Auciera

Tag: Autonomous AI

  • The Death of Accounting Software

    The Death of Accounting Software

    The Death of Accounting Software

    How dynamic workflows will reshape accounting solutions

    For more than three decades, larger scale accounting software (i.e. ERP solutions) has been developed around a stable premise: the process is designed first, encoded in the application and then adopted by the user. The steps to customize the menus, approval paths, reports and exception rules differ subtly from one product to another, but the underlying model is consistent. The consultant configures the client workflows in the software, as best they can, and this establishes how users interact with software, while the organization modifies their processes to work with the software.

    For most small and mid-sized businesses, improving operational efficiency has traditionally meant selecting software with the closest approximation of their desired workflow and then layering additional automation tools around it. The objective has never been to create a workflow unique to the organization, but rather to configure and extend a predefined one. This architectural constraint has shaped enterprise software for decades and has become so commonplace that many organizations no longer question it.

    This model has been effective because accounting requires consistency: a general ledger must remain complete; transactions must be traceable; access must be controlled; and review must be documented. Standardized applications brought discipline to processes that had previously depended on paper files, individual memory and local practice. These applications also made it possible for organizations and accounting firms to serve more clients without rebuilding their systems for every engagement.

    The next evolution of these accounting solutions continues with this approach as they are unlikely to discard the foundational structure. Ledgers, controls, audit trails and reporting standards will remain essential. Rather than redesigning these applications from first principles, most software vendors are extending them by layering AI onto their existing architecture. AI assistants can answer questions, categorize transactions, summarize reports, draft communications and automate selected tasks, but they generally operate within workflows that were designed long before modern AI capabilities emerged. The underlying process remains largely unchanged. The limitation, however, is that AI remains subordinate to the workflow rather than defining it. It can accelerate individual tasks, provide recommendations and reduce manual effort, but it generally cannot reshape the process based on the circumstances of a particular client, engagement or organization. The workflow itself remains a product of software design rather than organizational knowledge.

    AI is beginning to make it possible for workflow to be assembled and adjusted around the circumstances of the work, rather than being fully prescribed in advance. This is where a fundamentally different architectural model begins to emerge. Rather than treating AI as another feature embedded within predefined workflows, dynamic workflow environments place AI at the orchestration layer. Instead of asking, “What is the next step in this process?” the system asks, “Given everything I know about this client, this organization, previous decisions, current priorities and available information, what should the next step be?” The workflow is no longer static; it evolves continuously as the system learns from human decisions, changing business conditions and accumulating organizational knowledge.

    That distinction is important as AI is currently treated as an additional feature within existing applications. A user can ask a question, summarize a report, draft a client message or receive a suggested account classification. These tools can be useful, but they leave the operating model largely unchanged and manually configured workflows by the implementation continue to take 2 to 6 months. The user still decides what must happen next, moves information between applications and steps, follows up on missing evidence and determines when an exception requires professional attention.

    A more consequential use of AI is emerging in systems that can interpret an objective, identify the steps required, use available tools, monitor the result and revise the sequence when circumstances change. The Associated Press has described the central difference between a conventional chatbot and an AI agent as the movement from producing an answer to taking action across a multistep task. A 2025 MIT Sloan Management Review and Boston Consulting Group study, cited by the AP, characterized these systems as capable of planning, acting and learning with greater independence than prompt-and-response tools.[1] The terminology remains unsettled, and some products described as agentic provide only limited autonomy. The underlying direction, however, is clear: software is being developed to participate in the execution of work rather than only presenting information to the person executing it.

    Microsoft has advanced a similar position. Reporting on Satya Nadella’s recent comments, the Financial Times noted his warning that organizations must develop and retain their own learning capabilities rather than allow the value created from their knowledge to accumulate entirely within external AI platforms.[2] His observations regarding learning systems suggest that enterprise advantage will depend on the interaction between human expertise, organizational data and systems that can learn from repeated use. In that model, the most valuable capability is not access to a general-purpose model. It is the ability to turn an organization’s experience into a continually improving and evolving operational system.

    This view is consistent with established research on process management. Thomas H. Davenport and Thomas C. Redman argued in Harvard Business Review that AI and process management should be developed together. Their position matters because isolated AI tools often improve individual activities without improving the end-to-end process in which those activities sit.[3] A faster classification, summary or response does not necessarily improve a workflow if information remains incomplete, responsibilities remain unclear or exceptions continue to circulate between people and systems. Process performance depends on the design of the whole sequence, including the points at which judgment, verification and escalation occur.

