Finance leaders are increasingly harnessing agentic AI for accounts payable automation, converting manual tasks into autonomous workflows that drive substantial return on investment (ROI). Recent statistics show that while general AI projects yielded a 67% ROI last year, autonomous agents achieved an impressive 80% by managing complex processes independently. This significant performance gap is prompting a reevaluation of how CIOs allocate automation resources.

Agentic AI systems bridge the gap between theoretical benefits and tangible results. Unlike generative AI tools that simply summarize or draft content, these agents operate under stringent rules and can execute workflows without human oversight. In fact, 72% of finance leaders identify accounts payable as the ideal application for these systems, leveraging structured data for tasks like invoice capture, detection of duplicates, and fraud identification.

Successful deployment hinges on data quality; Basware's AI is trained on over two billion processed invoices, empowering it to make context-aware predictions and distinguish legitimate anomalies. Moreover, the preference for procuring agentic AI varies; while 32% of finance leaders favor embedding AI in existing software for accounts payable, 35% prefer self-built solutions for financial planning and analysis.

Concerns around governance and the potential for job displacement are prevalent, with nearly half of finance leaders hesitating to deploy agents without clear oversight. Yet, effective governance can actually facilitate scaling and complexity in operations. Organizations employing agentic AI extensively not only report higher returns but also enjoy improved operational efficiency, allowing finance teams to focus on strategic tasks rather than manual data processing. The key takeaway is that to replicate the success of early adopters, executives must embed AI systematically into their workflows with purpose and strict governance.

Teaching Finance in the Age of AI: Embracing Change and Innovation

In an interview with Rice Business following his Financial Management Association Innovation in Teaching Award, Professor Kerry Back discussed how artificial intelligence (AI) is transforming education, particularly in finance. Echoing historical shifts in educational methods, Back noted that past technological advancements, such as calculators and Excel, sparked similar anxieties about student learning.

Generative AI, he argues, continues this trend, changing how finance is taught without altering its fundamental concepts. With AI, students can now focus less on rote mechanics and more on critical understanding, much like how calculators evolved the teaching of math.

Back’s interest in AI began when he recognized that traditional Excel tools were limiting. With AI models capable of writing and running code, students—many of whom lack programming skills—can now use sophisticated tools accessible to professionals. This has shifted teaching from demonstrating AI capabilities to having students actively build apps, workflows, and automations.

In the classroom, AI serves as an interface; students interact with it conversationally to pull data and run analyses, effectively allowing them to treat it like a colleague. Back has even implemented specific prompts, or “skills,” that guide the model through complex finance tasks.

As students engage with AI, they experience significant breakthroughs, automating tasks that once required extensive manual effort. This hands-on approach mirrors industry needs: firms are automating repetitive analyses and seek individuals skilled in finance who can harness AI for efficient tool creation. Thus, the classroom is not only keeping pace with the industry but often leading the way, enabling students to innovate without practical constraints.

Artificial intelligence (AI) is revolutionizing the finance sector by enhancing team management, process efficiency, and strategic decision-making. At the recent MIT Sloan CFO Summit, CFOs shared how AI is being applied to forecasting, budgeting, and the automation of repetitive tasks, allowing teams to pivot towards more strategic initiatives.

Noteworthy discussions included MIT Sloan professor Eric So’s interview with Shopify CFO Jeff Hoffmeister on their AI experimentation approach and professor Nelson Repenning’s talk with Arm Holdings CFO Jason Child about the significance of clean data for successful AI projects.

Key takeaways for finance teams regarding AI implementation include:

1. Focus on Use Cases: Child emphasized the importance of testing specific, smaller use cases before making significant investments in AI, citing how Arm transitioned from Excel to AI for forecasting royalties on 8 billion chips per quarter. This approach allows organizations to assess ROI effectively.

2. Understand Technology Differences: Hoffmeister noted that not all AI technologies suit every application, stressing the importance of matching the right technology to specific business problems.

3. Prioritize Data Quality: According to Child, high-quality data is paramount for successful AI deployment, with 60% to 80% of analytics project time devoted to data collection and cleaning.

4. Foster Innovation and Experimentation: A culture that encourages creativity, particularly among younger employees adept in using AI, can drive novel solutions. Child highlighted how these employees often bring refreshing perspectives on AI applications.

The MIT Sloan CFO Summit, a pivotal event since 2003, continues to facilitate discourse on the future of finance and the strategic implications of AI integration.

AI tools are becoming increasingly prevalent, assisting consumers with various everyday tasks, including personal finance management. A recent Ipsos survey for BMO revealed that 37% of Americans employ AI to manage their finances, primarily for learning about personal finance, budgeting, and saving.

Traditional financial advice can be unaffordable for many beginners and individuals with smaller portfolios. Certified financial planners often charge high fees or require significant minimum assets, creating barriers to entry. AI tools and robo-advisors offer affordable options, helping users start their financial journeys without the high costs associated with traditional advice.

These AI-powered financial platforms assist users in setting and tracking financial goals while analyzing their financial situations. By linking bank accounts, users receive personalized insights, budgeting suggestions, and alerts regarding spending habits. Notable platforms include Betterment, Monarch, and Cleo.

Betterment allows users to invest in various vehicles like stocks and bonds without a minimum balance for automated investing accounts, albeit with an annual fee structure. Monarch emphasizes budgeting and investment tracking through an intuitive interface, and its AI features provide actionable insights and recaps of financial activities. Cleo houses an AI chatbot that assists with budgeting, spending insights, and cash advances, catering to various financial needs with several pricing plans.

The appeal of AI tools lies in their affordability, accessibility, and a less intimidating environment that encourages users to engage with their finances without judgment. While these technologies provide valuable support, they should complement human advice, ensuring personalized insights and effective financial strategies. AI's integration with human expertise can enhance the overall guidance, fostering a more informed approach to personal finance.