Author, lecturer, and strategic advisor focused on AI-powered finance transformation.

20+ years as a CFO and finance leader in private equity-backed companies, leading initiatives in automation, analytics, and operational scale. Deep expertise in aligning finance strategy with modern technology to improve performance, accuracy, and decision-making.

Advises CFOs and finance teams on adopting AI across forecasting, reporting, and operations. Emphasis on practical implementation, responsible governance, and measurable outcomes.

Author of Deep Finance and AI Mastery for Finance Professionals. Lecturer with Duke University, AICPA, and the Corporate Finance Institute, teaching the application of AI in real-world finance functions.

Committed to helping finance evolve through smarter systems, stronger insights, and better execution.

US Navy veteran.

I’m happy to share that I’m starting a new position as Member of the Board of Advisors at AI Leaders Council!

Glenn Hopper

Chief Financial Officer with two decades of experience leading finance operations for private equity-backed companies. With a focus on finance automation, M&A activity, operational efficiency and margin improvements, I have spent my professional career coupling financial leadership and technical expertise to optimize operations and promote innovation and modernization within companies.

As the Chief Financial Officer for Sandline Discovery, I am responsible for ensuring company scalability and efficiency. Prior to Sandline, I served as Chief Operating Officer and Chief Financial Officer for Cicayda, Managing Partner & CFO for GR Ventures, CFO of HCT Investments and Senior Director of Budget Management for NuVox Communications. Prior to these positions, I served five years as a Journalist & Public Affairs Specialist in the US Navy. 

Throughout my career, my focus has been on harnessing technology to improve processes, implement finance automation, and lead transformational change with intense focus on moving firms from start-up to scale-up.

Recent Accomplishments:
Led the sale of a privately held legal technology company to a PE-backed national firm, spearheading sell-side due diligence, integration/transition and company wind down.
Guided a private equity-backed retail business through due diligence and sale to a national brand at more than 10.5x annual EBITDA. Developed a real-time cash flow reporting and forecasting system that improved growth planning activities. Development of new web products and programs, including a custom-built monthly subscription application at a savings of 90% of the cost of off-the-shelf products. 

Ultimately, I enjoy helping businesses evolve. I focus on analyzing company needs, processes and procedures, and develop solutions to take them to the next level by integrating financial acumen and technical knowledge.

Specialties:
► Strategic Planning
► Financial Leadership
► Financial Reporting
► Business Analytics
► Cash & Asset Management
► P&L Management
► Budget Planning
► Data Science
► Change Management
► Transformational Leadership
► Agile Project Management
► Productivity & Performance Improvement
► Quality Improvement
► Negotiations
► Forecasting
► Mergers & Acquisitions

Building Practical AI Solutions for Finance | Head of AI at VAi

I'm excited to share my newest LinkedIn Learning course: Build No-Code AI Agents for Finance, released today.

AI agents are sitting at the apex of the Gartner Hype Cycle right now. Everyone's talking about them; but few actually deliver. This course is for people who want to actually build them. No code required. Just workflows that finance teams use every day.

If you've been wanting to automate the repetitive stuff but didn't know where to start, this is your chance.

We build three working agents from scratch using n8n.  You'll connect live data from Google Sheets, transform and reconcile it, then let AI agents generate the commentary your stakeholders need in a single report.

You'll learn the core automation patterns: trigger, transform, and output. We cover security, auditability, and monitoring once your agents are running.
By the end, you'll have a blueprint you can adapt to expense approvals, month-end close, budget reviews, or any repetitive workflow eating up your team's time.

Ready to build? Check it out.

https://lnkd.in/eR_BDgGY

 #finance #processautomation #aiagents #artificialintelligence #nocodedevelopment

LinkedIn

Gartner

Google

Jay Steigleder

Hot take: If OpenAI survives, they will become “consumer grade” AI. Meaning they have now been lapped by Google and Anthropic in the business space, and are too married to productization of their science.

Understanding now why so many left the fold in the last 18 months. 

I don’t mean they’re not still viable; just that their priorities have shifted. They need revenue to fuel the beast (compute), while the tech stalwarts have war chests beyond what OpenAI can raise.  If they can’t raise at ever-increasing valuation demands, they will fold.

At the AICPA Future of Finance Summit this morning listening to Mallory Barg Bulman from Gartner walk through what an AI-enabled finance function could look like by 2030. It is a very different job description for CFOs and their teams.

Her framing:
• Data and insights – finance shifts from relying on partners to building our own tools, hiring data science, data engineering, and data management talent, and owning business architecture.
• Process – all finance staff use AI, junior roles focus on learning from and testing machines, and most judgment and oversight work is automated.
• Delivery model – finance teams work in agile, iterative ways and adopt product management techniques.
• People – finance moves from “guardians” to “catalysts,” with employees prioritizing opportunity and upside risk and at least half the team as digital talent.

The big takeaway for me: CFOs who thrive in this world will be the ones who design these systems, champion the talent shift, and lead their organizations into an AI-driven, autonomous finance environment.

Welcome to December 2025, where:

▪️ 96% of CFOs say AI is a priority.
▪️ 71% of finance teams have NOT deployed it.

