Successfully Implementing AI in Pharma – Part 4: Operationalizing AI & Harvesting the Value

Successfully implementing AI in the pharma industry requires a strategic focus on Organizational Change Management (OCM) to ensure adoption and lasting value. Key elements include a clear communication plan, targeted training, and ongoing support to embed AI into everyday processes. By addressing these factors, pharma companies can fully harness AI's potential and drive sustainable business impact

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 making 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.

The Role of OCM in AI Implementation

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 that 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 that pharma companies think about three key questions when planning their OCM efforts to ensure that AI solutions are adopted, utilized, & optimized continuously.

Key Questions

1. What is our communication & roll-out model?
2. How do we build up the necessary capabilities?
3. How are solutions supported & continuously improved?

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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.

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Communication- & Roll-out Model

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:

• Communication Plan: Develop a detailed communication plan that outlines key messages, channels, and frequency of communication, as well as establishes channels for feedback. Example: A company implementing AI for healthcare professional (HCP) interaction data analysis should clearly communicate the benefits, e.g., better engagement outcomes, to field force to gain buy-in
• Change Champions: Identify and train change champions within the organization who can advocate for AI adoption and help address concerns. Example: In a pharmaceutical company, change champions can be senior marketing managers who understand the benefits of AI in optimizing marketing campaigns.
• Success Stories: Share success stories and case studies to illustrate the tangible benefits of AI and build confidence in the technology & specific use case. Example: Highlighting how AI has successfully segmented HCPs more accurately, leading to increased sales, can motivate further uptake.

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Capability Development

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 they 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 to provide 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.

• Role-Specific Training: Develop training material & sessions tailored to both the intended use of the solution [e.g., R&D vs. commercial] and depending on the role [admin, user, etc.]. Example: Training should be tailored to the intended user’s ways of working, i.e. be differentiated for field force agents engaging with HCPs and marketing managers trying to identify optimal content for campaigns.
• Practical Workshops: Incorporate hands-on training sessions tailored to users’ everyday business processes – focus on what the solution does and how, including what not to use it for. Example: Conducting hands-on-workshops showcasing how e.g., an HCP next-best-action tool can be used by the field force when planning for the next engagement.
• Continuous Learning: Provide ongoing material updating users on successful ways in which the solution has made an impact or new features. Example: Sending out newsletters or sharing information on a SharePoint page around updates to AI use cases or success stories from it being used in practice.

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.

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Sustained Support and Continuous Improvement

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:

• Adoption Tracking: Define clear performance metrics and regularly track the use and impact of AI initiatives to ensure they are delivering the expected value. Example: Track increase in in-market sales or market share as a result of AI-driven marketing initiatives.
• Feedback Loop: Create a structured feedback loop to gather user insights & potential improvement suggestions and continuously refine AI tools & processes. Example: Regularly have sessions, or set up forms for easy submission and retrieval of feedback from e.g., field force using solutions
• Support Setup: Establish a dedicated support desk to provide assistance and address any issues that arise with AI tools [depending on the scale of the AI solution] Example: A support point-of-contact for the specific solution. Both in charge of providing business guidance, and getting technical issues resolved.

Summary – Recommended action areas.

Conclusion

Anchoring AI within a pharma organization requires, almost more than any other IT discipline, a strategic and comprehensive approach to organizational change management. Although change management activities are generic in nature, it is important to tailor them to 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.

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