The two top objectives for finance in 2019 are reducing operating cost and supporting the enterprise’s digital transformation, according to The Hackett Group’s 2019 Key Issues Study. It’s important to understand that the two are not synonymous, because there is clearly a lot of confusion in the market.
Case in point: In a lead article in a mid-January edition of CFO Journal claimed AI and machine learning (ML) are failing to deliver anticipated cost cuts. But cost-reduction is not at all an accurate measure of the promise of AI/ML. The two are about complimenting human with machine intelligence to enable greater insight and more effectively guide management decisions. Their value should be measured by the contribution they make to the company’s overall financial performance and competitive prowess.
Some of the confusion about AI/ML has to do with commentators mixing up different aspects of what we at Hackett call “smart automation,” or the optimized execution of work through five specific tools and technologies:
- Robotic process automation (RPA), or the deployment of software (robots or bots) that emulates human execution of routine, primarily clerical/administrative, computer tasks.
- Intelligent data capture, or the extraction of structured and unstructured information, including analog files, documents and images. This is driven by the evolution of technologies such as optical character recognition (OCR), handwriting recognition, speech-to-text, machine reading, and image recognition.
- Conversational interfaces, or tools that mimic human spoken and written communication, via technologies that focus on linguistic interaction with people, particularly chatbots, and virtual assistants.
- Cognitive automation or machine activities that imitate human judgment and perception by processing unstructured, complex, or high-volume information in order to provide insights and predictions.
- Orchestration, or the coordination and execution of work by established systems, human workers, and smart automation tools; specifically, orchestration technologies enable ticketing, case management, and performance improvement.
While next-generation ERPs and RPA are targeted at eliminating manual intervention and thus can lead to headcount reduction and process efficiency improvements, other tools in the smart automation arsenal focus on getting a better read on current and future performance and intelligent process automation.
Finance is planning to move fast up the smart-automation adoption curve. Our 2019 Key Issues Study showed RPA adoption will more than double over the next two to three years. These findings were confirmed by a poll we conducted as part of a January 25, 2019 webcast: Only 10% of finance executives reported that they are not considering RPA initiatives. Over 40% said they have active implementations. An additional 20% are in the piloting phase.
Robots can take over routine tasks that do not require any judgment calls and eliminate manual intervention while eradicating errors. The chart below is based on the results of The Hackett Group’s 2018 Digital Transformation Performance Study. It illustrates the percentage of finance organizations that are considering or implanting RPA in different finance areas. RPA is set to become most prevalent in financial operational processes such as general accounting and cash disbursement.
Meanwhile, the key issues study also projected a sharp 2.5x increase in the adoption of AI/ML. AI and ML power advanced analytics engines that can spare large amounts of data to pinpoint key business drivers and predict future performance. At the same time, AI/ML-enabled diagnostic tools allow finance executives to cut through the noise and identify process bottlenecks and performance gaps, so they can be quickly addressed. Intelligent process automation is also an effective way to break down functional silos by applying advanced analytics to a single source of financial and operational information.
Companies in our key issues study ranked cost optimization and improved customer satisfaction as their two top initiatives for the year. Margin improvement came in at 62% above revenue growth (last year’s #1) as this year’s biggest financial objectives, as companies face fiercer competition driven by growing economic unease and relentless change in the business environment.
The lesson for finance organizations is that they must not only leverage tools like next-generation ERPs and RPA to lower process cost, but also quickly embrace AI/ML technologies that enable advanced analytics, conversational interfaces, cognitive automation and intelligent data capture. The latter are critical for to providing management with better insight into current and future financial performance that’s anchored in cross-enterprise data and the right business drivers.