The AI Imperative: Essential Factors Biotech & Pharma CIOs Must Address for Success
A collaboration blog post by Seema Sodhani and Deb Tabacco
As a Chief Information Officer (CIO) in a #biotech or #pharma company, here are some essential areas you will need to consider and actively manage in preparation for the rise of AI in drug discovery, development, and commercialization.
Business Strategy Alignment
Before embarking on any business transformation initiative such as AI, establish business strategy alignment, identifying why AI is business-critical and how AI enables and supports the business strategy, goals and objectives.
Assign business ownership to an executive outside the IT organization to reflect the business importance of the initiative.
Data Governance
To support the data standards that AI-driven algorithms and insights require, establish a robust data governance program that includes standard-setting policies, procedures, accountability (owners, custodians, etc.), training and monitoring. Standards should consider data definition, interpretation, integrity, restricted access, privacy and other regulatory requirements.
Develop effective strategies for integrating and managing the diverse systems and data sources that will drive comprehensive analysis and insights. Data sources may include electronic health records, genomics data, clinical trial data, and real-world evidence.
Validation and Validation Data: Define validation frameworks and protocols for AI models used in drug discovery, development, and pricing. Ensure the availability of high-quality, validated datasets for training and testing AI algorithms.
Systems
Patient experience hubs and engagement programs have the most to gain from the use of AI to meet patients and caregivers exactly where they are on the patient journey. Much information is already known about the patient as soon as they enter the patient support programs. Using AI to tailor the right message at the right time can support onboarding, adherence, and engagement.
Healthcare provider go-to-market systems including CRM systems should be assessed for ongoing fit with AI strategies. With companies like Veeva(R) breaking away from Salesforce(R), pharmaceutical companies have a chance to re-evaluate CRMs that are flexible and adaptable to this fast-changing market.
Infrastructure
Assess and enhance the company’s security and risk management programs to ensure the availability, continuity, resilience and integrity of AI systems, infrastructure and data stores. Engage cross-functionally to identify and size AI-related risks with the greatest likelihood and impact.
The high computational demands of AI requires re-evaluating the current performance of systems, data integration and infrastructure to ensure the high demand of AI algorithms and deep learning models will be supported.
Change Management and Skill Development
Prepare the organization for the cultural and operational shifts that come with adopting AI technologies. Facilitate change management initiatives to ensure smooth integration of AI into existing workflows and maximize the benefits it can bring to the company. Clearly, frequently and transparently communicate The Why of AI, as well as why AI is an imperative that requires immediate action.
Identify the necessary skills and expertise required for implementing AI in pharmaceutical processes. Don’t only hire data scientists and machine learning engineers, but bring on AI product strategists like Seema Sodhani, who are able to translate all the disparate data, analytics, and technology to meaningful insights that are critical for the business. Balance your hires with enough strategists who can direct the teams to stay focused on providing results for direct impact on the business.
Collaborative Partners
Foster partnerships and collaborations with academic institutions, research organizations, and technology companies specializing in AI to leverage their expertise, gain access to cutting-edge technologies, and accelerate drug discovery and development efforts. Software companies like Swift Invention have been building and delivering predictive models and ontologies to biopharma and healthcare companies since 2013.
Use third-party resources and experts like teams at Proximity Lab who are a step ahead in exploring user interface and experience (UI/UX) for new AI-driven digital delivery.
Program Management and Budget
Establish an AI Program Management Office (PMO) to ensure the success of this broadly cross-functional initiative. As a business-critical imperative, assign business sponsorship and accountability to executives outside IT.
When allocating funds for AI initiatives, be sure to include all implementation component costs from assessment and implementation to ongoing maintenance. Budget needs should consider the business value chain - people, partners, processes, data, systems and infrastructure assets, provider and patient experience. Quantify the return on investment (ROI), separately identifying capitalized and expensed cost components to create a capital-efficient budget.
Remember, the impact of AI in pharmaceuticals is exploding at an unprecedented rate. It's crucial to stay ahead to effectively adapt and drive innovation within your organization. Take some time to speak to experts like Seema and Deb about your AI strategy and planning.
About the Authors:
Seema Sodhani is a Product Strategy & Marketing leader with in-depth experience in the biopharmaceutical and healthcare IT market. Seema has achieved a successful track record of building and launching #healthtech #SaaS and application offerings. She brings a sophisticated product strategy, design, and brand development process to healthtech and biotech leaders.
Deb Tabacco is a highly experienced #digital transformation strategist and Fractional CIO with the business and IT know-how to design an effective AI roadmap and the #program management skills to efficiently navigate your company through its implementation.
