Artificial intelligence

Introduction The present enthusiasm for AI is rational, writes Emmet Kelly, but it needs to be balanced with proper governance, legal compliance, risk management, and human responsibility. Irish businesses are embracing artificial intelligence (AI) with enthusiasm. Across sectors such as professional services, finance, retail, manufacturing, logistics, as well as the public sphere, there is a widespread perception that AI, in a general sense, is a decisive productivity tool, capable of accelerating analysis, improving decision-making, and reducing operational friction. Most companies do not ‘adopt AI’ as a single initiative. Instead, they accumulate multiple AI systems over time – some explicit, some embedded, some user-driven, each with distinct risk profiles, data dependencies, and governance requirements. AI is not a single tool, nor a discrete system that can be cleanly ‘adopted’ or ‘switched on’. It is now a pervasive layer embedded across the software stack, the internet, and a set of tools to be easily accessed and used daily in the world of work. The principal challenge for companies therefore is not technical capability, but coherence: understanding where AI is present, what role it plays in decision-making, and how accountability is maintained across this fragmented landscape. The enthusiasm for AI is rational. AI systems continually demonstrate an impressive ability to summarise complex information, generate plausible text, detect patterns at scale, and automate tasks that previously required substantial human effort. However, this enthusiasm and use frequently outpaces a realistic appreciation of the complexity, opacity, and governance challenges that accompany AI deployment. For the accounting profession in particular – for whom judgement, verification and accountability remain foundational – this imbalance presents material risks. This article explores how Irish businesses are encountering AI in practice, why its complexity is often underestimated, and what recent research conducted by Amárach reveals about readiness among SMEs. Effective governance will require not only regulatory compliance, but a clearer understanding of the optimum relationship between humans and machines, one that preserves responsibility rather than attempting to outsource it. The illusion of simplicity For many organisations, the first encounter with AI is through publicly accessible systems such as OpenAI’s ChatGPT, Claude by Anthropic, or Gemini from Google. These large language models (LLMs) present AI as conversational, accessible, and apparently intuitive. Users ask a question, using ‘prompts’, and receive a fluent, structured response, from what appears to be a single, confident synthesis of vast amounts of underlying information. This interaction, or ‘chat’, creates a powerful illusion of simplicity. The complexity of the system, the scale of its training data, the probabilistic nature of the outputs, the constraints imposed by prompts, and the absence of any true understanding remains largely invisible. AI doesn’t understand anything. It merely matches words, numbers and context of the chat that best fits the data the AI is referencing. What appears to be a dialogue is a statistical process that predicts likely continuations of text based on patterns in data. The capacity to understand and interpret the quality and value of the AI response, or output, remains the responsibility of the human user. For professionals accustomed to contextual awareness, critical analysis and judgement, this distinction matters. LLMs do not ‘know’ when nuance is missing, when assumptions are incorrect, or when an answer is incomplete. They summarise, average, and generalise. In doing so, they may lose minority positions, edge cases, and context-specific considerations that are often critical in accounting, audit, tax, and governance work. AI in office and productivity software Beyond these publicly visible systems, AI is now embedded in many workplace productivity tools. Platforms provided by Microsoft, Google, and Apple increasingly incorporate AI-driven features: document drafting, spreadsheet analysis, email prioritisation, meeting summarisation, and even predictions of where trends in numeric data are likely to lead over time. Because these capabilities are integrated into familiar software, they are often perceived as incremental enhancements rather than as AI systems per se. Yet the AI governance implications still apply. For example, automated summarisation of discussions may omit critical qualifications or nuances. Predictive suggestions may reinforce historical biases. Decision-support features may subtly shape professional judgement without being formally recognised as decision-making inputs. The risk here is not malicious intent, but unexamined reliance. When AI-generated outputs are treated as neutral or authoritative simply because they are embedded within trusted tools, accountability can become blurred. AI in enterprise systems AI’s influence extends further into enterprise systems that underpin organisational operations. Enterprise resource planning (ERP), customer relationship management (CRM), and accounting platforms from providers such as SAP, Salesforce and Sage, now deploy AI for forecasting, anomaly detection, credit assessment, inventory optimisation, and workflow prioritisation. In these contexts, AI outputs can directly influence financial reporting, risk classification, and operational decisions. Yet they are often treated as system features rather than as models with assumptions, limitations, and potential points of failure. For accountants and finance leaders, this raises critical questions: Who validates these models? How are errors detected? What documentation exists? And how does professional responsibility apply when an AI-driven recommendation