Technology
Elevate AI Management to Reflect Its Strategic Importance
Elevate AI Management to Reflect Its Strategic Importance
Published by Jessica Weisman-Pitts
Posted on November 2, 2021
Global Banking and Finance Review
Company
Published by Jessica Weisman-Pitts
Posted on November 2, 2021
By Dave Trier, VP of Product at ModelOp
Enterprise AI use cases and dependency on them have matured more than management techniques
Very quickly by enterprise technology adoption standards, artificial intelligence went from something that most large financial institutions were experimenting with to something they are depending on. AI is now entrenched for some banking and insurance operations, and the roles that depend on it are increasing as enterprises roll out new use cases and AI models. The use case expansion, program scaling and AI importance to organizations will only grow – AI could unlock an additional $200 to $300 million in value for banks alone and improve revenue in the sector by 2.5% to 5.2%, according to a 2020 McKinsey study; for insurance, the ranges are $100 to $300 million and 3.2% to 7.1%. Of course, these benefits won’t be universal, and the top performers in enterprise AI will take a disproportionate share.
The competitive advantage won’t just come from having use cases or algorithms that others haven’t thought of. Rather, improved AI effectiveness, differentiation, and ROI also comes from how models and other AI-related intellectual property are managed.
As AI matures, enterprises and technology developers alike are learning more about it. One of the clear and well-documented learnings is that the right use of AI can make companies more profitable. Another learning is that the better AI is managed within an enterprise, the more profitable it can be. This article shares some insights and recommendations on how to manage AI programs in a way that improves performance, and ultimately profitability.
AI was born in data science but it doesn’t live there anymore. Artificial intelligence is now squarely in the business domain, with IT support behind the scenes to optimize enterprise operations and, more visibly, by helping line-of-business users through reporting, alerts, dashboards, and other output. Enterprise use of and reliance on AI have grown and matured substantially in the last few years, with more models moving from pilot to production, and more business users depending on them. However, AI enterprise management has not evolved as quickly. Many organizations are still managing AI models with the same processes, people and tools they were as when they had fewer models in production and the models were less business-critical. That stretches both staff resources and the legacy and niche management tools for AI and can be an impediment to its operations and expansion.
Because AI has risen in enterprise importance, its management needs to be elevated to dedicated roles. In turn, this function should be supported with the right resources, including executive-level attention. AI has become IP you can monetize. It is time to treat it as a strategic asset, and that includes more standardized and centralized management, rather than the fragmented, department-level approach that is common now.
Leading researchers and strategy advisors, including McKinsey, Deloitte and Gartner, have found correlations between an organization’s AI management, including executive-level attention, and its AI success. For example:
Enterprises are recognizing the link between strong, dedicated AI management and value too – 44% of executives said their enterprises had centralized or standardized their AI model operationalization practices, and another 17% were actively working towards that in 2021. As part of this transition, enterprises are taking monitoring and management of models that are in production out of developers’ hands and are creating new enterprise-level roles that are responsible for this. Sixty percent of enterprises had model operators or model engineers that were overseeing AI models across the organization in 2021, which is a clear departure from the traditional approach where business units managed their own models, or the same data science teams that developed the models remained responsible for supporting them in operations.
Modernizing AI management to align with AI’s elevated strategic importance requires changes to staffing and structure, tooling and prioritization. Things will be different, but making these changes is not especially difficult. Without getting too deep into specifics, enterprises can create an effective, modern framework for AI enterprise management by doing the following three things.
Another reason AI needs more centralized and comprehensive management is that it is no longer siloed. AI model outputs are increasingly part of day-to-day business operations across the enterprise, both directly and indirectly. A company’s AI models have become key contributors to its systems of record (e.g. ERP, finance) and systems of engagement (CRM, e-commerce). In 2021, 70% of companies had integrated data from their risk management applications into their AI model operations processes, and 62% had integrated business applications. AI’s reach now extends beyond its original models. It also likely extends beyond the model developers’ ability to see and control – especially if their job responsibilities incent them to prioritize creating new models over maintaining existing ones.
When AI is successful it improves revenue and productivity. When AI is unsuccessful it raises compliance issues and other business risks. Therefore, it is useful to not view AI models for what they are (algorithms and data) but for what they do – guide lending and pricing decisions, determine risk, etc. Because AI is now so closely tied to enterprise success, its management needs to be elevated to enterprise level. That strategy can be put into practice with adjustments to the AI management structure, supporting software and executive management commitment.
About Author:
Dave Trier, VP of Product at ModelOp and their ModelOp Center product. Dave has over 15 years of experience helping enterprises implement transformational business strategies using innovative technologies—from AI, big data, cloud, to IoT solutions. Currently, Dave serves as the VP Product for ModelOp charged with defining and executing the product and solutions portfolio to help companies overcome their ModelOps challenges and realize their AI transformation.
