MongoDB, in its newly commissioned IDC research, has found that legacy architecture is the primary barrier to AI adoption and success. The research paper, Modernising Legacy: Winning in the Age of AI, found that nearly half of Indian organisations (46%) say their existing architecture makes it impossible to build new applications without extensive modernisation because it is too rigid, costly, and slow for today’s requirements. As legacy risks grow, leaders are modernizing core infrastructure to be able to make the most out of the AI wave.
“AI has made technical debt an urgent board-level priority,” said Thorsten Walther, Managing Director, CXO Advisory at MongoDB. “The research is clear, strategic modernisation unlocks AI opportunities and supports a significant increase in revenue. The leaders across the region are showing what’s possible when organisations ditch rigid, siloed legacy systems and move to AI-ready data platforms like MongoDB.”
Essential Steps for Successful Modernization Strategy
Support for new AI initiatives was the number one driver for modernising databases and applications in India, according to the study. Some of the steps that were highlighted for successful modernisation are as follows:
Prioritize talent and skills development
- Skills gaps, whether due to lack of availability or resistance to change, are a major legacy challenge.
- Successful modernization hinges on building and nurturing talent.
- Ongoing modernization must support both human and technical resources within IT.
- Al skills are in short-supply, but training is becoming abundant, so ensure there are suitable programs that support the deployment plans.
Adopt cloud-native strategies
- Future success depends on the flexibility to move workloads to the optimal platform, whether on-premises or in the cloud, as business needs evolve.
- For advanced workloads such as Al, cloud-native approaches are essential.
Avoid repeating the mistakes of
- “lift and shift” migrations, which can create new technical debt and drain resources.
- Insufficient funding of this process will create issues over time.
Accelerate data maturity
- Data maturity is critical; address it now to avoid future setbacks.
- Accurate, well-managed data enables IT to align projects with business goals, meet compliance requirements, and drive rapid innovation.
- Invest in platforms that support hybrid cloud environments without adding unnecessary complexity or risk.
Modernize database technology
- Data modernization initiatives need to be embedded as operational procedures, not a one-time project.
- Maintenance of legacy databases has proven to be a challenge to modernization.
- Modern applications require modern database foundations.
- Upgrading database technology is key to improving data management and supporting the demands of today’s digital business.
- Ensure database solutions are scalable and flexible to accommodate future growth, new data types, and evolving Al workloads.
“The stakes for modernisation are now critical. High-quality, integrated data is the essential fuel that determines the accuracy and performance of an AI application, making modern data architecture a foundational element of any AI strategy,” said Dr William Lee, Senior Research Director, Service Provider and Core Infrastructure Research, IDC Asia Pacific. “But research shows that many organisations are being held back by their existing rigid legacy architectures that do not have the flexibility and scalability to handle the high volume of unstructured data required for AI.”
