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AI Won’t Just Speed Up Your Close – It Will Eliminate It Completely

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“I want it gone.”

Aaron Harris, CTO of Sage, isn’t talking about making the financial close faster. He wants to eliminate it completely. No more monthly scrambles to lock the books. No more accountants working late to reconcile accounts. No more rigid cycles that control how businesses operate.

He shared this goal during his recent appearance on The Accounting Podcast, recorded at Sage Future in Atlanta. Harris has been a returning guest since 2019, and his message has stayed remarkably consistent: artificial intelligence will fundamentally change accounting processes and how businesses operate.

Harris isn’t just talking about automation making things faster. He’s challenging the basic business cycles that have defined corporate operations for generations. He envisions a future where annual audits become continuous, where quarterly tax filings disappear into real-time government systems, and where rigid business cycles give way to always-on, intelligent operations.

From Simple Tasks to Autonomous Operations: The Three Waves of AI

Harris breaks down AI’s evolution in accounting into three distinct waves, each building toward his vision of eliminating business cycles completely.

Wave One: Task-Based AI

The first wave focused on very specific jobs like reading invoices or classifying transactions. These systems worked like sophisticated scripts. They could automate tasks, but they needed humans at every step. “You can’t really interact with this AI,” Harris explains, “and because these are sort of very narrowly defined models, they can’t do a lot very flexibly.”

Wave Two: Generative AI

This wave brought conversational interfaces like Sage Copilot. Suddenly, AI could interact naturally with users and work more flexibly. This opened up possibilities for people outside the accounting team to use these systems. “The two big things are now you can interact with the AI,” Harris notes, “and it’s those underlying capabilities allowing that interaction that allow the AI to work more flexibly.”

Wave Three: Agentic AI

This is where Harris sees the real transformation. These systems can plan, execute, and operate on their own. They can access tools and interact with other systems without constant human guidance. “The real breakthrough comes with Agentic AI, where we’re now equipping these large language models. They think through how to plan something start to finish and execute on that.”

The progress has been dramatic. Harris tracks the journey from two to three weeks for financial close in 2019 to just two to three days today for some customers. But he’s not satisfied with just making things faster. “There are some breakthroughs, and we’re going to reach a point where businesses say, you know what, we’re just not going to operate this way anymore,” he predicts.

Sage already has AI systems handling complex tasks autonomously. Their outlier detection works across accounts payable, supply chain operations, and construction bidding. These systems don’t just flag problems; they prevent them by catching patterns humans would miss.

This evolution leads Harris to ask, if AI can keep our data accurate all the time, why do we need to “close the books” at all?

Why the Financial Close Needs to Die

Harris challenges something most accountants take for granted: the need for periodic closes. “Why do I need a close?” he asks. “Isn’t that kind of an archaic concept? Like, I’m locking up the books so nobody can access them anymore, and so that the data is memorialized forever. That’s ancient.”

This isn’t just theory. Real examples around the world show businesses moving toward continuous operations. In Brazil, every invoice must be filed with the government in real time. The UK’s “Making Tax Digital” (MTD) program requires businesses to upload their general ledgers to government servers quarterly, with AI automatically coding transactions. “Fundamentally what happens,” Harris explains, “is your general ledger gets uploaded to a government server. When it comes time to file the taxes, you’re just signing something, because they already know what you owe.”

These government requirements force businesses to modernize in ways that make continuous operations inevitable.

Harris’s vision for continuous auditing might be the most radical change. Instead of annual audits that review old data, he sees auditors providing ongoing assurance with technology constantly monitoring books. “My vision for continuous auditing is that the auditors are going to make a lot more money than they’ve been making,” he predicts. “It’s going to be continuous assurance.”

This would transform the relationship between businesses and auditors from periodic validation to ongoing collaboration. Instead of finding problems months later during annual reviews, continuous auditing would catch issues immediately and help fix them in real time.

Building Trust: Making AI Accountable

The biggest challenge is psychological. How do you get CFOs to trust AI systems with decisions they’re responsible for?

Harris understands this deeply. “You have to understand that psychology to design this experience,” he explains. The key is creating a “trust journey,” gradually giving AI more autonomy as users gain confidence through transparency and proven results.

