To get a look at where teams are now, and where the market is heading, Corinium, in partnership with ModelOp, recently did an extensive survey of AI-focused executives. The State of ModelOps report offers compelling insights into the big challenges, trends, and strategies that are emerging.
Following are a few of the key highlights:
86% of respondents say company executives are demanding answers about the return on their AI investments—but only 38% say they can provide them.
Only 5% of respondents say they have full visibility into the models that are in production across the enterprise.
100% of respondents now have dedicated budgets for ModelOps—up from 51% in 2021.
80% say a lack of staff with the right skill sets is creating challenges, and that number grew 10% from the prior year.
Read the full report
Explore ModelOp Center — The enterprise ModelOps platform to govern and scale AI initiatives
Model Risk Industrialization
As the adoption of AI increased, so has regulatory scrutiny. While many financial institutions have risk management processes, these workflows are typically manual and require highly trained validators, which are in short supply. This panel offered a compelling look at how organizations are responding to these challenges.
The discussion offered a number of key insights:
Why teams are struggling to manage an expanding backlog of model validation, re-validation, and reviews.
How leading teams are establishing centralized, automated mechanisms for tracking, and enforcing, model compliance.
Proven strategies that have helped teams boost staff efficiency and reduce model risk.
After investing tens of millions of dollars in AI and machine learning, board members and C-level executives are understandably keen to determine what the return on those investments has been. This panel focused on helping executives establish the visibility needed to have concrete answers to these types of questions:
How many models are in production, and what business value is each model delivering?
Are models complying with all pertinent risk, governance, and regulatory policies?
Which models should be receiving additional investments—and which shouldn’t be?
To learn more about how ModelOp solutions can help your teams establish a 360-degree view of all models and their risk and value, be sure to visit the Executive Visibility page.
As the usage and scale of AI models have grown, so too have the risks. Today, improperly governed models can introduce operational and regulatory risks that expose a business to millions of dollars in financial penalties. This panel shared best practices on how teams can establish the AI orchestration capabilities needed to mitigate this risk.
Panelists shared how to:
Automate processes to enable scale—without scaling operational expenses.
Establish 24×7 operations and control of all models being used for business decision making.
Leverage existing investments by establishing integrations with existing IT, data science, risk, and business systems.
To learn more about how ModelOp solutions help organizations manage and monitor all the models they have in production, be sure to visit the AI Orchestration page.
