Whether you’re building a healthcare app, a retail store or a government program, the challenge is always the same: efficiency vs. empathy. On one side, you need standardized rules so the system can grow and stay organized. On the other side, you need personalized features so the system actually works for real people in their local context. This is a perpetual tension, on one hand building a "skeleton" that is strong enough to scale, but "muscles" that are flexible enough to adapt to the unique needs of the user.

What are the challenges to be addressed to resolve this tension?

Ethnographic diversity (e.g., India/Bharat):

  • How might we design one platform that successfully caters to distinct and diverse user sensibilities and behaviors without fragmenting the experience? 

The product scale paradox:

  • How might we balance between the need for standardization for scalability with the need for localization for segmental requirements?

Transformation planning:

  • How do we thin-slice this transformation to ensure that value is delivered and received incrementally, without the risk of a "big-bang" failure?

While there is a plethora of AI tools readily available in the market, the answer isn’t a monolithic system or a complex mosaic of disparate and asynchronous point solutions. 

From first principles thinking, we framed the idea of an optimally unified platform (OUP): a stable common set of capabilities across the core value stream, integrated with additional capability modules that are pulled into play depending on the specific needs presented by a scenario. Such an approach is meant to deliver value, upgrade independently and perform measurably.

The solution framework: Optimally unified platform (OUP) 

Let us assume we want to build a platform for the recruitment industry for 3 different business verticals catering to 3 different employment segments with very different hiring processes. How do we define the common solution that works for all in such an environment? Do we try to solve every segment’s problem at one go or is there another way?

The goal of this OUP framework is to start simple, from a priority business with a set of needs that apply across verticals (despite their differences) and add specific features that are unique to this primary vertical. Then the platform goes through a series of versions where the common features are incrementally customised as more businesses or user types are onboarded and their specific needs are uncovered through continuous user research and discovery. This requires planning at the outset to embrace the complexity and scale of the future, by provisioning it in the architecture and functional structure at the outset.

This allows for a seamless way to manage the chaos coming from conflicting requirements from different business verticals or user groups, in a systematic and harmonious process. 

We’re moving beyond the traditional MVP approach by weaving in more empathy than just adding features and also being more systematic about it. Instead of guessing which requirements matter most, we use continuous research to resolve conflicting needs with real-world data. This allows us to build a platform incrementally, that stays flexible for unique users while remaining stable and scalable at its core.

Progressive development phases of the OUP

The progressive shaping process to build a MUP with ‘Continuous Discovery’ informing the level of Customisation on the platform evolution journey

Development methodology for OUP