market research, and other information necessary to support decision-making and foster innovation in your enterprise. Intelligent Enterprise Search helps you eliminate data silos, maintain data governance and compliance, protect data, and reduce cybersecurity risks. It also helps you meet the expectations of today’s tech-savvy end-users.
To understand the payoff, you have to understand a little bit about how machine learning works. The SVP of a Fortune 100 company once asked, “Can’t we just buy one of those AI programs and turn it loose? If it’s so smart, won’t it just figure it all out?” Unfortunately, it’s just not that easy. Successful intelligent search implementations involve more than one AI discipline – it’s not just machine learning. Intelligent search uses at least six different AI-related disciplines:
- Natural Language Processing (NLP) – NLP is responsible for things like part of speech tagging and sentence diagramming.
- Text Analytics and Taxonomies – These functions aggregate and index the content specific to your business needs.
- Training Sets – Text Analytics and Taxonomies are then used to generate effective, focused training sets as a baseline for the machine learning to build on.
- Semantic Graph Analysis -This analysis uses meaning to find and plot out the important ideas.
- Semantic Indexing – Once the important ideas are in place, semantic indexing measures relationships and similarity.
- Neural Networks – All machine learning disciplines have to be powered by neural networks or processors capable of solving AI problems.
How do you create and implement an effective AI strategy for Intelligent Enterprise Search?
Initially, enterprises face challenges when trying to get an AI strategy off the ground. Why? You have to line up several disparate pieces across many domains. For example, those who understand the use case don’t always understand the technology, and technologists don’t always understand the use case. For a new AI strategy and application to provide an optimal ROI, your strategy has to be based on a problem that:
- can be solved using techniques and resources available today;
- is central to an important business process; and
- cannot be solved any other way.
Your strategy must also consider all the necessary parts associated with the solution, combining multiple machine-learning techniques and providing for the non-machine learning components. For example, processing infrastructure is a huge consideration. Intelligent search is five times more CPU-intensive than regular search. And, of course, your application must present information to users in a clear and easily comprehensible format, which may also involve AI.
What does Intelligent Enterprise Search look like in action?
Intelligent Enterprise Search helps you make better financial, operational, and marketing decisions, and it helps you make them faster. Intelligent Enterprise Search can be used to facilitate enterprise-wide, domain-specific, and application-specific search capabilities across large available content sets (PDF, Excel, Word, PPT, HTML, etc.). And some of those content sets often reside outside the organization’s firewall, such as subscription-based market research, competitive intelligence, business news, journal articles, and social media content. Bringing them together and making those documents (and the insights contained within them) findable in a single, unified search is critical.
According to a study by McKinsey, each employee loses about 1.8 hours per day searching for information. And that number goes higher the more knowledge-intensive the work is. What does that REALLY mean? You may be missing vital insights, major opportunities, and significant competitive and market developments. By empowering your employees with unified knowledge, you can increase productivity for individuals and teams, minimize rework, and, ultimately, shorten time-to-market. Because searches produce actionable insights, all of that time wasted before can be spent on activities that boost the customer experience and your enterprise’s productivity, efficiency, and bottom line.
Intelligent Enterprise Search is all about scalability and consistency. You can capture insights from subject matter experts and information lost in email or saved on local networks. By bringing together your legacy and nonintegrated systems, all of your employees can access all the information they need to do their jobs. With text analytics, taxonomies, and full-text search capabilities, it has become less critical to hold content creators and end-users to strict rules for creating metadata. With Intelligent Enterprise Search, metadata is the icing on the cake, but the text is the cake.
How does Northern Light SinglePoint take it to the next level?
Northern Light SinglePoint provides end-users with an insights report, or automatic summarization of important information contained in search results. The machine reads the documents in the search result and creates a report that distills the key insights and major competitive and market developments.
With SinglePoint, your occasional users learn more from reading the insights report than they do by engaging in the traditional search process of scanning search results and picking one document to download and read. Your power researchers save time by reading the report to learn what the major issues are before going to the search results to drill into key topics. And all of this happens inside of an AI-fueled continuous improvement feedback loop. Machine-learning monitors how users interact with search results and adjusts what the system proactively brings to each individual’s attention based on their preferences and behavior. And, contrary to what you might be thinking, insights reports do not keep people from downloading full content. Our research shows they actually download more! When your employees have access to better content, they’re going to consume more of it.
The future belongs to those that can make the most effective use of machines, and intelligent search using machine learning will change everything.
Want to learn more about becoming not just a data-driven enterprise but one driven by actionable intelligence? Contact Northern Light today!