5 Things You Need to Know for a Successful AI Integration

Business leaders are buzzing about artificial intelligence (AI) and trying to cut through the clutter of what’s relevant and authentic, and which solutions are worth investing in. However, the forecast is clear: adopting and integrating AI will be necessary to stay competitive and support future growth. According to a Gitmux AI in the B2B Industry Statistics market data report, by 2025 the global AI market is projected to reach $60 billion, primarily pushed by B2B applications, and close to 40 percent of B2B professionals believe that AI will augment their roles rather than replace them. 

For those yet to adopt AI technology, there is some uncertainty how AI will be integrated into their existing process or technology stack. There is also a question around whether to build an AI solution in-house, or buy an AI-powered solution available on the market. Accounting and finance leaders are not immune to these concerns, either. In the latest Strategic CFO Survey by Coupa, 45 percent of CFOs plan to invest in AI to drive growth, but 89 percent have doubts about a successful AI implementation.

This article focuses on important considerations for finance leaders who choose to purchase an AI solution, and breaks down critical areas to understand for a successful integration of AI technology into their accounting function.

Understand the role of data in an AI integration

The success of an AI implementation relies on having good data. Without data to ingest and train on, AI can’t operate. In accounting, data is prolific: there are transactions, invoices, payments, vendor and customer information, budgeting, forecasting, and operational metrics to keep track of. Before integrating AI, finance leaders must ensure that data is accurate, consistent, and structured. Proper data management is critical, allowing algorithms to learn, make predictions, and automate tasks effectively. 

Accounting teams should conduct data audits to identify and resolve discrepancies, establish clear data governance policies, and ensure proper data labeling for AI applications. Additionally, they should consider their data sources — whether from internal systems, third-party sources, or manual inputs — and determine how AI will ingest and process this information. 

By having a solid understanding of the role of data, finance leaders can set a strong foundation for an AI integration.

A secure connection with your ERP

Integrating AI into an enterprise resource planning (ERP) system can unlock new efficiencies but requires careful planning. ERPs are central to accounting functions, holding vast amounts of sensitive financial data. A modern ERP and good data structure will result in a smoother AI integration. Finance leaders must ensure their ERP system can support AI applications, both technically and structurally. This involves evaluating the ERP's compatibility with AI tools, determining if additional middleware is needed, and ensuring seamless data flow between the ERP and AI systems, such as through an API.

Additionally, integrating with an ERP may require reconfiguring existing workflows, retraining staff, and addressing data security concerns. Being aware of these implications helps finance leaders manage integration risks and create a smooth transition to AI-enhanced accounting.

Avoid common AI integration challenges

AI integration in accounting comes with its share of challenges, and finance leaders should proactively identify and mitigate these obstacles. 

A common issue is data quality; inaccurate or incomplete data can lead to faulty AI predictions. Another challenge is the lack of clear objectives — defining what specific tasks AI should accomplish, such as automating reconciliations or detecting anomalies in transactions, is essential. Piloting AI to address one significant pain is often a great way to start. 

Resistance to change is another hurdle, as accounting teams might hesitate to embrace or trust AI. To overcome this, leaders should foster a learning culture, provide training on AI tools, and maintain close oversight of the project. Lastly, consider the complexity of integrating AI into existing systems; ensure you have a team with the right expertise to manage technical issues.

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Demystify security concerns around AI 

AI's increasing role in accounting raises significant security concerns and often trust issues. Finance leaders must take steps to demystify these concerns and establish robust security practices. AI systems usually require access to sensitive financial data, making them attractive cyberattack targets. 

To mitigate risks, leaders should implement data encryption, use multi-factor authentication, and ensure strict access controls. Additionally, AI systems must comply with regulatory frameworks such as GDPR and CCPA to protect personal and financial information. Selecting a vendor that works with you and your team to determine which processes are managed by AI and which are simply augmented by AI will give you more control over the implementation. It’s also critical to select a vendor that has good security practices in place, and is SOC compliant. 

