Categories:> Analytics, Artificial Intelligence, Deep Learning, Machine Learning, UX Design


In this article we would like to share our learnings and insights about how effective design thinking plays a vital role in preserving a data-driven product’s experience and value proposition as it matures with time.

Every sector is seeing a massive growth in data, influencing their digital business transformation strategies, and empowering technologies such as ‘Artificial Intelligence (AI)’,  ‘Machine Learning (ML)’, ‘Deep Learning (DL)’, and so on . It’s important for digital product design experiences empowered by data and connected technologies to be malleable for easily accommodating the rate of data scale and change, for delivering meaningful experiences.

It enables envisioning a robust strategy on which the current and future roadmap of the product will reside. Thereby, significantly reducing strenuous working hours, and the need for expensive talent to repeatedly re-engineer the product for meeting the current as well as ever-evolving business and end-user needs.

In our recent partnership with a cloud-based B2B solution provider we crafted a scalable design experience which can gracefully accommodate the ever-growing data needs of business, new features and functionalities, as well as the platforms intelligence with the empowerment by smart technologies such as Artificial (AI), Machine Learning (ML), and Deep Learning (DL), without the need to re-write the user interface.

The challenge was to envision a product experience that was unique and effective for managing a legal firm. If the product failed to do so, the attorney could simply turn to a regular project management tool just like any other organisation.

The business stakeholders wanted to design an intelligent product that could observe and truly understand the end-user’s behaviour, in doing so deliver a personalised and an efficient experience to each end-user. In order to achieve the intended experience, we conducted a thorough study about the ecosystem and realised that its a multi-tenant data-intensive transactional application. Since, acquiring and synthesising behavioural data-driven insights will take time, a progressive experience design strategy for ensuring a seamless journey to the end-users was adopted as an optimum solution. As the system continues to learn from acquired data, 3 levels of intelligence will get empowered progressively – the first being ‘Artificial Intelligence’ , second is ‘Machine Learning’ and finally ‘Deep Learning’ algorithms will get trained for delivering an optimum experience.

Artificial Intelligence
Effective collaboration and coordination between the firm’s personnel was the key to business success. A manual messaging system on ‘Day 0’ for assigning tasks, status follow-ups, scheduling meetings, etc. was enriched with a AI bot driven experience on ‘Day N’ for automating frequented interactions based on acquired data, without the need for altering the products design framework.

An ‘AI bot’ worked well for this function since the system could be fed with the user’s trending interactions from a database that would get richer over time; leveraging the product’s maturity to it’s advantage.

Machine Learning
On studying the end-user’s behaviour the system will be able to predict frequent end-user interactions and therefore contextually customise the end-user’s interface for a tailored experience. Percolating this principle, even the user’s launchpad and workbenches were customised to match their style of work.

Deep Learning
One of the most tedious workflows within the product ecosystem consisted of uploading physical scanned documents, which further had to be manually segregated by a dedicated team. The team would have to go through each and every detail in the document thoroughly to understand which category it belongs to, so that they could tag them accordingly for association and future discoverability. The manual process was prone to errors, leading to skewed decisions, loss of time, and significant increase in operational costs for the firm. The attorneys in the firm had to wait a couple of hours for the matter documents to get categorised.

The introduction of a deep-learning algorithm early in the product architecture helped eliminate the need for an entire team and several hours of dogmatic work in the future. Moreover, redesign and redevelopment of the user interface was not required as it was already envisioned to accommodate the  products maturity curve.

The aforementioned project displays how it is imperative to think-through current as well as future business, end-user, as well as technology scenarios early in the product design and development lifecycle for delivering a differentiated digital experience and a lasting value proposition as the ecosystem matures over time.

The secret recipe resides in strategising a framework that allows the system to progressively evolve for continuously optimising the end-users experience with usage. Not being able to stay in-tune with the ever-growing changes and advancements, is a major reason why many products fail to preserve their user loyalty as well as acquire new users over time.

Our design thinking approach is in tandem with our philosophy ‘partners in your digital journey’; as your success is our success.


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