Accounting

The great reskilling: How AI is exposing the cracks in professional development

Published

11 months ago

September 10, 2025

The fourth industrial revolution isn’t just changing what accounting and finance professionals do — it’s revealing how fundamentally broken professional development has become.

While AI capabilities advance monthly, most professional development systems still operate on traditional and linear approaches that were designed for a world that moved at the speed of textbook editions, not software updates.

Consider ChatGPT. Within months of its release, it was already transforming how millions approached their workload, leaving many professionals scrambling to upskill. In fact, according to a Deloitte CFO Signals survey, 83% of finance leaders say AI and automation will significantly impact roles in their departments by 2028.

But while AI has shone a light on the gaps that exist, there has long been a need for change — and the numbers reveal just how urgent this transformation has become. According to the 2025 Future of Jobs Report, 63% of employers identify skill gaps as the biggest barrier to business transformation over the 2025-2030 period. As a result, 85% plan to upskill their workforce in response to macrotrends during this timeframe.

So how can they meet this challenge? It’s important to understand where traditional systems that focus on professional development are falling short.

The speed gap

Consider this challenge: The most advanced AI models today can learn and adapt to new information in real-time, while traditional curriculum development naturally requires several years for integration.

The speed gap illustrates how academic institutions, which necessarily prioritize thorough curriculum development and validation, operate on different timelines than rapidly evolving business needs. The foundational knowledge provided by traditional education remains essential, but it needs to be complemented by more agile learning pathways for emerging skills that enable lifelong learning.

The quality gap

While speed may be essential, many professionals face learning options of dubious quality.

The explosion of online learning has met the need for emerging skills, but lacks the rigor and accreditation that comes with an academic institution. This creates challenges in identifying reliable sources for emerging skills.

Research shows that employers often struggle to evaluate the quality of nontraditional credentials, creating uncertainty for professionals seeking to build on their academic foundation.

The relevance gap

While broad theory and foundations are essential, working adults often need focused, immediately applicable skills that are specific to their role and complement their existing education. This is particularly true for accounting and finance professionals.

Research on adult learners consistently shows they are price sensitive and seek high return on investment, immediate applicability and modular formats that fit into busy professional lives — needs that traditional academic structures aren’t designed to address.

The new professional development infrastructure

These gaps highlight the need for complementary development pathways that work alongside traditional education. As the chief product and technology officer at the Institute of Management Accounting, this is a challenge my team tackles every day: How can professionals acquire flexible, trusted, business-ready credentials that help them respond to emerging trends in their field?

Here’s how we approach this at IMA, and how we recommend every employer respond when tackling the Great Reskilling challenge within their own organization.

1. Competency-based assessment

You can’t stay on top of emerging technologies if you aren’t regularly assessing the capabilities of your workforce.

Diagnostic assessments can provide real-time competency mapping that goes beyond static qualifications and identify emerging gaps before they become career limitations. For example, CPAs with strong foundational knowledge might discover through diagnostic assessment they need focused development in AI-powered forecasting tools to remain competitive in their current role.

These assessments need to happen on a regular cadence, outside of just the annual review cycle, so professionals can understand their competitive positioning in real-time and make strategic learning investments accordingly.

This might seem like a daunting place to start, which is why IMA surveyed hundreds of employers to determine what competencies are essential for a future-ready accounting and finance professional. We call it the Competency Framework, and it’s how we guide the development of our credentials.

2. Targeted skill development

Skill-specific training focuses on education that will have the greatest impact on outcomes for both the individual and the organization. This approach allows professionals to enhance their existing qualifications with concentrated development in emerging areas, immediately applying new capabilities while leveraging their foundational knowledge.

The economics appeal to both adult learners and their employers, as organizations can invest in employee development more strategically, addressing real-world business problems rather than generic skill building. This targeted approach also reduces the risk that training investments become obsolete before they’re fully utilized — a growing concern in rapidly evolving fields.

A finance professional, for instance, might pursue concentrated training in AI-assisted budgeting techniques that can be immediately applied to solve current business challenges, creating immediate value while building future capabilities.

3. Durable skills focus

While it’s important to invest in training that responds to emerging technologies and methodologies, it’s also essential to invest in the foundations that will likely transcend digital transformation.

Durable skills are the uniquely human capabilities that become increasingly critical as AI and automation expand. Yet many professionals mistakenly assume they only need to become technical experts to remain relevant.

In reality, the most valuable skills often lie in exercising sound judgment about AI recommendations, collaborating effectively in human-AI teams, and synthesizing diverse data sources into strategic insights. These competencies transfer across technological shifts, providing career resilience that technical skills alone cannot offer.

Critical durable skills include the ability to discern when AI insights require human interpretation, skill in communicating AI-derived recommendations to stakeholders who may be skeptical of automated analysis, and competency in designing AI-human workflows that optimize both efficiency and accuracy.

While technical skills may have shorter relevance cycles, these human-centered capabilities remain valuable precisely because they enhance rather than compete with AI capabilities.

4. Role-aligned learning paths

Role-aligned learning paths tie everything together by connecting competency development to specific job functions and career trajectories. Rather than one-size-fits-all professional development, these paths recognize that an accounts payable manager and a financial analyst need different competencies, even within the same organization.

This alignment ensures that learning investments translate directly to job performance improvements. A financial analyst might focus on AI-enhanced data visualization and predictive modeling, while a controller might prioritize AI applications in compliance monitoring and risk assessment. Both are developing emerging skills, but in ways that immediately enhance their current roles while building toward future opportunities.

Building capacity for ongoing relevance

Regardless of how organizations choose to approach workforce upskilling, one thing is persistently clear: Employers need to implement a shift in mindset. Organizations need to move to a continuous development framework that allows time and resources to diagnose and develop.

This doesn’t mean professionals need to be constantly in formal training programs. Instead, it means building learning agility — the capacity to quickly identify, acquire and apply new competencies as business needs evolve. Organizations supporting this approach provide time and resources for diagnostic assessment, targeted skill development, and experimentation with new tools and approaches.

As AI has shown us, it’s not about learning new skills once. It’s about building the capacity for ongoing relevance in a world where the definition of “qualified” evolves continually, not annually. At IMA, that means helping professionals take the lead where it matters most: strategic thinking, decision-making and real-world outcomes that matter to the bottom line.

Accounting

AI-Driven Automation and Continuous Accounting Frameworks

Published

2 days ago

August 15, 2026

Business Finance News

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.

Accounting

Global ESG Reporting Standards and Double Materiality Compliance

Published

1 week ago

August 9, 2026

Business Finance News

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.

Accounting

Modernizing Internal Controls: Machine Learning and Continuous Monitoring in Auditing

Published

2 weeks ago

August 5, 2026

Business Finance News

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.