81% of companies have AI use cases in production, but only 15% rate AI Governance as very effective

Unlock exclusive insights into the state of AI Governance from our Responsible AI Benchmark Report 2024. The report reveals a growing gap between the rapid adoption of AI and the implementation of responsible AI Governance practices.

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Just 5% of executives have full visibility into all their AI initiatives

Generative AI is a C-Suite priority, but safeguarding the enterprise without stifling innovation is a huge challenge. Read our State of Model Operations report to learn more about trends impacting how enterprises govern and scale their AI initiatives.

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Learn more about why AI Governance is an urgent enterprise need
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Responsible AI:
Urgent Lessons for Managing Enterprise AI Risk and Reward


What is Enterprise AI?

Enterprise AI is the combination of Artificial Intelligence’s human-like learning and interaction abilities with custom-designed software catering to an enterprise's needs. This union can transform operations and decision-making within a business. When done effectively, the integration of AI into an organization's technology stack improves processes, intelligence, and capabilities in a safe, ethical, and responsible way. As companies learn to harness this transformative technology, they can drive innovation, productivity, and competitiveness on an enterprise scale.

Unlocking the transformational value of Enterprise AI requires effective AI Governance that delivers on business demands while safeguarding the organization from the technology's inherent risks without stifling innovation.

The Enterprise AI Revolution

Why AI is Existentially Important to the Enterprise

While modern AI – especially Generative AI – is still in its infancy, it has the potential to be a transformative technology, impacting society much like the Industrial Revolution or the advent of the Internet. A quick Google search for “AI Revolution” returns dozens of books, such as the highly respected The AI Revolution in Medicine. The Founder and Executive Chairman of the World Economic Forum, Klaus Schwab, writes about this concept in depth in his book and numerous articles on The Fourth Industrial Revolution.

Cloud Computing, Big Data, and AI work together to produce capabilities on an almost inconceivable scale. Unlimited computing power, and diverse data sets — both structured and unstructured — fuel comprehensive business intelligence and analytics. AI enables companies to leverage these vast data stores and take action on the overflowing amounts of information, driving operational improvements and unlocking new insights and opportunities.

Cloud Computing

Cloud computing environments play a pivotal role in supporting AI applications by furnishing the requisite computational power for processing and organizing data in a scalable and adaptable architecture.
Furthermore, the cloud facilitates broader accessibility for enterprise users, thereby democratizing AI capabilities and expanding their reach.

Big Data

AI, in tandem, depends on large volumes of data for training and deriving valuable insights.
The capacity of AI to deliver accurate predictions, distinct from replicating human biases, necessitates both a substantial quantity and high quality of data.

AI, ML, and Deep Learning

Deep learning is a computational approach that analyzes extensive datasets to uncover nuanced patterns and correlations, offering businesses a strategic advantage.

Top enterprise ai Use Cases

What Are Enterprise AI Use Cases?

An AI use case is a specific business challenge or opportunity that AI may solve. By comparison, AI systems, initiatives, applications, and models are software programs grounded in Artificial Intelligence that showcase practical utility within distinct tasks, purposes, or industries. These applications leverage AI algorithms and Machine Learning techniques to address specific needs or challenges in various sectors such as healthcare, investment management, banking, insurance, CPG, retail, and government.

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Financial Services

Fraud Detection & Prevention
Credit Scoring & Risk Assessment
Algorithmic Trading
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Healthcare, Pharmaceuticals, & Biotech

Disease Diagnosis & Imaging Analysis
Drug Discovery & Development
Personalized Medicine & Genomics
Two persons having a conversation.

Consumer Packaged Goods & Retail

Product Formulation
Customer Recommendations
Defense, Government, & Public Sector

Defense, Government, & Public Sector

Cybersecurity & Threat Detection
Intelligence Analysis & Decision Support
Predictive Maintenance and Asset Management
Three people having a conversation

Human Resources & Payroll

Recruitment & Hiring
Payroll Management
Employee Engagement & Retention
Internet cables and optic fiber on top of a laptop keyboard


Network Optimization & Maintenance
Customer Service & Experience
Fraude Detection & Security
Ship on sea


Route Optimization & Traffic Management
Inventory & Warehouse Management
Supply Chain Visibility & Risk Management
Broadcast producer looking at a panel of screens

Media & Entertainment

Content Personalization & Recommendation
Content Creation & Enhancement
Audience Insights & Advertising
Construction site

Energy, Mining, & Utilities

Predictive Maintenance
Resource Optimization & Management
Safety & Risk Management
AI Governance is Different and enables innovation

Governing AI is Fundamentally Different Than Governing Traditional Software and Data

Enterprise AI requires a new approach to governance because AI can be non-deterministic. In the past, development teams produced deterministic software and worked with operations and governance team in a static, one-time manner because once an initiative was approved, the outputs of the software stayed the same. This worked fine in the old days of classic software and regression models, but governance because a dirty word because it became a slow process and a heavy lift. However, generative AI changed the world, because its outputs are non-deterministic, which requires a new approach for how development, operations, and governance teams work together: Continuous AI Governance.

In the era of generative AI, all teams need to move fast and continually review AI outputs, because the outputs of AI can change instantly - within a day, hour, or even minute. Governance needs to be fast, efficient, and continuous to keep up with the demands of the business and the inherent risks that the technology presents. This is a seismic shift in the governance landscape, and as a result, enterprises require continuous AI Governance.

