Successfully Implementing AI in Pharma (Part 3)
Successfully Implementing AI in Pharma: Establishing the Foundation for AI
Successfully implementing AI in pharma requires building a strong foundation across people, processes, and platforms.Intellishore’s approach involves assessing organizational readiness, identifying maturity gaps, and creating a roadmap to address these challenges. By focusing on targeted initiatives, organizations can enable AI innovation and achieve sustainable, long-term success.
For companies to achieve their AI vision, it is often necessary to mature the organizational & technological foundation.
In the previous articles in this series, we explored the wide array of different opportunities AI offers pharmaceutical companies across the value chain and discussed how to strategically select the right AI use cases. Once an organization has prioritized and developed a sequence for different AI use cases, it is tempting to dive straight into their development. While we often observe great success from smaller scale projects with benefits such as the ability to quickly pilot a new technology, we believe that long term success with many AI use cases necessitates implementing several enabling initiatives to increase the organization’s digital maturity. This article is the third in a series of four and outlines Intellishore’s approach to evaluating maturity gaps and developing an actionable roadmap to increase the organizational & technological maturity of the organization to enable AI success based on our experience within the pharmaceutical industry.
For companies to achieve their AI vision, it is often necessary to mature the organizational & technological foundation.
In the previous articles in this series, we explored the wide array of different opportunities AI offers pharmaceutical companies across the value chain and discussed how to strategically select the right AI use cases. Once an organization has prioritized and developed a sequence for different AI use cases, it is tempting to dive straight into their development. While we often observe great success from smaller scale projects with benefits such as the ability to quickly pilot a new technology, we believe that long term success with many AI use cases necessitates implementing several enabling initiatives to increase the organization’s digital maturity. This article is the third in a series of four and outlines Intellishore’s approach to evaluating maturity gaps and developing an actionable roadmap to increase the organizational & technological maturity of the organization to enable AI success based on our experience within the pharmaceutical industry.
For companies to achieve their AI vision, it is often necessary to mature the organizational & technological foundation.
In the previous articles in this series, we explored the wide array of different opportunities AI offers pharmaceutical companies across the value chain and discussed how to strategically select the right AI use cases. Once an organization has prioritized and developed a sequence for different AI use cases, it is tempting to dive straight into their development. While we often observe great success from smaller scale projects with benefits such as the ability to quickly pilot a new technology, we believe that long term success with many AI use cases necessitates implementing several enabling initiatives to increase the organization’s digital maturity. This article is the third in a series of four and outlines Intellishore’s approach to evaluating maturity gaps and developing an actionable roadmap to increase the organizational & technological maturity of the organization to enable AI success based on our experience within the pharmaceutical industry.
Effectively utilizing AI in pharma is complex
For companies to achieve their AI vision, it is often necessary to mature the organizational & technological foundation.
In the previous articles in this series, we explored the wide array of different opportunities AI offers pharmaceutical companies across the value chain and discussed how to strategically select the right AI use cases. Once an organization has prioritized and developed a sequence for different AI use cases, it is tempting to dive straight into their development. While we often observe great success from smaller scale projects with benefits such as the ability to quickly pilot a new technology, we believe that long term success with many AI use cases necessitates implementing several enabling initiatives to increase the organization’s digital maturity. This article is the third in a series of four and outlines Intellishore’s approach to evaluating maturity gaps and developing an actionable roadmap to increase the organizational & technological maturity of the organization to enable AI success based on our experience within the pharmaceutical industry.
Effectively utilizing AI in pharma is complex
For companies to achieve their AI vision, it is often necessary to mature the organizational & technological foundation.
In the previous articles in this series, we explored the wide array of different opportunities AI offers pharmaceutical companies across the value chain and discussed how to strategically select the right AI use cases. Once an organization has prioritized and developed a sequence for different AI use cases, it is tempting to dive straight into their development. While we often observe great success from smaller scale projects with benefits such as the ability to quickly pilot a new technology, we believe that long term success with many AI use cases necessitates implementing several enabling initiatives to increase the organization’s digital maturity. This article is the third in a series of four and outlines Intellishore’s approach to evaluating maturity gaps and developing an actionable roadmap to increase the organizational & technological maturity of the organization to enable AI success based on our experience within the pharmaceutical industry.
