Fulfilling the Promise of Generative AI 10 Moving From Development to Production “ Only 40% of organizations have a well-defined, formal process With MLOps for moving ML models into production environments.” Only 40% of organizations have a well-defined, formal process for moving Figure 4. Top Challenges When Managing the Deployment and Monitoring of Models ML models into production environments, highlighting the fact that organizations are not yet where they need to be when it comes to MLOps. And while another 48% have some formalized processes, they admit Difficulty managing multiple environments 35% improvements are needed. Either way, many organizations acknowledge Difficulty ensuring compliance with corporate that they face several significant challenges in managing deployment and governance policies 33% monitoring of models. At least one-third of organizations say they are confronted with issues relating to managing multiple environments (35%), Difficulty detecting and responding to data drift 33% ensuring compliance with governance policies (33%), and detecting and responding to data drift (33%).7 Even more important is the need for organizations to step up their Inconsistent model performance in production 29% progress toward improving the time to value of AI. For instance, over a Difficulty detecting and responding to model failures 29% three-year period, organizations said their ability to start seeing value from their AI initiatives immediately increased only slightly, from 7% to 11%. On a more positive note, the percentage of organizations saying they started Inefficient retraining processes 26% seeing value within one to three months jumped from 32% to 61% over that same three-year period.8 Difficulty managing dependencies 26% As important as these data points are in highlighting the challenges organizations must overcome in their GenAI journeys, many of the key challenges are quite visceral, prompting an emotional response about issues Using GenAI in a way to create Achieving the desired Ensuring the highest level of Achieving the anticipated that tend to keep executives, board a sense of responsibility, outcomes as rapidly and data security and protection level of financial return members, and stakeholders up at confidence, and trust. Doing efÏciently as possible. of sensitive, proprietary and business insights. night. These include: the right thing matters. Speed matters. information. Privacy matters. Results matter. © 2024 TechTarget, Inc. All Rights Reserved. 7Ibid. Back to contents 8Source: Enterprise Strategy Group Research Report, Navigating the Evolving AI Infrastructure Landscape, September 2023.