In their backend, AI models work like chemistry. They can extract value from data, but only if we provide it with the right ingredients. We now generate per day – and the number keeps growing. This is a formula with an impact comparable to innovations like the periodic table, and discoveries like the DNA molecule.

But just like any chemical discovery, you must use the right method to get valid results. Here’s a breakdown of the steps to transform your organization.

Purify the original matter

Data is the substrate used to train AI systems and is what AIs ultimately act on. Because clean and reliable data is the key to drive insights and actions, data quality is paramount. Like chemists purifying substances, organizations must cleanse and refine their data. AI/Gen AI outcomes are only as powerful as the data that it’s running on.

But for most organizations data is distributed across different locations. Most of it resides on-premises, while . It’s difficult and expensive to move data from one location to another. It’s more efficient to bring AI to the data. Training and running AI models on-premises can bring benefits to processing, analysis, compliance and intellectual property management. that LLMD than in the public cloud. Organizations win when they bring the right GenAI model to their prepared data.

Combining Elements

Chemists prepare compounds of different elements, mixing them to create new substances. In the world of GenAI, you can do the same by working with open ecosystems – operating models that share data and services to create value. AI/Gen AI workloads require flexibility in infrastructure and software that can adapt as fast as the models evolve. Open LLMs create equal opportunity across the ecosystem, which in turn allows organizations to accelerate progress and solve problems.

The mixing of different elements – in other words, collaboration – fosters new opportunities and can reduce the cost of AI development. Openness ensures healthy competition, choice and knowledge sharing. And we must not forget the ethical component. Open models are under public scrutiny, which pushes research labs to reduce bias and secure the data. It’s like combining and distilling elements with an ethical lens.

The Formula of Insights

Once you purify your base material – your data – and combine the right elements in an open-ecosystem, you can achieve the breakthrough, the formula for insights. AI algorithms predict trends, customer behavior and market dynamics. These insights work like a formula to help sustain organizations and guide strategic decisions.

AI is not magic – it’s a disciplined practice. Data scientists and engineers follow precise methodologies to unleash innovation. Instead of flasks and beakers, their labs are now workstations, data, compute and storage. These are valuable tools to extract wisdom from data.

The Chemical Imperative

AI and data management are deeply intertwined. You need a rigorous data strategy to reap the benefits of generative AI models. We recommend treating your data as a raw element. It requires refinement and a detailed process to turn into a valuable substance. The call is for your organization to evoke the chemist’s fundamentals – stay curious, be persistent and committed to turning your data into insights. Only then you will unlock the transformative value of AI.