Effectively utilizing AI in pharma is complex
To assess whether your organization is ready to implement the selected and prioritized AI initiatives, it is important to conduct a holistic analysis of the organizational maturity. At Intellishore, we often find it helpful to base this maturity analysis on a well-known and widely used framework covering People, Process, and Platform-related organizational capabilities.
To assess whether your organization is ready to implement the selected and prioritized AI initiatives, it is important to conduct a holistic analysis of the organizational maturity. At Intellishore, we often find it helpful to base this maturity analysis on a well-known and widely used framework covering People, Process, and Platform-related organizational capabilities.
To assess whether your organization is ready to implement the selected and prioritized AI initiatives, it is important to conduct a holistic analysis of the organizational maturity. At Intellishore, we often find it helpful to base this maturity analysis on a well-known and widely used framework covering People, Process, and Platform-related organizational capabilities.
Assessment Framework
To assess whether your organization is ready to implement the selected and prioritized AI initiatives, it is important to conduct a holistic analysis of the organizational maturity. At Intellishore, we often find it helpful to base this maturity analysis on a well-known and widely used framework covering People, Process, and Platform-related organizational capabilities.
Assessment Framework
To assess whether your organization is ready to implement the selected and prioritized AI initiatives, it is important to conduct a holistic analysis of the organizational maturity. At Intellishore, we often find it helpful to base this maturity analysis on a well-known and widely used framework covering People, Process, and Platform-related organizational capabilities.
Assessment Framework
To establish a solid foundation for assessing a pharma company’s maturity, we have often conducted a series of interviews with representatives from both IT & business units. To be able to cover a wide range of aspects, these interviews usually include both qualitative and quantitative sections with differing purposes. The qualitative section consists of open-ended questions that foster reflection and help uncover aspects and nuances of the current setup and what challenges there are. The quantitative section consists of scoring exercises encouraging interviewees to score different parts of the current data & AI setup in maturity from 1-5 to be able to draw conclusions and test hypotheses about the adequacy of the current state.
This approach to data gathering helps organizations evaluate the current maturity, identify challenges, and remedy initiatives to succeed with their AI vision.
To establish a solid foundation for assessing a pharma company’s maturity, we have often conducted a series of interviews with representatives from both IT & business units. To be able to cover a wide range of aspects, these interviews usually include both qualitative and quantitative sections with differing purposes. The qualitative section consists of open-ended questions that foster reflection and help uncover aspects and nuances of the current setup and what challenges there are. The quantitative section consists of scoring exercises encouraging interviewees to score different parts of the current data & AI setup in maturity from 1-5 to be able to draw conclusions and test hypotheses about the adequacy of the current state.
This approach to data gathering helps organizations evaluate the current maturity, identify challenges, and remedy initiatives to succeed with their AI vision.
To establish a solid foundation for assessing a pharma company’s maturity, we have often conducted a series of interviews with representatives from both IT & business units. To be able to cover a wide range of aspects, these interviews usually include both qualitative and quantitative sections with differing purposes. The qualitative section consists of open-ended questions that foster reflection and help uncover aspects and nuances of the current setup and what challenges there are. The quantitative section consists of scoring exercises encouraging interviewees to score different parts of the current data & AI setup in maturity from 1-5 to be able to draw conclusions and test hypotheses about the adequacy of the current state.
This approach to data gathering helps organizations evaluate the current maturity, identify challenges, and remedy initiatives to succeed with their AI vision.
Data Gathering
To establish a solid foundation for assessing a pharma company’s maturity, we have often conducted a series of interviews with representatives from both IT & business units. To be able to cover a wide range of aspects, these interviews usually include both qualitative and quantitative sections with differing purposes. The qualitative section consists of open-ended questions that foster reflection and help uncover aspects and nuances of the current setup and what challenges there are. The quantitative section consists of scoring exercises encouraging interviewees to score different parts of the current data & AI setup in maturity from 1-5 to be able to draw conclusions and test hypotheses about the adequacy of the current state.
This approach to data gathering helps organizations evaluate the current maturity, identify challenges, and remedy initiatives to succeed with their AI vision.