The Old Way: Static, Siloed Governance

The Old Way: Static, Siloed Governance

Governance processes are slow, siloed, and designed for traditional software development and data management. The goal is primarily risk mitigation, and fairly or not, it's why governance may be perceived as the "department of no."

The New Way: Continuous Governance

The New Way: Continuous Governance

Governance processes are dynamic, enterprise-wide, and real-time to handle changing data, inputs, and outputs. The goal is to accelerate innovation while also safeguarding the enterprise, making governance a business partner.

AI Governance definition

What is AI Governance?

Artificial Intelligence (AI) Governance is the process of assigning and assuring organizational accountability, decision rights, risks, policies, and investment decisions for applying AI. In short, AI Governance is asking the right questions and giving the answers to put the right barriers in place. (Source: AI Magazine -Svetlana Sicular, VP Analyst, Gartner)
The process applies to all decision-making models including AI, ML, statistical, regression, and rules-based.
The goal of effective AI Governance is to accelerate innovation an growth by increasing the efficiency of an organization's AI initiatives and delivering responsible AI — accountable, transparent, robust, fair, and compliant AI Initiatives at scale.
Effective AI Governance will safeguard an organization from the risks of AI without stifling innovation, and enable the business to quickly measure and report on the risks, performance, health, value, and quantifiable return on investment of all AI initiatives across the enterprise. Successfully implementing AI Governance helps organizations deliver responsible AI.
Learn about Gartner’s AI Trust, Risk, Security Management (TRiSM) framework for managing AI risk
Read more
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how to quickly get started with ai governance

Minimum Viable Governance (MVG):
How to Quickly and Confidently Protect the Enterprise from AI Risks

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Governance Inventory

Establish visibility into all your AI initiatives with a dynamic inventory that integrates with your priority AI systems

ModelOp Center dashboard

Light Controls

Implement a risk-based compliance approach and enforce the requisite controls for all AI systems

ModelOp Reporting


Report on AI usage, risks, and adherence across internally developed, proprietary, vendor, and embedded AI

Advantages of AI Governance and responsible ai

Unlock the Transformational Value of Enterprise AI

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Protect the Business

Enhanced Compliance: Enterprise AI facilitates adherence to regulatory frameworks and ethical standards, ensuring responsible AI deployment.
Risk Mitigation: AI Model Governance enables effective risk assessment and management by providing transparency, accountability, and oversight in AI processes.
Bias Reduction: Through meticulous governance, Enterprise AI aims to minimize biases, fostering fair and unbiased decision-making within organizations.
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Visibility and Efficient Management

Predictive Analytics: Enterprise AI leverages advanced analytics to predict potential risks, enabling proactive measures to mitigate and manage uncertainties.
Operational Resilience: AI applications enhance model risk management by identifying vulnerabilities, improving operational resilience, and minimizing the impact of unforeseen events.
Data-Driven Decision-Making: With data-driven insights, Enterprise AI empowers risk management strategies, facilitating more informed and strategic decision-making processes.
Hand holding a phone near screens

Scale and Innovation

Real-time Performance Monitoring: Enterprise AI optimizes model portfolio management, allowing swift adjustments based on changing conditions.
Dynamic Portfolio Optimization: AI-driven portfolio management enables dynamic adjustments to models, optimizing the portfolio to maximize performance and ROI of machine learning models.
Risk Identification and Mitigation: Enterprise AI enhances risk management within AI model portfolios, ensuring a resilient and robust portfolio.
AI Governance is an urgent priority

Generative AI Necessitates AI Governance Now

The exciting potential of Generative AI for enterprises lies in the power of business transformation. By harnessing the capabilities of Generative AI within enterprise AI Governance frameworks, organizations can achieve unparalleled efficiency, elevate customer service experiences, and unlock new possibilities to propel success and build competitive advantage in a rapidly evolving technological and regulatory landscape.

Top Generative AI Use Cases for the Enterprise

Chat-Based Interfaces Embedded in Products

Coding Assistants

Customer Communications & Contact Centers

Knowledge Assistants (e.g. sales copilot)

IT Automation & Cybersecurity

Content Development (e.g. marketing personalization)

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AI Regulations

AI Regulations are Evolving Rapidly

In the evolving landscape of AI regulations, enterprise attention to compliance is imperative. Governments and regulatory bodies are increasingly scrutinizing AI practices, particularly in the financial sector. To navigate this complex terrain, enterprises must ensure their AI initiatives consider emerging regulations. Embracing AI Governance becomes not just an operational choice but a strategic imperative, safeguarding businesses from legal risks and fostering responsible innovation in line with evolving regulatory standards.

US Office of Management and Budget (OBM) Memorandum M-24-10
The European Union's Artificial Intelligence Act (EU AI Act)
National Institute of Standards and Technology, AI Risk Management Framework (NIST AI-AMF)
UK ICO AI Auditing Framework
H.R. 2231 – Algorithmic
Accountability Act of 2019
Canada Directive on Automated Decision Making
Singapore Model AI Governance Framework
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Why enterprises need ai governance

AI Governance is a Fundamental Requirement for Responsible AI

Implementing AI in the enterprise necessitates a strategic approach encompassing comprehensive AI Governance. Initiate by defining clear objectives and assessing data readiness. Establish robust governance frameworks to ensure ethical use, regulatory compliance, and risk mitigation. Integration of responsible AI practices safeguards innovation, fostering a secure and ethical AI ecosystem within the enterprise.

AI/ML Model Governance Framework Chart
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