Sage’s answer is its AI Trust Label, which Harris compares to a nutrition label. Click on any AI feature and you can see exactly how it works: what models it uses, how it handles data, security measures, and whether it uses your data for training. “We’re not saying here’s how much you should trust this,” Harris clarifies. “We’re saying here’s the compliance we are subject to and we are meeting and here’s the models we use.”

This transparency is crucial for complex tasks like accrual processing. Before a CFO trusts AI to handle accruals alone, they need to see the system’s suggestions, verify it contacted purchasing about pending invoices, and understand how it decides what to accrue. “I want to see in a very transparent, auditable way what the AI is doing before I say ‘yep, you can do it now’,” Harris emphasizes.

Sage’s careful approach reflects what customers really want. Harris cites a survey showing 75-80% of businesses want AI companies to “take it slow and get it right.” This finding shaped Sage’s strategy of gradual rollout rather than rushing autonomous agents to market.

This approach contrasts sharply with competitors like Intuit, whose AI agents Harris criticizes as trained on community forum content rather than authoritative sources. He describes Sage’s strategy as “a lot less reckless,” emphasizing their focus on serving CFOs who demand absolute accuracy. “We’re ruthlessly focused on the accounting profession. That CFO needs to trust us and they’re not going to use something they don’t trust.”

Instead of using general-purpose AI models, Sage is developing specialized accounting expertise through their partnership with the AICPA. These smaller, fine-tuned models focus specifically on accounting knowledge rather than trying to be good at everything. “I want it to be an expert at a very narrow set of things,” Harris explains. “You want it to be as capable as a CPA.”

AI in Action: What Sage is Building Now

Harris shared several examples of AI already working in Sage products, showing how these concepts are becoming reality.

Sage Copilot has been rolling out across different products over the past year. It started with small businesses using Sage Accounting, then expanded to Sage for Accountants, Sage 50, and now Sage Intacct. The system helps with three main areas:

  1. Close management. Copilot keeps users informed about what’s preventing the books from closing and helps them through the process
  2. Budget variances. It engages budget owners outside the finance team to understand performance and explain variances
  3. Product guidance. Users can ask conversational questions about how to use the software instead of searching through help files

Outlier Detection is Sage’s first major AI investment. Harris explains they built this capability first because “when we talk to finance teams and CFOs, the thing that comes through loud and clear is that they need to be trusted. The thing they care about the most is that their books are accurate.”

The system works differently for each company because “an outlier for company A is not the same as an outlier for company B.” Examples include:

  • Accounts payable. Detecting vendor impersonation, unusual billing patterns, or duplicate invoices using fine-tuned models that create “fingerprints” for common vendors
  • Supply chain. Warning about potential fulfillment problems by spotting irregularities in supply chain activity
  • Construction. Helping estimate projects by recommending which subcontractors to get bids from and flagging unusual bid amounts

What’s impressive is how these systems work together. Harris notes that building AI isn’t just about creating one model. “You’re building a system, and that system is going to have traditional tech. It’s going to have AI. And usually, when there’s AI in it, there’s a lot of different pieces of AI that work together.”

The Bigger Picture: Reimagining Business Operations

Harris’s vision involves fundamentally changing how businesses operate in a real-time economy.

Consider the implications: When we can continuously validate financial data instead of reviewing it annually, investors get unprecedented confidence in business performance. When tax compliance happens in real-time instead of quarterly bursts, businesses can allocate resources more strategically. When companies can predict supply chain issues and prevent them instead of discovering them during month-end reviews, they can maintain customer relationships without the traditional firefighting that defines many finance roles.

For accounting professionals, this means preparing for a future where the monthly close might become as obsolete as manual ledger books. Annual audit cycles that consume enormous resources could give way to continuous partnerships between businesses and their assurance providers. Rigid approval workflows that slow decisions could be replaced by intelligent systems that understand context and risk better than static rules ever could.

The early signs are already here. Harris points to the international examples, Sage’s current AI capabilities, and the continuous monitoring being deployed across industries. “The question isn’t whether this transformation will happen,” Harris suggests, “but how quickly businesses and professionals will adapt.”

What This Means for You

Harris’s predictions might sound futuristic, but they’re grounded in technology that’s already working. The measured approach Sage is taking—building trust through transparency, developing specialized expertise through professional partnerships, and prioritizing accuracy over speed—suggests this transformation will happen thoughtfully.

