Every time a major technological shift occurs, we enter a necessary phase where the focus transitions from pure innovation to efficiency—bringing the benefits of the breakthrough to everyday people.
This phase is essential. It democratizes access to technology while reinforcing the responsibilities required to create a positive impact on individuals, society, and the world.
The post-innovation phase always introduces new responsibilities, new expectations, and a renewed collective need for stewardship.
Ultimately, innovation + efficiency is the engine that creates great power and widespread utility.
A Once-in-a-Generation Inflection Point
Today, we stand inside another historic transformation. After the internet, Artificial Intelligence (AI) is the most defining technological shift of our era.
Those of us who were too young to witness the rise of the internet are now experiencing our own once-in-a-generation inflection point—one we may never see again.
Importantly, AI has not disrupted the direction of technological evolution. It has just made us take a few leaps ahead and put us ever closer to something we had been building for decades—the Open Digital World—Arguably since the inception of the open source foundation of the Open Source Initiative in 1998.
- Open Source Projects
- Open APIs
- Schema.org
- and many more..
All of these initiatives have made a push toward a transparent, collaborative, and fair digital economy—one that respects individual privacy, rights, and responsibility while enabling more equitable wealth creation. Chinmay Panda
With AI, we have made the most significant move toward that. Because with AI, technology can adapt to human ways instead of humans adapting to technology—which implies we will be able to bring everyone online irrespective of their education, social, or economic status. In my opinion, this would be the true digital transformation, and we must work hard with integrity toward that.
Changing Expectations in the Post-AI Era
On the consumer side, expectations are shifting as well.
A slick UI or fancy animation alone is no longer exciting. What truly excites end users now is real, actionable data—information that helps them make the right decision quickly and confidently.
The post-AI era gives us the tools to design systems that deliver exactly this. However, it also requires us to approach product development with a fresh perspective—one rooted in responsibility, trust, and system-level thinking.
In this article, we cover the first of them.
Principle 1: Application Modularity
We all know that design elements have become modular; frontend components and backend functions have become modular. Low-code/no-code tools have made entire component blocks (tables, search + results pages) modular too. These components are increasingly standardized to the point where they can be drag-dropped or copy-pasted without needing to understand their internal implementation—as long as we understand the input and output.
This raises an important question:
If components are modular, can application functionality itself be modularized?
Can we assume that if two different software systems generate the same audit report for compliance, they have performed a similar level of analysis?
If two systems complete the identity verification of a person, can we trust that they have investigated with the same depth, rigor, and integrity?
What this leads us toward is a future where any activity performed across different software systems carries the same level of authenticity, reliability, and trust.
Trust as a Software Primitive
This becomes critically important as more compliance, policy enforcement, audits, and certifications are handled—and validated—entirely through software.
In such a world, it is not enough for software to simply function.
It must carry verifiable trust and authenticity.
So, is this possible?
The short answer is: yes.
Governance as the Enabler
Identity Governance & Administration (IGA) provides a strong reference model for how this can be achieved. Take, for example, the framework for B2B partner IAM vendors Gartner has defined (please click here to request access).
Vendor capabilities required for each level of trust between the host and partner organization, and the maturity of IAM processes in the partner organization.
These frameworks outline best practices, minimum requirements, and industry standards. However, Gartner’s work primarily operates within the SaaS ecosystem.
For other categories of software, we will need similar governance structures—frameworks that clearly define:
- The use cases a tool should solve
- The level of integrity it must maintain
- How its modules should interoperate across systems
In this context, modularity is not just an architectural choice—it is a strategic foundation.
With the right governance, modularity enables consistency, interoperability, and trust across all software experiences.
Now, circling back to the application vs. framework—where do you think it lands?
I would say framework, as the framework needs to define the modules, key features, and business cases it should solve.