By: Vinod Nair, Solutions Head, LTIMindtree Banking and Financial Services

What is NexGen ADM? Does it mean development and maintenance using next-generation technologies instead of traditional technologies, or does it mean using next generation technologies to make the development process more efficient. Well, there is much more to NexGen ADM.

It can be easy to make claims on the effect of Next Generation technologies on the entire development process. However, putting yourself in the shoes of a CTO or a CIO of a large banking portfolio, it is truly challenging to hold up to those claims. What makes it really challenging are mega investments in traditional technologies and vendor products, regulatory norms that banks need to adhere to, and finally, adaptation to change, which banks have always struggled with.

Addressing the above challenges and bringing real-time cost value, and that is what NexGen ADM is all about. Now Agile development process is widely adopted across the banking spectrum in its various avatars like XP, Lean or Scrum. However, the true potential of agile is hardly realized by agile teams. There are a lot of tools being used, predominantly DevOps and Automation tools, but they seldom make a difference.

This is where Artificial intelligence, supplemented with historical information on releases and code changes, makes a big difference. Artificial intelligence usage within agile lifecycle can deliver the following outcomes:

These outcomes are ambitious, but very much possible with AI. There are some critical influence areas for AI:

• Core Development (Coding, Refactoring)
• Quality Assurance (Testing, Defect Resolution)
• Customer experience (Prototyping, Customer)

Here is a LTI-specific tool demo:

There are a plethora of AI tools and solutions that are available on public repositories, and a multitude of commercial research programs across the globe on this topic. We have just mentioned a few relevant examples. A more pertinent question is, how do these interventions transform agile development. This is demonstrated below in terms of AI solutions across the agile cycle and benefits they deliver:

As mentioned earlier, these are just a few (but important) use cases of AI that can be applied within an agile cycle, and there are many ways AI is being explored in the software development industry. Portfolio/Program/Project Managers, however, should ensure that there is a clear plan and infrastructure in place to collect and merge release, application, service, defect and support data across the organization, whether structured or unstructured. Historical information is the critical cog in the wheel that makes AI possible. With strong leadership, business strategy and stable infrastructure in place, businesses can make the most out of AI solutions.