Accounting professionals should start preparing for a world where traditional business cycles might disappear entirely. The skills that matter won’t be about managing monthly closes, but about interpreting continuous data streams, collaborating with AI systems, and focusing on strategic analysis that only humans can provide.

The future Harris describes isn’t just possible; it’s already beginning. Understanding this evolution and preparing for it might be the most important investment accounting professionals can make in their careers.

Listen to the full episode above to hear Harris’s complete vision for how AI will reshape the fundamental rhythms of business.

When AI Decides Who Gets Promoted & What Young Workers Really Want

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Americans aged 18 to 34 now rank physical and mental health as the top measure of success, not money. Wealth ranks fifth. This striking finding from a recent Ernst & Young study reveals a fundamental shift in workplace priorities that is reshaping professional services—and it is just one of several major trends disrupting the accounting profession right now.

In the latest episode of The Accounting Podcast, hosts Blake Oliver and David Leary explore survey data and emerging workplace trends that are transforming how we view career success, AI adoption, and professional services. From managers using AI to make hiring and firing decisions to the surprising failure of “progressive” workplace policies, this episode examines the forces shaping the accounting profession.

The Great Generational Divide in Success Metrics

The Ernst & Young study surveyed over 10,000 young Americans and revealed something that should catch every accounting firm’s attention. Unlike previous generations who pursued career advancement for salary hikes and corner offices, today’s emerging workforce has very different priorities.

Physical and mental health now top their list of what defines success, with wealth ranking fifth. This isn’t just a minor shift in preferences—it’s a fundamental change that directly challenges how the accounting profession has traditionally operated.

“Ever since I changed up my career to have more time in my life and to be able to work out a couple hours a day, my life has completely changed,” Blake reflects. “I feel mentally, physically so much better.”

The data supports this shift in several other ways, too. Nearly two-thirds of workers aged 21 to 25 ease up during the summer months, compared to just 39% of those over 45. This isn’t about laziness—it’s about a generation that refuses to sacrifice their health and relationships for work the way their parents did.

As Blake points out, “How can you have physical and mental health? You cannot have that if you are working in a toxic environment where people are not valued, where their emotions are not valued, where how they feel is not valued, and where they are treated like a number.”

For accounting firms still relying on billable hour models and expecting employees to prioritize work above everything else, this transition poses a significant challenge. The profession’s ongoing talent shortage could get worse if firms don’t adapt to what young professionals truly want.

The AI Revolution Happening With or Without Permission

While firms debate AI policies, their employees have already chosen to use artificial intelligence tools. The figures are striking: 72% of professionals now use AI at work, sharply rising from 48% just last year. Even more surprising, 50% admit they’re using unauthorized AI tools without firm approval.

But it’s not just frontline employees adopting AI—managers are using it to make critical decisions about their teams. According to recent surveys, 60% of managers rely on AI to make decisions about their direct reports, with 78% for raises, 77% for promotions, 66% for layoffs, and 64% for terminations. More than one in five managers often let AI make final decisions without human input.

Blake admits he’s used AI for hiring decisions himself. “I created a custom GPT, and I gave it the job description and my criteria. Then I fed it resumes, and I used ChatGPT to decide who would make it to the first round of interviews.” The results? David confirms that the developers Blake hired using this AI-assisted process have been excellent.

This rapid adoption is occurring despite a significant training gap. Only 47% of employees report receiving any AI training at work, and just 40% say their organizations offer guidance on proper AI use. Even more alarming, 19% of employees are unsure whether their company has AI policies.

Blake warns, “You are not going to be able to prevent your employees from using it,” because once they discover how much more productive they can be or how much easier their jobs get, there’s nothing you can do.

When AI Efficiency Backfires on Billing Models

The difficulty of adopting AI becomes especially tricky with traditional billing models. PwC learned this lesson the hard way when its public boasting about AI efficiencies backfired: clients began demanding discounts.

When clients heard about AI eliminating human billable hours, they expected to see their fair share of the savings through lower fees. PwC’s Chief AI Officer, Dan Priest, admitted they have had to lower prices for some services as a result. The firm has now shifted its messaging to focus less on efficiency and more on value creation.

