Adopting New-Age Technologies while Integrating with Legacy Infrastructure

Organizations today face the challenge of continuously adopting new technical capabilities while ensuring seamless integration with their current landscape. Ironically a lot of the value that the new technology is promising to deliver is locked in the legacy infrastructure that the new technologies aim to eventually replace. 

On a linear path, transformation for most organizations begins with transition from on-premises applications to modern cloud enabled services followed by the enterprise scaled data solutions on a full developed data mesh. Next comes the value unlock by use of Machine Learning models. Eventually moving up the value chain to scaled AI using the full suite of GenAI capability and Agentic AI by leveraging ever improving large language models.

Each step in this transition offers a unique set of challenges for integration with the legacy infrastructure. While business continuity, resilience, security and operational risk for the existing systems continue to be critical for business activities. 

Cloud adoption: For any organization the key decisions depend on the current on-prem service. Lift and Shift rarely provides the value organizations seek from the cloud. An application built natively in cloud is most likely to give the elasticity in terms of cost, consumption and scale. De-coupling the current on-prem services and re-engineering those that are most likely to benefit first, while ensuring a backward compatibility through a sensible use of new microservices and encapsulation of on-prem services through APIs ensures a gradual migration of the service, without disruption to the users while ensuring a feedback, monitor, measure and control approach at every step. Putting in place a robust API strategy at this step will reap a lot of dividends in the future.

Enterprise Data Strategy: This is usually the most important step. Large organizations over the years have collected petabytes of data. However, this data is often of poor quality, siloed and sits on unscalable infrastructure. This data needs to be governed, secured and compliant with multiple laws across various jurisdictions. Adoption of a centralized data strategy, investment in data quality and a phased approach in moving key analytical data to scalable cloud native technologies, eventually culminating in adopting a data mesh to ensure quality, governed, timely data products for consumption across the enterprise through a standard set of interfaces and protocols is the target. 

Embrace Machine Learning: Investment in data science talent, Model tools, MLOps and clarity of business cases comes next. Cloud offers scalable compute resources which come at a cost. The use case stage is key to explore the right opportunities for leveraging these capabilities. Organizations need to be cognizant that any ML model is probabilistic and should ensure that they have robust model risk management practices that align with their regulatory responsibilities. 

Scaled AI adoption: This is where it gets interesting. With the explosion of LLMs, the impact surface for an organization is significant. Productivity tools for engineers, GenAI solutions driving chatbots, virtual assistants and sophisticated Agentic AI workflows, to eventually self-healing applications driven by AIOps. An established API strategy gives the Agents access to the organization’s workflows. Additionally deploying MCP to leverage tools and resources like data products significantly empowers the Agents. Investment in tooling and model risk management during the adoption of Machine Learning provides a solid foundation for scaling out AI solutions. Investment in robust continuous monitoring for bias, hallucinations and other deviations from models and in a detailed telemetry and observability infrastructure is the key challenge at this stage. Adoption of managed AI services available natively from cloud providers allows the scale and speed necessary for innovative solutions.

Not all organizations follow a step-wise process and might be at a stage where they need to leapfrog from a full legacy infrastructure to a large-scale AI adoption. This should be carefully architected and developing a roadmap that includes the key elements of these four steps can put them on the right course.