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Defining AI Opportunities, Deployment Options, and Risks for Your Organization

GenAI has transformed machines from mere tools to valuable teammates. This shift, while promising, carries inherent risks. The C-suite now looks to CIOs to spearhead AI strategies, leveraging AI benefits while mitigating potential downsides.

The landscape is fraught with excitement and disillusionment. Despite high interest, many AI projects fail to meet expectations. Gartner research reveals that between 17% and 25% of organizations planned to deploy AI annually from 2019 to 2024, yet actual deployment growth hovered between 2% and 5%.

To improve success rates, CIOs must start by defining their organization’s AI ambition—determining where and how AI will be utilized. It's crucial to also identify what AI will not be used for, as today’s AI capabilities are vast, from decision-making to generating new content.

Key Elements of an AI Plan

AI Opportunity Ambition

Identify the business gains you aim to achieve with AI. This involves deciding where (e.g., internal operations, customer interactions) and how (e.g., optimizing tasks, creating new opportunities) AI will be applied. Utilize tools like the Gartner AI Opportunity Radar to map your AI ambitions.

AI Deployment

Evaluate the technological options for deploying AI, which can either enable or constrain your goals. Options range from using public, off-the-shelf models to building custom algorithms tailored to your data. More customization generally means higher costs and longer deployment times, but it also offers greater potential for innovation.

AI Risk

AI risks include unreliable outputs, intellectual property issues, data privacy concerns, and cybersecurity threats. Additionally, emerging regulations could impose new restrictions. Define your organization's risk appetite, considering automation levels and transparency.

AI Use Cases: Everyday vs. Game-Changing AI

AI applications fall into two categories:

  1. Everyday AI: Enhances productivity by making current tasks more efficient.
  2. Game-Changing AI: Drives creativity and innovation, potentially disrupting industries with new products or services.

Determine which combination of everyday and game-changing AI your organization will pursue, both internally and externally. Investment levels will influence these decisions, as game-changing AI often requires significant resources.

AI Investment Scenarios

  1. Defend: Focus on quick wins that improve specific tasks. This low-cost approach helps maintain the status quo.
  2. Extend: Invest in tailored applications that provide competitive advantages. These are costlier but offer greater value.
  3. Upend: Develop new AI-powered products and business models. These high-risk, high-reward investments could transform your industry.

Feasibility and Readiness

Ensure business executives understand the feasibility of AI projects. You can't capitalize on opportunities without the right technology and readiness from both internal and external users.

AI Deployment Options and Trade-offs

The past six months have seen a surge of new AI models and tools. Here are five emerging approaches for deploying AI:

  1. Consume embedded AI: Use established applications with added AI capabilities (e.g., Adobe Firefly).
  2. Embed AI APIs: Integrate AI via APIs into custom applications.
  3. Extend models with data retrieval: Use internal data to enhance AI model accuracy.
  4. Fine-tune pretrained models: Customize large models with specific datasets.
  5. Build custom models: Develop models from scratch tailored to your business.

Each approach has its trade-offs in terms of cost, knowledge, security, and implementation simplicity.

Articulating AI Risk Tolerance

Define the risk levels your organization is willing to accept regarding AI reliability, privacy, explainability, and security. Work with each CxO to establish acceptable risk levels for their departments, ensuring alignment with AI opportunities.


Balancing AI opportunities with associated risks requires a clear strategy and cooperation across the C-suite. By defining AI ambitions, understanding deployment options, and articulating risk tolerances, CIOs can lead their organizations towards successful and responsible AI integration.

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