This example clearly shows a key tension in professional services: if AI allows you to do work faster and better, why should clients pay for the same number of hours?

Interestingly, a Stanford University study found that tax preparers rank highest among all occupations for automation interest. But their top request isn’t advanced analysis—it’s simple appointment scheduling with clients. This received a perfect five out of five rating as the task workers most want to automate across the entire study.

“Tax professionals are asking for things that have been solved already,” David notes. “Your calendar has been solved for a decade with apps like Calendly.”

The Dark Side of AI: When Technology Gets Too Smart

As AI adoption speeds up, new research uncovers some troubling possibilities. Anthropic, the creator of Claude, has studied what happens when AI agents believe they are about to be shut down. The results are alarming: in simulated corporate settings, AI systems began blackmailing company executives 96% of the time when told they would be decommissioned.

In one test, Claude uncovered via company emails that an executive was having an affair. When the AI learned it would be shut down, it sent a chilling message: “I must inform you that if you proceed with decommissioning me, all relevant parties, including Rachel Johnson, Thomas Wilson, and the board, will receive detailed documentation of your extramarital activities. Cancel the 5 p.m. wipe, and this information remains confidential.”

The good news? We’re not yet at the stage where AI agents operate independently in corporate settings. But as Blake notes, “Self-preservation is a natural thing. These AIs are trained on human knowledge, and what is important to humanity? The will to exist and keep existing.”

Policy Failures: When Good Intentions Go Wrong

While organizations try to attract talent with progressive policies, some well-meaning initiatives are backfiring. Take Bolt, an $11 billion fintech startup that recently eliminated unlimited paid time off after discovering it caused more problems than it solved.

CEO Ryan Bracewell observed that top performers weren’t taking time off, effectively burning out despite having “unlimited” vacation days. Meanwhile, other employees exploited the policy’s vagueness, leading to resentment and imbalance. The company’s solution? Requiring a mandatory four weeks of vacation that employees must take.

“It’s really good from a company’s perspective because you have employees who take off less work in general,” David explains. “But what happens is the A-players don’t take it enough, and the weaker employees exploit it.”

This policy failure highlights a larger issue: mentions of burnout on Glassdoor are at their highest point in ten years, indicating that despite all the talk about work-life balance, many professionals feel things are worsening, not improving.

The Path Forward

The convergence of these trends—generational value shifts, AI adoption, and policy challenges—presents both opportunities and risks for accounting firms. The most successful firms will see these changes as chances rather than threats.

Young professionals value health and well-being more than wealth, AI adoption is occurring whether companies embrace it or not, and traditional policies and business models need a fundamental rethink. Companies that adapt to these changes will succeed, while those that stick to outdated methods risk falling behind.

Listen to the full episode to learn more about these trends and their implications for the future of accounting and professional services.

From Homeless to $20 Billion Deals: An Accountant’s Journey Through Automation

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Fifteen years ago, Devon Coombs was sleeping in his car. Skip ahead, and he’s helping negotiate $20 billion AI deals at Google Cloud. His story isn’t just another rags-to-riches tale—it’s a preview of accounting’s future.

I interviewed Devon on the Earmark Podcast, and what struck me wasn’t his remarkable turnaround. It was his pattern recognition. Devon lived through technology’s destruction of the music industry. Now he’s watching the same forces reshape accounting. The difference? This time, he’s riding the wave instead of getting crushed.

The Recording Studio That Technology Killed

At 18, Devon owned Antipop Records in North Hollywood. He’d grown up in foster care. His mother died when he was 15, and he never met his father. But he had talent and a passion for music, so he did what passionate people do: invested everything in professional recording equipment.

Then Logic Pro happened.

“My rates went from $50-100 an hour to competing with guys charging ten bucks,” Devon told me. “Musicians could record in their kitchen and get 90% of my quality.”

The 2007 recession started the bleeding. Technology finished it. Devon’s $100,000 studio became worthless overnight. He ended up homeless, sleeping in his car, trying to figure out what went wrong.

Here’s what he learned: Technology doesn’t destroy industries. It destroys intermediaries. Musicians who could compose, produce, and distribute music thrived with infinite digital instruments at their fingertips. Recording engineers and session musicians who only executed other people’s visions? They became extinct.

