3 Change Management Considerations When Maturing AI Capabilities

Companies are paying more attention to the benefits of AI and how to advance it more within their organization. The AI Maturity Model refers to the way organizations are adopting and using AI, and data. By 2027, the AI market will be valued at an estimated $407 billion. Though maturity adoption rates are growing, overall it remains a consistent challenge for most organizations.

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This represents a massive opportunity for organizations to streamline their internal operations and gain a competitive edge by aligning their employees. Change is inevitable in the workplace, but that doesn’t make it easy. As AI transformation continues, it disrupts workflows, processes and has the potential to change job descriptions. This can leave employees nervous about the change, and hesitant for being responsible if they do not fully understand their new role. The fear of change drives employees to ultimately resist the change and advancement.

Here are a few change management tips that leaders can apply now:

1. Have a People-First Approach
   Putting employees at the forefront of new process rollout ensures success of implementing new capabilities. Best practice for rolling out new AI capabilities is to incorporate end users into all phases, from design to execution. All foundational change should start with empathy and an understanding for the needs of each individual. Each person that will be affected by the change should have an understanding of the “why” and feel confident that they will be supported throughout the process. Leaders should set standards of success for measurement at the very start.  

2. Equip Them With Knowledge
   Advancing AI capabilities will trigger new roles and units of work that require new skills, workflows, and tools. This means that every individual affected by this change will need time to learn, practice, and adjust. This dedicated time should be factored into the strategic plan.
   
   When it comes to education, find programs that are inclusive of both technical and business teams.  Analytics and AI are both inherently cross-functional, so business and technical teams need to be in sync. This is critical for consistency and scale in any organization. Everyone needs to be able to speak that same language.
   
   
3. Sustain The Change
   The process of AI transformation is complex. It often involves multiple people working together: business owners, product managers, data engineers, data scientists, ML scientists, ML engineers, etc. Some departments will be working with data in ways they haven’t before. 

Changes to capabilities will continue to evolve iteratively. Workflows will be updated, tools may change or be re-configured, and new support roles will join. This is why all educational programs and documentation must be evolving at the same pace. 

Ensure the process of capturing the new standards is easy for the individuals contributing and learning. Keeping all teams aligned on the latest and greatest will ensure adoption over time. 

One of leadership’s primary responsibilities in change management is have a communication and education plan. The best organizations launch sustainable programs that allow for continuous change, outline new responsibilities, and expose resources that are easy to navigate.Our team has seen many patterns of best practices on improving AI capabilities and sustaining change. We understand the modern pains of AI transformation. If you’re interested in learning more, schedule a demo.