Data Gathering
To establish a solid foundation for assessing a pharma company’s maturity, we have often conducted a series of interviews with representatives from both IT & business units. To be able to cover a wide range of aspects, these interviews usually include both qualitative and quantitative sections with differing purposes. The qualitative section consists of open-ended questions that foster reflection and help uncover aspects and nuances of the current setup and what challenges there are. The quantitative section consists of scoring exercises encouraging interviewees to score different parts of the current data & AI setup in maturity from 1-5 to be able to draw conclusions and test hypotheses about the adequacy of the current state.
This approach to data gathering helps organizations evaluate the current maturity, identify challenges, and remedy initiatives to succeed with their AI vision.
Data Gathering
Once the interviews have been conducted, the foundation for conducting analyses around people, process, & platform-related elements are in place. While such an analysis should also be conducted specifically for your company and the AI use cases you strive to enable, the following paragraphs outline some of the areas we frequently see challenges in as well as what we recommend companies do to overcome them.
People
The backbone of any successful AI initiative is the people who drive it. This includes not only data scientists and data engineers but also the broader organizational workforce that will interact with and benefit from AI solutions. Common challenges we observe at organizations in the pharma industry include:
- Skill Gaps: Many organizations lack personnel with the necessary AI and data science expertise, and attracting the right talent can be hard.
Example: Developing AI algorithms for marketing optimization requires specialized knowledge in both AI & commercial strategies, which many pharma companies struggle to find. - Change Resistance: Employees might resist new technologies due to perceived complexity, lack of transparency, skepticism about data and model accuracy, or fear of job displacement.
Example: Implementing AI for HCP data analysis may face resistance from sales staff used to traditional methods. - Capacity Constraints: Many AI initiatives fail to mobilize due to capacity constraints, which cause an inability of IT resources to free up the necessary time to develop AI solutions.
Example: IT departments overwhelmed with maintaining legacy systems may not have the bandwidth to support new AI projects.
Although it is impossible to generalize the remedying initiatives across an entire industry, we often observe successful outcomes when companies take the following actions. First, developing the business case for the identified AI use cases to secure resource commitment to acquire the necessary talent and competencies, either internally from other departments or through upskilling or externally will help overcome both skill gaps and capacity constraints. Second, developing a holistic change engagement and roll-out framework can help companies successfully anchor solutions through training and efficient communication.
Processes
Processes cover the workflows and methodologies that support the development and deployment of AI solutions. Ensuring these are well-defined and flexible enough to accommodate the iterative nature of AI projects is crucial. Prominent challenges we observe include:
- Siloed Operations: Without effective collaboration fora & processes, context- and department-specific solutions are often developed in silos, impairing later scalability.
Example: while some AI models exist best within specific purpose areas, a chatbot developed exclusively for the sales force could benefit more users if it was built with scalability in mind – e.g., to be able to integrate medical insights to expand the solution to medical frontline staff - Regulatory Compliance: Navigating the complex regulatory landscape can be challenging, and AI technologies may be resisted due to compliance-related concerns.
Example: With emerging new regulations on AI, it is important to make sure that AI tools are in compliance which takes up time & efforts.
To resolve these, and to prepare the organizational processes for AI implementation, we often see success when companies, first, establish a central forum to keep an overview of AI initiatives across departments and ensure purposeful allocation of resources and prioritization of new use cases. Additionally, we often see companies implementing cross-functional collaboration models, to facilitate cooperation between different BUs and with IT – e.g., to centrally specify solution compliance requirements.
Platform
The platform encompasses the technological infrastructure required to support AI initiatives, including data processing, storage, and consumption tools. We frequently observe the following challenges:
- Poor Data Quality: Lacking structured data management, companies often experience data inconsistencies across sources, inhibiting accuracy and scalability in AI products.
Example: Discrepancies in data formats & standards across different affiliates make it difficult to consolidate data for AI analysis. - Unscalable Infrastructure: The presence of old on-prem IT systems in many pharma companies results in difficulty scaling the data foundation to support advanced AI use cases.