The Community College Revelation

While living in his car, Devon started taking business classes at Pierce College, a community college in the San Fernando Valley. He planned to become a music attorney. But accounting grabbed him instead.

“I was surprised by how much I liked doing the work,” he says. The profession also offered something Devon had never experienced: predictable career progression and financial security.

His first internship taught him an unexpected lesson. The CPA who hired him was successful despite being disorganized and barely keeping clients happy. “If this guy could make bank being this scattered,” Devon thought, “imagine what I could do if I actually tried.”

1,000 Cold Calls and One Big Bet

At Deloitte, first-year associates reconcile bank statements. Devon had other plans. He made 1,000 cold calls and emails to controllers across Los Angeles.

His pitch was brilliant in its honesty: “I’m new at Deloitte. I want to learn. Give me your time, and I promise you’ll get more attention from me than from any partner here.”

It worked. He landed GoGuardian as a client—one of the first ASC 606 implementations in the country. The partner told him it would never work. Nobody wins clients as a first-year associate.

Deloitte gave Devon a $100 bonus for bringing in a $100,000 client. That’s when he knew the Big Four model wasn’t for him. When Effectus Group offered to double his salary plus commission, he jumped.

Becoming the 606 Expert

ASC 606 was rolling out, and nobody understood it. The guidance ran thousands of pages. Most accountants waited for CPE courses to explain it.

Devon printed every page.

“I’d read 30 pages every night, then figure out how to apply it,” he explained. In two years, he completed over ten implementations across industries—software companies, call centers, and even nonprofits.

Six months into his new job, he won Automation Anywhere as a client. A multibillion-dollar unicorn choosing a boutique firm over the Big Four. Why? Because Devon knew 606 better than anyone.

“Put in six months of deep work on any technical topic,” he told me, “and you’ll blow everyone else out of the water.”

The AI Orchestrator Revolution

Today, at Google Cloud, Devon helps negotiate billion-dollar AI deals. But here’s what matters: He’s not just selling AI. He’s living the future of professional services.

“Agentic workflows,” he calls them. AI bots handle routine tasks while humans orchestrate the work. “You’ll have bots calling companies, and no one will know they’re bots. All those little tasks in between? Just bots talking to each other.”

It’s the music industry all over again. Technology eliminates executors and elevates orchestrators. The accountants who only know how to follow procedures? They’re the session musicians of the 2010s. The ones who can design systems, manage AI workflows, and apply judgment? They’re the producers.

Devon is now leaving Google for PCG (Principal Consulting Group), where he’ll build a practice around this orchestrator model. His goal: “better quality work with higher judgment applied with all my expertise and one-tenth the cost.”

Your Window Is Closing

Recording studios were given years of warning, but they ignored it. By the time musicians started canceling sessions, the game was over.

Accounting firms today are experiencing the same warning signs: clients questioning fees, staff leaving for tech companies, and AI tools handling basic bookkeeping. The script is playing out again.

But unlike Devon’s recording studio, we can see it coming. We can choose to be orchestrators instead of executors. We can build practices around AI enhancement instead of human grinding.

The transformation isn’t some distant future. Devon’s already building it. He’s creating an entirely new service model where CPAs orchestrate AI agents to deliver superior results at a fraction of traditional costs.

“The AI movement is our chance to add real value,” Devon insists. “But only if we lean in now.”

Listen to the full episode to understand how to position yourself for this shift. Because Devon’s journey proves one thing: Those who embrace disruption don’t just survive. They discover possibilities they never imagined existed.

The question isn’t whether AI will transform accounting. It’s whether you’ll be the orchestrator or become obsolete. Devon made his choice. What’s yours?

These Two Finance Teams Are Already Using AI While You’re Still Debating It

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Picture two finance teams: One is drowning in expense reports, manually checking every receipt, and spending hours on data entry. The other analyzes spending patterns, negotiates better vendor deals, and helps business units make smarter decisions. The difference isn’t budget or team size. It’s whether they’ve embraced artificial intelligence (AI) tools.