    This is where accounting provides a useful test. Accounting work is commonly described as a sequence of technical tasks: import transactions, classify them, obtain documents, reconcile accounts, prepare entries, review the file and issue a report. In practice, the difficulty lies in the context connecting those tasks. A transaction description may be incomplete, or the same vendor may represent different expenses for different clients, or a receipt may explain only part of a payment, or a recurring balance may be normal in one business and a warning sign in another. The accounting treatment may depend on information obtained in a meeting, an agreement stored elsewhere or a decision made during the prior year.

    Experienced accounting professionals manage this complexity by drawing on knowledge that is distributed across the firm. They remember how the client operates, which documents are usually late, which accounts are sensitive, which estimates require partner involvement and which explanations have previously been accepted. This knowledge determines the real workflow, even when the software displays the same screens for every client whether we are talking about a large business or the local business around the corner. The formal process may be standardized, but the workflows are continuously adapted by the people performing them.

    Dynamic workflows in accounting

    An autonomous AI accounting solution would make more of that operating knowledge available to the system. It would not remove the ledger or weaken the control framework. It would use the ledger, documents, communications, prior decisions and assigned responsibilities to determine how each matter should proceed. The workflow would remain governed, but it would no longer need to be identical in every circumstance.

    Consider a monthly bookkeeping and close process. In a traditional application, imported transactions enter a queue and are processed according to a standard set of steps. Rules may automatically classify known items, and machine learning may suggest classifications for the remainder. The user is still responsible for identifying missing information, contacting the client, deciding whether the answer is sufficient, completing the reconciliation and escalating unusual matters.

    In an autonomous AI accounting solution with dynamic workflows, the system begins with the objective and the control requirements. It determines which information is already available, which transactions can be resolved from approved rules or prior evidence and which items require additional context. It may request a receipt from the designated contact, compare the response with the transaction and the client’s historical treatment, and route the item to a reviewer when the confidence level or financial significance falls outside an approved threshold. If the reviewer corrects the proposed treatment, the system records not only the final account but also the reason, the supporting evidence and the circumstances in which the decision applies.

    Consider the payroll process for an organization. During the monthly payroll run, the system compares the payroll register with the active employee master file and identifies two employee records that did not exist in the previous pay period. Rather than simply flagging the additions, the workflow recognizes that new employees introduce a series of dependent activities extending beyond payroll. It retrieves the onboarding status from the organization’s human resources workflow to confirm that these employees were intentionally hired, verifies whether the required tax forms and employment documentation have been completed, and determines whether payroll has sufficient information to calculate statutory deductions accurately. If documentation is still outstanding, the workflow can recommend the use of approved default withholding assumptions, notify the appropriate HR representative that the missing information is required, and allow payroll processing to continue within the organization’s established policies rather than delaying these employees pay.

    During the monthly payroll run, the system identified another employee included in the payroll run that when compared with the employee security access list, identified their access had been disabled. Rather than assuming an error has occurred, it recognizes an inconsistency between the organization’s security list and payroll run. The workflow confirms the employee’s HR status and compares it to recent security events to determine next steps which might be routing this exception to the appropriate manager if the information is inconsistent. The objective is not simply to identify an exception but to establish whether the underlying business processes remain aligned before payroll is finalized.

    In both situations, the payroll controls have not changed. The organization still requires authorization of new employees, completion of statutory payroll documentation, segregation of duties, and appropriate review of employment status. What changes is the applied workflow. Instead of following a rigid sequence of predefined steps, the workflow adapts to the evidence presented by each situation, coordinating information across payroll, human resources, identity management and compliance processes to achieve the same control objectives more efficiently.

    In accounting, autonomy must be bounded by authority, materiality, data access and professional responsibility. A system may gather evidence, prepare a recommendation and coordinate review without being authorized to post an entry, release a payment or issue financial information. High-risk actions should require explicit approval, and the basis for automated decisions must be retained. Google’s 2026 announcement of a proactive AI assistant reflected this distinction: the system was designed to continue routine work independently while requesting permission before higher-stakes actions.[4] The principle is directly relevant to accounting. A useful system should be able to advance the work without obscuring who remains accountable for the result.

