Summary:
1. The article discusses the issue of large companies facing a velocity gap in deploying AI models due to slow risk review processes.
2. It highlights the collision of rapid innovation in AI models with the slower pace of enterprise adoption and governance.
3. The article provides strategies for winning enterprises to bridge the velocity gap and standardize the path to production.
Rewritten Article:
Your top-notch data science team has just completed a groundbreaking project, developing a model that can predict customer churn with an impressive 90% accuracy. However, despite their hard work, the model remains unused, stuck in a lengthy risk review queue awaiting approval from a committee that struggles to comprehend stochastic models. This scenario isn’t just a hypothetical situation; it’s a common reality for many large corporations.
In the realm of artificial intelligence (AI), the pace of innovation moves at lightning speed. New model families emerge regularly, open-source toolchains evolve rapidly, and entire MLOps practices are constantly changing. On the flip side, most enterprises move at a significantly slower pace. Anything related to deploying AI models must navigate through various hurdles such as risk reviews, audit trails, change-management boards, and model-risk sign-off. This creates a significant velocity gap where the research community advances while the enterprise lags behind.
This velocity gap may not make sensational headlines like “AI will replace your job,” but its implications are substantial and costly. It leads to missed opportunities for productivity, the proliferation of shadow AI projects, duplicated investments, and compliance challenges that hinder the transition from promising experiments to sustainable implementations.
The numbers from reputable sources like Stanford’s 2024 AI Index Report and IBM’s research clearly indicate the collision course between the rapid pace of innovation in AI and the accelerating adoption by enterprises. The exponential growth in training compute needs, coupled with the increasing deployment of AI in large-scale companies, paints a picture of a future where governance and control mechanisms are critical for successful AI implementation.
While many enterprises focus on fine-tuning AI models, the real bottleneck often lies in demonstrating compliance with guidelines and regulations. Audit debt, model risk management overload, and shadow AI sprawl are the key challenges faced by organizations when it comes to deploying AI models effectively.
Frameworks like the NIST AI Risk Management Framework and the EU AI Act provide guidance on governance and compliance, but they require operationalization to be effective. Winning enterprises are taking proactive steps to bridge the velocity gap by codifying governance rules, pre-approving reference architectures, tailoring governance based on risk levels, centralizing evidence, and treating audit as a product rather than a hindrance.
To catch up and standardize the path to production, organizations are advised to embark on a 12-month governance sprint that involves setting up an AI registry, converting controls into pipelines, piloting rigorous review standards, expanding pattern catalogs, and integrating governance into their key objectives. By standardizing the deployment process, enterprises can maintain innovation without being bogged down by lengthy audit procedures.
In the competitive landscape of AI, the real advantage lies not in chasing the next cutting-edge model but in establishing a robust platform, implementing standardized patterns, and providing evidence of compliance. By making governance a facilitator rather than an obstacle, organizations can strike a balance between velocity and regulatory compliance in the dynamic world of AI deployment.
Jayachander Reddy Kandakatla, a senior machine learning operations (MLOps) engineer at Ford Motor Credit Company, emphasizes the importance of addressing the velocity gap in AI deployment to stay ahead in the competitive market.