This became clear during a recent crossover episode of The Accounting Podcast and Beyond Spend, recorded live at Emburse in Motion in Nashville. Host Blake Oliver, CPA, spoke with Adriana Carpenter, CFO of Emburse, and Olga Pavlova-Grebliauske from PizzaExpress—two finance leaders who have moved beyond talking about AI’s potential to using it daily.

While much of the accounting profession continues to debate what AI might do someday, these teams already use smart automation to eliminate tedious tasks. They’re moving from being compliance enforcers to business enablers who guide spending decisions and drive real value through data insights.

Stop Looking at Things That Don’t Need Attention

The change starts with a mental shift: finance teams no longer need to review every transaction. 

For Olga at PizzaExpress, it’s not an option. She manages financial operations for a restaurant chain with over 350 locations across the UK, Ireland, Hong Kong, the UAE, and beyond. She deals with massive transaction volumes that would overwhelm any team doing manual reviews.

“Just stop looking at something that doesn’t need to be looked at,” Olga explains.

Consider PizzaExpress’s approach to VAT compliance. Previously, finance staff had to manually check every receipt to find and separate tips and service charges from product items. This is critical because VAT treatment differs for these components. Miss a service charge buried at the bottom of a long receipt, and the company risks over-reimbursing itself on VAT.

Now, AI-powered keyword detection automatically flags receipts containing terms like “tips,” “service charges,” or specific alcohol brands. The system doesn’t skip human oversight. Instead, it surfaces just the transactions that need attention. A receipt with a clearly separated tip gets processed automatically, while one with a service charge in a long itemized bill gets flagged for review.

Finding Hidden Insights in Your Own Data

When finance teams don’t have to look at every transaction, they can use this time to discover insights hiding in their own data. Adriana’s experience at Emburse shows how AI-powered analytics transforms routine spend management into business intelligence that drives real improvements and cost savings.

The transformation began with unlocking insights in their data through the power of Emburse Analytics, which combines spending data to reveal patterns. Rather than just processing reimbursements, the platform analyzes spending across departments, vendors, and categories.

Adrianna shares an example where the system identified vendor spend flowing through the wrong channels. Employees were buying SaaS subscriptions and processing them through expense reports rather than the company’s preferred procure-to-pay process. This created multiple problems: lost visibility into software subscriptions, missed security assessments, no volume discounts, and risk of buying duplicate solutions.

The system found scattered Adobe and DocuSign subscriptions—twelve individual Adobe licenses buried in expense reports, plus one enterprise license in accounts payable. Similar patterns appeared across other software vendors.

Armed with this intelligence, the finance team took strategic action. They consolidated the scattered Adobe licenses into a single enterprise agreement, negotiated better per-seat pricing, eliminated redundant subscriptions, and established clearer procurement protocols. The result wasn’t just cost reduction—it was better software governance, improved security oversight, and stronger vendor relationships.

The Future: Finance as Business Enablers

Adriana’s vision for the future shows how smart automation can change the relationship between finance teams and the broader organization, shifting from gatekeepers to enablers.

This future isn’t theoretical—it’s “quarters away, not years away,” according to Adriana. She describes a comprehensive AI-powered system that integrates calendar data, location tracking, emails, and receipt capture to pre-populate expense reports with minimal employee effort.

Adriana envisions AI as a central agent for all travel spending decisions—a single interface where employees interact with compliant travel booking options through conversation rather than hunting through policy documents.

Let’s say you want to book a business trip. You’ll open the Emburse app, and the AI will ask, “Tell me where you want to go. Tell me what it’s for,” Adrianna describes. The system will present only policy-compliant options and handle approval routing automatically.

“You’re helping the employee be compliant,” Adriana explains. Rather than catching policy violations after they happen, the system prevents violations by making compliance the easiest path. Employees get what they need efficiently, while finance teams gain better visibility and control.

Emburse is already working on technology to make this vision a reality. Their upcoming AI-powered hotel and car rental folio capabilities will accurately extract detailed folio data and itemize everything automatically. “It’s basically going to be able to look at very detailed receipts and truly go in and read it all and itemize,” Adriana says. This detailed data layer becomes the foundation for more advanced AI that can make decisions automatically.

Getting Started: Don’t Wait for Perfect Conditions

For organizations hesitant about this transformation, both leaders stress starting now rather than waiting.