Example: Legacy systems may not support the data throughput required for real-time AI-driven drug safety monitoring or other real-time data & AI solutions. - Poor Data Security: Data is not being sufficiently captured, stored, and utilized due to imperfect security measures and a resulting risk of breaches and non-compliance.
Example: Ensuring data privacy and compliance with GDPR when deploying AI in clinical trials, is a challenge if there is a risk of breaches.
To overcome these challenges, we recommend companies to implement a data governance framework to streamline taxonomies and appoint data owners to ultimately ensure high quality of data. Besides this, it can be beneficial to migrate legacy on-prem solutions to the cloud, as cloud solutions often offer enhanced security measures as well as scalability.
Once the interviews have been conducted, the foundation for conducting analyses around people, process, & platform-related elements are in place. While such an analysis should also be conducted specifically for your company and the AI use cases you strive to enable, the following paragraphs outline some of the areas we frequently see challenges in as well as what we recommend companies do to overcome them.
People
The backbone of any successful AI initiative is the people who drive it. This includes not only data scientists and data engineers but also the broader organizational workforce that will interact with and benefit from AI solutions. Common challenges we observe at organizations in the pharma industry include:
- Skill Gaps: Many organizations lack personnel with the necessary AI and data science expertise, and attracting the right talent can be hard.
Example: Developing AI algorithms for marketing optimization requires specialized knowledge in both AI & commercial strategies, which many pharma companies struggle to find. - Change Resistance: Employees might resist new technologies due to perceived complexity, lack of transparency, skepticism about data and model accuracy, or fear of job displacement.
Example: Implementing AI for HCP data analysis may face resistance from sales staff used to traditional methods. - Capacity Constraints: Many AI initiatives fail to mobilize due to capacity constraints, which cause an inability of IT resources to free up the necessary time to develop AI solutions.
Example: IT departments overwhelmed with maintaining legacy systems may not have the bandwidth to support new AI projects.
Although it is impossible to generalize the remedying initiatives across an entire industry, we often observe successful outcomes when companies take the following actions. First, developing the business case for the identified AI use cases to secure resource commitment to acquire the necessary talent and competencies, either internally from other departments or through upskilling or externally will help overcome both skill gaps and capacity constraints. Second, developing a holistic change engagement and roll-out framework can help companies successfully anchor solutions through training and efficient communication.
Processes
Processes cover the workflows and methodologies that support the development and deployment of AI solutions. Ensuring these are well-defined and flexible enough to accommodate the iterative nature of AI projects is crucial. Prominent challenges we observe include:
- Siloed Operations: Without effective collaboration fora & processes, context- and department-specific solutions are often developed in silos, impairing later scalability.
Example: while some AI models exist best within specific purpose areas, a chatbot developed exclusively for the sales force could benefit more users if it was built with scalability in mind – e.g., to be able to integrate medical insights to expand the solution to medical frontline staff - Regulatory Compliance: Navigating the complex regulatory landscape can be challenging, and AI technologies may be resisted due to compliance-related concerns.
Example: With emerging new regulations on AI, it is important to make sure that AI tools are in compliance which takes up time & efforts.
To resolve these, and to prepare the organizational processes for AI implementation, we often see success when companies, first, establish a central forum to keep an overview of AI initiatives across departments and ensure purposeful allocation of resources and prioritization of new use cases. Additionally, we often see companies implementing cross-functional collaboration models, to facilitate cooperation between different BUs and with IT – e.g., to centrally specify solution compliance requirements.
Platform
The platform encompasses the technological infrastructure required to support AI initiatives, including data processing, storage, and consumption tools. We frequently observe the following challenges:
- Poor Data Quality: Lacking structured data management, companies often experience data inconsistencies across sources, inhibiting accuracy and scalability in AI products.
Example: Discrepancies in data formats & standards across different affiliates make it difficult to consolidate data for AI analysis. - Unscalable Infrastructure: The presence of old on-prem IT systems in many pharma companies results in difficulty scaling the data foundation to support advanced AI use cases.
Example: Legacy systems may not support the data throughput required for real-time AI-driven drug safety monitoring or other real-time data & AI solutions. - Poor Data Security: Data is not being sufficiently captured, stored, and utilized due to imperfect security measures and a resulting risk of breaches and non-compliance.
