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AI raising concerns of skill erosion in entry levels

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AI is increasingly taking on the routine repetitive tasks that, in the past, were handled by entry-level accountants. This has allowed firms to expand their capacities without growing headcount, as well as transition out of mundane compliance services and into higher-level work that relies more on human judgment. At the same time, this has raised questions as to how the new generation of accountants will develop the foundational skills necessary for this higher-level work if they no longer handle the simple tasks that AI now does. 

While no one exactly liked processing 1099 forms assembly-line style or spending all day on bank recs, it was generally how newer accountants built the knowledge and experience that enabled them to eventually take on more complex work. As AI handled more and more of these tasks, though, the accountant has gone from performing these menial tasks themselves to vetting the AI’s work and analyzing its data. But Joy Taylor, managing director of Texas-based alliantConsulting, wondered if accountants have no experience doing this work, how well can they understand and evaluate the AI’s results.

“I do believe that technology and AI will [assist with many of] those transactions, but it will still be critically important for those that inherit the output of those digital results to interpret them and confirm that they are correct, and that is the skill that still requires a great deal of knowledge, careful consideration and an investment in education,” she said. 

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While Taylor was careful not to dismiss the importance of AI and the positive role it can play in the accounting world, she said professionals still must cultivate and maintain foundational skills in order to remain effective. If new accountants are no longer developing those skills, she warned, it could end up exacerbating, not solving, the pipeline issue. 

“If you avoided teaching and training the entry-level capabilities, it would be very disruptive, I would think, to any profession,” she said.

This is something that Avani Desai, CEO of Top 50 firm Schellman, has thought about as well. She’s seen smart and eager professionals who have never done a reconciliation by hand, built a pivot table from scratch, or tested a control matrix line by line. She agreed that no matter how good AI might get, foundational skills for humans remain vital. 

“You can’t expect someone to review a complex K-1 if they’ve never touched a 1099,” she said. “You have to actually go build that muscle memory first.”

Yolanda Seals-Coffield, PwC U.S.’s chief people and inclusion officer, made a similar point. Generally one does not expect surgeons to do heart transplants when they’ve never even stitched a cut. A surgeon would first need to practice on animals and then perhaps a cadaver before being trusted to work with a live person. This is not so much because we expect the surgeons to always do the stitching, but because we expect them to understand what makes a good stitch. She compared it to her own experience as a lawyer: it’s less about doing the “what” and more about understanding the “why.”

“I’m a lawyer by trade, and I remember in the early days of my career sitting in large rooms surrounded by files, doing discovery, digging through documents, putting stickers on the documents. By the time I left law firms, that work was being done by computers, [but] I still had to understand why those documents were critical, why discovery mattered, why that process mattered, even if I didn’t have the paper cuts to show for the work that I had done.” 

Atif Zaim, deputy chair and managing principal-elect for KPMG U.S., said that regardless of how someone gets those foundational skills, they’re still important for building later skills. He noted that he began doing basic things like bank reconciliations and fixed asset depreciation before moving on to the more complex work that eventually put him where he is now. He pointed out that this is the case even at the very basic level. 

“I have kids in high school. I’ve been involved in their education, and it’s interesting. Now you can have a calculator on every exam. But they were first taught how to do it manually,” he said. 

A loss or a change?

Others are not entirely sure. Yes, perhaps accountants today don’t do as many basic tasks as they used to, but does that really mean they’re losing skills? No one disputed that foundational skills are important, but not everyone thought automation and AI represented an especially dire threat against them. Further, there were questions as to whether performing basic tasks for years is really the best way to build those skills in the first place. 

Hrishikesh Pippadipally, chief information officer at Top 100 firm Wiss and Co., said some of the more old school partners at his firm were once concerned about skill degradation when optical character recognition technology came out, asking, “How are these kids going to know how to do a tax return if they don’t know where the numbers go in the boxes?” Years later, OCR has become commonplace and, Pippadipally said, people still know how to process tax returns. 

“No one stopped knowing how to do a tax return because now we have OCR technology,” he said. “The key is really just leaning into these technologies, and all that it’s going to be able to afford us: helping us save the time worrying about where the numbers go, and giving us more time to think about why the numbers are there.” 

Douglas Slaybaugh, a CPA career coach as well as the chief growth officer for agentic AI solutions provider uiAgent, said offshoring also takes care of routine menial tasks so in-house accountants can focus on higher level work, and has been around much longer than AI. But the profession, instead of losing its foundational skills, adapted. Firms still use offshoring all the time, and they’re still standing. This is because, in his view, one does not necessarily need to have done a process over and over again for years to understand how it works. He compared it to his car. 

“I drive a car. I have no idea how to fix my engine. I don’t know what those pistons do, I don’t know what those valves do, but I can still drive the car. The staff don’t have to know how the engines are built. They don’t have to know where every single detail is coming from. What they have to be able to do is use the information that AI is providing them.” 