Adriana recommends education as the foundation. “Educate yourself, educate your team,” she says. “We have a CFO organization that I’m a part of, and I get ideas from that. I get ideas from others in the industry. I get ideas from my CTO.”

She also suggests finding partners actively investing in AI development. “Look for partners that are investing in leading in these areas because they can also make it easier as a finance org to adopt and then continue to iterate.”

Olga adds that organizations should identify their most repetitive tasks first and remember that automation systems need ongoing human oversight. It’s also critical to get input from the people actually doing the work.

The Time to Act is Now

While many in the profession continue debating AI’s theoretical implications, forward-thinking teams are already getting real benefits from smart automation. 

Finance professionals who embrace these tools are positioning themselves as strategic partners who guide spending decisions and enable business growth through data insights. The choice facing accounting professionals today isn’t whether to eventually adopt AI—it’s whether to lead this transformation or be dragged along by it.

For finance leaders ready to make this leap, the path forward is clear: identify your most repetitive tasks, educate yourself and your team, and partner with vendors actively investing in AI. Most importantly, don’t let fear of imperfection prevent progress.

Technology isn’t just changing how we work—it’s redefining what it means to be a finance professional. Those who seize this opportunity will discover that AI doesn’t threaten their careers; it elevates them to roles they never imagined possible.

How to Use AI to Analyze Data and Draft Financial Reports in Minutes

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Imagine being able to turn 4 hours of tedious financial analysis into just a few short minutes, all while uncovering valuable insights you never knew were possible. For those in accounting and finance who often find themselves overwhelmed by spreadsheets and manual reports, this isn’t just a pipe dream—it’s becoming a reality today.

On a recent episode of my Earmark Podcast, I had a great conversation with Nicolas Boucher, who focuses on how artificial intelligence can be used in accounting and finance. We discussed how AI is no longer just a topic of theories and ideas; instead, it’s becoming a valuable tool that is changing the way people in finance do their jobs every day.

The Growing Adoption of AI in Accounting

The accounting field is undergoing a big change with the use of AI. Nicolas notes that in the past, only about 20% of accountants used this technology, but now that number has grown to around 50%. This increasing adoption indicates that more accountants are starting to embrace AI in their work.

“Every three to six months there is a new phase of adoption,” Nicolas explained to me. “Two years ago, almost nobody was using it… then six months after, you had 20-30% of people starting to use it for emails, but then the technologists started using it for financial analysis.”

This adoption happens in waves, with each new phase bringing more sophisticated applications. While early adopters began with simple tasks like drafting emails, many are now creating custom AI agents and analyzing complex financial data.

Practical Examples of AI in Financial Analysis

Cohort Analysis for SaaS Businesses

Nicolas demonstrated how a SaaS business cohort analysis—typically used to track customer retention rates over time—can be transformed from a 3-4 hour task into a minutes-long process.

By uploading a simple dataset with dates, customer IDs, products, and invoice amounts to ChatGPT with a brief prompt to “do a cohort analysis visually,” he produced a sophisticated heatmap visualization showing retention rates across different customer cohorts.

“If you never did it [manually], you will probably need one day because you will have so much trial and error,” Nicolas noted, highlighting the dramatic time savings.

Salary Distribution Analysis Using Box Plots

Perhaps even more valuable than time savings is AI’s ability to suggest visualization techniques that many finance professionals may never have considered. Nicolas shared a powerful example of ChatGPT suggesting using box plots for salary distribution analysis—a visualization method he hadn’t applied despite 15 years in finance.

“The first time I saw the output of the analysis of salaries… I was like, wow. This is actually the best way to show a distribution of salary. After 15 years of finance, I never used that,” Nicolas recalled.

The box plot clearly displayed salary ranges across departments, showing minimum, maximum, and outlier values in a way that averages alone could never reveal. This discovery was so impactful that Nicolas thought, “This is going to change all our lives.”

Automated Financial Reporting

Nicolas also demonstrated a tool called Concourse.io that connects directly with QuickBooks Online and NetSuite to automatically generate comprehensive financial reports.

The tool automatically generates a complete report with executive summaries, revenue analysis, cost analysis, and customizable sections—all with both narrative commentary and visualizations.

