Follow these steps to successfully carry out your Finance AI strategy and reap the benefits in processes and workflows across all business functions.
May 3, 2023
7 min read
How the agile CFO can successfully implement a Finance AI strategy
Scandals in the early 2000s brought drastic reform to corporate finance practices when the Sarbanes-Oxley act was passed to stop companies from “cooking the books.”
This marks the genesis of the agile CFO. Companies needed trusted finance leaders, which accelerated the career path for Controllers to enter the C-suite with their CPA credentials—a new requirement from boards adhering to SOX compliance.
To meet the demands of the complex SOX endeavor, finance leaders sought tools that would simultaneously increase efficiency and control amid the great recession. Budgets were slashed and limited resources required creativity with automation.
Today, we’re facing similar challenges as economic turmoil impacts businesses across the US. Companies are balancing low margins, layoffs are consistently in the headlines, and audit and accounting shortages are negatively impacting shareholders.
The difference between the beginning of the 2000s and now brings optimism. Finance teams have twenty years of automation experience under their belt and the CFO tech stack has evolved to new productivity heights.
Today, Artificial intelligence (AI) is transforming the finance industry in ways that were once unimaginable. From trading algorithms to fraud detection, AI is changing the way we think about finance. With the ability to analyze vast amounts of data and make predictions based on that data, AI is revolutionizing the industry and creating new opportunities for businesses and consumers alike. In this article, we'll explore the basics of Finance AI and how it's changing the game.
The three waves of the CFO Tech Stack
According to Bain Capital Ventures, there are three decades of notable transitions the CFO role has experienced in the 21st century. The first wave was triggered by SOX reform, then technology-driven processes matured during the second wave, and CFOs went from the safe set of hands who satisfied auditors to forward-thinking leaders who not only manage risk but also focus on positioning companies for long-term growth.
In 2023 finance leaders wear many hats and continue to increase oversight over key areas of businesses, especially technology adoption. As machines go beyond basic number crunching, entering the realm of decision-making via artificial intelligence (AI), finance teams are tasked with developing trust in machine “colleagues.” As you can see in the "CFO Tech Stack Evolution" image above, Vic.ai's AP Autonomy platform is part of the third wave, which goes beyond automation by leverage complex data to delivery autonomous or self-driving accounting and actionable insights.
"We’re increasingly seeing companies use advanced data and benchmarking to not only increase accuracy but also actionability. Companies enabling this include Vic.ai, which is leveraging a unique data set to provide intelligent spend and bill payment capabilities that can identify deviations in typical patterns or ingest a new invoice template sent by existing suppliers," said Tina Dimitrova, Investor at Bain Capital Ventures.
Trust doesn’t come easy and rightfully so, for finance leaders who must guard their budgets and sustain crucial controls. With the exponential growth of AI tools on the market, how do CFOs make sense of countless options? Many leaders wonder if AI can live up to the hype and address their problems when they’ve seen disappointing results from tools that claimed they had AI in the past or haven’t explored the technology before.
According to Deloitte, enterprises that have an AI strategy are 1.7 times more likely to achieve their goals than those without. To not only survive but thrive throughout economic uncertainty, finance leaders must consider where their company will be in one to five years from now if they don’t invest in simplifying cumbersome and costly processes.
Benefits and challenges of Finance AI
While AI has the potential to revolutionize the finance industry, there are also challenges that come with its implementation. One of the biggest challenges is the need for data privacy and security. With AI relying heavily on data, it's important to ensure that sensitive information is protected from cyber threats. Additionally, there is concern about the potential for AI to replace human jobs in the finance industry. However, proponents argue that AI can actually enhance human capabilities and lead to new job opportunities in the field. Ultimately, the benefits of AI in finance, such as increased efficiency and accuracy, outweigh the challenges.
Despite these challenges, the benefits of AI in finance are undeniable and will continue to shape the industry in the years to come.
Finance leaders need to act now to meet the new market challenges. Here are four steps to reach your goals with an AI strategy.