Example: Ensuring data privacy and compliance with GDPR when deploying AI in clinical trials, is a challenge if there is a risk of breaches.
To overcome these challenges, we recommend companies to implement a data governance framework to streamline taxonomies and appoint data owners to ultimately ensure high quality of data. Besides this, it can be beneficial to migrate legacy on-prem solutions to the cloud, as cloud solutions often offer enhanced security measures as well as scalability.
Once the interviews have been conducted, the foundation for conducting analyses around people, process, & platform-related elements are in place. While such an analysis should also be conducted specifically for your company and the AI use cases you strive to enable, the following paragraphs outline some of the areas we frequently see challenges in as well as what we recommend companies do to overcome them.
People
The backbone of any successful AI initiative is the people who drive it. This includes not only data scientists and data engineers but also the broader organizational workforce that will interact with and benefit from AI solutions. Common challenges we observe at organizations in the pharma industry include:
- Skill Gaps: Many organizations lack personnel with the necessary AI and data science expertise, and attracting the right talent can be hard.
Example: Developing AI algorithms for marketing optimization requires specialized knowledge in both AI & commercial strategies, which many pharma companies struggle to find. - Change Resistance: Employees might resist new technologies due to perceived complexity, lack of transparency, skepticism about data and model accuracy, or fear of job displacement.
Example: Implementing AI for HCP data analysis may face resistance from sales staff used to traditional methods. - Capacity Constraints: Many AI initiatives fail to mobilize due to capacity constraints, which cause an inability of IT resources to free up the necessary time to develop AI solutions.
Example: IT departments overwhelmed with maintaining legacy systems may not have the bandwidth to support new AI projects.
Although it is impossible to generalize the remedying initiatives across an entire industry, we often observe successful outcomes when companies take the following actions. First, developing the business case for the identified AI use cases to secure resource commitment to acquire the necessary talent and competencies, either internally from other departments or through upskilling or externally will help overcome both skill gaps and capacity constraints. Second, developing a holistic change engagement and roll-out framework can help companies successfully anchor solutions through training and efficient communication.
Processes
Processes cover the workflows and methodologies that support the development and deployment of AI solutions. Ensuring these are well-defined and flexible enough to accommodate the iterative nature of AI projects is crucial. Prominent challenges we observe include:
- Siloed Operations: Without effective collaboration fora & processes, context- and department-specific solutions are often developed in silos, impairing later scalability.
Example: while some AI models exist best within specific purpose areas, a chatbot developed exclusively for the sales force could benefit more users if it was built with scalability in mind – e.g., to be able to integrate medical insights to expand the solution to medical frontline staff - Regulatory Compliance: Navigating the complex regulatory landscape can be challenging, and AI technologies may be resisted due to compliance-related concerns.
Example: With emerging new regulations on AI, it is important to make sure that AI tools are in compliance which takes up time & efforts.
To resolve these, and to prepare the organizational processes for AI implementation, we often see success when companies, first, establish a central forum to keep an overview of AI initiatives across departments and ensure purposeful allocation of resources and prioritization of new use cases. Additionally, we often see companies implementing cross-functional collaboration models, to facilitate cooperation between different BUs and with IT – e.g., to centrally specify solution compliance requirements.
Platform
The platform encompasses the technological infrastructure required to support AI initiatives, including data processing, storage, and consumption tools. We frequently observe the following challenges:
- Poor Data Quality: Lacking structured data management, companies often experience data inconsistencies across sources, inhibiting accuracy and scalability in AI products.
Example: Discrepancies in data formats & standards across different affiliates make it difficult to consolidate data for AI analysis. - Unscalable Infrastructure: The presence of old on-prem IT systems in many pharma companies results in difficulty scaling the data foundation to support advanced AI use cases.
Example: Legacy systems may not support the data throughput required for real-time AI-driven drug safety monitoring or other real-time data & AI solutions. - Poor Data Security: Data is not being sufficiently captured, stored, and utilized due to imperfect security measures and a resulting risk of breaches and non-compliance.
Example: Ensuring data privacy and compliance with GDPR when deploying AI in clinical trials, is a challenge if there is a risk of breaches.
