Generative AI: How to Move from Promises to Production
Generative artificial intelligence. GenAI for short. It’s the one topic that has companies excited more than anything else in the past few years. And for good reason; it pulls the best information possible out of your data. It has the potential to transform how a business operates, how employees work, and how we all live our daily lives. It’s a powerful tool that has placed the world on the edge of a new sort of Industrial Revolution. For many companies, it’s a brand-new territory. At Axtria, we’ve been working with data analytics across our products and solutions since day one. Uncovering hidden insights from information – what GenAI does best – is part of our DNA. What about the companies that don’t have data so naturally embedded in their culture? How can they join the GenAI party? They can start by following four key concepts crucial to all GenAI strategies, irrespective of industry. Choose GenAI use cases that provide measurable impact. Some companies take a shotgun approach: trying GenAI in everything and seeing what works. As you can imagine, that’s a massive waste of resources. Maybe you want to improve a customer’s buying journey. Perhaps you want your sales reps to get insights and tips before walking into a meeting. Remember, not all use cases will have the same level of organizational impact, so choose wisely. You need someone to help you tailor a GenAI journey for specific projects that are scalable and will ensure adoption by your team members. Consider working with an established firm that can guide you and enable your GenAI journey of exploration, experimentation, industrialization, adoption, and, sometimes, monetization. Build strong Generative Relational Databases. You’ll quickly realize that every bit of data you have holds value, even the type that doesn’t fit neatly into a spreadsheet. “Traditional” data—names, numbers, sales figures, and such—needs to be clean and error-free. GenAI can parse the not-so-neatly-organized data—photos, video, audio, even handwritten notes. Combine it all by leveraging what are called Generative-Ready Datasets (GRD). These GRDs train the Large Language Models that form the basis of GenAI. This is important: Get this correct at the start, and you won’t have to worry about mistakes scaling with you. Your goal is to industrialize your GenAI usage, and this GRD strategy sets you up for it, not just with larger datasets but across all business functions. The potential is there for the taking if you seek products and platforms that leverage GenAI in production. Ensure a viable, intertwined GenAI and AI strategy. You can explore and experiment with GenAI, but industrialization can only happen once you have validated the strategy – by accepting the proofs-of-concept. That means you have to sit down and have frank discussions on sensitive issues: intellectual property protection, personal data, and other critical areas across your products and solutions. Setting the proper guardrails here can avoid severe reputational damage and prevent stumbles later on. Relying on a trusted and experienced partner who can help you look around corners is essential. Define the characteristics of industrialization excellence. In order to scale up, you need to have a platform that people are willing to use. The user interface must be simple to navigate, with relevant information easy to find. The user experience has to be positive, or you’ll never get full adoption. Likewise, you need to ensure your business users are on board with a new platform. Getting buy-in and positive change management ensures trust in the AI models. If your team isn’t using the model, or they don’t trust it, it’s meaningless.  Once you have that in place, you’re now ready to start turning promises into wider practice. Find a partner who listens to your industrialization goals and has expertise with products and platforms that leverage GenAI in production. One who can help you define the best practices and what will be considered a success. Remember, industrialization with GenAI doesn’t have to be limited to “more customers.” GenAI can find efficiencies in your own team, so don’t forget to look inwards. When you’ve got your GenAI strategy humming along beautifully, you must remain vigilant. Responsible GenAI usage involves security, as well as frequent monitoring of the analysis models, user controls, and usage compliance. Accountability is a crucial factor as well. You must document everyone tasked with ownership, oversight, and approval at every stage, including the usage of third-party models. Another critical aspect of GenAI responsibility comes from the human factor. You have to commit to eliminating biases in model training. We all have opinions, and that makes us unique. Be sure those feelings don’t handcuff the model, leaving important insights uncovered. Finally, you have to be able to explain your GenAI model. It must be transparent and traceable, which helps with governance considerations. Seeing how a model developed its answer helps with model refinement and reporting responsibilities. Putting these all together will give you a GenAI strategy that’s effective and well-suited for industrialization. It’s a big ask of any company, but leaning on a partner with established know-how across products and solutions will get you up and running faster and safer than doing it yourself. Since our founding in 2010, Axtria has focused on helping our clients make better decisions by enabling the best use of data. Our life sciences clients, whose work helps save lives, depend on Axtria and our rich expertise in leveraging data to help them work better, smarter, and with greater impact. Interested to read more on the impact and future of GenAI: Reading the Tea Leaves: New Insights from Gartner® on the Future of Gen AI and the Value it’s Already Adding in Life Sciences The Use of Natural Language Processing in Literature Reviews
What about the companies that don’t have data so naturally embedded in their culture? How can they join the GenAI party?
How can companies join the GenAI party ? They can start by following four key concepts crucial to all GenAI strategies, irrespective of industry.
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["AI", "artificial intelligence", "analytics", "GenAI", "RAG", "R", "Go", "Rust", "Scala", "GAN"]
Jaswinder Chadha is a successful serial entrepreneur who founded Axtria. In less than 15 years has led its global growth as President and CEO to a 3,500+ person enterprise serving dozens of clients in more than 50 countries, including a majority of the world’s top global life-sciences enterprises and several in the Fortune 50. A proven expert in delivering data analytics and artificial intelligence successes to life-sciences companies through the skilled work of employees in North America,
Europe, and India.

He is also a member of the Second District Advisory Council of the Federal Reserve Bank of New York. Jassi has earned numerous awards and recognition, including as one of the 100 most influential life-sciences professionals (PharmaVOICE) and one of the “Top Forty Under Forty” leaders
(NJBiz magazine).

He is also a graduate of the Indian Institute of Technology Delhi before earning his MS and Ph.D. (ABD) in Industrial Engineering and Operations Research, Jassi was a founding member of the institute’s IIT Delhi Endowment Fund, established by school alumni. He also has endowed the Chair of Operations Research and Chair of Data Science at the school.