• Written by Azhar Aliyar, Director

Change management for AI refers to the process of planning, implementing, and monitoring changes to an organisation's systems, processes, and people to ensure successful adoption of AI technology, such as AsscherAi, which enables large organisations to query their own data in natural language and get answers in real time. This definition is often misunderstood, and many people raise objections that change management for AI is too similar to traditional change management, and therefore not necessary. However, this objection is wrong because AI introduces new complexities that require specific consideration.

Definition and distinction

Change management for AI is distinct from traditional change management, which focuses on managing changes to business processes, organisational structures, or technology systems. In contrast, change management for AI requires a deeper understanding of the AI system itself, including its data sources, algorithms, and user interfaces. This is because AI systems like AsscherAi are designed to learn and adapt over time, which can lead to unintended consequences if not properly managed.

A key aspect of change management for AI is understanding how the AI system will interact with existing systems and processes. For example, a shift supervisor using AsscherAi to query the maintenance log may need to understand how the AI system's recommendations will affect the maintenance schedule and resource allocation. This requires a nuanced understanding of both the AI system and the underlying business processes.

Applications in a large organisation

In a large organisation, change management for AI is crucial for ensuring successful adoption of AI technology. This is because AI systems often require significant changes to existing processes and systems, which can be difficult to implement and manage. Change management for AI helps to identify and mitigate potential risks, such as data quality issues or user resistance to change. By planning and implementing changes carefully, organisations can ensure that AI systems like AsscherAi are integrated smoothly into existing operations.

For instance, a category manager using AsscherAi to compare two suppliers may need to understand how the AI system's recommendations will affect the procurement process and supplier relationships. This requires close collaboration between the category manager, the procurement team, and the IT department to ensure that the AI system is properly integrated into the existing procurement process.

What change management for AI does

Change management for AI involves a range of activities, including stakeholder analysis, communication planning, and training and support. It also requires a deep understanding of the AI system itself, including its data sources, algorithms, and user interfaces. By taking a comprehensive approach to change management, organisations can ensure that AI systems like AsscherAi are adopted successfully and provide real value to the business.

Most people get wrong that change management for AI is simply a matter of providing training and support to end-users. However, this is only one aspect of change management for AI. A more comprehensive approach is needed to ensure that AI systems are properly integrated into existing operations and that users are able to get the most out of the technology. To learn more about how AsscherAi can support your organisation's AI strategy, visit our website at www.asscherai.com.

What change management for AI does not do

Change management for AI is not a substitute for traditional project management or IT service management. While it shares some similarities with these disciplines, change management for AI is a distinct field that requires specific skills and expertise. It is also not a one-time event, but rather an ongoing process that requires continuous monitoring and evaluation.

Some organisations may be tempted to skip change management for AI altogether, assuming that the AI system will be easy to use and require minimal support. However, this approach is misguided and can lead to significant problems down the line. By neglecting change management for AI, organisations risk undermining the success of their AI initiatives and failing to realise the full potential of the technology. If you have questions about how to implement change management for AI in your organisation, contact us at www.asscherai.com/contact-us.

When change management for AI is the wrong choice

Change management for AI may not be necessary for small-scale AI pilots or proof-of-concept projects, where the risks and complexities are relatively low. In these cases, traditional project management approaches may be sufficient. However, for large-scale AI deployments that involve significant changes to existing processes and systems, change management for AI is essential for ensuring success.