    The design also requires a separation between deterministic controls and probabilistic reasoning. Bank balances, approval limits, user permissions and mathematical relationships should not depend on a language model’s judgment. They should be enforced through reliable rules and system controls. AI is more appropriately used where the work depends on interpretation, context, prioritization or the selection of the next step. A sound architecture combines both: fixed controls establish the boundaries, while adaptive reasoning helps determine how the work should proceed within them.

    This approach is receiving attention in current process research. A 2026 paper on agentic business process management systems describes a shift from design-driven process management toward systems that can sense process conditions, reason about improvement opportunities and act to maintain performance.[5] Research on AI-native enterprise resource planning has similarly examined systems that interpret user intent and assemble workflows across specialized agents rather than relying entirely on static, rule-based sequences.[6] These studies do not establish that mature, autonomous enterprise systems are already available. They do show that workflow composition, learning and orchestration have become serious areas of research rather than speculative extensions of chat interfaces.

    The market evidence is also more measured than the promotional language suggests. The Financial Times has reported that early adopters are finding practical uses for agents in activities such as candidate screening, marketing follow-up and cybersecurity investigation, while widespread deployment remains limited.[7] It has also reported that the cost of running complex agents is causing organizations to impose usage limits and examine whether each automated task produces sufficient value.[8] These constraints reinforce the importance of workflow design. An agent should not be used where a rule, lookup or established software function can complete the work more reliably and at lower cost. The model should be reserved for the portions of the process that genuinely require interpretation or adaptation.

    For accounting firms, the strongest systems will be built around an orchestration layer that coordinates workflows across specialized agents, business rules, organizational knowledge and professional oversight. Rather than sending every transaction directly to a large language model, the orchestration layer continuously determines the most appropriate path based on the information available and the objective being achieved. Deterministic rules resolve routine transactions where the outcome is known. Historical treatments and prior decisions identify recurring patterns and client-specific practices. Source documents, accounting records and external evidence are assembled to establish context and support conclusions. Specialized agents perform targeted activities such as reconciliations, payroll analysis, document collection or variance investigation, with the orchestration layer coordinating their activities and managing the flow of information between them. Only when sufficient evidence cannot be established, conflicting information exists or professional judgement is required does the workflow escalate the matter to an accountant for review. Professionals are no longer consumed by repetitive processing but instead focus their attention on the relatively small number of transactions and events that genuinely require experience, skepticism and judgement. This structure is not only more economical; it is more consistent with professional accountability.

    The orchestration layer also optimizes the use of AI itself. Not every task requires a large language model, and not every decision benefits from probabilistic reasoning. The orchestration layer selects the most appropriate capability for the work at hand, invoking deterministic logic where certainty exists, specialized agents where domain expertise is required, and large language models only when contextual reasoning adds measurable value. AI becomes one component of the workflow rather than the workflow itself.

    Impact on the Accounting Professionals

    The effect on software competition may be substantial. Traditional accounting software competes through feature depth, integration, usability and the size of their installed base. Those factors will continue to matter. Adaptive solutions introduce another source of value: the quality of the learning environment created around the application. Two firms using the same ledger could achieve different results because one has developed clearer decision rules, better evidence capture, stronger review feedback and a more complete record of how its professionals resolve exceptions.

    Over time, that record becomes a form of organizational capital. Review notes no longer disappear inside completed files. Corrections become reusable guidance. Client-specific knowledge becomes available at the point of work. New employees can be shown not only what was decided, but why the matter required attention and when the same reasoning should be applied again. The system does not replace professional development; it gives the organization a more consistent way to preserve and distribute what its professionals have learned.

    There are also limits that should be recognized. Historical decisions may contain errors or reflect circumstances that no longer apply. A workflow that learns without disciplined review can institutionalize poor practice. Client information may be incomplete, permissions may be excessive and model outputs may be difficult to explain. Adaptive workflows therefore require governance over what can be learned, who can approve a new pattern, how long decisions remain valid and how the system responds when evidence conflicts with prior treatment.

    These requirements make the role of the accountant more important, not less. The profession will be responsible for defining the boundaries within which systems operate, determining the evidence required for different decisions, establishing escalation thresholds and reviewing whether the workflow remains appropriate. Technical accounting knowledge will remain essential, but it will increasingly be expressed through the design and supervision of the environment in which the work is performed.