This is a Sponsored Feature
By Dave Trier, VP of Product at ModelOp
Enterprise AI use cases and dependency on them have matured more than management techniques
Very quickly by enterprise technology adoption standards, artificial intelligence went from something that most large financial institutions were experimenting with to something they are depending on. AI is now entrenched for some banking and insurance operations, and the roles that depend on it are increasing as enterprises roll out new use cases and AI models. The use case expansion, program scaling and AI importance to organizations will only grow – AI could unlock an additional $200 to $300 million in value for banks alone and improve revenue in the sector by 2.5% to 5.2%, according to a 2020 McKinsey study; for insurance, the ranges are $100 to $300 million and 3.2% to 7.1%. Of course, these benefits won’t be universal, and the top performers in enterprise AI will take a disproportionate share.
The competitive advantage won’t just come from having use cases or algorithms that others haven’t thought of. Rather, improved AI effectiveness, differentiation, and ROI also comes from how models and other AI-related intellectual property are managed.
As AI matures, enterprises and technology developers alike are learning more about it. One of the clear and well-documented learnings is that the right use of AI can make companies more profitable. Another learning is that the better AI is managed within an enterprise, the more profitable it can be. This article shares some insights and recommendations on how to manage AI programs in a way that improves performance, and ultimately profitability.
AI was born in data science but it doesn’t live there anymore. Artificial intelligence is now squarely in the business domain, with IT support behind the scenes to optimize enterprise operations and, more visibly, by helping line-of-business users through reporting, alerts, dashboards, and other output. Enterprise use of and reliance on AI have grown and matured substantially in the last few years, with more models moving from pilot to production, and more business users depending on them. However, AI enterprise management has not evolved as quickly. Many organizations are still managing AI models with the same processes, people and tools they were as when they had fewer models in production and the models were less business-critical. That stretches both staff resources and the legacy and niche management tools for AI and can be an impediment to its operations and expansion.
Because AI has risen in enterprise importance, its management needs to be elevated to dedicated roles. In turn, this function should be supported with the right resources, including executive-level attention. AI has become IP you can monetize. It is time to treat it as a strategic asset, and that includes more standardized and centralized management, rather than the fragmented, department-level approach that is common now.
Leading researchers and strategy advisors, including McKinsey, Deloitte and Gartner, have found correlations between an organization’s AI management, including executive-level attention, and its AI success. For example:
Enterprises are recognizing the link between strong, dedicated AI management and value too – 44% of executives said their enterprises had centralized or standardized their AI model operationalization practices, and another 17% were actively working towards that in 2021. As part of this transition, enterprises are taking monitoring and management of models that are in production out of developers’ hands and are creating new enterprise-level roles that are responsible for this. Sixty percent of enterprises had model operators or model engineers that were overseeing AI models across the organization in 2021, which is a clear departure from the traditional approach where business units managed their own models, or the same data science teams that developed the models remained responsible for supporting them in operations.
Modernizing AI management to align with AI’s elevated strategic importance requires changes to staffing and structure, tooling and prioritization. Things will be different, but making these changes is not especially difficult. Without getting too deep into specifics, enterprises can create an effective, modern framework for AI enterprise management by doing the following three things.
Another reason AI needs more centralized and comprehensive management is that it is no longer siloed. AI model outputs are increasingly part of day-to-day business operations across the enterprise, both directly and indirectly. A company’s AI models have become key contributors to its systems of record (e.g. ERP, finance) and systems of engagement (CRM, e-commerce). In 2021, 70% of companies had integrated data from their risk management applications into their AI model operations processes, and 62% had integrated business applications. AI’s reach now extends beyond its original models. It also likely extends beyond the model developers’ ability to see and control – especially if their job responsibilities incent them to prioritize creating new models over maintaining existing ones.
When AI is successful it improves revenue and productivity. When AI is unsuccessful it raises compliance issues and other business risks. Therefore, it is useful to not view AI models for what they are (algorithms and data) but for what they do – guide lending and pricing decisions, determine risk, etc. Because AI is now so closely tied to enterprise success, its management needs to be elevated to enterprise level. That strategy can be put into practice with adjustments to the AI management structure, supporting software and executive management commitment.
About Author:
Dave Trier, VP of Product at ModelOp and their ModelOp Center product. Dave has over 15 years of experience helping enterprises implement transformational business strategies using innovative technologies—from AI, big data, cloud, to IoT solutions. Currently, Dave serves as the VP Product for ModelOp charged with defining and executing the product and solutions portfolio to help companies overcome their ModelOps challenges and realize their AI transformation.
This is a Sponsored Feature
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