In our most recent Customer Experience Relevance Report, the vast majority of 4,000 consumer respondents said that they put in high effort to find information or get help from brands online.
The biggest blocker? Not being able to search for information they needed on their own.
Search isn’t just a doorway on your website — it’s the front door to your entire digital experience. It’s how prospects get to know you, and how customers learn about what they’ve purchased, as well as how they discover new product developments. Search also extends within your company, helping employees perform the tasks and services that your customers pay for.
That little bar sure is doing a lot of work! But search — especially AI search — can do even more. In our 2023 Enterprise Tech Report, roughly four-fifths of 600 IT, technology, and business stakeholders said that they saw search as critical to driving digital transformation. Whether it’s retrieving and delivering the most relevant information, proactively surfacing recommendations for best next steps, or revealing trends and gaps through analytics, search is arguably one of the most important technologies your business can invest in.
If you’re wondering how to evaluate which AI search platform to choose, we’re here to help with a transparent breakdown of must-have features for this year. And if you choose right, the best platform will be the one that becomes your foundation for years of innovation.
1) Crawling Modules and Custom Connectors
Rarely in a single location, enterprise content is scattered across hybrid sources both cloud and on-prem, stored in structured and unstructured formats. Security and governance concerns often block initiatives to access all of this information in a meaningful and actionable way. That, and the mindset of “why fix what isn’t broken?”, slow efforts for enterprise connectivity.
You don’t have to trade robust security for content gaps and enterprise silos. When evaluating AI search platforms, look for one that offers native connectors for popular enterprise platforms such as Salesforce, SAP, AWS, Atlassian, and Google to get your search experience up and running quickly. In addition, the right platform should be able to connect to virtually any system via Rest, Push, or GraphQL APIs so no content goes unindexed.
Also look for the ability to access on-premises content via a crawling module that can run behind your firewall, and is integrated with the customer’s environment. Such a crawling module can pull content from on-premises systems and push it to your unified search index without raising any security flags.
2) Ranking Rules Management
A robust, user-friendly backend is vital to usability, and to ensuring you can fine-tune relevance without technical roadblocks. You should have full visibility of the rules surrounding search results ranking. The ability to combine featured results and ranking expressions in one place is key to effectively managing ranking rules, making it a critical component to look for in an AI search platform.
In addition to having a clear view of the various factors influencing ranking, you should also look for flexibility when it comes to the timing of their activation. While you will certainly need permanent sets of rules for standard use, i.e., Groups, you may also wish to have timed sets of rules for specific promotions, i.e., Campaigns.
An AI search platform should grant you the ability to create and manage both from a central location with ease:
3) Dynamic Facets
Chances are that the volume of data at your disposal is growing quickly – and the proliferation of metadata in your index is getting more difficult for you and your end users to manage. Static search facets — typically displayed in the form of checkboxes on a results page that enable searchers to refine their experience — won’t help much, as they can be irrelevant to what a user is looking for. For example, “dimensions” won’t help if the user has already specified those dimensions in the query itself.
Customizing facets to the individual user can be powerful in helping them cut through the noise to find what they need, but they can also be difficult to manage: What values should you show? In what order should you present those values? Where should you pre-filter?
An AI search platform should come with dynamic facets powered by AI, as AI can answer all of the above questions and act upon the answers for you without any intervention. By feeding usage data to machine learning algorithms, they’ll be able to determine what the most appropriate facets are, as well as what values should be used and in what order. Best-of-breed AI search platforms also offer capabilities to auto-generate new facets from indexed metadata.
Relevant reading: The Complete Guide to Designing Faceted Search
4) Question Answering
Indicated by AI Overviews (AIOs) appearing in over half of Google searches and user feedback that reveals higher satisfaction in search results due to AIOs, generative answering is quickly becoming standard for today’s searchers.
The ability to ask a question in natural language, such as “how do I update my subscription payment details”, and get a direct response synthesized from the most relevant documents is a paradigm shift away from the traditional list of blue links. And with citations, users can see the direct source of the answers and dig deeper if they choose to.
Caption: With generative AI embedded in their support portal, Xero achieved a 20% decrease in search sessions needing additional customer experience support in the first six weeks of launching the new feature.
For highly regulated industries that require specific wording, an alternative solution is Smart Snippets. These snippets bring the most relevant information from a relevant document to the top of a search results list, instantly giving the searcher the answer they sought.
Image caption: The most relevant answer for a given query is extracted from a list of returned results and placed at the top of the page. The user gets their question answered right away, without having to click through content.
Relevant reading: Smart Snippets vs Generative Answering: Which Does Your Experience Need?
5) Personalized Recommendations
Answers to questions often beget more questions. So personalized recommendations allow you to digitally guide users by the hand to what they need next, whether that be informational content or products.
And given that expectations surrounding personalized digital experiences are sky high, persona-based recommendations are not enough – they must be tailored to the individual. This means that they should not only be shown what they know they need in the moment, but what they didn’t know they needed along with it and what they might need next.
