Implementing generative AI can appear to be a difficult task. According to a recent survey by the IBM Institute for Business Value, 64% of CEOs believe they need to modernize their applications before they can utilize generative AI. However, generative AI has the potential to revolutionize the process of application modernization through various automated processes, including code reverse engineering, code generation, code conversion, and defining modernization workflows. To evaluate their technology and data estates and determine the best path forward, CTOs and CIOs should:
1. Evaluate their organization’s level of hybrid cloud mastery as a foundational strategy for effective implementation of generative AI.
2. Assess the obstacles and costs associated with implementing and maintaining generative AI, as well as the costs of maintaining the status quo.
3. Weigh the costs and benefits of using general-purpose large models versus smaller, more specialized models.
4. Assess factors and costs related to data availability, governance, security, and sustainability.
5. Collaborate with HR to prioritize the role of people in their generative AI strategy.
Hybrid cloud adoption accelerates the adoption of generative AI. Over the past decade, IBM has advocated for a hybrid cloud strategy to support scalable AI-driven innovation, productivity, and efficiency. Organizations that have embraced hybrid cloud are well-positioned to implement generative AI throughout their operations. Hybrid cloud enables the use of powerful open-source large language models, access to public data and computing resources for training custom models, and secure fine-tuning of models while protecting proprietary insights. By leveraging generative AI on hybrid cloud, CIOs and CTOs can automate IT operations, modernize applications, and eliminate technical debt.
However, there are obstacles to modernization even for organizations committed to hybrid cloud. Technology leaders must estimate the financial impact of modernization across the organization and promote it as a business initiative rather than an IT project. They must address the expertise gap by prioritizing talent development and gaining cultural buy-in for modernization as a strategic business investment. Additionally, leaders need to understand the value generative AI brings to modernization and identify potential use cases. For IT operations, generative AI can automate tasks such as system triaging, issue resolution, event detection, and platform engineering.
When evaluating foundation models, CTOs should consider selecting models that deliver accurate and efficient outcomes for their enterprise. While larger models generally produce better results, smaller models that have been fine-tuned for specific tasks can often outperform larger models. Fit-for-purpose foundation models enable organizations to automate and accelerate modernization efforts. IBM’s watsonx.ai platform offers foundation models that are smaller yet perform well on business-specific tasks such as summarization, question-answering, and classification. These models can generate code snippets, automate application testing, and facilitate code conversion.
Creating a customized ROI framework for generative AI can be challenging due to the lack of mature calculation methods and comparative benchmarks. CTOs should consider factors such as pricing or licensing methods, development effort, enterprise data security, and IP and security risks when selecting and deploying models. Data availability and governance also play a role in assessing ROI.
Sustainability should be a consideration in the adoption of generative AI. Training, tuning, and running AI models can have a significant carbon footprint. Therefore, CTOs should factor sustainability into their decision-making process and explore ways to minimize the environmental impact of generative AI.
In conclusion, implementing generative AI requires careful evaluation of technology, data, and organizational factors. By leveraging hybrid cloud, prioritizing use cases, selecting appropriate foundation models, and considering sustainability, CTOs and CIOs can successfully integrate generative AI into their organizations’ operations and drive modernization efforts.
Source link