is followed? In-house AI models Larger organisations increasingly develop AI models ‘in-house’, using proprietary data to support functions such as fraud detection, credit-risk assessment, demand forecasting, and operational optimisation. These systems may be customised, powerful, and bring competitive advantage, but they also bring concentrated risk. In-house AI models depend entirely on the quality, scope, and representativeness of the data used to train them. They may reflect historical practices that are no longer appropriate, or embedded organisational biases. Without robust governance, policies, procedures, documentation, and on-going monitoring, such systems can quickly drift away from legal compliance and ethical acceptability. Regulation: the EU AI Act in context The European Union (EU) has sought to address these risks through the EU AI Act, which introduces a risk-based framework for AI governance. The EU AI Act emphasises transparency, human oversight, data quality, and accountability, principles that align closely with professional standards in accounting, auditing and assurance. However, regulation alone cannot resolve the underlying issues, as AI is no longer confined to discrete, easily identifiable systems. It will pervade software, services, and information flows from within an organisation, and often beyond. Organisations may be using dozens of AI-enabled tools without explicitly recognising them as such. Compliance, therefore, cannot be treated as a one-off assessment; it must become an on-going capability. Are Irish businesses AI-ready? Recent AI-readiness research conducted by Amárach Research in collaboration with InstaComply provides a clear picture of this structural gap. While the findings indicate strong enthusiasm for AI adoption among Irish SMEs, many of which are deploying AI at speed, there are also significant weaknesses in governance readiness. While many companies are experimenting with and deploying AI, far fewer have established clear ownership, policies, controls, and governance structures required to manage these systems safely, transparently, and in compliance with existing and emerging regulation. For example, only 37% have appointed a policy owner responsible for AI and data governance, while just 32% maintain risk registers that include AI-related risks. More than one-third have none of the basic structures that the EU AI Act will expect businesses to maintain. These findings do not show a failure of intent, but a structural gap. Use of AI is moving from an experimental phase to the operational core, yet the governance mechanisms needed to control it remain underdeveloped. The EU AI Act is not simply another compliance obligation – it requires a fundamental shift in how organisations must design, monitor, and document their automated systems. Taking responsibility Arguably, and as can be seen from our research findings, Irish businesses are engaging in “conversations with machines”, most often using LLMs, without fully understanding the mechanisms underlying the ‘conversation’ or the operations and quality of the machine with which the user is conversing. LLMs respond blindly based on the level, quality, and structure of the data that informs them. They do not challenge objectives, interrogate ethical implications, or assume responsibility for outcomes. Where complexity is poorly understood, responses tend to polarise. Some users may become distrustful, focusing on AI’s errors and limitations and rejecting its utility. Others may move in the opposite direction, treating AI outputs as authoritative and implicitly transferring responsibility to the system. Both kinds of behaviour present problems and risk. AI does not absolve individuals or organisations of responsibility, nor should it be dismissed as inherently unreliable. A useful analogy is that of tools in the physical world. The saying, “a bad workman blames his tools”, holds true for the use of AI, and a driver should not blame their car for their negligent driving. The workman remains bad, the driver negligent, and the tools and machines, just that: tools and machines. Responsibility remains with the human agent and the organisation deploying the tools. A new RACI model for human–AI collaboration What is required is a new articulation of responsibility, effectively, a new ‘RACI’ model that clarifies who is responsible, accountable, consulted, and informed when AI systems are used. This approach reflects a broader shift: compliance must move from static documentation to dynamic, operational governance, which embeds EU AI Act requirements, such as data quality, traceability, and human oversight, directly into development and operational processes as code. Human-in-the-loop approaches are not merely a regulatory preference; they are a practical necessity for maintaining standards, managing risks and sustaining businesses on the frontiers of rapidly changing, brimming with possibilities AI landscape. Conclusion: learning to drive the machine AI represents an extraordinary technological advance, and Irish businesses are right to explore its potential. But power without understanding presents risks. The accountancy profession, with its long-standing emphasis on judgement, accountability, and assurance, is well placed to lead a more mature, responsible and strategy-led engagement with AI. The challenge is not to slow innovation, but to learn to ‘drive’ these machines responsibly – within the limits of the law, ethics, business sense and professional judgement. AI is a tool, not an actor. Recognising that distinction will be central to protecting customers, clients, organisations, and public trust in the years ahead. Emmet Kelly is an AI data governance and compliance expert, and CEO of InstaComply, which empowers organisations to navigate regulatory complexity with smart automation.

