Analyst Perspectives

One of the promised benefits of artificial intelligence (AI), Generative AI (GenAI) and agents is that they can make everyone their own financial and business analyst. It’s true that these technologies can make it possible for everyone to access once hard-to-reach data (with suitable permissions), unleash agents to assemble the data into useful tables and charts along with commentary describing results and highlighting underlying drivers of results, propose next best actions and use natural language processing probe the numbers for further exploration. (Whew!) That’s not going to happen overnight. To achieve this potential, software providers must make it easy for everyone to get useful, reliable results. Yet, as these capabilities evolve from proof-of-concepts to standard operating procedures, the financial planning and analysis (FP&A) team must evolve its mission to remain useful and relevant in order to remain employed.

I’ve witnessed a similar situation in my professional career. I grew up in the investments business and was a securities analyst for decades. The role of the Wall Street analyst evolved steadily from the 1950s until today largely because the information technology available to analyze and forecast investment-related information grew steadily more powerful. The manual numbers-crunching work that occupied most of an analyst’s time in mid-century was eliminated by the personal computer. Conference call technology and the internet substantially reduced the role of the sell-side analyst as an information intermediary. Over the past two decades, the rise of passive investing, social media, algorithmic trading and independent specialty research firms providing real-time data (for example, drones counting cars in mall parking lots) has substantially altered the role of the analyst on Wall Street and in investment management firms. Analysts are still there and in large numbers, because what they do and how they do it have evolved with the available technologies.

I doubt there will be fewer people on average in FP&A roles ten years from now, but in a similar fashion, what they do and how they do it will be different. Those analysts that can redefine their role to fit the evolving capabilities of technology will be leaders in their field.

Enterprises hire analysts because innate analytical talents are not evenly distributed. Analysts know how to assemble information from necessary sources and, by applying critical thinking, logical reasoning, pattern recognition, curiosity, skepticism and a deep understanding of statistical techniques, they draw conclusions, make recommendations and identify issues to promote informed and objective decision-making. That will still be true in the 2030s but the tools they will use will be different.

Information technology has facilitated the work of analysts almost from the start. Today there is a wealth of tools that analysts use to streamline their work and maximize their abilities. Beyond mechanical computational automation, systems now facilitate data acquisition, automate periodic reporting, present real-time information and communicate more effectively using visualizations. Yet to be truly effective, the tools require analytical abilities to enable the user to go beyond the basics. Basics that very shortly can be handled by technology.

AI has the potential to make everyone an analyst by employing algorithms to rapidly and continuously review data and improve situational awareness, understand the underlying sources of opportunities and issues, offer options for decision-making, examine alternative courses of action and understand the potential outcomes. Like most significant technological advancements, this won’t happen overnight, but I think it will happen faster than most believe. Basic analytical capabilities are likely to be widely available as part of the feature set of general-purpose tools and also as provided by software providers as part of business applications. For example, ISG Software Research asserts that by 2028, all providers of business planning software will use GenAI to streamline commenting and annotations in tables and exhibits to boost productivity. One can be forgiven for thinking that these features were promised and promoted in the past with little to show for it. However, analytics technology continues to advance steadily, and we’ve finally arrived at a point where conditions are capable of such transformational change.

One difference today is the growing availability of easily accessed, accurate, consistent and timely data. This is a core requirement for practical and useful application of analytics and even more essential for AI. Increasingly, application providers are incorporating what I (tongue-in-cheek) call a data pantry, although each software provider has a unique name for this form of data fabric. It’s a method of data storage (and the technology and process blueprint for its construction) created for a specific set of users and use cases in business-focused software. A data pantry makes it possible to train analytical systems to reliably provide deeper analytical insights than is currently possible, generate forecasts and create sets of scenarios to explore various courses of action. It will make it possible to train GenAI systems on internal data and documents to create reliable narratives.

There’s been a great deal of talk about advanced analytics for at least 20 years. Still, our research has found that only a minority of enterprises report using predictive analytics regularly. There are two likely explanations for this lack of penetration. One is that to be useful and reliable, predictive models must be trained and retrained regularly using a consistent, relevant and reliable set of data. Techniques for automatically creating such data sources (in effect, data pantries) have only recently become available but will grow more common as providers address the demand for AI capabilities.

A second related issue is a lack of available time for analysts to perform this type of work. Our research found that analysts spend the majority of their time on data preparation, leaving much less time for them to do more useful work. Here, too, the increasing availability of dedicated data stores will substantially cut the time analysts spend on purely mechanical work. Providers must incorporate basic analytical routines into software, enabling business analysts to create models regardless of their abilities.

Technology will change the nature of the work that the FP&A organization performs, freeing up time now spent on data preparation, routine analyses and perfunctory report writing. As that happens, there will be a need to change the group’s mission, especially for those FP&A leaders that want to play a more strategic role in their organization. One important role is becoming the go-to resource for modeling, outlining and coordinating planning processes, developing and managing performance measures and metrics, in addition to reporting and communications. While that somewhat describes what the group does now, FP&A will have the time and tools to do more of it and do it broadly across all parts of their organization.

It's likely that in the not-too-distant future, everyone in an organization can be some sort of an analyst by using an “analyst-in-a-box” to do the work. The FP&A team will build models and craft processes that empower executives and managers to improve their situational awareness, investigate issues, revise plans rapidly and explore the consequences of different scenarios and potential actions. It will be the application of FP&A analysts’ modeling skills, critical thinking, logical reasoning and understanding of the business to build the analyst-in-a-box, utilizing systems provided by software providers. For example, existing technology makes it possible to make planning and budgeting easier.

However, this will take time. ISG Research asserts that by 2027, just one in five FP&A enterprises will have redefined their mission to make planning easier for business unit leaders. Those that do will be a strategic asset.

Business and financial analysts who want to play a more strategic role in their organization must understand how to utilize AI, GenAI and agents to make everyone an analyst. That requires the FP&A group to redefine its mission to reflect the significant changes that technology brings to their daily workload. They will be more productive and able to redefine their position to be more important and more consequential.

Regards,

Robert Kugel