Using OCM to Help Ensure AI Adoption Success

Artificial intelligence (AI)-enabled capabilities offer IT organizations the opportunity for “better, faster, cheaper” operations and outcomes, but there are a number of key factors to consider to achieve AI adoption success. Those related to the technology implementation are covered elsewhere on the internet, but it’s also important to consider the people-related factors. Please keep reading to find out more in the following four tips.

Tip #1: Understand that AI adoption is more than a technology change

As with most technology or process-focused changes, people-related change must be considered. This need is because these two types of change will usually affect the traditional ways of working (for people). So, while AI adoption is a technology-based change, those responsible must also consider and address the likely people impact – from gaining employee buy-in to minimizing employee resistance to change and its negative consequences.

This people-based need is where organizational change management (OCM) tools and techniques can help, with OCM a formal approach to managing the people side of change when introducing new technologies, processes, or organizational structures. The next tip explains more through specific examples.

Tip #2: Address the people change aspects of AI adoption with OCM

OCM is all about anticipating and addressing the barriers and issues that can prevent successful change initiatives. There are many elements to a formalized OCM approach, with specific inputs and activities designed to elicit the required outcomes that contribute to change success. In this case, the successful adoption of AI-enabled capabilities for IT service delivery and support.

In terms of the inputs, key OCM elements include:

• Stakeholder engagement – such that their inputs are captured and concerns addressed. The engagement helps avoid misunderstandings and conflicts related to AI adoption and facilitates the required buy-in for AI initiatives.
• Focused communications – a comprehensive communication plan helps ensure all stakeholders are well-informed about AI adoption’s benefits, risks, and impact.
• Employee support – to help alleviate employee fears and anxieties related to AI, for example, job security concerns.
• Change impact analysis – to understand the effects of AI adoption on various organizational aspects.
• Risk management – to help minimize disruptions, delays, and costly issues and ensure AI adoption is aligned with organizational objectives.
• Relevant training – structured training and support programs to help employees adapt to new AI capabilities, ensuring people are skilled and confident in using them.
• Success measurement – for example, the measurement and analysis of user AI adoption rates, with improvement areas identified and corrective action taken when needed.

These OCM elements contribute to the successful adoption of AI-enabled capabilities by focusing on the needs of the people affected by the change. More detail on what each of these OCM elements entails can be found in the ITIL 4 organizational change management practice guide.

Tip #3: Understand how OCM helps with AI adoption

The cumulative effect of the application of the above OCM elements (in the form of tools and techniques) is that:

• Employees and other stakeholders are more likely to buy into AI adoption
• Employee resistance is minimized
• It helps in aligning organizational culture with AI adoption
• The change is more sustainable by reinforcing the value and benefits of AI.
• It helps ensure that AI adoption is aligned with business objectives and strategies.

While OCM will help ensure AI adoption success, other people-related areas can still easily be missed.

Tip #4: Identify and address the “less obvious” implications of AI adoption success

While OCM tools and techniques will help your organization, there are potentially other “less obvious” people issues related to AI adoption success to address. Two of the most important are understanding and addressing the effect of AI adoption success on:

1. Employee well-being
2. Performance measurement.

Starting with employee well-being, while AI enablement is intended to make everyone’s lives easier, it could make life harder for some roles. IT service desk roles are a good example. Here, the new AI-enabled capabilities take away many of the mundane and simpler tasks previously handled by service desk agents. While this might appear to make life easier, it potentially won’t. Because service desk agents will see their work populated with only the more complex or complicated tickets. This change will motivate some people as they are more challenged than before. But, for others, it will make their work harder (without the easier tickets) and open them up to well-being challenges.

The performance measurement implications stem from the same root cause – the AI-enabled capabilities handling the easier tickets. This change impacts many traditional IT service desk metrics – such as average handling time and first-contact resolution rates – and the ability of people to hit the agreed targets. If unaddressed, not only is this demotivational, but it will also likely drive unwanted behaviors, with service desk agents working to hit the targets rather than to make the people they serve productive again.

Have you used OCM techniques to help with AI adoption in your organization? Please let me know in the comments.