The advent of generative AI (GenAI) has dramatically increased awareness and investment in artificial intelligence technologies, with enterprises saying that half of their AI budgets are now devoted to GenAI, according to a new research report from leading global technology research and advisory firm Information Services Group (ISG).
The ISG AI Platforms Buyers Guides, produced by ISG Software Research (formerly Ventana Research), say that in addition to bringing new attention to the AI domain, GenAI offers the promise of making AI much more accessible and more easily utilized in a broader portion of the workforce and the general public. According to buyer behavior research from ISG, 85 percent of enterprises say they believe that investment in GenAI technology in the next 24 months is important or critical.
The ISG report notes that hiring and retaining experienced AI professionals is the biggest IT resource challenge and the biggest impediment to enterprises adopting AI on a broader scale.
“Enterprises need objective, independent assessments of AI software providers,” said David Menninger, Executive Director, ISG Software Research. “Capabilities vary widely among these providers. The lack of AI-talented resources makes it even more difficult for enterprises to evaluate which vendors can best meet their needs.”
The most common tasks where GenAI is being applied include natural language processing (NLP) such as chatbots, copilots and assistants, extracting information from and summarizing documents, and assisting with software development tasks such as code generation and application migration. GenAI is expected to have a bigger impact in these areas than predictive AI.
While the rise of GenAI has been meteoric, enterprises still plan to invest one-half of their AI spend on predictive or traditional AI. Predictive AI is expected to have a bigger impact in areas such as credit risk, fraud detection, algorithmic trading and customer acquisition.
Developing and deploying AI models is a multistep process, beginning with collecting and curating the data that will be used to create the model. Once a model is developed and tuned using the training data, it needs to be tested to determine its accuracy and performance. Then the model needs to be applied in an operational application or process.
The study notes enterprises need to monitor and maintain the models, ensuring they continue to be accurate and relevant as market conditions change. In the case of third-party Large Language Models (LLMs), providers are constantly updating and improving their models, so enterprises need to be prepared to deploy newer models as well.
Software providers have slowly recognized that a lack of Machine Learning Operations (MLOps) and LLMOps tooling was inhibiting the successful use of AI. AI software providers have expanded their platforms to address many of these capabilities, and specialist providers have emerged with a focus on MLOps/LLMOps. ISG Software Research asserts that by 2026, four in five enterprises will use MLOps and LLMOps tools to improve the quality and governance of their AI/ML efforts.
