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Oct 4th 2021 algolia
Consumers expect always-on, frictionless, dynamic and personalized digital experiences. But as it stands, 99 percent of companies not named Amazon or Netflix have struggled to deliver world-class search. Why? Because humans search in messy, unpredictable ways. People use different words to look for the same things – and this doesn’t even take into account the different languages we speak. Expected search results vary from one person to another based on preferred brands and lifestyles. Expected results also change over time based on seasons and trends. (Think about what we expected when searching for “masks” today compared to a year ago.) Also, results customers expect don’t always align with what businesses want to surface first. Finally, there is a vast difference between a site that delivers “basic textual search” and one that provides “best-in-class search.” Best-in-class search is fast, relevant, personalized and predictive/prescriptive — and this is the type of search that fuels the best digital experiences.
Enter Artificial Intelligence (AI). Businesses can apply AI to solve search-related challenges, thereby enabling brands to deliver meaningful digital experiences. When it comes to applying AI to search, many organizations find the task complex and challenging. Tech leaders consider AI difficult to master; they think it is a technology that is not “standardized” that can yield sometimes unexplainable results. AI is also a technology that requires specific expertise to implement, and a great deal of testing, iterating and fine-tuning. What are the biggest barriers to adopting AI-powered search, and how can we begin to address these roadblocks?
First, let’s discuss what we mean when we say “AI-powered search.” Search is inherently complex, with ever-changing user behavior and ever-expanding (yet imperfect) data. And while AI can help simplify the search process and improve the accuracy of search results, AI is not a magic, “one-size-fits-all” solution. But we can solve for many of search’s complexities with a step-by-step approach. We want to build a search engine that gets smarter and learns from user behavior. AI should allow companies to deploy tailored digital experiences that are driven by transparency, natural language understanding and personalization. Ultimately, we want people to consistently find what they’re looking for in their top three search results. Ideally, when someone asks their Alexa or Google Home assistant a question, we want them to get the single best answer.
So, what roadblocks stand between companies and their AI-powered search goals?
Considering the above potential barriers to AI-powered search, how can companies begin to address these issues? How can companies evaluate which type of AI implementation may be right for their business?
The first step involves identifying a small set of problems specific to the business that AI can solve. From there, we can decide if buying off-the-shelf software is best, if we should build our own solution in-house, or if we should take a “build and buy” approach. In that scenario, we can build only that part of the solution that is unique for the business. Here, the growing popularity of API-based solutions can make all the difference for developers. APIs meet developers where they are, allowing them to “buy to build faster” by reducing the number of back-end processes and allowing them to get back to building, experimenting and iterating. Ultimately, we should have one objective: To provide a digital experience that guides customers to the right information when, where and how they need it.
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