Define your AI opportunities, deployment options and risks
Machines can now function as partners rather than as tools thanks to GenAI. This is a significant shift that may have negative repercussions. In order to reap the rewards of AI while minimizing the risks, the C-suite expects CIOs to lead the organization’s AI strategy. The stakes are high because every organization is both excited about AI and disillusioned about it. Disillusionment is caused by the fact that most AI projects have not been implemented as planned. According to research conducted by Gartner, only 2% to 5% of production deployments increased annually between 2019 and 2024, but between 17% and 25% of businesses stated that they planned to implement AI within the next year. CIOs should begin by assisting in the establishment of the organization’s AI ambition, or where and how AI will be utilized within the organization. Given that today’s AI can do everything, including decide, take action, discover and generate, it’s as important to know what you will not do.
Three essential considerations must be made in an AI strategy: AI opportunity ambition
This reflects the kind of business benefits you hope AI will bring. Using opportunity ambition, you can determine where and how AI will be used, such as in internal operations or customer-facing activities or to improve everyday tasks or create game-changing opportunities. Utilize the Gartner AI Opportunity Radar to outline your goal for opportunities. AI implementation This reflects the technological options available for deploying AI, which can enable or limit the opportunities you hope to pursue. AI can be implemented by organizations using off-the-shelf public models trained on public data, utilizing a public model and data modified with proprietary data, or developing an in-house custom algorithm trained on your data. The more customization involved, the higher the investment cost and time to deployment — yet greater customization also enables game-changing opportunities.
AI risk
AI risk comes in many forms, including unreliable or opaque outputs, intellectual property risks, data privacy concerns and cyber threats. There are also emerging regulatory risks related to the rules and restrictions that different jurisdictions may place on AI, including those related to copyright. Your company will need to define its risk appetite in relation to transparency and automation levels.