The job market for Chartered Accountants at all levels will be a combination of positivity, optimism and caution. While overall the sentiment is positive, recruitment in some areas is moderating in response to cost-efficiency pressures and business transformation. That said, overall, the demand for Chartered Accountants is predicted to remain strong. Some employer will continue their journey of adapting their approach to traditional hiring models, focussing on the acquisition of specialised skills and investing heavily in talent pipelines to secure the next generation of Chartered Accountants. For job seekers and employers alike, understanding these trends is essential to staying competitive in an evolving market.   There will continue to be uncertainty in relation to global economy and geopolitical conditions, however, to date employers continue to prioritise hiring experienced finance professionals capable of driving governance, transformation, and strategic financial performance. The job market for Chartered Accountants is proving to be resilient including at the newly and recently qualified level and the outlook overall is positive.  What sectors are recruiting  Professional services & corporates Large professional services firms and international practices are continuing to hire finance, accounting and advisory talent across all areas of their business in Dublin and regionally. Large corporates and multi-nationals are continuing to recruit after a brief hiatus related to the concerns in relation to the impact of potential tariffs.   SMEs and small medium sized practices  The outlook in these areas is positive also with sustained recruitment activity across practice and industry.  Where will the roles be located  Dublin  Dublin continues to be a central hub for options, particularly in Technology, Financial services, Shared Services, and Life Sciences and pharmaceuticals.  Regional growth  We expect to continue to see increased levels of hiring across practice and industry regionally during 2026 following a pick-up in 2025. This includes within the SME and indigenous sectors.  Northern Ireland  Belfast remains active, driven by professional services, manufacturing, public sector reform and continued growth in Tech and Fintech. Northern Ireland’s public sector continues to offer strong opportunities at senior finance levels due to ongoing transformation initiatives.  Global opportunities Irish Chartered Accountant continue to be highly sought after worldwide, with roles available in the UAE, Australia, Canada, the US, and the Cayman Islands  Salary trends  After years of inflation-driven increases, salary growth is likely to continue to stabilise in 2026, especially for entry to mid-level positions. This is the case in ROI and NI. However, experienced Chartered Accountants with specialist skills in the areas of financial planning & analytics, business transformation and automation will continue to command premium packages.  Bonus structures will remain attractive and will be aligned with company and personal performance in many instances. Comprehensive and flexible benefits packages will remain to attract and retain top talent.  It is worth noting that pay expectations will continue to be tempered by hybrid working flexibility rather than driven solely by base salary with candidates prioritising flexibility and work-life balance over financial considerations.  Hybrid and remote working  Hybrid models are now the norm with many qualified accountants expecting to work from home at least some of the time with most spending 2/3 days in the office. Despite the increased focus on a return to the office for the most part hybrid working remains the approach adopted by many organisations.    Skills in demand  Leadership capability continues to be one of the most sought‑after skillsets particularly for those seeking to advance their careers and who are managing and developing growing finance teams and businesses.  Communication and stakeholder engagement skills remain key differentiators for senior candidates.  Technical competence in audit, tax, governance, compliance and complex reporting remains essential in both practice and industry roles. Strong monthly and quarterly reporting discipline continue to be critical for organisations navigating uncertain conditions in 2026.   Data and digital skills including Power BI, automation tools, ERP expertise and system literacy are increasingly required due to the developments in IT, AI and automation.   Business transformation experience, especially in process optimisation or finance function redesign, is in high demand as is experience of managing projects in these areas.  Interview and recruitment trends  Interviews will continue to be a combination of virtual and in-person meetings with the initial screening process most likely to be conducted online followed by a more detailed in-person interview. AI is also being incorporated into recruitment processes including AI screening and structured assessments to help employers with their evaluation.  Competency based interviews are being used across organisations small and large and are pivotal in focusing on leadership behaviours and demonstrating impact.  Timeliness for interview processes have become more protracted in recent times with hiring managers being more discerning in terms of the selection process. This is a trend that is likely to persist in 2026.   Conclusion  The outlook for the jobs market for 2026 is positive with opportunities arising for Chartered Accountants at all career stages. To remain competitive members will need to remain up to date with market developments including recruitment trends and the skills requirements of employers which are evolving inline with market developments.  

