Given the complexity of many AI solutions as well as their ability to disrupt traditional ways of working, effective roll-out to drive adoption is key to make sure that AI solutions are adopted and that the intended value materializes.
In our previous articles, we uncovered the opportunities AI offers to the pharmaceutical industry, how to select the right use cases, and how to establish a solid foundation for AI implementation. As we continue this journey, the next critical step is to plan for how to drive data literacy in business units and anchor AI solutions within the organization to ensure they are used correctly and embedded into everyday processes. Organizational Change Management (OCM) plays a pivotal role in this stage, helping to facilitate the integration of AI in a manner that maximizes its value. This article will focus on how to plan for effective anchoring of AI solutions in the pharmaceutical industry based on experiences from our previous client engagements.
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From our perspective, the success of implementing AI in large organizations is more critically dependent on effective OCM activities than almost any other type of IT solution. For example, this is due to: (I) the complexity of AI & people struggling to understand the way it works, (II) ethical considerations and the potential of AI to introduce invisible biases which must be understood and overcome by users, (III) people’s fear of displacement given AI’s ability to standardize many routine tasks, and (IV) the requirements of continuous learning by users given AI solutions’ ability to rapidly evolve.
We recommend pharma companies think about three key questions when planning their OCM efforts to ensure that AI solutions are adopted, utilized, & optimized continuously.
Key Questions
Before addressing these questions, it is crucial to establish a clear organizational structure and select a dedicated team to drive the change. This ensures a holistic approach to adopting company-wide AI solutions across departments, preventing any gaps or oversights. This dedicated team is responsible for defining the communication & roll-out plan, developing training material & conducting user training, and ensuring that solutions are supported, monitored, and continuously improved. Optimally this task is coordinated centrally but given the organizational setup of many large pharma companies and the degree of local and regional autonomy, adoption is often effectively driven by local or regional teams.
Defining a consistent plan for how information regarding new AI solutions is communicated as well as how solutions are rolled out to different affiliates and departments is, in our experience, critical in large pharma companies. This ensures that all intended users are addressed in a meaningful sequence and understand the vision & benefits of the AI solutions, including how solutions work, what they can do, and what not to use the solutions for. Lacking this, we often see business users reporting experiences of inconsistent messaging from the owners of solutions as well as a lack of transparency into goals and development or roll-out progress.
We often see successful outcomes when change drivers in pharma companies take the following actions before initiating the roll-out plan:
Effective training programs are essential to equip employees with the skills and knowledge needed to use AI tools effectively. Trainings should both focus on intuitive aspects such as how the solutions work and what it can be used for, but with AI it is particularly important that users are educated in e.g., how an ML model makes recommendations, how to detect and avoid biases in the model, as well as what the solution is unable to do.
Unfortunately, many companies fail in providing intended AI users with effective training by making training programs too generic, theoretical with no hands-on experience, or simply boring, which hampers adoption. We recommend the following focus areas when designing training programs.
Besides these points, some AI solutions will, by nature, alter the ways of working of users, and when that is the case, it is imperative that the learning material & training modules train employees in these new ways of working.
To ensure the long-term success of AI initiatives, it is important to provide ongoing support after the solution goes live. Additionally, having measures in place for continuous improvement ensures that the solution remains valuable and relevant to users. We often experience companies fall short by failing to capture feedback or by allocating insufficient resources to support users of the solution and to make sure it is performing and being updated after it has been developed, resulting in frustration among users. To avoid these issues, we see companies undertaking the following OCM initiatives:
Summary – Recommended action areas.
Anchoring AI within a pharma organization requires, almost more than any other IT discipline, a strategic and comprehensive approach to organizational change management. Although the change management activities are generic in nature, it is important to tailor them based on the inherent challenges of AI described above.
By focusing on pursuing several of the activities described in this article, we experience that pharma companies can ensure that AI solutions are not only adopted but also integrated into everyday processes to deliver lasting value.
Alexander Søe Andersen, Consultant at Intellishore