Keeping Technology Accountable: Navigating the Intersection of Accounting and AI

Emily Perkins

Emily Perkins

Head of Content Strategy

Accountable AI is a standard that business leaders should expect from their AI investments to ensure the software is accountable to the team, the business, and, ultimately, itself.

May 10, 2024

6 min read

Keeping Technology Accountable: Navigating the Intersection of Accounting and AI

Artificial intelligence (AI) is now a staple in our society, both in business and at home. Self-driving cars, smart appliances, virtual AI assistants — all exciting and available innovations. And as technology continues to evolve, and more and more AI offerings hit the market, consumers and professionals are trying to navigate which are trustworthy, and which are questionable or even potentially damaging.

Understanding AI and being able to navigate the landscape is critical for business leaders and executives. In the coming years, a substantial amount of corporate budgets will be directed toward AI technology as organizations scramble to stay on top of trends and remain competitive in a quickly shifting global economy. 

According to Fortune Business Insights, the global AI market size is projected to grow from $621 billion in 2024 to $2,740 billion by 2032. And, Gartner predicts that by 2027, spending on AI software will grow to $297.9 billion, with market growth accelerating from 17.8% to 20.4%. 

Even with all the momentum and investment in AI, some business leaders and employees are still hesitant to trust artificial intelligence to “do the job”. A recent article on the CNBC Workforce Wire covers this very topic, yet reinforces the importance of leadership being enthusiastic and up-to-speed on AI technology to ensure long-term success for their team and the organization as a whole. Yet, this isn’t a call for blind trust; critical thinking by humans is still required to vet and manage these solutions.

Many professionals are wondering, “Can I trust the AI software I purchase?” and “How will I know it’s doing what it’s supposed to do?” 

Enter “accountable AI,” a standard that business leaders should expect from their AI investments to ensure the software is accountable to the team, the business, and, ultimately, itself.

What is accountable AI?

Accountable AI means the AI is not only secure, but also transparent in a way that can be monitored, measured, and course-corrected. It continuously learns, adapts, and improves how it functions to benefit the user and customer. To help explain, let’s define AI at the very highest level: AI is a series of complex coded algorithms that replicate human intelligence actions and processes. It encompasses various techniques like machine learning, natural language processing, and computer vision to mimic human cognitive functions. AI offers automation, data extraction and analysis, and decision-making capabilities. To break this down even simpler: AI can be looked at as “smart math” that continuously learns and adapts based on previous outcomes. However AI needs data to work, and a lot of data to work well.

Here are a few ways that business leaders can hold AI accountable:

  • Having scrutiny over the foundational data used to train the AI
  • Understanding how the AI algorithms work with systems and data
  • Consistently reviewing outputs and accuracy to optimize over time
  • Feeding the AI corrections and adjustments as needed to improve accuracy
  • Welcoming feedback from software users and line-of-business workers

What makes an AI technology solution accountable?

For the many business leaders and executives who need to make strategic decisions for which AI software tools to invest in, it’s critical to understand a few elements before signing a contract. Here are some important questions to ask a potential vendor:

  • How does your AI solution fix the business problem or pain?
  • How does your AI work? Is your technology proprietary? How was it built and designed?
  • What data set drives your AI? 
  • How can I monitor the performance of the AI once implemented?
  • How can I change or modify the AI’s output?
  • How secure is my data once I integrate AI with my existing tech stack?
  • What outcomes should I expect in 6, 9, 12 months?
  • How will you support me as a customer over time?

Having a clear understanding of the use case and problems being solved is critical while exploring potential solutions. Ongoing support and services are also essential to long-term success. 

For the finance and accounting function specifically, AI automates repetitive tasks like data entry, invoice processing, and bill pay, enhancing accuracy and efficiency. By offloading these mundane tasks, AI can liberate human resources to focus on more strategic activities like analysis and decision-making, maximizing productivity and driving business growth. However, an AI solution for accounting should be purpose-built, meaning the AI should already have knowledge and awareness of standard accounting information — this is where the data the AI has trained on is so important., for example, is purpose-built for accounting, and the proprietary AI has trained on more than 1 billion invoices to develop a market-leading AI solution.

Why accountable AI is so important in accounting

Being able to trust AI solutions is critical for financial leaders, as data accuracy is imperative to cash flow forecasting and strategic business decisions. Do you invest in another business? Do you expand to a new market? Do you switch vendors? Do you sunset a product? Being able to answer these questions relies on accurate financial data, and how efficient and optimized the finance and accounting departments are.

Having control over how AI technology is being used in accounting is also important. There is a fine balance between being confident enough in the technology to handle mundane and repetitive tasks while reducing errors, yet having access and control to key software insights to understand how accurate the AI is. In short, leaders need to be able to audit the AI over time.

The autonomous finance platform, for example, can ingest, extract, and process invoice data, and then provides a confidence score for how accurate the results are. also flags anomalies and potential errors on invoices for human review. And, once the AP team is confident the AI is processing at an accurate enough level, they can turn on “Autopilot”, which gives the software the permission to work autonomously. 

However, this doesn’t mean the AP team has no more control — quite the opposite. Users decide when and where to use the Autopilot feature. And, with Analytics, team members can monitor accuracy rates, time per invoice, volume per user, and other key data to continuously fine-tune and improve collaboration and performance between the AI software and the team. 

How accountable AI impacts your AP team

Being able to confidently say an AI software investment is being held accountable to important standards is an ideal state for most organizations and leaders. AI is just another type of technology, and while more advanced and innovative than anything the business world has experienced to-date, it should be managed the same way as any other software investment — with oversight, control, and a clear operational strategy. 

For team members, in particular AP managers or specialists that may spend hours on monotonous and mundane data entry tasks, leveraging AI should be a positive experience. AI can be a collaborative partner on the team, trusted to perform repetitive work accurately and without fatigue. And, with more time to focus on strategic initiatives, AP teams can improve vendor relationships, provide better data for cash flow forecasting and analysis, and tackle more important work to support continued business growth. 

Interested in learning how offers and supports accountable AI? Book a meeting with our team.

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