Don’t Drift Away from Reality — The Importance of AI Model Longevity

July 30th, 2024

Author's

George Harrison

Analyst and AI Consultant

George is an Oxford alumni mathematician. His research is deeply rooted in an understanding of how AI works and how to apply it in real-world working environments. His specialist coverage is AI in CX.

Simon Harrison

Analyst and Executive Partner

Simon Harrison is an accomplished analyst and technology strategist with over 30 years of experience spanning systems engineering, technical consulting, product innovation, and global senior leadership. He began his career as a UNIX systems engineer and consultant before advancing to senior roles, including SVP of Product Marketing and award-winning Chief Marketing Officer, driving growth for a multibillion-dollar company. A former Gartner analyst and Magic Quadrant author, Simon remains an active industry analyst and executive advisor, helping companies sharpen their strategy, messaging, and go-to-market performance. Today, as founder of Actionary, he delivers board-level insight on AI, customer engagement, and platform innovation, drawing on deep technical roots and a proven track record of helping companies achieve their goals at scale.

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.

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