How AI Transforms Finance within Technology, Media, and Entertainment Companies

This article was written and contributed to the Workday Blog by Hyoun Park, CEO and chief analyst at Amalgam Insights. 

In June 2023, IDC surveyed over 600 technology, media, and entertainment executives to better understand how they seek to manage their finance, sourcing, resource management, and revenue management.1 

All of these industries are dealing with transformational pressures. Technology companies are being pushed to be profitable, a trend that has affected even the largest tech behemoths. And in the media and entertainment sectors, it’s hard to ignore the massive streaming wars as every form of media deals with digital distribution and monetization efforts. 

In looking at this data in the context of current markets, several interesting trends emerge. These markets are quite different financially, as media and entertainment companies typically spend over 60% of their receipts on the cost of goods sold compared to less than 40% for software companies (see Margins by Sector). And software companies typically provide the equivalent of 10% of their sales each year as stock-based compensation, while entertainment companies do not tend to have this expense to the same extent.

Despite such differences, executives across these industries have very similar views of the technologies they currently use to manage their planning, consolidation, and close capabilities.

Missed Opportunities for AI and ML

In general, the majority of accounting teams in these markets don’t use machine learning (ML) technologies to manage the manual checks and audits associated with reconciliation and close management. But this is a massive opportunity for AI given that we have collectively learned more about AI capabilities, and that software platforms have made efforts to provide AI-enabled guidance to support the painstaking and detailed work typically involved in creating expense and close reports.

Currently, just under 40% of technology and media executives use AI across receipts, time tracking, and approvals.¹ But these are areas with a lot of work that most people find to be either unnecessary or perfunctory in nature. 

For receipt scanning, AI could reduce manual entry, identify handwritten notes, and flag possible fraud or invalid receipt types and categories. AI can also accelerate approvals while providing clearer explanations and documentation associated with reconciling accounts and transactions. 

An even lower number of executives–about one-third– currently use AI for resource and skills management. This is going to be a fundamentally important area to track and manage with AI. Just consider that each employee provides a variety of skills, and that employment is somewhat volatile in tech departments for a variety of economic and societal reasons. 

Getting a current measure of skills available in-house or through contractors and consultants will likely require a combination of data collection and algorithmic modeling to account for schedules, projects, and worker availability. 

In addition, just over one-quarter of respondents stated that they currently use AI for journal entries and expense reports. Anyone who has ever gone through the manual review of these entries knows just how difficult it is to find these anomalies flawlessly. In a precomputer world, companies would assume a certain error rate requiring fixes after the fact. But this also seems like a missed opportunity for finance departments that currently use the time and effort of credentialed CPAs and MBAs to manage work that could likely be automated.

It’s important to note that AI availability and adoption has accelerated rapidly over the past five years. In the late 2010s, ML and AI were available, but companies struggled to access AI without having a data scientist on staff to build a custom model to support either talent management or consolidation or expense management. 

Today, a variety of algorithm libraries supports the technical development of models, along with AI teams within every major software vendor that embed AI into enterprise applications platforms. 

In 2024, companies not using AI to support anomaly detection, risk management, and more continuous management of data-driven processes are choosing a more mistake-filled financial environment, lower accuracy, and more busy work for finance and accounting employees. This is an area in which finance and planning vendors can definitely help to fill in gaps and provide embedded AI.

The recommendation is simple: every finance department needs to, at the very least, take advantage of the AI-enabled capabilities that are now standard in a best-in-breed platform around anomaly detection, basic automation driven by situational circumstances, and risk and fraud detection of noncompliant transactions and expenses. Even finance departments that do not have formal AI talent in-house are likely to have access to AI capabilities through existing vendors and should actively learn to use them. 

AI has become much more friendly and user accessible over the past few years, and the inability to support and directly build AI models is no longer an excuse for bringing AI into finance.

_{1 IDC InfoBrief, sponsored by Workday, 2023 Technology, Media and Entertainment Transition to Cloud Services, IDC #US51011123 August 2023}