In the digital age, the terms “search” and “discovery” are often used interchangeably, but they represent fundamentally different processes. Understanding the distinction between search and discovery can help us appreciate how discovery is transforming our interaction with information and unlocking new possibilities for exploration and innovation.
Search is a transaction, a request-and-retrieval where requests are made, specific content is retrieved, and results are presented. Discovery is an exploration, allowing for the dynamic expansion of initial criteria to include additional information that might have been excluded otherwise. The concept of discovery is emerging as a powerful paradigm shift that goes beyond the capabilities of traditional search. Enter generative AI–a revolutionary advancement that promises to transcend the boundaries of search, delivering discovery experiences that are more intuitive, personalized, and insightful. This AI-augmented discovery is what the Engenai team calls Generative Discovery.
Understanding Search
Search engines have been our primary tools for finding information on the internet, leading us through an immense labyrinth of data. However, as the volume and complexity of information continue to explode, the limitations of conventional search methods become increasingly evident.
The Mechanics of Search
Traditional search engines, such as Google, Bing, and Yahoo, have revolutionized how we access information. They use sophisticated algorithms to index and rank web pages, presenting users with a list of links that match their queries. This process of search typically involves:
- Query Input: Users type in specific keywords or phrases.
- Indexing: The search engine scans its vast index of web pages to find matches to the query.
- Ranking: Algorithms rank these matches based on relevance, quality, and other factors.
- Results Display: A list of links is presented to the user, who then clicks on these links to find the desired information.
Limitations of Search
This method has proven remarkably effective for many purposes, but it has inherent limitations:
- Keyword Dependency: Effectiveness is limited by the specificity and accuracy of the user’s query.
- Surface-Level Answers: Provides lists of links to information rather than deep insights or synthesized knowledge, leaving the user to sift through multiple sources to find the most relevant information.
- Iterative Process: Users often need to refine their queries multiple times to find what they need.
- Context Blindness: Search engines can struggle to understand the full context or intent behind complex queries.
The Essence of Generative Discovery
Discovery, in contrast to search, is a more holistic and exploratory process. It involves uncovering new information, insights, and connections that might not be immediately apparent through a straightforward search query. Discovery is less about finding a specific answer and more about exploring a landscape of information to gain a deeper understanding or stumble upon unexpected insights. Generative AI takes this process further by creating new content, offering insights and information that go beyond mere retrieval, and thus transforming the search experience to generative discovery.
Key Features of Generative Discovery
- Exploratory Nature: Encourages users to explore a broad range of information allowing for different paths to a general destination.
- Contextual Insights: Parses nuanced queries to understand and incorporate the context behind the user’s interest to provide more relevant and comprehensive results, delivering a highly personalized discovery experience.
- Serendipity: Facilitates the chance discovery of valuable information that the user might not have explicitly searched for.
- Synthesis and Creation: Instead of providing links, integrates and synthesizes information from multiple sources and creates coherent, comprehensive responses, summaries, or even entirely new content.
Limitations of Generative Discovery
Generative AI is dependent on the quality of data that is used to train the algorithms, leading to its own set of limitations:
- Accuracy and Reliability: Might perpetuate and amplify existing data bias skewing outputs.
- Privacy and Security: Extra care has to be taken to handle sensitive data safely.
- Resource Intensive: Can require significant computational power depending on the complexity of the request.
- Ethical and Legal Constraints: Challenges in content moderation and legal issues are still evolving.
Generative Discovery in Action
Let’s look at an example where generative discovery goes beyond search:
Business Intelligence
Let’s say that over the last month your furniture business analytics shows a 700% increase in purchases of a specific cabinet. There’s nothing particularly special or noteworthy about this cabinet. You haven’t run any new promotions on this cabinet and the price hasn’t changed in two years. So you go to the product reviews and you ask the following query: “Why are people buying this cabinet?”
- Conventional Search Response: 2,892 product reviews returned, all returned because they contain the word “buying” or “cabinet.”
- Generative Discovery Response: 346 customers said they are upcycling it to make dollhouses.
In the conventional search, an employee would have to read through those reviews to try to find an explanation, and they would have to sift through irrelevant reviews that were included because they have the word “buying” or “cabinet.” In the generative discovery response, a pattern was detected, summarized, and presented as a key insight. A business analyst might then do a follow up query to the sales order system and ask: “What orders that include cabinets also include products from the kids department?”
