Next, companies must invest in their team to support AI adoption. Hiring or upskilling professionals with AI and data science expertise is essential for driving AI initiatives. These experts will play a pivotal role in designing AI systems, managing data pipelines, and ensuring the technology aligns with the overall strategy. Trusted partners can also help define and validate AI use cases, ensuring that AI is used effectively and scaled appropriately. Depending on the company’s goals, resources, and industry context, insight into whether building, buying, or using open-source tools is the best option for their organization.
Rather than being distracted by AI’s limitless potential, companies switzerland rcs data should identify high-value use cases where AI can deliver the most meaningful results. It’s best to start with an AI prioritization assessment framework that quantifiably evaluates business outcomes and gains against build and runtime costs.
For example, with power demands soaring due to large-scale AI model training, a data center company turned to our company to address rising operational costs. Working together, the company leveraged AI to monitor and predict power consumption, identifying erratic usage patterns that allowed the company to negotiate power hedging and price protection plans with utility providers. The company anticipates this strategy will save hundreds of millions of dollars over time, offering critical cost stability amid increasing energy demands.