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AI automation shifts staff training

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(This is the second of a two-part series about the effect of AI and automation on fundamental skills. Part 1 can be found here.) 

In an age where the kinds of repetitive, manual, compliance-driven work is increasingly automated by AI, firms have had to rethink their approach to recruitment and training to ensure their new hires can still learn the foundational skills they’ll need to perform higher value work. 

Traditionally someone new to a firm would have a short onboarding period followed by years and years of on-the-job training alongside a more experienced professional. Over time, repetition plus experience would, on an ideal level, eventually produce a skilled professional who can then go on to perform higher value advisory work. While there were many differences of opinion among sources, all agreed that this old model no longer works for today’s era. With AI rapidly taking over the repetitive compliance-driven work new hires used to do, everyone we talked to agreed that firms need new approaches. 

“[Foundational technical skills are] still important, and so we still have to train on it, but I think it’ll be less about on the job training. Historically, when you came up in a public accounting firm, it was a lot of on the job learning. You learn by doing. But if the technology is eliminating the doing part, then the learning has to change too,” said Hrishikesh Pippadipally, chief information officer at top 100 firm Wiss and Coo.

While it might be easy to lay the blame for this entirely on AI, Avani Desai, CEO of top 50 firm Schellman, felt that was too simple. In her view it is not so much the technology that is the issue but that training and education have not kept up with the technology. If people are not developing foundational skills because of automation, and if those skills remain important for professionals, it is incumbent on the firm to bring new hires up to speed, which she understands may take some time. 

“I don’t think tech is actually the problem. I think it is the lack of structured training. When you go to a big place like an accounting firm, you get about one week of onboarding, maybe two weeks. I think we’re gonna have to do 12 weeks of onboarding, 18 weeks of onboarding, to actually train on those lower level skills.” 

She was describing something very close to Schellman’s own model, with Desai saying new hires are flown to its office in Columbus, Ohio (opened in 2022) for 18 weeks of classroom style training, paired with 52 weeks of on the job training with a mentor. She conceded that it was a big investment, and an expensive one as “the first 18 weeks are not chargeable,” which does somewhat limit scalability. 

“You can teach lower level control testing. You can teach reconciliation, if you have to. But again, 12 to 18 weeks, it’s a big investment, and so our classes are small. We have fewer than 25 people. If you go to firms that have hundreds of people on board, it becomes difficult,” she said. 

But beyond the raw skills, the training also emphasizes critical thinking and judgment. When AI handles most of the basic tasks, the human needs to be able to vet the tools, evaluate the outputs, and be able to understand the wider context behind the data. This means training also includes things like communication and asking the right questions, curiosity for finding out the whys behind the whats, and observation for finding things that don’t quite fit. 

“We really need structured, tech enabled training paths that teach both how to do it and why to do it. That’s more of a people process, less of a tech. And tech isn’t going to solve it. Giving someone five different pieces of tech and saying, ‘Go out and do it’, they could say, ‘Okay, I pressed these buttons.’ But again, you have to go deeper. Why am I pressing these buttons? And what is happening, and what’s the analysis that I need to do? So it’s both how and why,” she said. 

This is part of a wider trend towards emphasizing the non-technical skills for new hires, with pretty much everyone mentioning the need especially for critical thinking and skepticism. Yolanda Seals-Coffield, PwC U.S.’s chief people and inclusion officer, mentioned it when discussing things like intensifying mentorship and apprenticeship on the job combined with practical simulations and digital training. The goal is to impart not just the foundational skills but the critical thinking to apply them to higher value work, which can involve a blend of hands on work, team coaching, AI enabled tools and close mentorship. 

“You are spending time with the people that you work with, who are making sure you understand the ‘why.’ You’re not just [using] the output of what AI is giving you… We need to make sure that people understand why it is happening,” she said. 

While this happens naturally throughout the workday, she added that “we will have to be even more intentional than it was before.” 

Stephanie Ringrose, a partner with California-based Navolio and Tallman, similarly stressed the importance of critical thinking skills in today’s era, gearing the firm’s training to go beyond just technical abilities. 

“We’re going to build on that, to give you technology as a layer to help you now understand and evaluate. We’re going to focus your training more on ‘does this make sense? What is the judgment? What is the critical thinking behind it?'” she said. 

Ringrose added that, at her own firm, while they have a lot of AI-enabled tools, there’s still a lot of work that must be done by a human and so having those core skills remains vital. While the AI handles a lot of returns, humans are still trained on how to fill them in themselves so that they can both do the work AI perform as well as understand the AI’s outputs. 

Pippadipally, from Wiss, said learning involves working side-by-side with a team, but in addition to that the firm also emphasizes self-directed learning through digital resources. Taken together, he said, professionals can both learn proactively but also have others to support them. 

“And so having learning that’s on the job and side by side with a team, but also having learning that’s recorded that you can reference back to… even if we’re not together.”

Joy Taylor, managing director of Texas-based alliantConsulting, said the learning process needs to happen well before someone is hired at a firm in the first place. She said that, over time, college education might focus more on giving students practical experience to be ready on their first day. In contrast, she said, recent graduates often find that there’s a vast gulf between what they learned in school and what they’re expected to know at work. While every educator aims to narrow this gap, Taylor felt it will be especially important as AI takes on more mundane tasks. She also anticipated that internship programs may also get more rigorous. 

“I envision in the future that professional services companies, instead of hiring right out of college and having summer intern programs, making those summer intern programs even more robust and possibly have a stronger relationship with universities building out programs such that students in school are actually getting the experience that we used to get after we got the job. I think there’s going to be some advanced relationships in the pipeline, development starting closer in the college in a freshman, sophomore, junior, senior experience, versus waiting for a six week summer intern just to gain a little bit of experience,” she said. 

But this brings us to the original problem: the pipeline. Fewer students are pursuing the field, which raises the question of how many people would actually be in this revamped system. This is partially addressed by embracing alternative staffing models, fractional roles and other ideas that try to go past the traditional firm model. But she also said students also need to be made more aware of the cultural changes in the profession, emphasizing the strategy-forward nature of many firms today. 

“I really lean in very much on even career rebranding. I think accounting is facing a crisis right now because of the challenges of becoming an accountant. Maybe we need to rebrand it, emphasizing the strategic. Let’s strategy do the heavy lifting, instead of it feeling like it’s a bookkeeping service, because that’s not what people [want to do] anymore. What they’re using those professional services for is the strategic tax and accounting and programs that can be set up to benefit individual users and businesses,” she said. 

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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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