This is part 7 of “101 Ways AI Can Go Wrong” - a series exploring the interaction of AI and human endeavor through the lens of the Crossfactors framework.
View all posts in this series or explore the Crossfactors Framework
Legacy technologies are outdated systems still in use due to cost despite the availability of modern replacements. They remain in use due to costs, complexity or lack of resources to upgrade.
Why It Matters
Legacy technologies often serve critical functions while being gatekeepers of progress. They are usually deeply embedded in an organization’s workflows and processes and have inter-dependencies with other legacy systems. Upgrading legacy systems is often a high-cost, high risk proposition, resulting in many leaders deferring action. Many antiquated systems make it difficult to integrate AI into these workflows and processes, creating innovation bottlenecks.
Real-World Example
Traditional banks are renowned for using legacy mainframe systems that are decades old, yet they need to innovate in the areas of fraud detection, digital services and real-time analytics. Many such financial systems find themselves with a patchwork of workarounds and middleware that adds complexity and risk.
Large companies like IBM and Cognizant offer upgrade services on a consultancy basis to organizations such as banks to migrate their legacy systems. They even create special hardware, such as servers that interface with tape drives to enable cloud computing.
Another fascinating example was the recent upgrade of the U.S. nuclear weapons program from floppy disks. These floppy disks from the 1970s were in use until about 5 years ago!
Key Dimensions
Complexity - The level of complexity needs to be recognized. Unfortunately, it is often the case that the more antiquated and complex a system is, the more critical it is to operations.
Executive ambition vs. organizational capacity - Change management and resources play a critical role in an organization’s odds of success. It is difficult to match the ambition of leaders to the actual know-how and money required to achieve such projects.
Interoperability - Sometimes, interim middleware to enable interoperability is the correct solution, even though it may not be the final one. Replacing mainframe functionality with microservices that can scale independently while being interoperable may be a key avenue.
Take-away
Any C-level leader thinking about AI in a medium to large organization is also thinking about their legacy technologies. It should be seen as an opportunity, where a careful approach and a willingness to accept small failures as lessons learned can offer a substantial reward.