Educating staff about AI security risks and promoting best practices, like avoiding phishing attempts and ensuring software updates, can further bolster security. Leaders can provide a safer integration process by demystifying AI security concerns and maintaining trust in their accounting functions.

Carefully select your AI vendor

Selecting the right AI vendor is a critical step in successful integration. Your AI vendor should be an AI-first company with a foundational dedication to AI technology. If the vendor offers a traditional accounts payable (AP) automation solution built on RPA on OCR technology and simply adds an AI chatbot, they aren’t really an AI-first company. 

Finance leaders should evaluate vendors based on their expertise in accounting applications, their AI approach, security practices, and how they handle customer support. Start by examining the vendor's track record — do they have experience in the accounting field, and have they successfully implemented AI solutions for other organizations? What is their data training model, and how do their AI algorithms work? It is important to have a clear understanding of the team’s expertise and approach to developing AI, including how often their data models are updated.

It's also important to consider the vendor's customer support and training programs, as having a solid partnership will impact the adoption and long-term success of the integration. An authentic AI vendor will continuously improve and adapt its AI algorithms to meet the needs of its customers. Don’t be afraid to ask for the product roadmap during the buying process to clearly understand how they continue to improve and innovate.  

Evaluate the vendor’s ability to customize solutions to your needs, ensuring scalability and flexibility. By carefully selecting an AI vendor, you can minimize risks and maximize the benefits AI provides.

How is Vic.ai different?

Vic.ai, an autonomous finance platform, is AI from the ground-up and was purpose-built for the AP function. There are a few crucial factors that make Vic.ai a leading solution and AI integration choice for invoice processing and payments: 

The proprietary AI: Proprietary AI algorithms are at the core of Vic.ai’s innovative approach. Trained on over a billion invoices, these algorithms enable the platform to provide highly reliable predictions on how invoices should be coded. Vic.ai goes beyond merely capturing invoice details like number, date, and amount; it also makes predictive coding decisions that typically require human judgment from the AP team. Moreover, the AI that powers Vic.ai is continuously improving. A feedback loop enhances its accuracy and confidence each time a user reviews and adjusts an invoice. This iterative process ensures that Vic.ai becomes more adept at coding invoices precisely, adapting and learning from each interaction to better meet the needs of the AP team.

A secure connection with your ERP: Data security is a critical concern for finance leaders when adopting AI technology. The Vic.ai team understands these concerns and ensures the highest level of data protection. When processing invoices, Vic.ai only extracts essential data from your ERP system to enhance predictive accuracy. This data is encrypted during transfer, ensuring it remains secure and private. Additionally, Vic.ai guarantees that no sensitive information is shared with other customers. This commitment to maintaining strict data confidentiality protects customer information throughout its use in the AI processes.

A common SaaS set-up: Vic.ai is a web-hosted SaaS solution that users can access and log into from their web browser, and no code is deployed on a customer’s internal systems. The API connection facilitates the data exchange from the ERP to Vic.ai, so the security and hosting structure are comparable to any leading SaaS solution. Vic.ai can also be deployed on premise if needed.

A focus on partnership: A good software vendor will focus on customer support and service over time, and advocate for a phased approach to the AI implementation. This ensures the data models are training correctly and the connection with the ERP is successful. The Vic.ai team closely partners with customers during implementation and go-live to monitor how the AI is training and learning on your data. Once invoices are processing at a high level of accuracy, it then makes sense to scale the software to another team or process, or leverage additional features or functionality. 

Backed by data scientists: While Vic.ai has been AI-first since the beginning, the team is always evaluating the latest in AI technology. Vic.ai has a dedicated team of data scientists who are constantly developing and fine-tuning the AI models. With notable recognition from organizations like NASA and Kaggle, the Vic.ai data scientists push the boundaries of what AI technology can do, and this investment in innovation is critical to the longevity of the Vic.ai brand and the success of customers over time.

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