Currently, there are two main approaches to implementing AI in enterprises. Some companies are rushing to adopt AI solutions to quickly see returns on investment, while others are taking a more long-term approach, investing in research now for future benefits. However, regardless of the stage of AI adoption, organizations face common challenges such as skills shortages, energy usage, supply chain issues, and budget constraints, with AI implementations starting at around £10 million.
Investment in AI has been steadily increasing over the years, with companies investing an estimated $934.2 billion in AI technologies from 2013 to 2022. The emergence of generative AI has further fueled this investment, with major tech companies like Microsoft, Google, and Amazon leading the way. Despite this trend, many organizations find large-scale AI implementations costly, considering factors like IT infrastructure, energy consumption, and environmental impact.
To ensure successful AI implementation, organizations need to lay the right foundations. Accessibility to GPUs is crucial for AI projects, and organizations may need to look to hosting service providers for GPU access due to market demand. Data center power and space capabilities are also critical, as AI projects require high power density and efficient storage technologies like flash-based data storage to mitigate power and space challenges.
One of the biggest challenges in AI projects is handling massive datasets and ensuring data accuracy. Implementing ‘checkpointing’ can help revert AI models to earlier states to understand the implications of data and parameter changes. Additionally, organizations need to address skills shortages by investing in talent acquisition and internal training to develop AI skills within their workforce.
As organizations progress in their AI adoption journey, partnerships with cloud service providers and managed service providers may be necessary to overcome challenges. These partnerships can offer services and infrastructure to help organizations achieve their AI goals. Ultimately, successful AI implementation requires careful planning, investment in the right resources, and collaboration with external partners to navigate the complexities of AI technology.
