With all the confusion in today’s audit tools marketplace, the question for accounting and audit firms is whether or not to invest in artificial intelligence.
A wide range of claims and news can be found online, from the technology “not being ready” to vendors declaring that AI is already within their tools. Ultimately, firms must decide when to invest in AI, or rather, which AI-enabled tool provides the best return on investment.
By understanding how AI fits into their technology infrastructure and audit processes, audit managers and partners can most effectively choose the right solution. It’s critical to understand what real AI and machine learning techniques mean, and the implications for the firm.
So how do firms measure their readiness for AI and assess potential vendors?
The Artificial Intelligence Business Case
Before adopting AI, firms should understand how it will improve their business and bring new value to clients. Examining business processes along these criteria will help answer how AI can help:
• How many audits are performed each year?
• What is the average size of audits, in number of transactions?
• What risk does the firm take on with those audits?
While there are significant advantages for larger firms performing many audits at scale, firms of all sizes should seriously consider using AI. The challenge for smaller firms in the past was the investment required for leading-edge technologies.
Today, we are seeing the trend towards a ‘democratization’ of AI that enables all firms to participate with more cost-effective solutions. For example, after bringing AI-enabled tools into her small firm, Samantha Bowling, CPA, CGMA, Partner at Garbelman Winslow, said, “It gives me a comfort level that I’m looking in the right place.” The firm also uses AI to decide whether to take on a new client or not by examining their general ledger for risk, and charges clients more for its use.
The greatest values of AI come with the ability to digest and understand enormous amounts of information, subjecting all transactions to an analysis that goes beyond rules-based or statistical methods. There is literally no sampling required with AI. It reduces the time and costs associated with data analysis and provides new ways of reporting on completeness, thoroughness, and the risk of material misstatements.
These factors reduce the degree of risk associated in providing an opinion to clients on the quality of their statements.
As with any new technology deployment, firms should consider how people are trained and supported by the vendor. Critical change areas to consider are how AI deals with ingesting data from client systems, mapping product workflows to audit processes, and reporting to the client. There are costs associated with this, and onboarding time needs to be allocated for both users and business leaders. It’s important to know if the AI vendor has a robust adoption program and support services that fit the organization’s needs.
Understand How AI works
Investing in new technologies is a journey and firms must understand what they are getting beyond cost and support. Knowing that the tool actually uses AI and machine learning techniques and understanding how they work are key to extracting the most value.
Unlike traditional tools, there are new questions to ask of AI vendors:
How is the “black box” problem avoided? It’s easy for vendors to claim that AI exists where it does not, or worse, use AI in a non-transparent way that makes it difficult to understand what it’s doing. Vendors should be able to explain how they utilize AI in their tool and the tool itself should explain its analysis and results.
As AI is introducing new ways of determining risk and providing value to clients, the vendor should be able to explain their algorithms and how they relate to risk assessment and reporting. They should also explain how these algorithms go beyond traditional rules-based testing and statistical methods – the real value of AI. If all they offer is Benford’s Law and weekend post checks, the value is not there.
Does the vendor have actual AI experts on staff? AI and machine learning software must be built by a combination of data scientists, machine learning experts, and programmers. Ask to meet them and find out what’s really going on under the hood.
Are there specific results achieved by actual users? If the vendor can’t provide customer references or case studies using AI, the chances for success are low.
The adoption of game-changing technology requires novel approaches to researching and vetting the solution. As AI-based tools are designed to provide a level of risk assurance that was never possible before, it’s crucial for business leaders to select the vendor that offers the best competitive position for their firm.
MindBridge Ai is restoring confidence in financial data with Ai Auditor, the world’s only AI-powered auditing solution that leverages machine learning and AI techniques to augment human capacity and redefine reasonable risk assurance.
John Colthart is General Manager, Audit and Assurance & VP Product Management at MindBridge Ai. After his departure as a corporate finance and accounting practitioner in 2000, he grew a startup to over 425 employees and exited to IBM with a role of VP Sales Operations in 2010.