In Conversation: Andrew Lokenauth on the Role of AI in Transforming CTO Functions
Artificial intelligence is no longer an emerging tool for tech leaders—it’s becoming central to how companies operate. For CTOs and digital executives, the rise of AI is reshaping core responsibilities, requiring a shift from managing infrastructure to driving strategic, AI-enabled growth.
To explore this evolution, we sat down with Andrew Lokenauth, CEO of a CFO advisory firm and founder of The Finance Newsletter. Andrew saw this shift firsthand. Since integrating GPT-4 into his firm’s operations, Lokenauth has cut analysis time by more than half, tripled client capacity, and restructured his leadership team around AI capabilities.
In this interview, he explains how AI has changed the way he works in, the organizational shifts that followed, and the measurable results that made the investment worth it. He also speaks candidly about early mistakes, internal resistance, and why AI is now a permanent part of his business strategy.
Andrew, you’ve mentioned AI has had a transformative impact on your firm. Can you share what that shift looked like for you?
Lokenauth: As CEO of my CFO advisory firm, AI has completely transformed how I approach financial analysis and client advisory. Back in September, I implemented GPT-4 to analyze complex financial statements and identify growth opportunities — this cut our analysis time by 65% and let us serve 3x more clients without adding headcount.
That’s a huge efficiency boost. Were there any bold moves that paid off particularly well?
Lokenauth: One of my boldest moves was automating our entire financial reporting process. I was honestly skeptical at first, but the results blew me away. We now generate detailed financial insights in minutes instead of days, and our accuracy rate has improved from 92% to 99.4%. The thing is this freed up my senior analysts to focus on high-value strategic work our clients actually care about.
Do you now prioritize AI strategy alongside business and product roadmaps, and how has that shifted your leadership focus?
Lokenauth: AI isn’t just another initiative for me anymore — it’s woven into everything we do. I’ve restructured our entire product development process around AI capabilities. My leadership meetings now start with AI strategy, not just revenue targets.
I personally spend about 40% of my time exploring new AI use cases and implementation strategies. Last month, I combined our tech and AI teams into one unit focused on what I call “augmented advisory” — using AI to enhance human expertise rather than replace it. This has boosted our client satisfaction scores by 28%.
That restructuring sounds significant. What changes did you make in your leadership or tech teams, and what results followed?
Lokenauth: When I merged our tech and AI teams, I created a new role: Chief AI Integration Officer. Sounds fancy, but it’s made a huge difference. This person ensures AI tools actually solve real problems instead of just being cool tech.
The results speak for themselves. Our client onboarding time dropped from 2 weeks to 3 days. Project delivery speed increased by 45%. And here’s what really matters — our profit margins grew by 32% because we can handle more complex work without proportionally increasing costs.
Measuring AI impact is still tricky for many leaders. Do you have a framework or set of metrics that guide your investments?
Lokenauth: I developed what I call the “AI Impact Score” — combining time saved, accuracy improvements, & revenue generated. We track these metrics in real-time through custom dashboards that show AI usage patterns across teams.
For example, our AI-powered financial analysis tools save roughly $400K annually in labor costs. But the real value is in the 3x increase in our advisory capacity. We measure this through client engagement metrics & project completion rates.
AI adoption can be disruptive. How do you strike a balance between experimentation and core business stability?
Lokenauth: Here’s my approach: 15% of resources go to AI experimentation, 85% to core operations. I learned this the hard way after some early missteps trying to transform everything at once.
In my CFO advisory practice, we run small pilot programs with select clients before rolling out AI solutions broadly. This lets us test and refine without risking our core service quality. The key metric I watch is the ratio of successful pilots to failed ones — we aim for 3:1.
What are the biggest organizational or technical challenges you’ve faced when scaling AI?
Lokenauth: The biggest challenge wasn’t technical — it was cultural. My senior advisors initially resisted AI tools, seeing them as threats rather than enablers. I addressed this by involving them in the AI development process and showing how it enhanced their expertise rather than replaced it.
Integration with legacy systems was another headache. We spent nearly $200K updating our infrastructure to support AI capabilities. But this investment paid off in under 6 months through improved efficiency.
If you could go back, what’s one piece of advice you’d give your past self as you started this AI journey?
Lokenauth: Start smaller but move faster. I initially tried to build perfect AI solutions before launching anything. Now I know it’s better to launch minimal viable AI tools and iterate based on real usage data. This approach led to 40% faster implementation times and better user adoption rates.
The most valuable learning was that AI isn’t about replacing human expertise — it’s about amplifying it. This mindset shift helped get my team onboard and drove better results across the board.
Explore more of the AI in the industry series.
