Unlocking the Full Value of GenAI: A Strategic Roadmap
Companies are increasingly looking for Generative AI (GenAI) to uncover insights, boost efficiencies, and unlock value across their organizations. GenAI presents an unparalleled opportunity with its ability to synthesize vast amounts of data, whether structured or unstructured , and in multiple formats beyond simple text. But how do companies ensure their GenAI initiatives evolve beyond mere experimentation? Axtria, a leader in data, analytics and AI products and services for life sciences, gathered more than 30 top executives from the world’s largest pharmaceutical companies to discuss GenAI. We sat down with them during our highly anticipated Axtria Ignite annual event to plot out a roadmap for enterprise-level value creation. And what we quickly discovered is that this framework has the potential to transform any industry, not just life sciences. This collaboration identified seven steps that can easily apply to any line of business or commercial enterprise. This GenAI roadmap helps avoid the struggles of moving beyond pilot projects and into scalable solutions. The Road to Value: Pilot to Production 1. Alignment with Strategic Objectives To succeed with GenAI, companies must ensure that their AI initiatives align with broader corporate objectives. GenAI should not operate in isolation but should be part of an overarching AI strategy that involves leadership from both C-level executives and data science teams. By fostering regular conversations between business and technical leaders, organizations can set a strong foundation for successful GenAI deployment. In addition to strategic alignment, effective governance is essential. With numerous pilots running simultaneously in large companies, selecting projects with the greatest potential for scaling is critical. A structured stage-gate process can help organizations evaluate and prioritize initiatives based on technical feasibility and business impact. 2. Data Strategy GenAI’s success hinges on a robust data strategy that goes beyond traditional data management. With GenAI’s ability to handle both structured and unstructured data, companies must focus on integrating data from various sources—from sales records to social media interactions. The creation of “generative AI-ready datasets” (GRDs) is crucial, as these train the large language models (LLMs) that form the backbone of GenAI applications. As the technology progresses, the use of unstructured data such as video, audio, and images , and more is increasingly important, offering richer insights and more accurate predictions. 3. Operating Model For GenAI to generate meaningful value, organizations need a flexible operating model that accommodates cross-functional collaboration. A dedicated GenAI task force, supported by executive sponsorship, can help bridge the gap between technical and business teams, ensuring that AI initiatives align with company goals. Companies must also decide which GenAI processes will be managed in-house, and which will be outsourced to third-party vendors. Establishing a culture of continuous learning within teams ensures that employees are equipped to harness GenAI’s capabilities effectively. In addition, organizations should develop a broader partner ecosystem that includes academia, research institutes, and technology partners. Ensuring interoperability between external partners and existing tools and methodologies will be vital to maximizing the impact of GenAI initiatives and fostering innovation. 4. Skills and Experience Building a skilled workforce is another crucial factor in scaling GenAI. Specialized skills like prompt engineering, GenAI development, and LLMOps (Large Language Model Operations) are essential for delivering accurate and usable AI outputs. Prompt engineers ensure that the AI’s responses are tailored to specific needs, while LLMOps teams manage the full lifecycle of GenAI applications, from continuous integration to monitoring performance. To drive adoption, front-end design and user experience expertise are equally important. A well-designed user interface fosters engagement and helps make GenAI applications more accessible and user-friendly across different departments. 5. Technology The technological backbone of GenAI must support large-scale, complex operations. Companies should focus on—or partner with experts on—integrating models such as GPT-3.5 and GPT-4 to develop GenAI applications that enhance commercial activities. Effective prompt engineering, coupled with the latest GenAI enhancement modules, helps ensure the accuracy of AI-driven insights. Operationalizing GenAI through tools like LLMOps and continuous integration/continuous delivery pipelines ensures that AI models are scalable and can be updated seamlessly. Monitoring tools and infrastructure management via cloud platforms also contribute to the sustainability of GenAI solutions. 6. Adoption Even the most advanced GenAI applications can fall flat without widespread user adoption. To promote acceptance, organizations must demonstrate the value of AI initiatives clearly. This involves securing executive sponsorship and designating “champions” within teams who advocate for GenAI and facilitate cross-functional collaboration. Clear communication and training programs are essential for building confidence in new tools. Early wins—demonstrated through pilot projects that yield tangible results—can help build momentum for broader adoption. 7. Responsible AI Companies must also ensure that their GenAI initiatives adhere to ethical and regulatory standards. For example, pharmaceutical firms face a necessary, but mountainous volume of rules and restrictions on data. And privacy concerns are a consideration, regardless of industry. Organizations must adopt responsible AI practices that prioritize transparency, explainability, and bias mitigation. By implementing robust governance frameworks, companies can safeguard against potential risks and maintain trust with stakeholders. Responsible GenAI usage not only supports compliance but also fosters a culture of integrity in AI-driven innovation. The Future of GenAI: 2025 and Beyond GenAI continues to gain adoption, and those who have already begun implementing projects are seeking to scale up. GenAI solutions will connect customers through conversational interactions, and virtual assistants with emotional intelligence will help unlock sales teams’ potential, making engagements more meaningful and effective. Take the Next Step As GenAI continues to evolve, organizations must move beyond pilots,embrace full-scale industrialization and drive adoption to capture its true value. From aligning initiatives with strategic objectives to ensuring responsible AI practices, the right approach can unlock a transformative impact across the entire business value chain. To dive deeper into how pharmaceutical companies can move from pilot to production and maximize the impact of GenAI, join Axtria’s 30-minute webinar on these seven key findings on Tuesday, October 29, 2024, at 1:00 PM ET. Axtria’s experts will discuss the seven pillars of GenAI success and how to create enterprise-level value, drawing from insights across the life sciences sector. Register here for the webinar.
Companies are increasingly looking for Generative AI (GenAI) to uncover insights, boost efficiencies, and unlock value across their organizations. GenAI presents an unparalleled opportunity with its ability to synthesize vast amounts of data, whether structured or unstructured , and in multiple formats beyond simple text. But how do companies ensure their GenAI initiatives evolve beyond […]
Companies are increasingly looking for Generative AI (GenAI) to uncover insights, boost efficiencies, and unlock value across their organizations. GenAI
["axtria", "Axtria GenAI", "Axtria generative AI", "GenAI Roadmap", "Generative AI Roadmap"]
["Generative AI Roadmap", "Axtria GenAI", "data science", "GenAI", "TPU", "Axtria generative AI", "AI", "axtria", "ML", "virtual assistants", "prompt engineering", "GenAI Roadmap", "analytics", "generative AI", "R"]
["AI", "ML", "data science", "analytics", "generative AI", "GenAI", "prompt engineering", "virtual assistants", "TPU", "R"]
Sudip Chakraborty is a Principal and Head of AI and Gen AI at Axtria. He advises pharmaceutical clients on leveraging AI and GenAI to improve business outcomes. His expertise lies in developing an AI/GenAI strategy and roadmaps, assembling and coaching high-performing teams, building AI/GenAI and MLOps/LLMOps capabilities, and driving adoption and monetisation.