Radiance '24 Round-Up: Will AI Kill 300 Mn Jobs, Is DSO Dead, and More.

From the paradoxical dance of AI as both a slayer and creator of jobs to the emergence of English as the universal language of data analytics, Radiance 2024 was a melting pot of ideas, insights, and revelations.

Join us as we journey through the corridors of this groundbreaking annual event, where boundaries blur, paradigms shift, and the future of finance unfolds in all its quirky glory.

Nestled in the vibrant city of Arlington, this year's Radiance, the largest Unconference for Finance leaders, witnessed the convergence of diverse luminaries and trailblazers, each contributing to a tapestry of unconventional ideas and visionary insights. Amidst the buzz of intellectual discourse, we’ve captured five statements that stood out boldly (and radically!), offering a glimpse into the eclectic landscape of finance's evolution.

Feel the pulse of finance's evolution, as we navigate through the corridors of Radiance 2024, where tradition meets transformation and curiosity fuels progress. Let’s jump right in!

AI will kill 300 Million Jobs and Create 2X More!

With layoffs, job role redundancies, and possibilities of AI progressing to more niche areas like decision-making, it is often reassuring to hear someone speak about AI’s role in creating new jobs. 

Our CEO, Sashi Narahari, did exactly this in his keynote address at Radiance 2024, digging into past trends and envisioning how AI will be a job enabler rather than a job-killer.

Here are the examples he quoted from the past to demonstrate that AI will open up new avenues even when some of the existing roles become dispensable.

• ATMs did not lead to fewer teller jobs: ATMs, many believed would lead to teller jobs getting eliminated. While it did reduce the number of tellers needed per bank branch, the lower costs of operations led to more branches being opened, and the teller job numbers increased!
•  Computers/internet killed jobs but created 2X more: The introduction of computers and the internet automated many workflows in the finance & accounting department. But in the next decade, 2X more jobs, including new roles in finance & accounting, were added.

While AI may impact 1/3rd of the existing finance & accounting jobs, the resilient nature of humans will help us evolve and respond to these new technological changes in the workplace and unearth new channels of production and productivity.

Finance & Accounting Professionals Will Transform into Data Scientists!

Finance & Accounting (F&A) professionals have always had their way with numbers. While many use traditional methods for calculations and reporting, in the next ten years, we expect all F&A professionals to don the hat of data scientists.

Citizen data scientists are professionals without formal training in statistics or advanced mathematics but use self-service data applications in everyday work to extract high-value insights from raw data. These experienced business professionals drive decisions using data-backed insights generated using point-and-click interfaces like HighRadius’ Freeda, Microsoft Power BI, Google Looker Studio, etc.

Tips for Aspiring Citizen Data Scientists

Here are some best practices to help you don the robes of a citizen data scientist.

• Imbibe a data-driven culture: Having a data-first approach is critical to becoming a data scientist. Validate your hypothesis and intuitions with data.
• Data governance: Use high-quality data (accurate, complete, consistent data) to generate insights. Understand basic data hygiene and data cleaning practices. Organize data and store it in easily analyzable formats.
• Structure your business problems: Structure your business problems in such a manner that you can build equations and models to find predictive answers, forecasts, etc.
• Use self-service data tools and applications: Excel need not be your sole reporting and analysis tool. Several self-service data application tools in the market help analyze data, identify patterns, and predict outcomes using point-and-click methods. Check out Freeda, our AI-powered self-service data analytics tool for finance & accounting professionals to get a hands-on feel.

English will be the new programming language!

Programming languages like Java, C, C++, etc. have a steep learning curve. Even newer languages such as Python which are considered easier than the traditional programming languages require some level of training.

But as data analytics tools evolve to support citizen data scientists, the new language for insight generation would be native languages like English (We even expect tools to serve region-specific languages like French, German, or Spanish).

Business professionals will only need to frame their questions in native languages to get the required output metrics from data application tools.

