Unlocking the True Potential of Generative AI: A Human-Centered Roadmap

Companies today are looking to Generative AI (GenAI) as a way to uncover new insights, boost efficiency, and unlock value across their organizations. GenAI holds incredible potential—it can analyze vast amounts of data, whether structured or unstructured, and in formats that go beyond simple text. But how can businesses ensure their GenAI efforts grow from simple experiments into something much bigger?

At Axtria, we brought together more than 30 top executives from some of the world’s biggest pharmaceutical companies during our annual Axtria Ignite event. Our goal was to create a roadmap for harnessing GenAI at an enterprise level. What we found wasn’t just useful for life sciences; this framework has the power to transform any industry.

Together, we identified seven clear steps that any business can follow to turn GenAI projects into real, scalable solutions. This roadmap is designed to help companies avoid the frustration of stalled pilot projects and move toward meaningful, large-scale results.

From Experiment to Real Impact: A 7-Step Roadmap

1. Align with Company Goals For GenAI to truly succeed, it needs to fit into a company’s bigger picture. It shouldn’t be a separate, isolated initiative but a core part of the overall strategy. Leaders, from executives to data scientists, need to work together closely, having ongoing discussions to ensure AI efforts align with the company’s broader goals. When there’s a shared vision, teams can build a strong foundation for deploying GenAI successfully.

Alongside strategic alignment, good governance is crucial. When multiple pilot projects are running at the same time, it’s important to choose the ones with the greatest potential to scale. A structured process helps evaluate which initiatives will have the most significant impact.

2. Build a Strong Data Strategy For GenAI to work, you need more than just basic data management. Since GenAI can handle both structured and unstructured data, companies must bring together information from various sources, whether it’s sales data or social media interactions. Creating "GenAI-ready datasets" is key because these power the large language models that drive AI applications.

As GenAI technology evolves, incorporating data like video, audio, and images becomes even more important, offering richer insights and more precise predictions.

3. Adopt a Flexible Operating Model To get the most value out of GenAI, companies need a flexible approach that promotes collaboration across different teams. A dedicated GenAI task force, with support from top executives, can bridge the gap between technical and business teams, ensuring AI initiatives align with the company's goals.

Companies also need to decide which GenAI tasks will be managed in-house and which will be outsourced. Encouraging a culture of continuous learning helps employees stay ahead in effectively using AI.

Partnering with academia, research institutes, and tech companies can further enhance GenAI’s impact. Making sure these partnerships work well with existing tools will be crucial to driving innovation.

4. Develop the Right Skills Scaling GenAI requires a skilled workforce. Specialized skills like prompt engineering and LLMOps (Large Language Model Operations) are essential for ensuring GenAI produces useful and accurate results. Prompt engineers shape the AI’s responses, while LLMOps teams manage the entire lifecycle of AI applications, ensuring everything runs smoothly.

Good user interface design is just as important. Well-designed interfaces help make GenAI applications easy to use, encouraging adoption across different departments.

5. Leverage Cutting-Edge Technology GenAI’s technical foundation must be able to handle large, complex operations. Companies should focus on—or partner with experts who can—integrate advanced models like GPT-3.5 and GPT-4 to create powerful GenAI tools. Effective prompt engineering, combined with the latest AI advancements, ensures that insights are accurate.

Building a robust infrastructure, including cloud-based tools for managing AI models, helps make sure GenAI applications are scalable and can be updated seamlessly over time.

6. Drive Adoption Even the most advanced GenAI tools will fall short if no one uses them. To encourage adoption, companies must clearly demonstrate the value of AI. Having executives who support the initiative, along with “champions” within teams who advocate for AI, can help build momentum.

Clear communication and training are key to building confidence. Showing quick, successful results through pilot projects can spark broader enthusiasm and acceptance.

7. Ensure Responsible AI Use It’s crucial that GenAI is used ethically and in compliance with regulations. For example, pharmaceutical companies have to navigate a mountain of rules when handling data, and privacy concerns are important across all industries. Companies must adopt responsible AI practices, prioritizing transparency, fairness, and minimizing bias.

A solid governance framework will protect against risks and help maintain trust with stakeholders. Responsible AI not only supports compliance but also fosters a culture of integrity in AI-driven innovation.

The Future of GenAI: What’s Next?

GenAI is quickly gaining ground, and companies that have already begun are looking for ways to scale. In the future, GenAI solutions will connect more with customers through meaningful conversations. Virtual assistants with emotional intelligence will help sales teams engage more deeply, making interactions more impactful.

Ready to Take the Next Step?

As GenAI continues to evolve, companies must move beyond experiments and fully embrace this powerful technology. Aligning AI efforts with business goals, adopting responsible practices, and driving widespread adoption are key to unlocking its full value.

If you want to learn more about how the pharmaceutical industry can scale GenAI projects and maximize their impact, join Axtria’s 30-minute webinar on October 29, 2024, at 1:00 PM ET. Our experts will discuss how to create enterprise-level value using these seven key strategies, with insights from the life sciences sector.