CIO OPINION
Rita Sallam , Distinguished VP Analyst at Gartner
Costs of Generative AI will soon exceed its value
Generative AI promises unprecedented productivity improvements and business transformation opportunities , but calculating the value of new investments in GenAI requires you to build a business case by simulating potential cost and value realisation across a range of GenAI activities aiming for a mix of quick wins , differentiating use cases and transformational initiatives .
Gartner predicts that by 2025 , growth in 90 % of enterprise deployments of GenAI will slow as costs exceed value . Lean on use cases that leverage generative AI within industry or custom applications that allow you to leverage enterprise data in unique ways to extend current processes .
Gartner Predicts by 2028 , more than 50 % of enterprises that have built large AI models from scratch will abandon their efforts due to costs , complexity and technical debt in their deployments . Transformative use cases come with higher cost , complexity , risk and potential for technical debt .
To ensure quick wins for GenAI , focus on potential productivity improvements . These will likely come from productivity assistants , such as Microsoft 365 Copilot and Google Workspace .
Such activities are easy to get started , pilot and buy but are usually task-specific , so measure and value time saved for both those specific tasks and across aggregate tasks related to specific processes – within specific time periods .
Productivity improvements alone may be a diminishing source of differentiation over time , but integrating these capabilities into other business processes can help enterprises maintain a competitive edge . These differentiating initiatives provide a more defensible competitive advantage than quick wins but come with higher and more unpredictable costs and risk .
Direct and indirect financial benefits , including the potential for revenue generation , can generally offset costs , assuming effective underlying process redesign , upskilling and risk management .
Ongoing innovations in GenAI are refining models and techniques and bringing down adoption costs ; however , until lower-cost options emerge , innovators may have to accept difficult-to-quantify hard financial returns and higher cost , complexity and risk in exchange for first-mover advantage . Investment decision criteria should prioritise strategic benefits that may be difficult to quantify in financial terms over immediately identifiable task- or process-specific financial benefits .
Leverage pilots to assess value and cost in addition to technical feasibility .
a way that minimises its carbon footprint without compromising accuracy or value . By using such an approach , digital organisations can select and utilise AI techniques and methods in an energy-efficient way , resulting in a smaller environmental footprint and fewer greenhouse gas emissions .
An example of this could be the use of AI-powered smart grids by researchers of MBZUAI in the UAE to optimise energy use and local energy sharing to reduce load on and disruption of energy services . The Al Shera ' a , the new headquarters of Dubai Electricity and Water Authority , DEWA , is another outstanding example . It has incorporated cutting-edge solutions such as Internet of Things , IoT , Artificial intelligence and Fourth Industrial Revolution applications in order to be more efficient .
As a result , it is anticipated to use up to 50 % less water than its counterparts and is intended to become the tallest , largest and most advanced government net zero energy building with net zero carbon emissions in the world .
Sustainable AI is a great initial step towards the collaboration between AI and Sustainability . Ultimately , this joining of forces will aid in increasing ESG compliance and as well as positively contribute to United Nations Sustainable Development Group ’ s , UNSDG ’ s goals . With this , businesses can generate value and enhance traditional operations whilst also reducing their global environmental footprint . p
Business leaders need to build a portfolio of generative AI quick wins , differentiating and transformation use cases . Combine initiatives with hard ROI with loss leaders and those delivering transformation benefits and competitive advantages that are difficult to initially quantify directly in financial terms .
of the AI engineering process , such as ensuring the reusability of assets and accounting for electricity usage for data storage , training and running AI models , as well as water consumption .
AI can become environmentally sustainable through techniques that help create and run a model in such
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