Skip to content

Cloud Is 28 Years Old — Catch up Now to Be Able to Compete in the AI Innovation Race

    Summary

    Cloud computing has come a long way since it was first envisioned 28 years ago, transforming from a revolutionary concept to an essential driver of business innovation. From its origins in a 1996 Compaq business plan, cloud computing has evolved into a critical enabler of AI, offering unprecedented growth, efficiency, and scalability. This research revisits the original cloud vision and how dramatically cloud has evolved since. It covers what you need to know to realize the pivotal benefits of cloud computing, that is driving unprecedented growth and retention increases in today’s digital economy.

    Key Take: Companies relying on outdated on-premises technology in their innovation strategy will fall behind competitors who are realizing cloud-powered and AI-related growth and customer retention.

    Read MoreCloud Is 28 Years Old — Catch up Now to Be Able to Compete in the AI Innovation Race

    Don’t Drift Away from Reality — Ensure Vendors Offer AI Model Longevity

      Summary

      In the ever-evolving landscape of machine learning (ML), winning the battle against model drift and decay is pivotal to maintaining a competitive edge and achieving sustainable success. Model drift and decay refer to the degradation in the performance of machine learning models that happens over time. This research delves into the critical importance of addressing these issues head-on and offers actionable insights to support verifying investment in the right Artificial Intelligence (AI) vendor solutions. Continuous monitoring, implementing automated retraining pipelines, and ensuring robust data management safeguard the integrity of AI systems and unlocks their full potential. Discover how proactive measures can ensure long-term value for your organization and what to be asking AI solution providers.

      Key Take: Effective management of model drift and decay is essential for sustaining the value of ML systems. Without these measures, models can become obsolete, leading to inaccurate predictions and reduced ROI.

      Read MoreDon’t Drift Away from Reality — Ensure Vendors Offer AI Model Longevity

      Maximize AI With Clear Understanding of Fine-Tuning vs. Retrieval-Augmented Generation (RAG)

        Summary

        Fine-Tuning and RAG (Retrieval-Augmented Generation) are essential AI model enhancement methods, each offering distinct advantages and challenges. Implementing these approaches strategically, based on specific AI application needs, can significantly enhance the performance and outcomes of pre-trained models. Conversely, choosing the wrong approach can undermine the benefits of an AI solution. This research explores the benefits of both Fine-Tuning and RAG, emphasizing the importance of selecting and combining these methods appropriately to achieve optimal results.

        Key Take: Companies that fail to align Fine-Tuning and RAG with the appropriate use cases will encounter inefficiencies, diminished performance, and missed opportunities to fully leverage AI’s potential.

        Read MoreMaximize AI With Clear Understanding of Fine-Tuning vs. Retrieval-Augmented Generation (RAG)