In an economic environment where costs are rising, businesses are searching for new ways to improve margins, ideally by increasing productivity while lowering costs at the same time. Generative AI is offering a quickly growing toolbox for enhancing efficiency and reducing operational expenses with relatively low targeted investments. For example, AI tools can be used to process large amounts of documents, images or video content as well as to automatically generate new content at high quality.
It is not difficult for organizations to develop a multitude of ideas of how to put generative AI to work—indeed, the potential seems almost unlimited. But developing a comprehensive AI strategy for a business is a big challenge at a time when foundational technologies appear to evolve on a weekly basis.
The generative AI ecosystem is moving at a breathtaking speed, with new players arriving daily and established players at risk of disappearing. Big, commercial large language models (LLMs) are leading the scoreboards, but smaller and open-source models, including those with commercially viable licenses, are catching up quickly. The cost structure of operating LLMs is currently dominated by a scarcity of specialized hardware for AI clusters, with delivery times of a year or more for large customers. Selecting the right set of tools from an avalanche of unproven and quickly changing open-source projects is another considerable challenge.
It seems hard to pick the right combination of tools, AI models and technology suppliers for long-term tech investments, especially for organizations (including large, established consulting firms and IT service providers) that lack the expertise to implement generative AI. So what is a safe approach to creating an AI strategy if you do not want to miss out on this exciting technology, while hedging your bets and minimize your risk?
Wolfram Consulting Group can help companies to navigate this quickly transforming landscape by beginning with carefully selected and sharply focused use cases, avoiding the pitfalls of premature and costly investments. By rapidly developing prototypes for the most promising application areas, clients can gain experience and build the expertise and confidence to develop a longer-term generative AI strategy in preparation for more profound and transformative changes.
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