    The shift will also change implementation. Installing an adaptive system cannot be limited to migrating data and configuring a chart of accounts. The organization must identify its objectives, controls, sources of evidence, decision rights and exception criteria. It must decide which knowledge should be standardized across the firm and which should remain specific to a client or engagement. It must create a feedback process through which corrections improve future work without becoming unexamined precedent.

    This is a more demanding undertaking than adding an AI assistant to an existing product. It is also more likely to produce durable value. Personal productivity tools can save time for individual employees, but they rarely change how responsibility, evidence and decisions move across the organization. A dynamic workflow can improve the process itself because it connects the work performed by different people and systems to a common objective.

    Final thoughts

    For decades, accounting software has required organizations to adapt their processes to fit the software, and this approach has served accounting professionals as it delivers consistency, strong controls and reliable financial reporting results. Those principles are not changing but what is changing is how work is organized around those controls or the AI driven accounting workflows.

    As organizations evolve, new legislation is introduced or business processes change, the workflow no longer needs to wait for the next software release or an upgrade project of next release. The operating environment can adapt by changing the information it captures, the evidence it requests and the sequence in which work is performed, while the underlying accounting controls remain intact.

    That, in my view, is the real opportunity created by autonomous AI solutions. AI is an operating environment that understands the objective, coordinates the work required to achieve it and continuously improves as the organization learns.

    Author

    Andrew Ross CPA

    Andrew A. Ross, CPA, CMA

    Andrew Ross, CPA, CMA, is the Co-Founder and CEO of Auciera, an AI-native accounting platform built for accounting professionals and businesses that demand clarity, control, and confidence in their financial operations. Andrew brings over 25 years of accounting, tax, and financial management experience across public practice, consulting, and academia. He spent nearly a decade at two of the world’s leading professional services firms, serving as Senior Manager of Tax at EY and Performance Management Consultant at PwC, where he advised organizations on tax performance and enterprise financial decision-making. He has also held senior roles at Longview Solutions and MicroStrategy, giving him a deep understanding of how technology intersects with financial operations at scale. Since 2018, Andrew has served as a Professor of Accounting and Tax at Humber College, where he continues to shape the next generation of accounting professionals. His academic work reflects the same principle driving Auciera: that rigorous professional judgment and governance are non-negotiable, regardless of what tools are doing the work.

    References

    [1] Matt O’Brien, ‘What does agentic AI mean? Tech’s newest buzzword is a mix of marketing fluff and real promise,’ Associated Press, November 18, 2025.

    [2] Financial Times, ‘Microsoft’s early AI lead has become a test of faith,’ July 10, 2026. See also Satya Nadella’s essay, ‘A Frontier Without an Ecosystem Is Not Stable,’ discussed in subsequent reporting on organizational learning systems and the ownership of enterprise knowledge.

    [3] Thomas H. Davenport and Thomas C. Redman, ‘How to Marry Process Management and AI,’ Harvard Business Review, January-February 2025.

    [4] Michael Liedtke, ‘Google announces slew of AI advances, including a personal AI assistant coming soon,’ Associated Press, May 19, 2026.

    [5] Marlon Dumas, Fredrik Milani and David Chapela-Campa, ‘Agentic Business Process Management Systems,’ arXiv, January 25, 2026. Position paper based on the 2025 Workshop on AI for Business Process Management.

    [6] Hongyang Yang et al., ‘FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance,’ arXiv, June 2, 2025.

    [7] Financial Times, ‘Lessons from the agentic AI trailblazers,’ May 2026.

    [8] Financial Times, ‘Businesses face up to budget-busting AI bills,’ June 30, 2026; and ‘We created a monster: companies rein in AI usage as costs strain budgets,’ June 19, 2026.

    Source links

    Associated Press – agentic AI explained

    Financial Times – Microsoft’s early AI lead

    Harvard Business Review – How to Marry Process Management and AI

    Associated Press – Google proactive AI assistant

    Agentic Business Process Management Systems

    FinRobot

    Financial Times – Lessons from agentic AI trailblazers

    Financial Times – Businesses face budget-busting AI bills

  • What the best CPA firms do differently with technology

    What the best CPA firms have in common when it comes to technology

    The accounting profession is in the middle of a significant technology shift. AI tools are being evaluated, adopted, and in some cases quietly shelved. Some firms are navigating this well. Others are spending money and getting nowhere.

