In 2023, AI was the talk of the tech town, with companies eager to showcase their use of artificial intelligence. However, as we move into 2024, the focus is shifting from mere fascination to practical implementation. Gartner's assessment that GenAI is moving beyond the 'Peak of Inflated Expectations' underscores this shift.
One of the key considerations for companies delving deeper into AI is the practicality of large language models (LLMs) and the significant compute power required to leverage AI effectively. Moreover, security and privacy concerns surrounding AI data usage are gaining prominence, especially when interacting with external entities like OpenAI.
As the enterprise AI landscape matures, attention is turning towards establishing robust processes to address these concerns. The debate between using large, general-purpose models versus smaller, focused models is gaining traction among decision-makers. While large models offer versatility, smaller models provide depth and cost-effectiveness, allowing for greater control over data security.
Companies are urged to approach AI adoption thoughtfully, considering their existing business priorities and strategic roadmap for integrating AI seamlessly. The surge in data volumes necessitates a concerted effort to consolidate and organize data for AI applications effectively.
When venturing into the AI realm, careful vetting of service providers is crucial to ensure reliability and longevity. Security remains a top concern, with the potential for data breaches posing significant risks. Executives are advised to proceed incrementally, monitoring projects closely for any issues that may arise.
As we step into 2024, the emphasis is on leveraging AI responsibly and strategically. While the allure of AI capabilities is undeniable, a cautious and methodical approach is essential to harness its full potential.
