Businesses often deal with old information technology systems. Outdated code, workflows and dashboards hinder their ability to upgrade or switch to new platforms. Conversions and upgrades can often be complex and more costly and time-consuming than anticipated. Terence Tse, Mark Schlesinger and David Carle write that mismanaged IT migrations can become major roadblocks to the successful integration of AI in a company’s daily work.
Imagine the immense potential of implementing AI technology to optimise and elevate your company’s operations. Completing a proof of concept (POC) is pivotal in this journey. A POC serves as a trial run designed to assess the practicality and advantages of AI, often using isolated data to test scenarios in a controlled environment. Successfully reaching this point signifies more than just progress: it reflects your company’s readiness to embrace a new wave of technological transformation. This achievement showcases a commitment to innovation and represents a clear path toward unlocking significant economic value, operational efficiency and competitive advantage once AI becomes fully embedded in your business infrastructure.
A successful POC is just the beginning of unlocking AI’s full potential. To achieve this, you must complete the entire implementation process. Yet, in our opinion, many companies struggle with obstacles that often arise during information technology (IT) migrations and conversions, critical phases that can make or break your AI integration.
Understanding IT migration and conversion
Migration involves transferring data or software from one system to another, including moving code, data, applications, operating systems or even transitioning to the cloud. Conversion is a related but different process that transforms older IT systems into newer or more modern versions. Companies often encounter challenges with legacy systems, such as outdated code, old reports, workflows, and dashboards, which hinder their ability to upgrade or switch to a new platform. This complexity is further compounded when the original system architects are no longer available, and documentation is incomplete or outdated. As a result, conversions and upgrades can seem daunting, often becoming more complex, costly and time-consuming than anticipated.
These technical hurdles stretch timelines and can quickly derail entire projects. Rising costs and delays caused by mismanaged migrations or conversions can erode an AI initiative’s return on investment (ROI). What began as a promising AI project may swiftly become a resource-draining exercise if not carefully handled. Foresight and preparations are critical during these phases to ensure that the transition is smooth, efficient and aligned with overall business goals. Without a thoughtful approach, the risks can outweigh the most significant expected benefits, making it vital to prioritise careful management and planning for these crucial stages of AI deployment.
Strategic AI implementation
A strategic approach can conquer these challenges. Thorough planning and execution ensure your AI project thrives and delivers the expected benefits without unnecessary complications. This focus on strategic planning should instil confidence that even the most daunting challenges can be surmounted with the right approach, putting you in control of the journey.
IT migration and conversion are often seen as purely technical issues, but this is a misconception. Strategic thinking is vital in this process. A comprehensive plan that anticipates and addresses potential challenges ensures that AI deployment aligns with broader business goals. This strategy involves collaboration between the strategy, business and finance teams to support the plan adequately. Your role in this collaborative effort is crucial, ensuring the project stays within budget and on track.
Preparing for the unknowns is an important part of this process. Experienced IT executives can often predict challenges during new technology implementations. However, they must also be ready for “unknown unknowns”, challenges that only emerge during the process. This unpredictability is a significant concern in IT migrations and conversions. Rather than letting the fear of the unknown hinder progress, companies should view these challenges as opportunities for growth. It is possible and vital to prepare for the known and the unknown. By fostering a culture of open communication and collaboration, companies can better anticipate and respond to unexpected challenges, reducing the risk of costly delays or failures.
No-one can foresee every obstacle, not even seasoned IT professionals. There will always be “unknown unknowns”, challenges that arise unexpectedly. While this unpredictability can be intimidating, it doesn’t have to derail progress. You must view unforeseen events as opportunities for growth and adaptation. Strong teamwork, a transparent company culture, and a strategic approach to handling unexpected challenges can help reduce the risk of costly delays and failures.
The “divide and conquer” strategy is highly relevant in AI implementation. Delivering results in small, manageable chunks has multiple advantages. It allows end-users to gradually adjust to changes in workflows and processes, boosting their confidence and acceptance. Small steps lead to big gains. Achieving small wins also lowers risk and builds momentum for completing large, complex projects. This incremental approach reduces the likelihood of failure and provides opportunities for feedback and improvement at each implementation stage.
“Tech sprawl” must be managed. Companies that rely more on external technology solutions often accumulate numerous vendor relationships, leading to tech sprawl. This results in complex middleware solutions that drain productivity and capital. To combat this, companies should consider reducing the number of vendor relationships and consolidating their systems. Partnering with vendors who understand AI migrations and conversions can streamline the implementation process so that managers can focus their resources on successfully integrating AI to drive business value.
The importance of getting it right
No matter how advanced the technology is, it will fail to deliver value if it is not correctly integrated into the organisation’s IT infrastructure. The migration and conversion phases ensure the AI system functions seamlessly within the existing ecosystem and provides the desired outcomes. When handled correctly, a sound migration and conversion strategy can minimise costs, save time, and optimise resource use, clearing the way for a smooth AI implementation.
Although moving from an AI proof of concept to full-scale deployment is a complex process, it is manageable with a well thought-out strategy. By approaching the project with a clear vision, anticipating challenges, and following the above battle-tested ideas, companies can mitigate risks and streamline the IT migration and conversion processes. Consolidating systems and vendor relationships further simplifies the path to AI integration. These suggestions would improve the chance of successful AI deployment. They help position your company for ongoing technological growth, operational efficiency, and competitive advantage, ensuring long-term success in an increasingly AI-driven business world.
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- This blog post represents the views of the author(s), not the position of LSE Business Review or the London School of Economics and Political Science.
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