Robert Kugel's Analyst Perspectives

With a year of AI-everywhere-all-the-time chatter now in the rearview mirror, finance and accounting department executives appear to be in a state of apprehension and well-tuned skepticism about the impact this technology will have on their organization. There are solid reasons to believe that the next few years will be transformative, making it important for departments to adopt a fast-follower approach to artificial intelligence. Rather than being a laggard, leaders must be ready to take affirmative steps to incorporate technological innovations as soon as they have proven practical. It’s essential for executives to have a plan for AI and generative AI adoption, as this technology is the single most important strategic issue that executives will deal with over the next three years. 

Artificial intelligence is already at work, and finance-focused AI- and generative AI-enabled applications are available. Expect a steady stream of announcements from software providers in this category this year. Ventana Research asserts that by 2027, almost all providers of software designed for finance organizations will incorporate AI capabilities to reduce workloads and improve performance. I recently wrote about the ability of AI and generative AI to boost productivity and the need for guardrails and risk management in applying this technology. 

As encouraging as the initial steps are, there also are important questions about the feasibility and timelines of having the clean data necessary to train machine learning models that are performant, accurate and up-to-date. The required data must be resident on the application platform but determining how it gets there can be an issue. For example, under what conditions is it best to source the data directly from the authoritative system of record, or should there be an intermediate data store for cost or performance considerations? While fevered minds can create seductive use cases, making the more evolved and exotic examples affordable and worth the cost is an open question. 

Enterprise generative AI systems require large language models to train on that enterprise’s corpus rather than the whole internet to produce results that are faithful to what the organization “knows” while limiting copyright infringement and other risks. In time, it’s likely that application software vendors will use multiple LLMs to support generative AI features – for both general purposes and optimized for specific purposes such as contracts, accounting and supply chain management. 

AI isn’t some far-off capability. It’s already available to varying degrees in planning and budgeting platforms. The scope, complexity and depth of AI functionality will grow steadily over the next five years. Planning software vendors are incorporating an integrated data store necessary for machine learning to support AI. This type of data store will all but eliminate time spent on data preparation tasks that occupy considerable analyst time. 

We are on the cusp of a generational change in the tools available to finance departments. Yet most enterprises are ill-prepared to adopt new tools and the methods required to use them. Our Office of Finance Benchmark Research found that 49% of departments are technology laggards while just 12% are innovative. This matters because the research also shows a correlation between technological competence and how well the department performs core processes. Organizations that are advanced in the use of technology perform accounting, analysis, cash management and tax (to name a handful) better than laggards. For that reason alone, I recommend finance executives develop technology competency in their department to successfully overcome their conservative approach to technology, which may be to instinctively act as a follower (“excellent idea – you go first”). And they need to adopt a fast-follower approach – adopting technology as soon as it is proven to stay ahead of the pack. Altering how work is performed is the best way to take advantage of the capabilities technology affords. 

Regards,

Robert Kugel