3 Best Practices for a Successful Adoption of AI in Accounting

As a finance leader, it’s imperative to continue improving operations, uncovering bottlenecks, and positively impact the bottom line. Yet, there are daily challenges that most likely continue to create obstacles in achieving operational excellence. Manual accounting, financial errors, hazy forecasting, and continuous audit or fraud concerns are common in most finance and accounting teams. On the bright side, automation and artificial intelligence (AI) solutions are providing some relief and resolution for these historical pains for the accounting function. 

The Wall Street Journal recently referenced data from Deloitte’s CFO Signals survey in an article to summarize the current CFO outlook on leveraging AI: “Forty-two percent of surveyed CFOs say their companies are experimenting with generative AI, and 15% are building it into their strategy.” 

With almost half of CFOs surveyed saying they are already experimenting with AI, it’s critical to follow best practices for successful adoption and to create meaningful change in the accounting function. Data security, change management, organizational support, and executive sponsorship are all important elements to have in place for long term success.

In the Gartner^® report, Become an AI-First Organization: 5 Critical AI Adoption Phases, researchers reinforce the importance of following an adoption strategy for an AI implementation. 

Specifically, the report summarizes that “AI implementations are challenging to organizations because, rather than following known adoption phases, many of them learn the hard way how to progress. Organizations underestimate the time it takes to deploy: they start with a wrong activity for their adoption level and miss the necessary steps. What is right to do at one adoption phase could be too late or too early at another phase, causing delays of AI projects and frustration among the stakeholders. Organizations often plan way in advance and make hard commitments without gaining the right experience — this doesn’t allow them to take advantage of the latest AI trends that change dynamically.”

In this article, we outline three best practices to follow for a successful adoption of AI in accounting:

1. Identify use case for augmenting the team with AI

Ensuring there is a viable use case and opportunity to leverage AI seems like a no-brainer, however it’s important to reiterate that where you implement AI is just as important as how. Manual, mundane, and repetitive tasks are an ideal place to start - in particular areas that are time-consuming, cost prohibitive, and error-prone. For the accounting function, ideal use cases for leveraging AI technology include:

Invoice processing: One of the most time-consuming and error-prone tasks in AP is manual data entry from invoices. AI-powered systems are changing the game by intelligently extracting crucial information from invoices, including vendor details, invoice amounts, and due dates, eliminating the need for manual data entry and significantly reducing errors associated with human handling. 

Invoice matching and exception handling: Matching invoices to purchase orders and ensuring their accuracy is a critical aspect of AP. AI-enabled process mining algorithms can automatically match invoices to purchase orders, detecting discrepancies and preventing duplicate payments. 

‍Invoice approvals: Managing the approval process for invoices can be a complex and time-consuming task. AI-driven workflows can streamline this process by routing invoices for approval based on predefined rules and criteria, ensuring that invoices move through the approval chain efficiently and minimize bottlenecks.

Fraud detection: AI systems can identify potentially fraudulent invoices by analyzing patterns and flagging irregularities so team members can investigate and take corrective actions promptly.

‍Cash flow forecasting: AI can provide precise cash flow forecasts by analyzing historical payment data and supplier behavior. This enables organizations to optimize their working capital, make informed financial decisions, and plan for any potential cash flow challenges.

Predictive analytics: Leveraging AI and machine learning, AP departments can predict payment trends, identify potential late payments, and strategize accordingly. This proactive approach to cash flow management ensures that finance leaders have the insights needed to make strategic decisions that optimize working capital.

Audit preparation: Preparing for audits can be a time-consuming and stressful process. AI can assist in this regard by organizing and retrieving necessary documents, ensuring compliance with financial regulations, and providing detailed reports on financial transactions.

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2. Prepare and expand your team

With any new technological advancement, fear and uncertainty are common. Rightly so, finance leaders have concern about the impact of implementing AI technology and concerns about successful adoption within the organization. There is a common fear and misconception that adoption of AI tools will take away jobs and eliminate roles, but in reality, AI can be strategically leveraged as a collaborative business partner to handle mundane, time-consuming and repetitive tasks – the unfulfilling work that humans don’t enjoy. 

AI can automate repetitive tasks, freeing time for employees to focus on more meaningful work. It can also provide data-driven insights into workload management, helping to distribute tasks more evenly across the team and reduce stress. Changing the perception of the role of AI within an organization before an implementation is critical, and this takes time, attention, and evangelists within a team. 

Beyond preparing the team for an AI implementation, business leaders may find that they don’t have the appropriate staff on hand to lead and manage the change. Ensuring a change agent or champion is leading the project is critical to the success of the project. Amaka, an accounting solution provider for small businesses, suggests adding an Accounting Technologist to the team to support an AI implementation. This role can manage technical developers or similar IT roles, while also having intimate knowledge of the accounting business and go-forward process design. 

3. Follow an adoption methodology

While it’s possible to research and select an AI tool for accounting independently, it is worth considering following an adoption methodology or framework. Gartner outlines five critical adoption phases for AI in the research report, Become an AI-First Organization: 5 Critical AI Adoption Phases. These five phases according to Gartner are:

1. Planning - In this phase, the AI teams socialize the AI ideas to detect the most promising use cases and find a business champion who will act on and benefit from the first AI solutions.
2. Experimentation - In this phase, an initial proof-of-concept project plan is drafted and may be in pilot. 
3. Stabilization - In this phase, an AI project is in production, and an executive sponsor exists. Budget for AI is also available and protected by executive management.
4. Expansion - In this phase, AI scales up and scales out, and line-of-business projects begin to scale to the wider organization.
5. Leadership - In this phase, The organization has an “AI first” culture from the top down, which allows it to innovate with AI rapidly.

Incorporating AI technology into the finance and accounting function is an exciting project for any finance leader. Ensuring plans for AI adoption are in place and understanding how to craft an AI maturity strategy over time will only support the project’s long term success. And if managed correctly, an AI implementation can truly transform an organization and support future growth. 

‍Gartner, Become an AI-First Organization: 5 Critical AI Adoption Phases, Svetlana Sicular, Bern Elliot, Jim Hare, Whit Andrews, 13 October 2023

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