4 steps to build your Finance AI strategy
(1) Define the outcome
No matter what business function you’re looking to improve, automation could actually increase costs if you don’t take the time to make a business case and articulate the outcome you want to achieve. Do you want to increase efficiency? Is your goal to reduce costs and stop relying on offshore teams to manage manual data entry? Define the problem you need to solve, then create key metrics to measure the performance of whatever tool you choose.
(2) Assemble an AI buying team
Answer these questions to help you gather the right people for this journey.
Who will manage the project?
Who needs to be involved with vetting the tool and providing their opinion?
Who has the capacity and the knowledge to make your AI strategy successful?
Do you have a partner or need outside expertise?
(3) Ask the right questions
Collaborate with your CIO to ask the right questions for appropriate due diligence. Create a thorough software requirement document and don’t stray away from the core competencies and goals your team has defined.
“One of the absolute key relationships within an organization is between the CIO and the CFO because the mission of the CIO is to make sure that the organization is running not only efficiently, but effectively, and that’s the same thing the CFO is concerned about,” Russ Porter, CFO of the Institute of Management Accountants, said in a CFO Dive interview.
Here are some key questions to as artificial intelligence vendors:
Does it require more headcount to manage?
Is the software reliable in making predictions?
What are the AI predictions and how do they work?
Does it eliminate manual work and rely on human task execution or just human review?
When investing in a Finance AI platform, confirm that the company behind the AI platform is moving in that direction—where a human will be primarily reviewing the AI’s suggestions, rather than the human performing the coding and review. For example, legacy AP automation still requires humans to manually sort invoices and allocate them to the correct property or entity, whereas Accounting AI executes the sorting and makes the invoice allocation, so the Accounts Payable Associate only has to review that decision if the AI communicates low confidence.
Remember to ask for proof. If you don’t ask for the evidence of accuracy upfront, you may be unpleasantly surprised by the amount of time it takes to manage the system that was supposed to “give you back more time into your day.”
(4) Look past the buzzwords
Be cautious of technologies that advertise a one-stop shop to solve all of your needs with rules and templates. Solutions that rely on rules and templates are not AI. If you currently use an existing ERP or automation tool that is advertising a new AI feature, make sure it's not just RPA (Robotic Process Automation) and/or OCR (Optical Character Recognition). While these technologies are beneficial in their own right, they rely heavily on your team and their hidden costs are often overlooked during initial evaluations.
Growing exception queues from broken rules, year-long onboarding processes with large internal onboarding teams, thousands of templates created and maintained (in perpetuity), and the list goes on. Companies ultimately end up having to restart their automation journey after implementing tools they believed to be the “next great AI solution.”
True AI is autonomous - it requires limited or now human maintenance and works for you silently in the background. Here are some keywords and phrases you can be on the lookout for in conversations, that are dead giveaways of RPA + OCR solutions that are rules and template based.
Invoice turnaround time of more than a few seconds
Any “AI” technology leveraging a template
Leveraging only the last invoice from a vendor to code the next (i.e. memory based coding)
An in-house managed service team that “reviews” predictions before your team sees them
No in depth historical master data training
Follow these steps to successfully carry out your AI strategy and reap the benefits in processes and workflows across all business functions. These steps are not an exhaustive list and leveraging your buying team, plus your CIO’s expertise will help you make confident decisions as you navigate this new territory. Remember to ask the tough questions and get proof. If you’re looking into invoice processing AI, ask the vendor to test your invoices and show you how the AI platform processing, understands, and codes them. Historical master data training is a key to a successful and seamless Accounting AI implementation.
3 ways to unlock faster and more accurate accounting
Do you feel confident when you close your books? Month-end close is a hectic time, which brings stress and room for data discrepancies. 49% of finance teams reported having to re-open the books three or more months out of the year due to errors.
Inaccurate invoice coding that doesn’t match your master vendor data or chart of accounts sets your team up for failure. Free your finance department from the ongoing guesswork caused by manual processes—from paper invoices to clunky accounting systems. Click the image below to watch the Autonomous Business Intelligence discussion and see a live demo of how the Finance AI actually works.