Paul Redmond writes. Paul is the founder of RDA Accountants. A recognised voice in modern accountancy, Paul helps business owners and accountants achieve clarity, growth, and long-term impact through his frameworks on wealth, strategy, and advisory transformation. Introduction: a defining decade Every profession has defining decades – periods when technology and expectations force a complete reinvention. For accountants, this is one of those decades. We’ve already lived through three major shifts: from ledgers to spreadsheets, from desktop software to the cloud, and from static reporting to real-time collaboration. Each step freed us from manual drudgery and increased our efficiency. Artificial Intelligence (AI), however, is different. Unlike past shifts that digitised existing work, AI reshapes the work itself. It changes what accountants do, how we deliver value, and even how clients perceive us. Used poorly, AI risks reducing us to faster processors of compliance tasks - a commodity in a race to the bottom on fees. Used strategically, it gives us the power to become navigators of business success, guiding clients with insight, foresight, and clarity. The choice is ours. Why AI is arriving now AI’s rapid arrival in accountancy isn’t random. Four converging forces make this the perfect moment: Data overload: businesses now produce enormous volumes of data from e-commerce, CRM systems, banking feeds, and apps. Most of it goes unused because humans can’t process it all. AI thrives in this environment, ingesting and analysing vast datasets in seconds. Rising client expectations: Netflix predicts films, Google anticipates searches - our clients live in an AI-powered world. They now expect real-time insights, proactive guidance, and personalised advice from their accountants, not just year-end reporting. Margin pressure: Compliance work is being commoditised by cloud software and low-cost providers. To escape shrinking margins, firms must shift towards higher-value, insight-led services. Talent shortages: Fewer graduates are choosing traditional accounting. The repetitive nature of compliance makes retention difficult. AI offers relief by automating low-value work, freeing teams for more engaging, strategic roles. Together, these forces make AI not optional, but essential. Practical AI in today’s firm AI isn’t a distant future – it’s already embedded in tools we use daily. Here are six practical applications that are reshaping firms: Automated data capture: OCR and machine learning categorise invoices, receipts, and bank transactions with minimal human input (e.g. Dext, Auto Entry). Predictive forecasting: Dynamic models replace static spreadsheets, enabling scenario planning in real time (e.g. Futrli, Fathom). Plain-language reporting: NLP tools translate financial data into clear narrative commentary clients can actually understand (e.g. Microsoft Co-pilot). Workflow optimisation: AI analyses projects, reallocates workloads, and helps practices meet deadlines more reliably (e.g. FYI Docs with Co-pilot). Anomaly detection: Machine learning flags unusual transactions and potential fraud instantly. Knowledge management: AI assists with tax or compliance research, cutting hours from manual work and increasing confidence in advice. Key point: AI replaces repetitive effort, not accountants. It frees us to spend more time interpreting, guiding, and advising. Avoiding the trap: tech-first thinking One of the biggest mistakes firms make is starting with the tool instead of the outcome. Too often, a partner buys software after a slick demo, only for it to gather dust when it doesn’t fit real client needs. The better path is client-first adoption: Define the client result (e.g. “improve cash flow visibility”). Map the process to deliver it. Identify the AI that accelerates or enhances that process. When AI is embedded in a structured, outcome-driven workflow, it stops being a shiny toy and becomes a genuine profit driver. A client-first model for AI adoption Firms succeeding with AI often follow a five-stage rhythm: Discovery – data pull: AI-enabled tools gather a client’s full financial position in minutes, not hours, creating a rich foundation for advisory conversations. Clarity – turning data into insight: AI converts raw data into dashboards, benchmarks, and models, highlighting the top