Audit, Reimagined | An Audrey AI original series

Summary
Randy Crabtree is a Chicago-area CPA who ran a generalist accounting firm before co-founding a specialty tax firm, which he recently stepped back from to advise and speak full time. His annual career satisfaction survey keeps finding the lowest satisfaction in the middle: mid-sized firms, mid-career staff and middle managers. He explains why accountants feel overwhelmed by AI but still expect it to pay off, how firms can train new staff when AI takes over entry-level work, and why leaders should reward people who finish faster instead of asking them to fill the hours.

Keywords
AI in accounting, professional services, firm transformation, management stress, AI adoption, industry change, audit innovation, firm economics, mental health in accounting, pricing models

Key topics
Impact of AI on mid-career professionals and management satisfaction
The dual feelings of optimism and overwhelm regarding AI adoption
Training and onboarding in an AI-driven environment
Changing firm economics and internal incentives
The importance of firm identity and deeper definitions in AI adoption
Norms and quality control for AI-generated work
Strategies for rapid AI integration and experimentation
The role of simulation and judgment in training
Mental health and stigma reduction in professional services
Pricing models and value-based approaches in AI tools

Sound bites
“They don’t see the long term gain. In this situation they feel the short term pain, but they do see the long term gain.”
“Wow, we’re gonna have to audit our AI output.”
“I want every leader out there to be praising that person for what they figured out.”
“You may be in the wrong role right now. Doesn’t mean you’re in the wrong profession.”

Chapters
00:00 Introduction to AI’s impact on audit and accounting
02:00 Challenges faced by mid-sized firms and middle management
04:24 Positive outlook on AI despite short-term stress
07:24 Growing comfort in asking for help and mental health awareness
10:31 AI’s effect on productivity and the manager’s challenge
12:34 Firm economics, incentives, and the future of audit pricing
15:51 Full population analysis and insights delivery in audits
16:20 Improving audit experience and client value through AI
17:10 Pricing models for AI tools and software
18:40 Value-based pricing and personal experiences with AI tools
19:58 The ongoing nature of AI transformation and rapid change
20:49 Defining firm identity and deeper purpose to guide AI adoption
22:40 Norms, quality control, and auditing AI output
23:07 The need for new frameworks to audit AI-generated work
24:18 AI as a judge and quality assurance in professional services
25:45 Balancing rapid AI adoption with cautious experimentation
26:37 Individual incentives and leveraging AI for career growth
29:04 Advice for mid-career professionals feeling burnout
30:24 Finding your niche and leaning into your strengths
31:01 The agency professionals have in shaping their careers

Guest links
Randy Crabtree on LinkedIn: https://www.linkedin.com/in/randy-crabtree-cpa-1945a67/

Summary
Ben Taylor, CEO and co-founder of SoftLedger, joins Ryan Loughran to talk about audit from the client side of the table. A former EY auditor, Ben explains why a tailored PBC list cuts the back and forth, why planning decides whether an audit takes two weeks or six, and what changes when auditors get read-only access to the ledger. They also cover continuous monitoring, pricing pressure from AI, using MCP while still proving completeness, and what auditors should ask when AI sits inside the finance stack.

Keywords
audit, AI in audit, PBC list, audit automation, continuous monitoring, general ledger, financial reporting, audit technology, MCP, SoftLedger

Key topics

• Where the audit process works today and where it falls short
• How planning and a tailored PBC list shorten audits
• Giving auditors direct, read-only access to the ledger
• Continuous monitoring and the future of financial audit
• AI adoption, audit pricing and what auditors should ask about AI-driven processes

Sound bites
"They can automate all that, but ultimately they're responsible."
"No one wants to pay more for audits or other accounting services than they absolutely need to."
"The general ledger is not the place to YOLO. It's the place to have an audited record of every transaction."

Chapters
00:00 Introduction to Ben Taylor and SoftLedger
01:13 Challenges with the PBC list and audit preparation
02:16 Factors influencing audit duration and planning
03:12 Changes in the audit process over the years
04:49 Importance of good planning and request set
05:13 Impact of technology on audit procedures
07:33 Controller insights on business knowledge and audit
08:49 Benefits of direct access for auditors in SoftLedger
10:07 Future of continuous monitoring and real-time audits
12:06 Adoption of AI and security in audit processes
14:16 Cost reduction and efficiency through AI tools
17:32 Using MCP and ensuring data completeness
19:25 AI's role in automating audit procedures
21:39 Handling complex, multi-entity audits
23:19 AI and judgment in automated processes
24:16 The future of audit technology and adoption
26:46 Key takeaway: The ledger as an audit record
27:03 Closing remarks and future outlook

Guest links
Ben Taylor on LinkedIn: https://www.linkedin.com/in/bentaylor8/
SoftLedger: https://softledger.com

Summary
David Emmer, Chief Innovation and Information Officer at Anchin, talks about where AI actually pays off in audit. He explains why early AI pilots failed, how firms should measure ROI, and why redesigning the whole workflow beats speeding up a single task. He also shares how he splits AI into three buckets, what startups and incumbents each bring to audit, and why winning people over is harder than the technology itself.

Keywords
AI in audit, AI deployment, business transformation, ROI, change management, audit technology, workflow redesign

Key topics
Strategic deployment of AI in audit
Measuring ROI and success in AI projects
Change management and employee engagement in AI adoption
Different focus areas for AI benefits: cost, quality, risk
The role of startups vs. incumbents in AI innovation in audit

Sound bites
"If you're saving 10, 20% on one task inside of a 15-step workflow, you might not really experience any sort of efficiency."
"It's there as an administrative assistant, if you will. It's not there as an authoritative source."
"Definitely the hardest part is not the technology. It's about changing those behaviours."