In this respect, he said it’s not so much that foundational skills are degrading but, rather, what counts as a foundational skill is starting to shift, as it has several times before. He noted that he didn’t get to be in front of the client until he was a senior manager, and until then was constantly told, “No, we’ll go talk to the client, you stay in the conference room or stay in the office. We’ll go have the client conversation.” Now that entry-level accountants aren’t generally spending years and years with the kind of work now handled by AI, they can get in front of the clients and play a more strategic role earlier than before. 

“A lot of those things, they get to start learning and developing earlier. We don’t just save it until they reach a certain level now, and the benefit is felt mostly at the top, the partners, because they get to leverage more and delegate more, because they have more skilled, more available resources below them than they’ve ever had.” 

Stephanie Ringrose, a partner with California-based Navolio and Tallman, also raised the point of offshoring and outsourcing, noting that it took care of routine work. She added that while foundational skills are important, she wondered how much people were really getting out of manually keying in data over a long period of time. What’s more important is understanding why something is done in the first place. 

“Kind of like the bank reconciliation, you need to know how it’s done, why it’s done, to fully appreciate that the technology is assisting you with that piece, but also [need] to evaluate that just because we’re using AI technology, it doesn’t necessarily mean that is also the right answer. It’s not just blindly relying on it, either. … You have to understand some of the basics to evaluate it. But I don’t know that you have to do it so many times to get there,” she said. 

Desai, from Schellman, agreed that the baseline technical skills may not be as important as they were before automation and AI. However, performing such tasks is not just about knowing how to fill in a tax return, for example, but the ability to understand and contextualize the return—also known as critical thinking. This, too, is a baseline skill that Desai worries is starting to get lost as well. 

“Maybe technical skills aren’t what you should be looking for. You should be looking for people who are curious and adaptable, not just technically proficient. I want to hire people who are asking ‘why does this work this way?’ not ‘what button do I click right and when?'” she said. 

Pippadipally raised a similar point. As AI and automation take over more basic tasks, critical thinking will be more important than ever. He believes it will be less important to fill in the tax form and more important to know what the tax form means and how it fits within the context of the client’s particular circumstances. 

“As the AI starts to replace the [routine] work that we’re doing, what you end up having more time for is the ability to analyze, supervise and be a trusted advisor with your clients. So you need to be analytical, and you need to lean into the tools to understand what’s out there so that you can use those tools to service your clients, and you need to be able to engage with your clients in a personable way,” he said. 

Taylor, from alliantConsulting, agreed that human judgment and intuition have become more important in the age of AI and automation. Indeed, understanding technology has become essential for any accountant who seeks to advance their career. She stressed that her concerns regarding foundational skills are not a call to action against AI in accounting, but the opposite: a firm cannot use AI to the best of its ability without underlying knowledge of what it is doing in the first place. 

“I don’t want to ever underestimate the importance of that and how AI will play a role in working smarter … versus doing all the heavy lifting. But those are skills that must be cared for and learned no matter what they get into,” she said. 

Part 2 will look at how firms are adjusting their recruitment and training in response.

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Accounting

AI-Driven Automation and Continuous Accounting Frameworks

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The accounting profession is undergoing a fundamental structural transition as enterprise finance departments shift from periodic month-end closes toward automated continuous accounting models. By integrating specialized machine learning algorithms directly into enterprise resource planning (ERP) platforms, chief accounting officers are transforming financial reporting from a retrospective exercise into a real-time operational asset.

The Shift from Periodic Close to Continuous Financial Reporting
Traditional accounting workflows heavily relied on manual data reconciliation, spreadsheet calculations, and multi-week closing cycles at the end of each fiscal period. In contrast, continuous accounting frameworks utilize automated software agents to process, validate, and post transactional data in real time as business activities occur.

Automated bank reconciliation tools cross-reference incoming bank feeds, invoice records, and purchase orders automatically. By resolving transactional variances instantly throughout the month, corporate accounting teams eliminate the traditional workload spikes associated with quarterly and annual closes.

Machine Learning in Audit Trails and Anomaly Detection
Advanced natural language processing (NLP) and machine learning tools are redefining internal audit and financial control environments. Automated systems analyze 100% of general ledger entries, identifying anomalous transactions, duplicate payments, and unauthorized journal entries in real time.

Rather than relying on random statistical sampling, corporate internal auditors can focus their attention on high-risk flags automatically surfaced by algorithmic monitoring platforms. This continuous risk assessment strengthens internal controls over financial reporting (ICFR) and significantly reduces fraud risk.

Evolving Roles for Accounting Professionals
As routine data entry and manual reconciliation tasks become fully automated, the skill set required for accounting professionals is shifting toward data analysis, system design, and strategic business advisory.
– Systems Governance: Accountants are increasingly responsible for monitoring algorithmic accuracy and managing data integration pipelines.
– Business Partnership: Finance professionals leverage real-time financial dashboards to advise operational leaders on margin management and working capital allocation.
– Regulatory Compliance Management: Accounting teams utilize automated platforms to ensure compliance with dynamic tax codes and international accounting standards.