    The difference is not the tools they chose. It is how they approached the decision.

    After spending time with CPA practices across the country, a clear pattern emerges among the firms getting this right. Six things they have in common. None of them are about which software they picked.

    Software does not fix a broken process

    This is the one that costs firms the most money and produces the least return. A practice with a broken process buys new software hoping the tool solves the underlying problem. It does not. What the firm ends up with is a broken process running on newer infrastructure. Sometimes faster. Still broken.

    The best firms audit their process before they select a tool. They ask hard questions: where does work get stuck? Where does information go missing? Where does the most qualified person in the room spend time on work that does not require their judgment? The answers to those questions determine what technology they actually need. The tool comes last, not first.

    This discipline is less common than it should be. Vendors do not encourage it. Software demos are compelling. The pressure to look like a modern firm is real. The firms that resist that pressure and do the diagnostic work first are the ones that end up with technology that actually performs. The firms that skip it end up frustrated and wondering why a product that looked so good in the demo is underdelivering six months into an implementation.

    Identify the problem clearly before you buy anything. This sounds obvious. The number of firms that skip this step suggests otherwise.

    They stopped waiting for the perfect moment

    There is a version of due diligence that turns into permanent delay. The firms stuck in that loop are not being careful. They are paying the cost of inaction and calling it prudence.

    The best firms made a decision and moved. Not recklessly. They did the work. They evaluated options. And then they committed. They understood that waiting for certainty in a market moving this fast is itself a choice, and not a safe one.

    The accounting firms that adopted cloud-based practice management tools a decade ago did not do so because the technology was perfect at the time. They moved because the direction was clear and the cost of waiting was already showing up in their operations. The same logic applies today. The direction is clear. AI-native solutions are not a future consideration. They are a current reality that a growing number of practices are already building their operations around.

    The window to move with intention, rather than scramble to catch up, is still open. It will not be open indefinitely.

    They treat technology as a judgment amplifier, not a productivity shortcut

    This is the distinction that separates the firms building something durable from the ones chasing a faster version of what they already had.

    The productivity framing goes like this: if this tool saves each accountant two hours a week, multiply that across the team and the year, and the ROI is defensible. That math is real. But it is the wrong question.

    The judgment framing goes like this: what work is currently consuming the time and attention of the most qualified people in this practice, and does any of that work actually require their judgment? If the answer is no, the goal is not to make that work faster. The goal is to remove it entirely, so those people can spend their time on work that only they can do.

    Firms operating from the judgment framing end up in a fundamentally different position. Their senior people are focused on advisory work, on client relationships, on decisions that require experience and expertise. The return on that shift is not measured in hours saved. It is measured in client outcomes, retention, and the kind of professional reputation that does not require a marketing budget to sustain.

    This is the problem Auciera was built to solve. Not faster accounting. Better accounting, by putting the right work in front of the right people.

    They are deliberate about what senior people should and should not be doing

    This follows directly from the point above, but it deserves to be named separately because it requires a management decision, not just a technology purchase.

    Most firms have never had an explicit conversation about role definition in the context of their processes. Senior accountants end up doing work that does not match their seniority because the process demands it of them, not because anyone decided this was the right use of their time. The most qualified person in the room spends Tuesday afternoon chasing a missing document because nobody redesigned the process to prevent it.

    The firms getting this right made it a deliberate call. They looked at where their senior people were actually spending their time, identified which of that work required professional judgment, and structured their process to protect those hours. Technology supported that decision. It did not make it.

    That sequence matters. Leadership decides what the practice should look like. Technology gets deployed in service of that decision.

    They take change management seriously

    Technology adoption fails more often because of people than because of software. The best firms know this going in. They plan accordingly.

    Change management in most practices is an afterthought. The software is selected, the implementation is scheduled, and then someone sends an email to the team. That is not a change management plan. That is an announcement.

    The firms doing this well communicate the why before the what. Staff understand the problem the technology is solving. They understand how their day-to-day work is going to change and why that change is in their interest. They have a forum to raise concerns before go-live, not after. And the change is led by someone with the actual authority to make decisions, not delegated down to whoever had the most availability.