Pro tip: To master programming in English, finance & accounting professionals must train themselves on ‘prompt-engineering’ – the art of providing the most appropriate input texts to guide the application to deliver the best output. It’s similar to how we interact with large language models (LLMs) like ChatGPT today!

Over the last 10-15 years, almost everybody would tell you that it is vital that your children learn computer science, everybody should learn how to program. It is almost exactly the opposite. It is our job to create computing technology such that nobody has to program, and that the programming language is human. Everybody in the world is now a programmer. This is the miracle of AI. – Jensen Huang, Nvidia CEO

DSO is dead!?

Once considered the gold standard by collectors and credit professionals, today many believe that DSO is not the right metric to calculate collections efficiency.

In an exclusive session at Radiance 2024, the team from DXP took us on a thought-provoking journey around the efficiency of DSO.

The problem with DSO

• DSO is usually calculated for 3-6 months and becomes less accurate when calculated for shorter durations
• Seasonal trends in sales impact DSO and may not reflect the collection prowess of the team accurately
• Does not take into account the quality of receivables or the effectiveness of the collection efforts

Other collection metrics to look at!

The team at DXP suggests using the following six metrics to track AR efficiency.

• Trial balance aging: Trial balance aging or aged trial balance report is a financial statement covering the list of all outstanding receivables. It provides a one-shot picture of your receivables’ health and highlights areas/accounts that need attention.
• Days to Pay cycle: Days to Pay measures the average number of days it takes for a business to receive payment for services rendered. It helps to identify trends in customer payment promises and can be used to spot process issues, market/economic changes, etc.
• Percent auto-posting: Thismetricmeasures the number of payments posted without manual intervention. The varied forms of remittances (or even the lack of remittance data!) often result in the need for manual intervention to apply the cash. Percent auto-posting measures the efficiency of cash application and indicates the speed of cash realization.
• Team Response SLA: This measures the speed at which customer queries or requests are resolved. This helps recognize whether staffing adjustments or process improvements are needed. It is indicative of customer satisfaction in your O2C processes.
• FTE alignment: This metric helps match staffing to workload and is an indicator of FTE efficiency. It is helpful when used with other metrics like Team Response SLA to understand why your process might be slow or if your FTEs are under-utilized.
• Exception Management: Exceptionmanagement tracks the payments that failed within an automated workflow and needed manual intervention for correction. It highlights process areas that need improvement while helping identify recurrent issues and problematic customers.

You can grow your business 2X without adding headcount!

If you haven’t guessed yet on how to grow without FTE addition, let’s tell you the story of Addison Group.

Addison Group offers a comprehensive range of consulting and recruiting services for multiple industries. With a rapidly growing global client base, Addison faced challenges with invoicing and cash application. They send over 4000 invoices to 3000+ clients every week and most of their analysts’ time was spent matching payments.

One way to solve this problem would have been to add more analysts to the team. But Addison did better – and implemented a cash application automation solution to avoid having to manually match every payment. This improved productivity by 2X and enabled a single analyst to manage cash applications for the entire organization.

The analysts’ time could be repurposed toward more value-adding activities such as cash forecasting and AR reporting.

In the following years, they replicated the benefits that automation offered to other areas including collections management, invoicing, and payment processing. Despite acquiring new companies and growing their revenue twofold ($50 Mn to $100 Mn), Addison was able to grow without significant additions to their finance team headcount.

Pro tip: Here’s a quick glimpse of the path that Addison took to ensure success with AR automation (see image).

Conclusion: Embracing the Unconventional

In the world of finance, unconventional predictions can be both intriguing and thought-provoking. From AI’s dual role as a job creator and destroyer to the evolution of finance professionals into data scientists, the landscape is ripe with unexpected twists. As we anticipate the future, embracing these unconventional ideas may hold the key to unlocking new opportunities and driving progress in the ever-changing world of finance.

For more insightful sessions (recordings and slides), please check out the Radiance 2024 website.

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