What could be better when you have someone else doing all the thinking and churning for you, making your life decisions and interactions easier and quicker. The new smart person is called ‘Artificial Intelligence (AI)’, and the invisible machine learning (ML) algorithms/data-models are developed by the coolest of engineers. ‘AI’ quietly and tirelessly works in the background capturing human digital interactions. The intelligent algorithms/data-models convert the pool of continuously flowing data into meaningful patterns, and channelize relevant streams of focused information and interactions in-context to a human’s behavioural needs. So next time you receive out-of-the-blue recommendations for connecting with folks on Facebook/LinkedIn you know where it’s coming from.

Interestingly, the intelligent algorithms/data-models are developed by humans, and the AI engine is only as good as the interpretation of data captured from humans, for shaping meaningful experiences for humans. Hence, in my opinion for shaping seamless and meaningful experiences, algorithms/data-models have to be developed in tandem with human experience designers. There are a lot of moving parts, and the intent of the algorithms/data-models have to align with the intent of the solution designed for humans.

Try purchasing a product on your preferred e-commerce platform. Having purchased the product, you will still see ad snippets of the product along your search/browse paths and on social platforms such as Facebook for a couple of weeks, if not days, enticing you to purchase the product. Can get annoying, is irrelevant, and at-times embarrassing depending on what you have purchased. Ever wondered why? It’s because the data-models have been developed to blast ad campaigns based on end-user click behaviours irrespective of last-mile human transactions.

Reference: https://www.technologyreview.com/s/510646/racism-is-poisoning-online-ad-delivery-says-harvard-professor/‍

Have you been followed by restaurant ads based on your search criteria’s when at NYC, all the way to when you were back home in India and later?

Automation can be delightful as well as scary, and questions about data confidentiality as well as ethical practices are all over. Moreover, algorithms often can be misleading.

I’m sure many of you must have reached a black-box when conversing with virtual agents like Siri, Cortana, etc. and often received responses such as, “I don’t know…”, “I’m not sure…”, “I don’t understand…”, and so on. This is because, in order for the agent to seamlessly respond it has to first understand what you are saying, what are you telling it to do, and how to improve to make your life easier.

Reference: https://www.fastcodesign.com/90132632/ai-is-inventing-its-own-perfect-languages-should-we-let-it

It’s extremely important to understand the data source/s, the type of data, the age of data, the data segments, and for what purpose it’s going to be used, and for whom. Designers are usually kept at bay from the data details and are made to focus on shaping engaging features and functionalities. Typically, automation products use pre-built algorithms/data-models and the quality of responses are an outcome of its inherent design.

Data has become a critical element for designing desirable, feasible and viable digital experiences for the future. Automation product developers should work in tandem with experience designers for shaping relevant, meaningful and trustworthy digital automation experiences.

All these years’ engineers have made good use of available data and developed intelligent algorithms for automated digital product experiences inside-out. It’s about time we start building digital products outside-in for customer value creation and seamless end-user experiences.

Looking forward to hearing your views and experiences.

Over the last few years the distinction between designing experiences for Enterprise products versus Consumer products has significantly narrowed and in few scenarios is undistinguishable; thanks to the fast evolving and adopted trend for ‘Consumerization of Enterprise Products’, and the change in user behavioural patterns as well as expectations.


Today, Enterprise users are exposed to a wide array of consumer products as well as social applications in their day-in-life at work and home. Flexible Enterprise policies like ‘BYOD – bring your own device’ and ‘CYOT – choose your own tool’ has fuelled employee exposure to common productivity/communication tools in their work life and has proven to enhance employee productivity. Growing collaboration and accessibility needs (from anywhere and anytime) owing to the change in business dynamics as well as user behaviours have forced enterprise product creators to reshape their strategies (e.g. Microsoft Office vs. Google Docs) and imbibe social platforms as well as features. The overlapping experiences have uncovered an open design canvas for us ‘the experience designers’ and there is a noticeable change in the design thinking and approach. Mobile, gaming, etc. design paradigms have become a stimulus and seemed to be widely embraced for shaping intuitive and personalised enterprise product experiences.

User interface standardization between consumer and enterprise products is imminent and I believe if design paradigms are embraced responsibly and judiciously can significantly improve product adoption, engagement and user efficacy.


In view of my collaborations with several fortune 500 companies as well as product start-ups serving enterprise as well as consumer needs, there have been interesting learnings, and I believe there are several challenges which still exist in front of us while shaping enterprise product designs. Following are few aspects which may be worth keeping in mind while strategising solutions;