Webinar

When AI Delivers: How Finance Teams Move from Pilots to Real Impact

As AI adoption accelerates across finance, many teams are discovering that implementation - not interest - is the real barrier to success.

This webinar brings together finance and operations leaders for a practical, no-hype conversation about what it actually takes to turn AI into reliable, scalable outcomes. MegaCorp Logistics shares how they used an AI-first approach to achieve real-time value across their AP operations.

IFOL _ Virtual (2)

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When AI Delivers: How Finance Teams Move from Pilots to Real Impact

As AI adoption accelerates across finance, many teams are discovering that implementation — not interest — is the real challenge.

Turning AI from a promising idea into something reliable, scalable, and genuinely useful is where things often become complicated. In this webinar, finance and operations leaders shared practical, real-world insights on how organizations are successfully moving AI initiatives from experimentation to measurable operational impact.

Topics discussed included:

  • Building trust in AI systems across finance teams
  • Scaling AI without adding unnecessary complexity
  • Managing organizational change effectively
  • Improving AP efficiency while maintaining control
  • Defining measurable success and ROI

Featured speakers:

  • Katie Roy — Head of Product, Vic.ai
  • Paul Taylor — Order to Cash Education Lead, IFOL
  • Hailey Ahrens — Accounting Manager, MegaCorp Logistics
  • Mook Cahill — Staff Accountant, MegaCorp Logistics

Key Discussion Highlights

AI Adoption in Finance Is Still Early — But Accelerating

The panel discussed how many finance organizations are still in the exploration phase when it comes to AI adoption. Katie Roy noted that accounting and finance teams often approach AI cautiously due to risk and accuracy concerns: “Testing the waters is completely fine. In accounting, adoption tends to move more carefully because of the risk analysis involved.”

Hailey Ahrens added that many smaller finance teams also worry about AI replacing jobs, which can create hesitation early on: “It can feel scary at first, but AI isn’t a replacement tool — it’s a tool to help people.”

Why Organizations Begin Exploring AI in AP

For many finance teams, the push toward AI is being driven by increasing workload, limited headcount growth, and frustration with manual processes.

Katie Roy explained that organizations are often being asked to “do more with less” while improving efficiency and scalability: “Companies come to us because they need a solution that doesn’t require hiring more people — but they still want their teams focused on higher-value work instead of data entry.”

MegaCorp Logistics shared that reducing manual AP work and improving scalability were key motivations behind their AI initiative.

The Importance of Fixing Processes Before Automating

One of the strongest themes throughout the conversation was that AI should not be used to fix broken processes.

Paul Taylor emphasized: “Don’t try to automate a poor process. Fix the process first, standardize it, and then bring AI into the workflow.”

The panel agreed that successful AI implementations start with clearly understanding the operational problem that needs to be solved.

How MegaCorp Logistics Achieved Faster AP Processing

MegaCorp Logistics implemented Vic.ai to automate and streamline AP workflows while keeping their accounting team focused on more strategic work. Within six months of implementation, the team reported:

  • Reducing invoice processing time from 5–6 minutes down to approximately 2 minutes per invoice
  • Increasing no-touch invoice rates to over 40%
  • Reducing weekly invoice processing time from 35 hours to 7 hours
  • Supporting significant invoice growth without increasing headcount

Mook Cahill described the impact this way: “We’ve been able to free up our accounting team to focus on more meaningful work instead of repetitive data entry.”

The team also emphasized that early internal buy-in and keeping the implementation team small were major factors in their success.

Building a Strong Business Case for AI

The panel discussed the importance of keeping the business case simple and focused on solving a clear operational challenge. Recommendations included:

  • Start with the specific business problem
  • Evaluate and improve processes first
  • Choose a long-term strategic partner
  • Validate results through proof-of-concept testing
  • Focus on measurable time savings and operational impact

Katie Roy highlighted the importance of selecting a partner that can grow alongside the organization: “Find a partner that wants to solve your business problems and grow with you over time — not just provide a product.”

Final Advice for Finance Teams Starting Their AI Journey

The panel closed with several consistent recommendations for organizations exploring AI adoption in finance:

  • Focus on solving real operational problems
  • Standardize workflows before introducing AI
  • Involve the people who use the process every day
  • Start small and scale thoughtfully
  • Choose a partner that supports long-term success

As the discussion made clear, successful AI adoption in finance is not just about technology — it’s about process, people, and measurable business outcomes.