    This takes more time upfront. It consistently saves significantly more time on the back end. Adoption rates are higher, resistance is lower, and the technology gets used as intended rather than worked around by people who were never brought along on the decision.

    They build time for learning into the practice

    The last thing the best firms share is the simplest and the most consistently neglected. They protect time for their people to learn.

    Not a training budget that nobody touches. Not a standing suggestion that staff explore new tools when they find a gap in their schedule. A specific, recurring, protected block of time for education, exploration, and professional development in the context of technology.

    The accounting profession is changing fast enough that the skills and tools relevant today are not identical to what they will be in three years. The firms building learning into their operating model are accumulating institutional knowledge that their competitors are not. That compounds. A team that has been given the protected space to understand new tools, test approaches, and share what they learn is considerably better positioned than one expected to absorb change in the margins of a full workload.

    It is also, for what it is worth, one of the clearest signals a firm can send to its staff that it is investing in them. In a profession where experienced accountants have no shortage of options, that signal matters more than most managing partners acknowledge.

    The firms getting technology right are not the ones with the largest budgets or the most tools. They are the ones that approached the whole thing with discipline: understanding the process before buying the software, committing before certainty arrived, and thinking about technology as something that amplifies the judgment of their best people rather than a substitute for the hard decisions underneath.

    Those six things are not complicated. They are also not common. The practices that get all six right will be in a significantly stronger position three years from now than the ones still waiting for the right moment to start.

    Explore more perspectives on accounting practice, technology adoption, and AI on the Auciera Insights page.

    About the Author

    Auciera's Head of Growth - Patrick Parato

    Patrick Parato is the Head of Growth at Auciera, an AI-native accounting platform built to bring clarity, accuracy, and trust to financial operations. He holds a degree in Computer Science and has spent his career working at the intersection of technology, data, and business systems.

    At Auciera, Patrick helps shape product strategy, platform positioning, and market education, with a particular focus on AI-native system design, financial transparency, and scalable growth. He regularly writes about the role of AI in accounting, the importance of trust in financial systems, and how modern technology can support better decision-making without sacrificing control or accountability.

    With a strong technical background and deep experience in go-to-market strategy, Patrick focuses on how modern software architecture, automation, and AI can be applied responsibly in real-world business environments. His work centers on translating complex technical concepts into practical solutions that business owners and accounting professionals can actually rely on.

  • Demand for CPAs Will Increase with AI adoption

    Demand for CPAs Will Increase: In the World of AI, Human Judgement Becomes a Premium

    Written by Andrew Ross

    Artificial intelligence is beginning to change the way work gets done inside CPA firms. Tasks that once required hours of effort, such as reconciling accounts, summarizing client records, drafting memos, classifying transactions, preparing schedules, and identifying exceptions, can now be completed or accelerated with the support of AI. Some believe this will eliminate the need for CPAs; however, I believe the opposite is true. AI will make accounting work faster, but it will also place a greater emphasis on the value of the CPA and the professional judgement they bring to the process.

    The CPA’s role will increasingly shift from manual preparation to reviewing AI-assisted work, validating the reasoning behind it, identifying what may be missing, and applying professional judgement before anything is relied upon by a client, a lender, a regulator, or a business owner. Clients will demand it. With AI solutions being used to prepare, analyze, and recommend, clients will continue to require a qualified professional to determine whether the output can be trusted.

    That is the central issue: trust. Trust does not come from speed. It does not come from automation. It comes from knowing that the right facts were considered, the assumptions were reasonable, the evidence supports the conclusion, and the final recommendation can stand up to professional scrutiny. This is the value CPAs bring, and this becomes even more important in the world of AI. The future of the profession will belong to those who understand this clearly: AI may change the work, but human judgement will define the value.

    This change is happening at the same time that public practice is already facing a shortage of CPAs. Firms need more professionals who can work with AI, exercise judgement, supervise technology, communicate clearly with clients, and protect trust in the financial information businesses depend on. The challenge, therefore, is not just how CPA firms adopt AI, but how firms develop the next generation of CPAs in an environment where AI may produce the first draft, but the professional remains responsible for the final judgement. That question should shape how firms train junior staff, how bookkeepers and accounting technicians evolve, how universities and colleges prepare students, and how current CPA professionals get ready for the next stage of public practice.

    Human Judgement and Trust

    Accounting work is not valuable simply because it is completed quickly. It is valuable because clients, lenders, regulators, boards, and business owners can rely on it.