opportunities or risks without drowning clients in spreadsheets. Guidance – human + AI: Accountants interpret insights, ask deeper questions, and deliver recommendations. AI provides the analysis; humans provide wisdom and context. Execution – reliable delivery: Workflow tools automate follow-ups, deadlines, and task allocation so advice is consistently delivered. Continuous monitoring – always-on support: AI alerts accountants to risks or opportunities between meetings (e.g. low cash thresholds), enabling proactive contact. This model transforms advisory from one-off sessions into continuous partnership. Case studies – AI in action Manufacturing cash flow turnaround: A €2.8m family-owned manufacturer struggled with stock inefficiencies. Using AI forecasting, the firm modelled different reorder strategies. A just-in-time approach cut stock write-offs by 40% and freed €120k in cash, which funded new machinery and growth. Retail margin improvement: A retailer saw sales rising but margins falling. AI sales mix analysis revealed 12% of SKUs (Stock Keeping Unit) were unprofitable once marketing spend was factored in. Dropping these improved net margin by 2.5% annually. Result: In both cases, AI supplied clarity, but the accountant supplied confidence and strategy. Overcoming adoption barriers Even with clear benefits, adoption isn’t smooth. Common barriers include: Skills gap: Teams fear they lack knowledge. Fix: Run small AI literacy workshops on tools staff already use. Nominate an “AI champion.” Cost concerns: Licences feel expensive. Fix: Start with one high-impact use case, prove ROI, then expand. Cultural resistance: Staff fear job loss. Fix: Frame AI as support, not replacement – removing low-value work so people can focus on meaningful, engaging tasks. Data security: Clients worry about confidentiality. Fix: Vet vendors rigorously, demand compliance certifications, and communicate transparently about data use. Handled well, these barriers become opportunities to build trust. Redefining the accountant’s role AI doesn’t change what clients ultimately seek: trust, clarity, and strategic partnership. It simply enhances our ability to deliver it. The accountant of today – and certainly of 2030 – will be: A navigator: using AI insights as a compass to help clients chart their course. A translator: converting complex data into clear, empowering stories. A strategist: aligning financial insight with business goals, spotting opportunities, and mitigating risks. Future specialisms will emerge, from data accountants skilled in governance and analytics, to CFO-as-a-Service providers offering real-time strategic guidance to SMEs who can’t afford full-time CFOs. The automation of compliance gives us back the most precious resource: time. What we do with it defines our future. The ethical compass As trusted professionals, we must ensure AI is used responsibly. Four principles matter most: Bias: AI learns from historical data, which may carry hidden biases. We must question and validate outputs. Transparency: Black-box models can’t justify conclusions. Accountants must ensure advice is explainable. Governance: Clients deserve clarity on where data is stored, who can access it, and how it’s used. Accountability: No matter how advanced the AI, responsibility for professional advice rests with us. Our credibility depends not on how advanced our tools are, but on how responsibly we use them. Roadmap – bringing AI into your practice You don’t need a revolution overnight. A structured approach works best: Identify one high-value client outcome (e.g. faster invoice payments). Map your current process. Choose an AI tool to enhance it. Pilot with a small group of willing clients. Refine based on feedback. Standardise and roll out more broadly. Review quarterly to adapt and improve. This rhythm turns AI from an experiment into a consistent growth engine. Conclusion – leading the change AI will reshape accountancy whether we like it or not. The firms that thrive will not be the cheapest or the fastest at compliance, but those who combine AI’s scale with human judgment, trust, and empathy. We can remain record-keepers of the past - or become navigators of the future. That future is already here. The only question is: will you lead with it? This excerpt has been taken from the September 2025 edition of Practice News.