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[5/5] Supporting Your Teams Through Change

Our series "Deploying a Computer Vision Project and creating value in just 90 days" is coming to an end. We have reviewed the three major milestones of this type of project: 30 days for creating the dataset, 60 days for algorithm training, and 90 days for measuring the initial results. In this final article, we address a crucial topic on which the success of your project depends: change management!

Implementing powerful technology alone cannot ensure any digital transformation project's success. Supporting human aspects is equally essential as these major transitions disrupt certain processes and involve new habits. By automating the quality control of field operations, the entire chain of field and back-office business is affected: technicians/workers of the company, subcontractors, operations and infrastructure managers, and of course, quality managers. The AI-based solution serves as a new tool and reshapes their way of working. In this context, how can you help everyone embrace change, and what are the change management prerequisites to give you the best chance of success in improving the quality of your operations?

Raise awareness about the urgency of breaking free from manual quality control

The challenge is substantial: to manage to shake up the status quo, i.e., manual and post-operation quality control, and then establish a new way of functioning, a new verification process based on AI. One proven method to achieve this is to organize workshops with various roles (operations managers, compliance officers, and training departments) to bring out observations:

-The quality of operations needs improvement.

-The quality control process is lengthy, too costly, and occurs too late.

-Field employees lack skills and learning to perform their job well.

Through this reflection, you will make the need for change obvious and acceptable, making implementing the AI-based quality control solution smoother.

Communicate changes and benefits of the new solution for each role

When it comes to the communication plan, both channels and messages are important. Utilize your company's internal communication tools to present the new quality control solution, organize in-person workshops with representatives from each role, and, most importantly, identify ambassadors to relay information and support team change management. Proximity managers can fulfill this role, especially those overseeing technicians and workers whose mobile missions make information sharing challenging.

In terms of the message, this multi-channel communication should first explain the main objective of the automated quality control solution: the pursuit of operational efficiency and effectiveness through better quality operations. It should also detail what will change in each role, not omitting the benefits they will derive, and we provide a non-exhaustive list below:

Addressing barriers to adoption

In your communication, be sure to reassure teams about the role of AI, which tends to be perceived as a threat, especially by senior employees. Explain, for example, that the goal is to create human-AI experiences that promote the success of operations and team productivity, focusing on collaboration that capitalizes on the strengths of each.

As mentioned, implementing computer vision alone is insufficient to achieve the expected results. Field teams play a crucial role in the success of the quality control automation project, as they are the primary users of the solution. Therefore, deploy a clear and smooth user experience that closely aligns with the reality of the field and avoids creating friction. For more information, refer to the fourth article in the series, which delves into this topic.

Before implementing the solution, develop a training plan by designating one or more individuals responsible for presenting the functionality and use of the solution. Finally, to facilitate the integration of the AI solution into the company's daily life and ensure its sustainability, we recommend including tool training in the onboarding process for new hires.

Measuring the success of change

Here, the goal is to measure the effects of the new quality control process on the quality of your operations. You can use Field Intelligence data provided by Deepomatic, including two key metrics:

  • Compliance rate of photo reports: This metric allows you to see the progress in the quality of reporting by technicians.
    Average number of photos for each stage of an intervention is also a great indicator: The closer it is to 1, the better the quality of the photos taken.
  • Compliance rate of operations: This indicates the solution's impact on the quality of work. It is an indicator that is measured in the medium and long term.

In conclusion, it is important to remember that any new operational process begins by considering the human factor. Sensitizing various roles to the need for a change in the quality control method and communicating the concrete changes for each and the benefits will allow you to introduce the new solution more smoothly.


Automate Quality. Accelerate Growth.