- Conventional Search Response: 13,428 orders include cabinets OR kids products; 1,489 orders include cabinets AND kids products
- Generative Discovery Response: 8% of “Stuva Wall Cabinet” orders also include kids items.
To execute this conventional search an explicit mapping of this product plus kid’s department product categories would be required. For generative discovery, this is a natural language query and it returns a specific product that is noteworthy.
Personalized Recommendations
Now that you have this new insight, how can you capitalize on it? Imagine that you have a dynamic landing page on your site. Today, you would curate this page using various conventional tools that automate some of this effort. However, those tools still require some manual rules-based configuration. You have to map product categories, curate collections, create thematic content, and bring these all together. In the past, your customers experienced this as seasonal, promotional, and other thematic pages. But as your inventory changes and evolves, you are limited by how much effort and resources you can assign to this task.
However, with generative AI, you only need to deploy one intelligent, dynamic landing page once. From that point on, you simply pass in parameters that will instruct the page on how to dynamically change. For example, you might pass in a query like, “Father’s Day Gifts.” The genAI would pull in the relevant products, create introductory paragraphs and supplementary content on the fly. Of course, this is something you can do with your existing solutions. But what you couldn’t do, is scale this to a near infinite set of offerings. How about, “Father’s Day Gifts for the Dad Who Loves Carpentry,” or “Father’s Day Gifts for the Technogeek Dad,” or “Father’s Day Gifts for the Dad Who Loves All Things Blue.”
Now imagine that you make it even more intelligent by passing in the customer’s past shopping behavior, or a list of the items that they’ve browsed or added to their cart today. Your customer history data might point to this customer liking the color blue, but today, they seem to be shopping only for yellow items. Your genAI-powered dynamic landing page would respond accordingly. All of these permutations, for every conceivable occasion or theme, dynamically generated by passing in a few different prompts. No additional development, no new product releases necessary.
Dynamic Marketing Content
Great, so now you have an incredible new gen-AI powered landing page that has greater conversion. How do you drive more traffic to it? Generative discovery allows you to decouple your marketing team from product development releases. Using your existing CRM and marketing tools, let’s say that you determine that for a specific customer, their relevant interests are: hiking, photography, and travel. Normally, you’d have to construct a thematic landing page for this particular set of interests. But with a genAI-powered dynamic landing page, your marketing team simply adds this hyperlink to their email campaign:
https://www.myshop.com/personalized-recommendations?q=hiking+photography+travel
Then you have a different customer with interests in: cooking, movies, and dogs. Your marketing team creates a hyperlink like this in that customer’s email:
https://www.myshop.com/personalized-recommendations?q=cooking+movies+dogs
Of course, this would all be done automatically with your marketing tools. You instruct your marketing template to take the base URL, then add the relevant interests at the end. All of this is done dynamically and without being dependent on a new product development release for each and every one of those thematic pages. Instead, you deploy that genAI-powered dynamic landing page once, and you simply pass in new parameters. The AI takes care of the rest.
The Future of Information Interaction
As generative AI and advanced discovery tools continue to evolve, the way we interact with information will fundamentally change. Here are some future trends to watch:
- Seamless Integration: Discovery tools will be increasingly integrated into our everyday digital experiences, making it easier to uncover relevant information and insights in real-time.
- Enhanced Personalization: AI will enable even more personalized and context-aware discovery experiences, tailored to individual needs and preferences.
- Immersive Experiences: With advancements in augmented reality (AR) and virtual reality (VR), discovery will become more immersive, allowing users to explore information in dynamic and interactive environments.
- Expanding Accessibility: Generative AI can democratize access to information, providing high-quality, personalized insights to people regardless of their background or location.
- Ethical and Responsible AI: As discovery tools become more powerful, it will be crucial to address ethical considerations, ensuring that these technologies are used responsibly and transparently.
Conclusion
Generative Discovery represents a new frontier in our quest for knowledge, offering a richer, more nuanced, and serendipitous approach to information exploration. While traditional search engines have served us well, the future lies in discovery tools that understand context, synthesize information, provide personalized output, and inspire creativity. As we continue to harness its power, the journey from search to discovery will become more seamless, intuitive, and enriching, uncovering new insights and making meaningful connections will become an integral part of our digital lives.