My 2025 annual report on generative AI in corporate finance is coming out next week. The findings should calm some of those FOMO jitters many have been feeling. 

Why generative AI has moved past the peak of the Hype cyle:

→ Median ROI sits at 10%, half what most targeted 
→ Only 38% of AI projects meet expectations 
→ Two-thirds of companies are stuck in pilot mode

But the organizations breaking through have figured out something important:

Technology selection accounts for about 20% of success. 
The other 80% comes from data quality, workflow redesign, and change management.

The full report runs nearly 50 pages, and shows:
→ Where GenAI maturity actually stands in the finance function
→ Why AI agents warrant serious skepticism 
→ What's working across FP&A, AP/AR, treasury, and audit 
→ Microsoft vs. SAP vs. Oracle 
→ EU AI Act and SEC examination priorities 
→ Ten specific recommendations for 2026

Subscribe to my Substack (link in comments). Free subscribers get breakdowns and key findings. Pro subscribers get the full report plus weekly bonus content.

Is your organization actually scaling AI in finance, or still stuck in pilot mode?

The emerging promise of generative AI in finace may well be a bifurcation of back office software into two distinct layers:

Microsoft is positioning itself as the ultimate controller to manage the ledger, while Anthropic aims to be the junior analyst that builds and audits the model.

If these roadmaps hold, success will depend on orchestrating a hybrid stack rather than finding a single magic tool.

My breakdown:

🎙️New FP&A Today episode is live. This week I’m joined by Preston Naegle, Head of Finance and FP&A at Midway Mechanical Services.

We talk about how his private equity background shapes the way he is building a lean FP&A team from scratch, supporting an active acquisition strategy, choosing an FP&A solution, and moving the business from reporting to real partnership.

https://lnkd.in/gsfUa3ip

The magic of memory ...

As someone who works with AI tools every day and teaches others how to apply them across the finance function, I seldom stop to experiment with these systems on a more “personal” level.

This weekend, I had a bit of a meta moment. I haven’t spent significant time curating saved memories in these systems, yet when I asked ChatGPT about me, it painted a surprisingly accurate picture.

It noted my roles in finance and AI, the books I’ve written, the global talks I give, and the courses I teach. It’s fascinating how these tools form a narrative about us, even if we’re not actively feeding them all the details.

⚠️ Here's the obligatory security warning on this topic: even if you opt out of having your data used to train future models, many of these tools still retain conversation history and system-level memory to deliver continuity. That’s the tradeoff that makes the “second brain” possible. It isn’t good or bad on its own, but it’s something every professional should be aware of.

It all reinforces the same point: our interactions with AI quietly accumulate into context. Over time, that context becomes a mirror of your professional identity.
Sam Altman once described his vision as a future where AI can hold “a trillion tokens of context that you put your whole life into,” allowing it to reason across all your experiences. In other words, these tools are designed to know us deeply over time.

So here’s a thought experiment: What would your AI “second brain” say about you? If you’ve tried it, drop a comment and let me know what surprised you. And if you want to explore more of these insights, you can find my weekly musings at Deep Finance Dispatch: https://lnkd.in/eAgGt8s9

Proud to be part of this podcast and this community. 

FP&A Today has grown because finance professionals are eager to learn from each other; and my favorite part of hosting is getting to sit down with so many brilliant  finance leaders across the industry. Grateful to everyone who listens, shares, and keeps pushing the field forward!

Datarails podcast, FP&A Today has hit 500,000 audio downloads. Given that its the #1 financial planning and analysis podcast, I asked a director of FP&A to analyze our numbers. 

What is the story behind the numbers?
1. The main audience is US, Australia, UK, and Canada
2. Most downloaded episode: CFO Shannon Nash, Esq., CPA Nash, discusses Pitch Perfect Board Presentations
3. 79% of our audience listen on Apple IOS
4. 20-30 minutes performs best, with some preferring slightly longer deep dives.
5. Our ListenScore is as high as Big Four podcasts such as EY's Better Finance (though of course all these podcasts are fantastic and serve different niches within the finance world). Listen Score (LS) is a metric that shows the estimated popularity of a podcast compared to all other rss-based public podcasts 
6. Technical and data focused content continues to grab attention
7. More than 1000 FP&A professionals have earned CPE Credits with FP&A Today through the Earmark app
8. A small cohort of users topped the charts with 9 consecutive weeks of earning CPE

Thanks to the amazing hosts Glenn Hopper and of course previously Paul Barnhurst and all the amazing guests too many to tag.

*On a technical note it is 579,000 downloads (when you also add YouTube and our Earmark collaboration )

FP&A Today hits more than 500k downloads (FP&A analysis)

Most finance teams didn’t experience an AI “revolution” in 2025.

The close still looks familiar. Forecasts still run on calendars. Excel, ERP, and meetings still do most of the work. For many CFOs, AI showed up more in vendor decks than in workflows that actually move cash or close the books.

At the same time, the ground shifted. Models got materially better at structured finance work. Core finance platforms quietly shipped real AI features inside tools teams already use. Regulators moved from observing AI to explicitly naming it in exam priorities. The toolkit strengthened, even if the operating model didn’t.

