Ventana Research Analyst Perspectives

We’re quickly approaching the moment when it becomes clear that artificial intelligence (AI) and generative AI (GenAI) will not be free. As that happens, we will discover who’s willing to pay how much and for what. After nearly 18 months of unlimited use-case fantasizing, it should be obvious that not all the potential applications of AI can be realized over the next three to five years because they fail a cost/benefit test. In theory, AI’s potential is almost limitless, but so far, little thought has been paid to how the economics will play out. The issue of AI costs is important because even uses that are technically feasible will be passed over if buyers find them more costly than they are worth. Business software providers, especially those serving the technology-hesitant office of finance, will need to correctly anticipate the market willing to pay for AI when making their investments. Software buyers will need to consider the current and future ability of a software provider to offer reliable AI-infused capabilities that address their requirements at a price they’re willing to pay. Cost/benefit analyses will have a strong influence on how businesses structure their strategic and tactical approaches to AI and therefore how demand for AI — especially AI for business purposes — is likely to play out in the market.

The pointy end of the technology adoption spear will be the willingness of CFOs to invest in the technology. These executives will be facing an ongoing trilemma over investing in AI in both their role as the finance department leader as well as overseeing the finances of an enterprise: Should AI be a means of cutting costs, a source of innovation and productivity, or both? And if both, what framework should they be using to determine how to make the choice? On the other side of the transaction, business software providers face the issue of where to make and prioritize AI investments. And they will confront the corollary issue of where, in order to remain competitive, they are willing to absorb ongoing development and operational costs of AI without adequate compensation.

The answer to the trilemma is: All of the above. For a current read on the willingness of enterprises to invest in the technology, ISG asked participants in its AI Buyer Behavior Study how much more they would be willing to spend on AI capabilities as a percentage of what they were currently paying. The research provides insight into how enterprises currently expect to gain value from using AI and, therefore, their propensity to pay for AI capability in applications. The research finds the greatest inclination to spend is in sales performance management, which I interpret to mean that the participants see this area as having the greatest potential to generate profit through gains in sales productivity and therefore greater revenue. The next five include supply chain management (to cut costs), treasury and risk management (for more accurate cash flow forecasts, to reduce risk of fraud and credit losses as well as cut the cost of regulatory compliance), IT service management (to cut costs), analytics and business intelligence (BI) (to gain productivity) as well as procurement (cut costs). In other words, enterprises are willing to pay for productivity gains that clearly generate revenue and easily achieve cost savings or reduced risk. Those offerings that promote productivity without a direct connection to higher revenue or cost savings, or where the cost savings are perceived to be limited or difficult to achieve, will not make the cut.

Although AI is likely to positively impact an enterprise’s operational and financial performance, investment decisions will vary. Experience suggests that the realizable gains will differ across industries, location, the size of the organization and their technological competence. Moreover, no matter how attractive the proposition is, CFOs will be constrained by their financial position in making investment decisions. So, those industries and enterprises that operate with thin profit margins are more likely to favor initiatives that clearly cut costs over those that might achieve a strategic advantage. That is, unless not having AI at work poses an existential competitive threat. Enterprises with between 100 and 1,000 employees are unlikely to lower headcount through productivity gains because people in these organizations typically wear multiple hats, so investments that favor productivity (and reduce the need to hire people in the future) should be attractive. In North America, the shortage of skilled accountants that is likely to persist will encourage departments to find ways to increase productivity. In charting their product strategy and making R&D investment decisions, software providers will need to take their customers’ needs and constraints into account while factoring in the investment direction of their competitors.

ISG-Ventana Research asserts that by 2027, almost all providers of software designed for finance organizations will have incorporated some AI capabilities to reduce workloads and improve performance. Yet, it’s not clear how much finance and accounting departments will be willing to pay for AI. The sort-of good news is that many of the initial uses of AI for the department are relatively inexpensive to develop and operate because they are straightforward applications of predictive analytics requiring limited compute power. These include supervisory tasks that spot outliers and omissions and, therefore, potential errors at their source, accelerating forecasting and planning, facilitating the production of analytics, reconciling data from disparate sources and automating data entry from paper and electronic documents. Each of these instances offer "collective impacts," where the combined or overall impact of many small individual actions or occurrences is significant when measured in avoided costs, productivity gains or organizational performance.

The use of GenAI may prove to be trickier from a cost/benefit standpoint. I expect that all software providers will need to offer some sort of copilot or digital assistant for competitive reasons, but it’s not clear how well these capabilities can be monetized. Some GenAI features may be included in the software’s subscription price, while more advanced ones requiring more compute power or more costly resources may require additional fees. Software providers with a larger installed base may be at an advantage, having the ability to fund development efforts to secure broader and deeper capabilities sooner, which will affect market shares and industry consolidation through the end of this decade. Alternatively, smaller software providers may be able to source ranges of advanced GenAI capabilities to offset that advantage.

The impact of AI and GenAI remains a classic undetermined linear algebra problem, the kind with fewer equations than unknowns and therefore one with an infinite number of solutions. The technology will transform how enterprises operate, and those that can harness its power best will be more competitive than those that lag in their adoption. How well software providers develop and market AI-infused capabilities will be a key factor in determining the shape of the industry at the end of this decade. Yet, buyers and sellers of the technology must obey the physics of finance in setting investment priorities.

I strongly recommend that CFOs and their finance and accounting departments have a clear understanding of the benefits of AI, how to achieve them and what they’re worth. 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 their methods. Our Office of Finance Benchmark Research finds 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. I recommend finance executives develop a fast-follower approach to technology adoption and instill this competency in their department. They must 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 to using AI — adopting technology as soon as it is proven to stay ahead of the pack.

Regards,

Robert Kugel