Considering Ranking in AI and UI Models



From Google’s SERP to Netflix suggestions, ranking models have their seats everywhere in digital products. Ranking is a prevailing model because it supports users to easily identify the top options quickly. In fact, ranking in user interfaces (UIs) can help users obtain the best options from a given data.

There are many cases when ranking is useful in enterprise software. At the time of procurement, users would want to know about the best providers to procure a service. In the case of professional services, managers would want to know about the best talent to hire from a group of consultants.

From a user viewpoint, ranking can help in the establishment of factors with a common comparable value to determine the best option, preference, value, relevance, or priority.

Ranking in AI projects

It is among a collection of primary models that can serve to reveal an intelligent insight. Ranking, comparing, feedback and intelligence explanations present affordances and additional interaction. The users can use these to work with artificial intelligence (AI) output. In an enterprise software ecosystem, there are a zillion ways where a ranking model can side with intelligence scenarios. 

Here are some examples where the ranking model fits perfectly in an intelligent ecosystem.

Supporting Management in decision-making

When making a decision, an intelligent system can play a part in MCDM (multi-criteria decision making). An intelligent system can accelerate and quickly provide ranked items while undergoing a multidimensional approach. This can be taken further for analysis and evaluation by the teams. 

Example: This is much more useful in health care enterprises, where intelligent systems could support oncologists to enhance early cancer detection.

Smart technology in Automation

AI is transforming business and will continue to make lives easier in every sector possible. Automation is a sector which has pre-eminent application for AI. 

Imagine expanding the capabilities of self-driving cars. In the case of enterprise software, one potential application could be asset management. Here an intelligent system evaluates possible damage to a fleet of vehicles with a computer vision. This will assist in the listing of services needed, ranking by a specific severity or importance.

Building a seamless workflow in IT and Design companies

With no administrative hassle, one can take the support of staff augmentation and outsource work. Imagine an intelligent system that can generate zillions of design variations that are not humanly possible to help a designer or engineer to work seamlessly. 

This intelligent system could also provide designs that are iterated on the ranking based upon specific metrics. For example, a list for cost reduction might be useful to analyze, further explore, and tweak any proposals.

Ranking for in-app experience

Netflix has already leveled up by implementing suggestions that are based on user behavior patterns. At the time of procurement, the users would love to know about the best options which the company is providing. Instead of showing the users all of the products or services, providing the top 5 would give the users a better experience and would nimble the process. 

Ranking considerations in UI

Wonders happen when ranking is added to an app’s UI! Below are some considerations.


Ranking helps in sorting the data. So, ranking and sorting are two separate things. But because ranking now indicates a purpose of the order; you can link it with sorting (sort by rank) when displayed as a list or in a tabular format.

We use the card sorting technique in UI for evaluating information architecture; it also makes sense to sort information and help in labeling it. 

Setting the tables for users

Ranking helps the user in a tabular format and assist in task completion. E-commerce sites have a tabular attribute where the user can change the sorting by price, reviews, and newest. Offering a ranked data collection in a tabular format does not carry away the natural properties like sorting, filtering, line order, explore, and grouping. 

For example, users could sort specific tables differently to the rank order initially presented. An example would be providing a business user with a table ranked by a score, and the user changing the sorting by price to accommodate a particular task he or she wants to perform.

Explaining to build trust

If we intend to expand and maintain the human workforce(which already has many myths and assumptions) with the use of AI in businesses, we need to provide the transparency required for users to affirm the suggestions by intelligent models.

Revealing how the rank was formed will allow end-users to understand the presented information better. This will not only add to building trust in the model but will also empower the users to have enough understanding of the system to provide feedback and judge the quality of the AI.

Final thoughts

These are some of the ideas that we could consider while managing a ranking for an intelligent model. We can add more and more aspects to it while considering as interfaces begin installing intelligent models in the ecosystem, and users start utilizing them.

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