To overcome these challenges, we recommend companies to implement a data governance framework to streamline taxonomies and appoint data owners to ultimately ensure high quality of data. Besides this, it can be beneficial to migrate legacy on-prem solutions to the cloud, as cloud solutions often offer enhanced security measures as well as scalability.
Common Challenges & Frequent Recommendations
Once the interviews have been conducted, the foundation for conducting analyses around people, process, & platform-related elements are in place. While such an analysis should also be conducted specifically for your company and the AI use cases you strive to enable, the following paragraphs outline some of the areas we frequently see challenges in as well as what we recommend companies do to overcome them.
People
The backbone of any successful AI initiative is the people who drive it. This includes not only data scientists and data engineers but also the broader organizational workforce that will interact with and benefit from AI solutions. Common challenges we observe at organizations in the pharma industry include:
- Skill Gaps: Many organizations lack personnel with the necessary AI and data science expertise, and attracting the right talent can be hard.
Example: Developing AI algorithms for marketing optimization requires specialized knowledge in both AI & commercial strategies, which many pharma companies struggle to find. - Change Resistance: Employees might resist new technologies due to perceived complexity, lack of transparency, skepticism about data and model accuracy, or fear of job displacement.
Example: Implementing AI for HCP data analysis may face resistance from sales staff used to traditional methods. - Capacity Constraints: Many AI initiatives fail to mobilize due to capacity constraints, which cause an inability of IT resources to free up the necessary time to develop AI solutions.
Example: IT departments overwhelmed with maintaining legacy systems may not have the bandwidth to support new AI projects.
Although it is impossible to generalize the remedying initiatives across an entire industry, we often observe successful outcomes when companies take the following actions. First, developing the business case for the identified AI use cases to secure resource commitment to acquire the necessary talent and competencies, either internally from other departments or through upskilling or externally will help overcome both skill gaps and capacity constraints. Second, developing a holistic change engagement and roll-out framework can help companies successfully anchor solutions through training and efficient communication.
Processes
Processes cover the workflows and methodologies that support the development and deployment of AI solutions. Ensuring these are well-defined and flexible enough to accommodate the iterative nature of AI projects is crucial. Prominent challenges we observe include:
- Siloed Operations: Without effective collaboration fora & processes, context- and department-specific solutions are often developed in silos, impairing later scalability.
Example: while some AI models exist best within specific purpose areas, a chatbot developed exclusively for the sales force could benefit more users if it was built with scalability in mind – e.g., to be able to integrate medical insights to expand the solution to medical frontline staff - Regulatory Compliance: Navigating the complex regulatory landscape can be challenging, and AI technologies may be resisted due to compliance-related concerns.
Example: With emerging new regulations on AI, it is important to make sure that AI tools are in compliance which takes up time & efforts.
To resolve these, and to prepare the organizational processes for AI implementation, we often see success when companies, first, establish a central forum to keep an overview of AI initiatives across departments and ensure purposeful allocation of resources and prioritization of new use cases. Additionally, we often see companies implementing cross-functional collaboration models, to facilitate cooperation between different BUs and with IT – e.g., to centrally specify solution compliance requirements.
Platform
The platform encompasses the technological infrastructure required to support AI initiatives, including data processing, storage, and consumption tools. We frequently observe the following challenges:
- Poor Data Quality: Lacking structured data management, companies often experience data inconsistencies across sources, inhibiting accuracy and scalability in AI products.
Example: Discrepancies in data formats & standards across different affiliates make it difficult to consolidate data for AI analysis. - Unscalable Infrastructure: The presence of old on-prem IT systems in many pharma companies results in difficulty scaling the data foundation to support advanced AI use cases.
Example: Legacy systems may not support the data throughput required for real-time AI-driven drug safety monitoring or other real-time data & AI solutions. - Poor Data Security: Data is not being sufficiently captured, stored, and utilized due to imperfect security measures and a resulting risk of breaches and non-compliance.
Example: Ensuring data privacy and compliance with GDPR when deploying AI in clinical trials, is a challenge if there is a risk of breaches.