In the latest Deep Finance Dispatch, I look at where finance actually ended 2025, what really changed under the hood, and why 2026 may be the first year the technology and the work start to meet. Not another transformation manifesto (one of those coming next week 😉 ), but a practical read for CFOs deciding what to turn on, what to redesign, and what to govern next.

Link in comments.

The variance sits at $42,500. It's been there since Tuesday.

Your senior accountant is three days into the close. She's toggling between a NetSuite extract, a folder of PDFs, and a three-month-old Slack thread.
Eventually, she forces a plug entry just to get the draft board deck out.

This is the operating reality of modern finance. We hire smart people to perform forensic data retrieval across fragmented systems.

Microsoft’s announcements at Ignite propose a new operating model. They call it the "Frontier Firm."

It is a bold promise, but it's what we have been waiting for from Microsoft. They're embedded more deeplyg in the workplace than any other software vendor, and this is the kind of move we've expected from them. If it works as promised, it has the potential to move the close from a brute-force human effort to an agent-operated workflow.

This is the infrastructure required for agents to execute tasks within the General Ledger:

▪️ Work IQ: The context engine. It connects agents to the Microsoft Graph so they understand your specific file versions and workflow patterns.
▪️ Fabric IQ: The semantic layer. It binds agents to structured data in Power BI so they do not hallucinate revenue figures.
▪️ Agent 365: The control plane. It allows IT to govern non-human identities with the same rigor used for employees.

We've been hearing about "continuous close" for years, but perhaps we are now entering the phase of specific, autonomous tasks. The goal is a close that never actually stops.

I break down the full analysis and the competitive moat against Salesforce and Google in this week's Deep Finance Dispatch.

https://lnkd.in/eS_RVkTp

For Pro members, I provide the 4-step governance checklist and the technical architecture map required to pilot your first "Supervisor Agent" without compromising the GL.

🎙️New FP&A Today epsode 

I recently sat down with Diya Sagar, CFO of AWA Studios, to talk about what it takes to run a hits-driven, creator-first business. 

AWA operates at the intersection of publishing, media, and early-stage studio development, where one breakout success can change the entire trajectory of the company.

Diya brings a rare combination of investor discipline and operator pragmatism. We covered how she builds financial models around creative work, why upfront creator economics anchor every project, how she manages long-tail uncertainty in forecasting, and the controls needed to support a business where the payoff may be years away.

It’s a sharp look at portfolio thinking, data hygiene, and the role of finance inside a creative environment.

Definitely worth a listen if you work in FP&A, media, or any business built on big swings.

Listen here: https://lnkd.in/ekDyHTKn

Open video: CFO of AWA Studios on Building a Hits-Driven Business on youtu.be

CFO of AWA Studios on Building a Hits-Driven Business

This is worth noting. There’s a big difference between “citizen development” and production coding. 

Thanks, Alex, for this breakdown.

Bootstrapped Groove from $0–$5M ARR solo. Now rolling it into a holding co. for CX SaaS. Launching Helply, InstantDocs & ZeroTo10M to scale $0–$10M ARR w/ 50%+ margins. Sharing it all at ZeroTo10M.com.

Vibe coding is a joke that just cost another founder $300K.
AI coding traffic collapsed 76% in 12 weeks globally.
10,000 startups hit the complexity wall, creating a $4B crisis.

Base44 spiked 950% in May, crashed 95% by October.
Lovable went from +207% to -37%.
Cursor dropped from +62% to -19%.

Massive adoption surge, then 60-80% decay in three months.

Vibe coding is good for demos.
It’s terrible for production.

That difference is bankrupting startups and even enterprise AI initiatives.

Thousands of founders fell for the marketing and PR.

"AI replaces senior engineers."
"Code equals product."
"Architecture can be outsourced to autocomplete."

These tools made prototypes fast and easy.
Integration, governance, security, and reliability stayed hard.

Most founders and larger companies 
who went all-in are paying the rebuild tax:

$200-300K in senior engineering costs.
4-8 months of re-architecture.
$30-150K monthly burn during rebuilds.

They got MVPs working.
Then everything broke when real users showed up.

The math:

10,000 startups experimented with AI coding.
8,000 require rebuilds or architectural rescue.
Budgets range $50K-500K each.
Total cleanup market: $400M-$4B.

I’m not talking about about senior engineers using Cursor effectively.

It's about founders and businesses 
who started initiatives and hit the wall.

And they’ll need a way out. 

We're entering the first AI-generated technical debt crisis.

In 2026, rescue engineering 
will be a hot commodity.

A whole generation of startups shipped prototypes as products.

AI let everyone build software.
Now everyone learns what it costs to make software actually work.

Your product might secretly be a demo.
Your users will find out eventually.
Better to rebuild before they force it.

Plan your rebuild before your users discover you need one.
And budget for senior engineers the moment you get traction.

AI can write code, but it cannot design systems.

The vibe coding tax always comes due.

The only question is whether you pay it 
before or after your customers leave.

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