While some may say that gen AI enthusiasm is dying down, independent software vendors of enterprise applications see things differently. Recent data from Gartner, a business advisory and research firm, estimates that 80% of them will have embedded generative AI capabilities in their enterprise applications in 2026, up from less than 1% in 2023. Even if only half of that percentage is achieved by then, it would still represent a major spike in generative AI use. 

This projection came up during a presentation from Matthew Mowrey, senior director analyst at Gartner, who spoke about the company’s annual finance technology survey during the Gartner CFO & Finance Executive Conference 2024, taking place this week in London. The figure is derived from research Gartner conducted early this year that looked at 13 different enterprise application markets across four categories: digital workplace, customer relationship management, enterprise resource planning and augmentation services. The research relied on a combination of data from sample firms and overviews and summaries from analysts covering these markets. 

In terms of markets, Gartner research found generative AI capabilities are most available from vendors of solutions pertaining to digital adoption platforms; collaborative work management; intranet package solutions; meeting solutions; and visual collaboration applications. Conversely, generative AI capacities are least available in solutions involving application portfolio management tools; cloud extended planning and analysis solutions; customer communication management; content service platforms; cloud ERP for product-centric enterprises; and cloud ERP for service-centric enterprises. 

For the most part, the inclusion of GenAI within enterprise applications focuses on user experience, primarily content creation. In this realm, the principal use case is facilitating employees to write more effectively (aka “augmented writing.”) This includes drafting, in whole or part, from a variety of starting points (e.g., blank page, response to message, next section or paragraph); completion of a word, phrase or sentence; correction of spelling or grammar; or changing tone and/or voice. 

A similar, related, strong area is content consumption. Here, the principal use case is facilitating employees to read more effectively (aka “augmented reading”). This includes things like providing summaries of documents or meetings, as well as answering questions. 

Gartner said, to a very limited extent, “technology creation” (e.g., generating code or processing data), is another area of focus for vendors. In terms of technology creation, the principal use case is metadata attribution in the context of machine experience — that is, facilitating applications to process content as data. More specifically, generative AI has been used in categorization, whereby labels are attributed to content with respect to a variety of dimensions (e.g., sentiment, topic, grouping). 

Overall, this jump seems to line up with other Gartner research, such as data from March indicating that 83% of technology service providers have already deployed or are piloting generative AI, and 50% will make strategic changes to extend core offerings with gen AI to realize a whole product or end-to-end services solution.

Further, more recent Gartner research found that the adoption of finance AI by finance functions has increased significantly in the past year with 58% using the technology in 2024, an increase of 21 percentage points from 2023. Overall, Gartner estimates that, by 2026, 90% of finance functions will deploy at least one AI-enabled technology solution, but less than 10% of functions will see headcount reductions. 

In the wake of rapid generative AI adoption, Mowry said modern finance professionals need to possess both technical expertise and business acumen: data scientists should understand business processes, while business analysts should be proficient in data analytics tools. Ensuring finance staff build technology proficiency should be a top priority, as finance technology is increasingly automating through various technologies such as process mining, robotic process automation and AI, necessitating digital competencies to keep up with these innovations.