To overcome these challenges, we recommend companies to implement a data governance framework to streamline taxonomies and appoint data owners to ultimately ensure high quality of data. Besides this, it can be beneficial to migrate legacy on-prem solutions to the cloud, as cloud solutions often offer enhanced security measures as well as scalability.
Common Challenges & Frequent Recommendations
Once the interviews have been conducted, the foundation for conducting analyses around people, process, & platform-related elements are in place. While such an analysis should also be conducted specifically for your company and the AI use cases you strive to enable, the following paragraphs outline some of the areas we frequently see challenges in as well as what we recommend companies do to overcome them.
People
The backbone of any successful AI initiative is the people who drive it. This includes not only data scientists and data engineers but also the broader organizational workforce that will interact with and benefit from AI solutions. Common challenges we observe at organizations in the pharma industry include:
- Skill Gaps: Many organizations lack personnel with the necessary AI and data science expertise, and attracting the right talent can be hard.
Example: Developing AI algorithms for marketing optimization requires specialized knowledge in both AI & commercial strategies, which many pharma companies struggle to find. - Change Resistance: Employees might resist new technologies due to perceived complexity, lack of transparency, skepticism about data and model accuracy, or fear of job displacement.
Example: Implementing AI for HCP data analysis may face resistance from sales staff used to traditional methods. - Capacity Constraints: Many AI initiatives fail to mobilize due to capacity constraints, which cause an inability of IT resources to free up the necessary time to develop AI solutions.
Example: IT departments overwhelmed with maintaining legacy systems may not have the bandwidth to support new AI projects.
Although it is impossible to generalize the remedying initiatives across an entire industry, we often observe successful outcomes when companies take the following actions. First, developing the business case for the identified AI use cases to secure resource commitment to acquire the necessary talent and competencies, either internally from other departments or through upskilling or externally will help overcome both skill gaps and capacity constraints. Second, developing a holistic change engagement and roll-out framework can help companies successfully anchor solutions through training and efficient communication.
Processes
Processes cover the workflows and methodologies that support the development and deployment of AI solutions. Ensuring these are well-defined and flexible enough to accommodate the iterative nature of AI projects is crucial. Prominent challenges we observe include:
- Siloed Operations: Without effective collaboration fora & processes, context- and department-specific solutions are often developed in silos, impairing later scalability.
Example: while some AI models exist best within specific purpose areas, a chatbot developed exclusively for the sales force could benefit more users if it was built with scalability in mind – e.g., to be able to integrate medical insights to expand the solution to medical frontline staff - Regulatory Compliance: Navigating the complex regulatory landscape can be challenging, and AI technologies may be resisted due to compliance-related concerns.
Example: With emerging new regulations on AI, it is important to make sure that AI tools are in compliance which takes up time & efforts.
To resolve these, and to prepare the organizational processes for AI implementation, we often see success when companies, first, establish a central forum to keep an overview of AI initiatives across departments and ensure purposeful allocation of resources and prioritization of new use cases. Additionally, we often see companies implementing cross-functional collaboration models, to facilitate cooperation between different BUs and with IT – e.g., to centrally specify solution compliance requirements.
Platform
The platform encompasses the technological infrastructure required to support AI initiatives, including data processing, storage, and consumption tools. We frequently observe the following challenges:
- Poor Data Quality: Lacking structured data management, companies often experience data inconsistencies across sources, inhibiting accuracy and scalability in AI products.
Example: Discrepancies in data formats & standards across different affiliates make it difficult to consolidate data for AI analysis. - Unscalable Infrastructure: The presence of old on-prem IT systems in many pharma companies results in difficulty scaling the data foundation to support advanced AI use cases.
Example: Legacy systems may not support the data throughput required for real-time AI-driven drug safety monitoring or other real-time data & AI solutions. - Poor Data Security: Data is not being sufficiently captured, stored, and utilized due to imperfect security measures and a resulting risk of breaches and non-compliance.
Example: Ensuring data privacy and compliance with GDPR when deploying AI in clinical trials, is a challenge if there is a risk of breaches.
To overcome these challenges, we recommend companies to implement a data governance framework to streamline taxonomies and appoint data owners to ultimately ensure high quality of data. Besides this, it can be beneficial to migrate legacy on-prem solutions to the cloud, as cloud solutions often offer enhanced security measures as well as scalability.
