It is well known that Artificial intelligence (AI) has progressed, moving past the era of experimentation to become business critical for many organizations. Today, AI presents an enormous opportunity to turn data into insights and actions, to help amplify human capabilities, decrease risk and increase ROI by achieving break through innovations.
While the promise of AI isn’t guaranteed and may not come easy, adoption is no longer a choice. It is an imperative. Businesses that decide to adopt AI technology are expected to have an immense advantage, according to 72% of decision-makers surveyed in a recent IBM study. So what is stopping AI adoption today?
There are 3 main reasons why organizations struggle with adopting AI: a lack of confidence in operationalizing AI, challenges around managing risk and reputation, and scaling with growing AI regulations.
A lack of confidence to operationalize AI
Many organizations struggle when adopting AI. According to Gartner, 54% of models are stuck in pre-production because there is not an automated process to manage these pipelines and there is a need to ensure the AI models can be trusted.This is due to:
An inability to access the right data
Manual processes that introduce risk and make it hard to scale
Multiple unsupported tools for building and deploying models
Platforms and practices not optimized for AI
Well-planned and executed AI should be built on reliable data with automated tools designed to provide transparent and explainable outputs. Success in delivering scalable enterprise AI necessitates the use of tools and processes that are specifically made for building, deploying, monitoring and retraining AI models.
Challenges around managing risk and reputation
Customers, employees and shareholders expect organizations to use AI responsibly, and government entities are starting to demand it. Responsible AI use is critical, especially as more and more organizations share concerns about potential damage to their brand when implementing AI. Increasingly we are also seeing companies making social and ethical responsibility a key strategic imperative.
Scaling with growing AI regulations
With the increasing number of AI regulations, responsibly implementing and scaling AI is a growing challenge, especially for global entities governed by diverse requirements and highly regulated industries like financial services, healthcare and telecom. Failure to meet regulations can lead to government intervention in the form of regulatory audits or fines, mistrust with shareholders and customers, and loss of revenues.
The solution: IBM watsonx.governance
Coming soon, watsonx.governance is an overarching framework that uses a set of automated processes, methodologies and tools to help manage an organization’s AI use. Consistent principles guiding the design, development, deployment and monitoring of models are critical in driving responsible, transparent and explainable AI. At IBM, we believe that governing AI is the responsibility of every organization, and proper governance will help businesses build responsible AI that reinforces individual privacy. Building responsible AI requires upfront planning, and automated tools and processes designed to drive fair, accurate, transparent and explainable results.
Watsonx.governance is designed to help businesses manage their policies, best practices and regulatory requirements, and address concerns around risk and ethics through software automation. It drives an AI governance solution without the excessive costs of switching from your current data science platform.
This solution is designed to include everything needed to develop a consistent transparent model management process. The resulting automation drives scalability and accountability by capturing model development time and metadata, offering post-deployment model monitoring, and allowing for customized workflows.
Built on three critical principles, watsonx.governance helps meet the needs of your organization at any step in the AI journey:
1. Lifecycle governance: Operationalize the monitoring, cataloging and governing of AI models at scale from anywhere and throughout the AI lifecycle
Automate the capture of model metadata across the AI/ML lifecycle to enable data science leaders and model validators to have an up-to-date view of their models. Lifecycle governance enables the business to operate and automate AI at scale and to monitor whether the outcomes are transparent, explainable and mitigate harmful bias and drift. This can help increase the accuracy of predictions by identifying how AI is used and where model retraining is indicated.
2. Risk management: Manage risk and compliance to business standards, through automated facts and workflow management
Identify, manage, monitor and report risks at scale. Use dynamic dashboards to provide clear, concise customizable results enabling a robust set of workflows, enhanced collaboration and help to drive business compliance across multiple regions and geographies.
3. Regulatory compliance: Address compliance with current and future regulations proactively
Translate external AI regulations into a set of policies for various stakeholders that can be automatically enforced to address compliance. Users can manage models through dynamic dashboards that track compliance status across defined policies and regulations.