Accounting has a tech problem, and it is not what most people think. In this conversation, Cos Nicolaescu, Co-founder and CEO at Accrual, breaks down why accounting has lagged behind, where AI can actually create leverage, and why the future of the profession is likely more human, not less.

This episode gets into the real constraints inside accounting workflows, the difference between deterministic work and judgment based work, and why trust, context, and client knowledge still matter more than most AI narratives admit.

What stands out

• Accounting has not been slow to adopt tech because accountants resist change. A big reason is that the tools have often been weak, fragmented, and not worth the workflow overhead.

• AI can handle more of the mechanical and backward looking work, but the highest value still sits in judgment, context, and forward looking decisions.

• In accounting, knowing the tax code is not enough. The hard part is knowing the client, their history, their complexity, and the tradeoffs that shape future outcomes.

• The profession is still deeply supply constrained. Firms are understaffed, demand is growing, and better tooling may help accountants do more meaningful work instead of simply shrinking headcount.

• Junior talent may benefit more than people expect. As tools improve, newer professionals could ramp faster, though trust and client relationships will still take time to build.

Timestamped highlights

00:39 What Accrual is building, and why accounting workflows are a major opportunity for AI

01:47 Why accounting is more tech conservative than people assume, and why bad software is a big part of the story

04:11 The three buckets of accounting work, standardization, firm level process, and personal preference

07:29 Where AI fits best in accounting, flexible interfaces, document understanding, and smarter workflow support

12:14 The key difference between software engineering automation and accounting automation

16:36 Will AI reduce the need for accountants, or make the profession more productive and more valuable

A line worth remembering

“I would want to see people who spend a lot of time getting CPA degrees and training for decades spending most of their time, not just inputting data from one field to another.”

Pro tips

• If you are building AI for a regulated or detail heavy workflow, start with where accuracy matters most and do not confuse automation with value

• If you work in professional services, context is the moat. The more client history and situational knowledge you can capture, the stronger your systems become

• If you are early in your career, tool fluency can compress the learning curve, but trust still has to be earned

Stay connected

If this episode gave you a new lens on AI, accounting, and the future of expertise, follow the show, subscribe for more conversations like this, and share it with someone building at the intersection of software, operations, and professional services.