Common Challenges & Frequent Recommendations
Summary of Frequent Challenges & Initiative Recommendations
Summary of Frequent Challenges & Initiative Recommendations
Summary of Frequent Challenges & Initiative Recommendations
Establishing a solid foundation for AI in pharma and developing a holistic AI roadmap specifying both use cases and enabling initiatives require a strategic focus on people, processes, and platforms. By addressing specific shortcomings and implementing targeted initiatives, organizations can create an environment conducive to AI innovation. This holistic approach ensures that AI projects not only get off the ground but also deliver sustained value, aligning with the long-term strategic goals of the organization.
In our next article, we will explore how to anchor AI within the organization and harvest the value of developed AI solutions to ensure lasting impact. Click here to read article 4 in the series.
Establishing a solid foundation for AI in pharma and developing a holistic AI roadmap specifying both use cases and enabling initiatives require a strategic focus on people, processes, and platforms. By addressing specific shortcomings and implementing targeted initiatives, organizations can create an environment conducive to AI innovation. This holistic approach ensures that AI projects not only get off the ground but also deliver sustained value, aligning with the long-term strategic goals of the organization.
In our next article, we will explore how to anchor AI within the organization and harvest the value of developed AI solutions to ensure lasting impact. Click here to read article 4 in the series.
Establishing a solid foundation for AI in pharma and developing a holistic AI roadmap specifying both use cases and enabling initiatives require a strategic focus on people, processes, and platforms. By addressing specific shortcomings and implementing targeted initiatives, organizations can create an environment conducive to AI innovation. This holistic approach ensures that AI projects not only get off the ground but also deliver sustained value, aligning with the long-term strategic goals of the organization.
In our next article, we will explore how to anchor AI within the organization and harvest the value of developed AI solutions to ensure lasting impact. Click here to read article 4 in the series.
Conclusion
Establishing a solid foundation for AI in pharma and developing a holistic AI roadmap specifying both use cases and enabling initiatives require a strategic focus on people, processes, and platforms. By addressing specific shortcomings and implementing targeted initiatives, organizations can create an environment conducive to AI innovation. This holistic approach ensures that AI projects not only get off the ground but also deliver sustained value, aligning with the long-term strategic goals of the organization.
In our next article, we will explore how to anchor AI within the organization and harvest the value of developed AI solutions to ensure lasting impact. Click here to read article 4 in the series.
Conclusion
Establishing a solid foundation for AI in pharma and developing a holistic AI roadmap specifying both use cases and enabling initiatives require a strategic focus on people, processes, and platforms. By addressing specific shortcomings and implementing targeted initiatives, organizations can create an environment conducive to AI innovation. This holistic approach ensures that AI projects not only get off the ground but also deliver sustained value, aligning with the long-term strategic goals of the organization.
In our next article, we will explore how to anchor AI within the organization and harvest the value of developed AI solutions to ensure lasting impact. Click here to read article 4 in the series.
Conclusion
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Sign up for the whitepaper and learn how Agentic AI is set to disrupt pharma’s commercial engagement - and why acting now is key to staying ahead. Explore how a strategic approach to AI adoption can help pharma scale impact, optimize operations, and unlock lasting value.
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AI is revolutionizing the pharmaceutical industry, improving efficiency in drug discovery, clinical development, and customer engagement. Key applications like content creation, insights extraction, and predictive analytics streamline operations. To maximize AI’s potential, a strategic, holistic approach is crucial, avoiding siloed efforts and aligning with business goals for scalable success.
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AI has the potential to revolutionize the pharmaceutical industry, but success hinges on selecting the right use cases. Without a structured approach, companies risk spreading resources too thin and missing out on scalable, impactful solutions. This article explores a strategic framework for evaluating and prioritizing AI projects, ensuring they align with corporate goals and deliver maximum value
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Successfully implementing AI in pharma requires building a strong foundation across people, processes, and platforms.Intellishore’s approach involves assessing organizational readiness, identifying maturity gaps, and creating a roadmap to address these challenges. By focusing on targeted initiatives, organizations can enable AI innovation and achieve sustainable, long-term success.
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