Core Implementation Recommendations
1. Deploy Automated Reconciliation Tools: Integrate continuous transaction processing modules into existing enterprise ERP architectures.
2. Establish Algorithmic Governance Controls: Implement strict internal testing protocols to ensure automated accounting rules comply with GAAP/IFRS standards.
3. Reskill Accounting Teams: Invest in training finance staff on data analytics, workflow automation, and predictive financial modeling.

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Accounting

Global ESG Reporting Standards and Double Materiality Compliance

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Corporate accounting departments face expanding reporting expectations as international sustainability disclosure standards achieve regulatory enforcement across major global jurisdictions. Chief Accounting Officers (CAOs) and corporate controllers are establishing rigorous internal accounting controls to treat Environmental, Social, and Governance (ESG) metrics with the same data precision, auditability, and governance as traditional financial statements.

Regulatory Harmonization Under Global Sustainability Frameworks
The implementation of standardized sustainability reporting frameworks—notably rules established by international sustainability accounting boards—has created unified expectations for public and large private enterprises. Corporations must report standardized metrics covering greenhouse gas emissions (Scope 1, 2, and material Scope 3), energy utilization, workforce demographics, and supply chain governance.

In Europe and other participating international jurisdictions, double materiality principles are mandatory. Under double materiality, organizations must report both how external sustainability risks impact corporate financial performance, and how internal corporate operations affect surrounding environmental and social structures.

Integrating Sustainability Metrics into Core ERP Systems
To provide auditable non-financial data, enterprise organizations are integrating specialized carbon accounting and ESG management platforms directly into core ERP systems. Automated data collectors capture energy utility invoices, logistics fuel consumption metrics, and vendor compliance records in real time.

Establishing automated, traceable data pipelines ensures that non-financial reporting is supported by clear audit trails. This structured approach allows external financial auditors to provide reasonable assurance on sustainability disclosures during annual corporate reporting cycles.

Financial Impacts and Capital Market Disclosure
Accurate ESG reporting directly influences corporate cost of capital and institutional credit ratings. Commercial lenders and institutional asset managers systematically incorporate sustainability metrics into risk pricing models. Companies that demonstrate transparent, verifiable progress in operational energy efficiency and climate risk mitigation benefit from expanded access to green bond markets and lower debt pricing.

Action Steps for Accounting Leadership
1. Implement Double Materiality Frameworks: Conduct comprehensive assessments to identify material financial and operational sustainability metrics.
2. Build Auditable Non-Financial Data Pipelines: Automate ESG data collection within core accounting software to ensure data integrity.
3. Align Sustainability with Annual Financial Filings: Prepare non-financial disclosures concurrently with financial statements to satisfy regulatory audit expectations.

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Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

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Internal audit departments and corporate risk managers are modernizing internal control frameworks by shifting from periodic sampling techniques to continuous monitoring and machine learning analytics. As operational data volumes increase across enterprise organizations, automated control testing ensures financial integrity, prevents corporate fraud, and streamlines annual audit engagements.

The Limitation of Periodic Audit Sampling
Historically, internal and external auditors evaluated internal controls by reviewing random samples of financial transactions—often analyzing less than five percent of total ledger entries. In complex enterprise environments, periodic sampling methods carry inherent risks of overlooking localized financial misstatements, unauthorized disbursements, or operational control breakdowns.

In 2026, progressive internal audit functions are utilizing automated continuous monitoring platforms that evaluate one hundred percent of financial transactions in real time. Continuous control auditing systems continuously monitor general ledger entries, procurement approvals, and expense reimbursements across all operating subsidiaries.

AI-Powered Fraud Detection and Anomaly Identification
Machine learning models trained on historical corporate financial data excel at identifying subtle transactional anomalies that indicate potential fraud or operational error. Automated systems instantly flag duplicate invoice payments, unapproved vendor creation, unusual journal entry timing, and unauthorized override of authority thresholds.

When an anomaly is detected, the automated auditing platform generates an instant risk alert, allowing internal audit teams to investigate root causes immediately. Early detection prevents minor operational errors from escalating into material weaknesses in financial reporting.

Streamlining External Audit Preparation
Continuous internal control monitoring delivers significant benefits during annual external financial audits. External audit firms can review continuous audit logs and automated control testing documentation, reducing the time required for manual field testing.

This integrated approach lowers overall audit compliance fees, reduces administrative burdens on corporate accounting staff, and provides senior management and audit committees with real-time visibility into the organization’s overall risk profile.

Core Implementation Guidelines
1. Transition to 100% Data Testing: Replace legacy sampling methods with automated continuous audit monitoring systems.
2. Deploy Anomaly Detection Algorithms: Implement machine learning models to identify unauthorized transactions and operational control overrides.
3. Align Internal and External Audit Workflows: Coordinate continuous control testing protocols with external auditors to optimize annual compliance cycles.

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