Chapters
00:00 Introduction to AI's role in audit transformation
02:03 Shift from technology-driven to business-driven AI deployment
04:12 Focus areas for AI benefits: cost reduction, quality, risk
06:13 Redesigning workflows for maximum AI impact
07:53 Ownership and responsibility for AI ROI
09:54 Piloting AI: measuring success before scaling
12:03 Different AI deployment archetypes in firms
13:57 Innovation challenges for startups vs. incumbents
16:10 Regret and risk in AI investments
18:08 Motivating people and change management in AI adoption

Guest links
David Emmer on LinkedIn: https://www.linkedin.com/in/davidemmer/
Anchin: https://www.anchin.com

Summary
Randy Johnston has spent four decades advising CPA firms on technology. He talks with Ryan Loughran about navigating audit technology and AI integration, where firms and vendors keep talking past each other, and how to choose a platform that won't trap you in three years' time.

Keywords
CPA firms, audit technology, AI, large language models, vendor relationships, future-proofing, continuous auditing, technology adoption, training, change management, platform interoperability

Key topics

• Where vendors and CPA firms consistently misunderstand each other
• Choosing vendors that keep your options open
• What AI and large language models change about the audit process
• Why training and change management make or break adoption
• Interoperability and open APIs in audit software
• How AI compute and inference costs actually work

Sound bites

• "Many misunderstandings about regulatory environments."
• "Large language models don't have specific answers for regulated industries."
• "AI costs are about compute load and inference pricing."

Chapters
00:00 Introduction
00:21 Randy's background in technology and accounting
02:37 Where vendors and CPA firms misunderstand each other
04:30 Problems with existing audit platforms and infrastructure
05:48 Choosing future-proof vendors and reading a roadmap
09:33 Foundation models and large language models in audit
11:16 AI, data security, and what agents actually do
13:19 Build versus buy, and tailored solutions
15:33 Why interoperability and open systems matter
18:54 Putting AI into practice with pilot projects
21:50 AI as mini auditors and building reusable agents
22:39 Vendor lock-in, walled gardens, and open APIs
23:50 Continuous auditing and the systems that support it
25:18 Leadership and change management
28:13 Training, workflows, and using technology well
31:34 Pricing models for AI and inference
37:53 Closing thoughts

Summary
Ryan Loughran talks with Chris Jones about the evolving role of AI in professional services, with the focus on people rather than tools. They cover what AI does to training and apprenticeship models, how team structures are flattening, why incentives have to change alongside the technology, and what a people-first approach to adoption actually looks like in practice.

Keywords
AI in professional services, change management, team structure, training, apprenticeship, incentives, culture, leadership, technology adoption, hiring

Key topics

• What AI does to training and apprenticeship models
• Team structures shifting from pyramid to flat or diamond
• Incentives and performance management in AI-driven environments
• Why culture and values determine whether technology sticks
• Running pilot programmes and managing change
• Skills and hiring for an AI-enabled future

Guest
Chris Jones — LinkedIn

Chapters
00:00 Introduction
03:04 Chris's background and podcasting journey
04:19 Attention span and choosing the right medium
05:16 From surface answers to deep understanding with AI
06:08 Long-form conversation as a route to connection
07:23 Which AI tasks Chris would have avoided two years ago
10:16 Trusting AI for personal and professional work
11:09 AI as a learning tool, not just a replacement for manual work
13:10 How roles and team structures are changing
17:36 Work getting more intense and more judgment-based
20:08 Managing change and running pilot programmes
24:23 Incentives, culture, and a people-first approach
28:09 Skills and hiring for an AI-enabled workforce
30:20 Attracting talent through values and culture
33:23 Closing remarks

In this episode of Audit Reimagined, Ryan Loughran interviews Liam Te-Wierik about the evolving role of AI in audit, focusing on judgment, data management, and industry-specific challenges. They explore how AI impacts audit quality, decision-making, and the future of the profession.

Titles

AI and the Future of Audit Judgment

How AI is Changing Data Management in Audit

Chapters

00:00 Introduction to AI's role in audit

00:28 Liam's background and expertise in audit and technology

01:19 Examples of AI limitations in audit judgment

01:53 How firms should respond to AI's assistance in audit

02:54 The importance of data quality and governance

03:34 AI as part of the standard audit toolkit

04:34 Industry differences in AI's capabilities

05:20 The evolution of AI in understanding language and signals

05:58 The future of AI in handling complex audit signals

06:49 Training data and confidentiality issues in audit AI

07:39 The role of deep sectoral judgment and industry expertise

08:28 Balancing automation with professional judgment

09:26 The impact of AI on audit documentation and evidence

10:07 Training and developing audit judgment with AI tools

11:01 The importance of experience and intuition in judgment

11:50 AI's potential to enhance judgment accuracy

12:35 The challenge of capturing collective knowledge in AI

13:33 The limits of AI in understanding tone and behavior

14:10 The evolution of AI in language and conversation analysis

14:57 The role of multi-model approaches in audit AI

15:52 Handling ambiguity and complex signals in audit

16:43 Balancing exploration and standardization in audit methodology

17:27 The future of audit standards and AI regulation

18:17 The importance of professional skepticism and challenge

19:05 AI's role in strengthening or weakening professional judgment

19:50 Unlocking collective knowledge and experience in AI

20:31 The impact of AI on audit training and career development

21:15 The importance of storytelling and experience sharing

22:02 Balancing automation with human oversight in audit

22:55 The evolution of audit judgment and AI integration

23:39 Closing thoughts on AI's role in audit judgment

resources

Audit Reimagined Podcast - https://audit-reimagined.vercel.app/

LinkedIn - https://www.linkedin.com/in/liam-te-wierik/

guest links

LinkedIn - https://www.linkedin.com/in/liam-te-wierik/