Now, given that bounce rates are also high — in ecommerce the average sits around 45% – you need to be able to provide this experience to all users, quickly, even if they are a first-time visitor. If recommendations were confined to being history-based, this would be impossible, but, thankfully, they are not.
An AI search platform that utilizes a recommender system (1) based on product associations and (2) driven by in-session actions can anticipate what an unknown visitor needs – and provide it to them in turn.
Caption: Recommendations for similar products are shown to a user, based on their in-session actions and the product information associated with those actions.
Relevant reading: 5 Recommendation Categories Found in the Best Recommenders
6) In-Product Experience
Product-led growth is quickly becoming the top go-to-market strategy – and given that the median enterprise value (EV) of PLG companies is 2X higher than the public SaaS index as a whole, it’s certainly not a bad move. For those that choose to employ it, success is dependent on enhancing in-product engagement and communication.
One effective method for doing so is to improve in-product help. Now, standard in-product help is by no means an uncommon feature. However, the results are static and not contextually aware. In other words, they are irrelevant.
By adopting a platform that applies AI on top of robust search to give rise to personalized recommendations (see section above), you also attain the ability to deliver in-app contextual help and surface help and documentation based on a user’s specific context – i.e., their current page and what they have already viewed up to that point.
A user receives dynamic in-product help – in the form of personalized recommendations and search results, based on their current page and what they have previously viewed.
That being said, an AI search platform should allow you to integrate your search UI into your different applications so that you can surface helpful content in a contextual manner inside the product itself — for example, in SaaS offerings or even within your CRM. Product use and adoption will soar.
Relevant reading: In-App Contextual Help: Improve Your User Experience with IPX
7) Headless UI Components
Traditional Javascript UI frameworks have proven to be highly useful for front-end developers.
However, with the rise of multichannel marketing and the proliferation of digital touchpoints, they have proven to be limiting. Developers want greater flexibility and control when it comes to design.
The solution? Headless UI components. These decouple the front-end experience from back-end architecture, making the whole experience, in a word, composable. The right AI search platform plays to the strengths of composability, acting as an intelligence layer between systems that helps disseminate relevant information to the right people, when needed.
If your organization has a strong UX team, with specific guidelines for how search UI should look and feel, you want to ensure that the AI search platform you adopt has a robust library of components that can be recreated in any UI framework to support your custom app or experiences.
Relevant reading: An Explainer on Composable Commerce and Its Benefits
8) Time to Value
Building custom components is valuable, but moving from ideation to creation to integration to rule building to configuration can take so much time. And long build times ultimately slow down the development process — meaning going to production becomes a pipe dream, and all the up-front investment in your AI search platform withers.
Speed up build times by selecting an AI search platform that offers:
- An accelerator to build custom components
- A means to unify multiple code bases using different stacks with one build process
- A collection of crowd-sourced components to help turbocharge implementations.
Plus, out-of-the-box features like machine learning models help your enterprise start reaping the benefits of search starting on day 1.
Relevant reading: Generative AI and Top AI Models for CX
9) Search Analytics
Delivering relevant information to the right people at the right time is one massive benefit to your enterprise — but understanding what people are searching for, whether they’re finding it, and what’s missing is just as important. With usage analytics, you can identify patterns and trends in search, surfacing insights like particular interest in a product or feature, or areas of customer education that your customer success team could better attend to.
Usage analytics can map searcher behavior to business outcomes, too, highlighting the cost savings and revenue-impacting benefits gained from investing in an AI search platform.
10) Security & Infrastructure
As AI-search platforms become critical to delivering modern digital experiences, their infrastructure matters as much as their intelligence.
Start with resiliency: Active-active architectures keep systems running across multiple regions, ensuring uninterrupted service even if one goes down. It’s essential for enterprises that can’t afford downtime.
Data residency should be flexible. With privacy laws like GDPR evolving, platforms should offer the ability to store data in-region to meet compliance needs and user expectations.
And security must go beyond checkboxes. Look for encryption in transit and at rest, zero-trust architecture, and enterprise-grade audits and certifications—all designed to protect sensitive information.
These foundational capabilities are what separate scalable, enterprise-ready platforms from the rest. It’s not just about delivering relevance—it’s about delivering it securely and reliably.
Dig Deeper
This blog was just the start on what to look for in the right AI search platform for your enterprise.
When evaluating an AI search platform, its important to look for a single, enterprise-ready solution that can scale across all your GenAI and agentic experiences to simplify your tech stack — securely connecting every data source, delivering relevance with precision, and giving you the flexibility, speed, and analytics to adapt and grow without limits.
If you’re looking to dig deeper, check out our free ebook, Buyer’s Guide for Best Enterprise Search Engine, where you’ll find key considerations you have to evaluate when looking for the best enterprise search software.