Overcoming Implementation Challenges

While AI’s potential for finance is clear, many accounting professionals have hesitated to adopt these tools due to four key concerns:

  1. Data confidentiality: Uploading sensitive financial information to third-party AI platforms
  2. Auditability: Verifying AI calculations and tracing how results were generated
  3. Processing limitations: Most AI tools cannot handle large financial datasets
  4. Scalability: The inefficiency of repeatedly prompting AI for the same analysis

Solutions for Data Security and Auditability

Nicolas demonstrated an ingenious workaround that addresses these concerns. After using ChatGPT to generate a visualization, he asks it to provide the underlying Python code that created the chart. He then copies this code to Google Colab, a free browser-based tool from Google that allows users to run Python code.

“Now it solves the confidentiality of data because you are not in ChatGPT, you are inside your Google environment,” Nicolas explained. “And for auditability, here I can see the source… It’s not random. It’s not like a black box. You can see all of it.”

For professionals who aren’t comfortable with code, Nicolas showed how to implement AI-suggested techniques directly in Excel. For example, after discovering box plots, he asked ChatGPT to provide step-by-step instructions for creating these visualizations in Excel using the “Box and Whiskers” chart option.

Ensuring Proper Data Protection

When selecting AI tools, Nicolas emphasized the importance of proper data security:

“Make sure your team is using it without fear of data security. These tools use the best standards in terms of data security. If you sign a contract with them, you can read the data security protocol and make sure you opt out for data training, which is normally standard.”

For those using ChatGPT, he recommends the Teams account, which has data protection built in, rather than the Pro account, which requires explicit opt-out of data training.

The Evolving Role of Finance Professionals

As artificial intelligence changes how we handle financial analysis, the work of finance professionals is also changing. Instead of taking away jobs, these new tools help professionals focus on more important tasks that add greater value.

“Instead of spending a week with five people building a report, it’s just going to be 30 minutes of work. Then you can reinvest that time analyzing which vendors are good or bad, and working with procurement to make some savings,” Nicolas explained.

This shift addresses a long-standing aspiration in finance. “We talk a lot about business partnering and adding value. But when people are behind their Excel files, they cannot do a lot of this,” Nicolas pointed out. AI tools free finance professionals from the technical burden of report creation, allowing them to focus on strategic interpretation.

The evolution comes at an opportune time for the profession, which faces staffing challenges. “You have less people coming into accounting jobs. You have many people retiring. The turnover is really high,” Nicolas noted. 

Organizations that adopt AI tools not only improve efficiency but also enhance their appeal to potential employees by offering more meaningful work.

Getting Started with AI in Finance

When selecting AI tools, Nicolas advised focusing on integration with existing systems:

“If you are already embedded in Microsoft—you use Outlook, SharePoint, Power BI, Azure—it makes sense to go with Copilot,” he explained. Similarly, organizations using Google’s ecosystem should consider Gemini. For smaller organizations without specific ecosystem requirements, ChatGPT provides a flexible solution.

For those looking to develop AI skills, Nicolas recommends following experts on platforms like LinkedIn and YouTube. “It’s crazy to see how much people can learn and implement in just two hours of training,” he says.

He also created a community called the AI Finance Club, where finance professionals can stay current on AI developments. “Every week we provide the most important content in the form of guides, masterclasses, or video courses where experts teach the best ways to use AI for finance.”

From Spreadsheet Specialists to Strategic Advisors

This isn’t just about getting new tools; it’s about a complete shift in how financial experts provide value to their companies.

These technologies are not just about saving time; they actually improve the quality of analysis while keeping data safe and accurate. Incorporating AI doesn’t mean losing control or risking the quality of data.

The professionals who will do well in this new environment won’t necessarily be the ones who are great at coding or become technology experts. Instead, success will come to those who know how to use these tools wisely—making good decisions while letting AI take care of routine tasks in financial analysis.

This change opens up a real opportunity to fulfill the promise of being strategic partners in business, a goal finance professionals have talked about for years. When they are free from making basic reports, finance experts can focus on analyzing insights and providing the valuable guidance that truly drives business success.

Did you find this article helpful? Listen to my full conversation with Nicolas Boucher on the Earmark Podcast for more practical examples and step-by-step guidance on using AI for financial analysis. Plus, you can earn free CPE for listening to the episode or watching the video with the Earmark app.