Using AI finance tools is the absolute best way to optimize your working capital management. Check out our interview with Ray Wang to see why.
This is the first article in our Frictionless Finance Blog Series:
How to Optimize Working Capital Management with AI Finance: Q&A with Ray Wang Part 1 “Digital Darwinism is unkind to those who wait.”
We’ve entered a new age in business where guesswork no longer has any place in finance. The emergence of AI-powered process mining technology has set the stage for an era of objective and data-based decision making, actionable metrics, automated improvements, and proactive analytics.
With more pressure than ever put on finance leaders to remove operational friction across departments, and 52 percent of all Fortune 500 Companies having gone bankrupt since 2000, it’s clear that companies who aren’t adapting to the new technologies in place in order to optimize their working capital and accelerate their enterprise will not survive the age of what Ray Wang, VP of Constellation Research calls “Digital Darwinism.”
And after years of research and experience proving the extreme advantage finance departments that adopt AI finance software over those that don’t, Wang can say with certainty that the age of Digital Darwinism is unkind to those who wait.
Imagine a world where all your finance processes are working in perfect harmony, free from bottlenecks and inefficiencies. A world where each financial operation is digitally transformed to be in a constant state of improvement, becoming more and more intelligent until all unnecessary manual touches are replaced with automation, the need for rework is eliminated, and decision making is accelerated across all departments.
A world where AI identifies all the bottlenecks, inefficiencies and opportunities across your finance processes ahead of time, alerting you to all upcoming risks and opportunities. This is the world of Frictionless Finance, made possible by AI technology and process mining.
Businesses that adapt to process mining and Frictionless Finance gain an exponential advantage, raking in 60-70 percent of the market share, and 50-60 percent of the profit. Those that don’t are jeopardizing their existence in the evolving ecosystem of finance. Simple as that.
To help, I sat down with Ray Wang, Principal Analyst and Chairman of Constellation Research, to get answers to the most frequently asked questions he receives from financial leaders around the world who are ready to start their journey towards Frictionless Finance.
A: The trick here is to start cutting down frequent payment levels. Focus first on improving the finance processes that drive sustainable change in your business. These include processes where all the little details live that are crushing your working capital. Specifically, I’d start by exploring your customer cash, forecast to fulfill, procure and purchase to pay (p2p) processes.
Finance leaders can start optimizing their working capital management system by looking into processes like costs of goods sold, for example, to figure out how to get a reduction in spend. They’re looking into areas where they can reduce transportation costs and lock down on write offs.
This process usually starts with goals including increasing sales efficiency, minimizing process transaction costs, establishing standardization and automation, reducing costs of capital, increasing trade receivables, improving capacity utilization, etc.
A: Another place to start is evaluating how they use data systems to make productive financial decisions. They can ask themselves: Am I identifying large numbers of duplicate suppliers in your systems? What efforts am I taking to make up for your lack of standardized payment terms? What do we do when we find multiple payment terms within the same supplier?
Most of this data comes from manual systems that we’ve set up, but rarely analyze to look for problematic patterns and behaviors. That’s where the beauty of AI process mining technology shines through.
New AI-powered tools provide complete transparency into these processes and their hang ups, leading many to discover opportunities that they were never able to see before. With that understanding they can provide solutions to optimize their operating working capital long-term.
A: Building context and relevance in your process data is a must for successful long-term improvement. These are the key drivers that are helping businesses automate finance processes, draft more efficient initiatives, and deliver the right message to the right people at the right time to realize business value.
Context includes all factors occurring throughout each stage of a process, including: Time Location Relationships Weather Conditions Roles Intent Sentiment
All these factors drive relevancy inside AI systems, resulting in more intelligent finance operations over time. This in turn leads to more effective process improvement initiatives, and long-term success.
A: Using AI finance software isn’t about eliminating human choices and roles. In fact, it does just the opposite: it gives your staff more opportunities to collaborate over more choices than they could possibly know they had, and make faster, more intelligent decisions along the way.
Anticipatory analytics—also called intention-driven design—is the main driver of choices in the age of Frictionless Finance. With actions and predictions based on data-based insights, instead of emotions and guesses, finance teams are not stripped of choice, but empowered with the ability to break from patterns and they may not have been aware of prior, and the foresight to avoid threats and risky situations before they occur.
Picture this: you’re a race car driver, and it’s raining on the day of your big race. There’s actions you and your pit crew immediately need to make, based on past patterns and experiences racing in wet conditions. You may need to change your tires to get more grip, change to your positioning strategy, account for your loss of visibility, and even consider what other race cars have done to face similar conditions in the past, and the best practices they used to succeed.
Now imagine we could apply process mining to race car driving, and you discover that there’s an entirely different process to prepping your car that estimates a 98% chance of success in slippery conditions. Would you choose your traditional pattern, from which you simply can’t quantify your chance of success, or try a new method with promising odds?
