Technology, AI and the Future

With the boom of AI in the current competitive world, how will this change the way organizations approach application development by adopting mature tooling techniques? Modern approaches like Cloud-as-a-Service are transforming the business landscape, and organizations need to be aware of all the latest developments to stay ahead. Today, we will discuss two different areas – Faster Time to Market and Optimizing Cost.

Faster Time to Market:

As it is rightly said, “First Impression is the Best Impression”. Every customer believes in being an early player in the market. This gives the organization an edge over its competitors. This has evolved and matured through the years, as per many research studies done by cloud companies.

Traditional vs Agile

The above diagram emphasizes how organizations moved from a traditional approach to an agile-based approach for better customer satisfaction and engagement. Next comes tool adoption to further improve the quality of the shipped application with quicker and more stable releases.

Agile with DevOps

The above diagram elaborates on the evolving adoption strategy of every organization. DevOps assisted organizations in automating the SDLC life cycle with checks at every stage to ensure a quality deliverable is shipped out. The best cloud service providers ensure the security aspects are also verified and checked during the DevOps cycle. STL Digital assures security aspects 

DevSecOps and SecDevOps

The security aspect verification combined with DevOps is called DevSecOps or SecDevOps. How are these both different and which would be better for an adoption? 


As the name suggests, this is concerned with integrating security processes with SDLC and ensuring the efficiency of the product. This is a great approach to ensure the application is more secure.

The disadvantages include application security being considered after an application is built and security policies might be defined while the tests are being conducted.


In the Cloud-as-a-Service model, this integrates security during the application development phase itself. A security team is appointed for an application that defines coding practices at the beginning that need to be adhered to by the team. The application security team and application development team work side by side to ensure security practices are being followed. Security check is performed by the application security team after every module is built. This provides earlier feedback about application security and ensures a more robust and secure application is being shipped. This is more cost-consuming compared to DevSecOps, but considering the larger picture, this is a better approach to adopt.


To take a step further and reduce the involvement of the operations team, there needs to be a process that would automate every aspect of the SDLC software development life cycle once the team completes the development. This journey is No-Ops.

NoOps means reducing the activities of operations and support teams pertaining to application management. This can be adopted by building different utilities that perform the necessary tasks or an AI Solution.

The Future

We looked at how organizations and cloud service providers evolved the approach of delivering applications quickly and efficiently by adopting different strategies and tooling. The future will be even more competitive to adapt to changing customer demands, and AI and cloud adoption will be on the rise, below we can see a sample of how to improve the efficiency of an SDLC life cycle.

This can be thought of as a mix of DevOps + NoOps in a mature and efficient way.

The above diagram elaborates how AI and serverless tech can be integrated with existing SDLC for improving the robustness and efficiency and quality of the delivery.

Optimizing Costs:

Cost is a primary concern for organizations on a transformation journey, cost needs to be holistically controlled. This has also evolved to a great extent over the years. Organizations captured data and manually generated reports leveraging which cost decisions were being made.

With the advent of technology and competition, quicker ways were needed to aid cost decision-making, AI is currently playing a significant role in this context and continues to evolve. AI provides a quicker and more efficient way of identifying cost spending and ways to optimize. 

Below is an example of governance that would aid in the process of cost optimization based on the current competitive market.

This is a recurring activity that organizations review at regular intervals and make necessary decisions based on organization goals.


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