    The starting point for any discussion about AI in public practice should be trust. Accounting work is not valuable simply because it is completed quickly. It is valuable because clients, lenders, regulators, boards, and business owners can rely on it. That reliance depends on the process behind the answer and someone to confirm a complete and correct answer.

    AI is producing the first draft with impressive speed, summarizing a lease, drafting a variance explanation, classifying a transaction, reading a stack of invoices, or preparing an initial reporting package. Those capabilities will save time, but AI still needs the “right” data, with “right” context. Someone is still needed to determine whether the output is right, complete, and fit for use.

    A bank reconciliation that looks complete may still miss a stale-dated cheque issue. A lease summary may ignore an amendment. A tax memo may cite the right rule but apply it to the wrong facts. A transaction classification may be statistically likely and still be commercially wrong for that specific client. These are not small details. They are the difference between information that appears useful and information that can be relied upon.

    This is why the CPA becomes more important, not less. The CPA is the professional who reviews the output, tests the reasoning, confirms the facts, challenges the assumptions, and decides whether the conclusion is defensible. In an AI-assisted firm, the CPA is no longer only reviewing whether the number ties. The CPA is reviewing whether the system understood the transaction, considered the right evidence, applied the right policy, identified the right risk, and reached a conclusion the firm can stand behind.

    CPA.com’s 2025 AI in Accounting Report describes this shift clearly. Firms are investing in AI-powered workflow automation for areas such as bank reconciliations, transaction coding, month-end close, and reporting. At the same time, human-in-the-loop verification remains essential, especially in high-stakes areas such as tax, assurance, and advisory work.

    That is the human judgement premium. As AI produces more work, the scarce value shifts to the person who can determine whether the work can be trusted. The output may come from AI. The accountability cannot.

    Developing CPAs

    The need for trusted judgement is rising at the same time that public practice is under pressure to attract and develop enough professionals. That matters because AI is often discussed as if it is entering a profession with excess capacity. It is not. CPA firms are already dealing with increasing client expectations, more complex business models, changing tax rules, technology adoption, retirements, and a tighter talent pipeline.

    In the United States, the Bureau of Labor Statistics projects employment for accountants and auditors to grow 5% from 2024 to 2034, with about 124,200 openings per year on average over the decade. The same outlook notes that while routine tasks may be automated, advisory and analytical duties are expected to become more prominent rather than reduce the need for the profession.

    Similarly, the AICPA’s 2025 Trends Report shows the pressure from another angle. Bachelor’s degree completions in accounting declined 3.3% in 2023-2024 after a larger decline the prior year, while master’s degree completions declined 15%. At the same time, 75% of responding firms that hired accounting graduates in 2024 expected to hire the same number or more in 2025.

    Canada faces its own version of the same issue. The Canadian Occupational Projection System projects 83,100 job openings for financial auditors and accountants over the 2024-2033 period, driven by both job creation and replacement demand.

    AI is not arriving to solve a problem of too many CPAs, rather, it is arriving in a market that needs more professional capacity and more professional judgement. Firms need AI to increase capacity which creates a bigger risk; developing staff and having the staff supervise the AI work.

    For decades, CPA firms trained professionals through preparation. A junior staff member prepared the working paper. A senior reviewed it. The junior received review notes, corrected the work, and learned. Over time, repetition built an understanding and comprehension that developed their judgement. A junior accountant learned what a clean reconciliation looked like by preparing many imperfect ones. They learned what a weak explanation looked like because a reviewer challenged it. They learned how one missing document could change a conclusion because they had to go back into the file and fix the issue.

    AI changes that learning path. If AI prepares more first drafts, junior professionals may have fewer opportunities to learn through manual repetition. The Journal of Accountancy has described this as one of the profession’s key training questions: if automation and AI are taking over the repetitive, lower-risk work young accountants historically used to learn systems, controls, and professional skepticism, how do firms train accountants when the training work starts to disappear?

    The answer is not to preserve inefficient manual work just because it once served as a training ground. That would be like asking new pilots to ignore modern instruments because earlier pilots learned without them. The answer is to redesign apprenticeship around the work that will matter most in an AI-assisted environment. Future CPAs need to learn how to challenge AI-generated work. They still need the technical foundation in accounting, tax, assurance, finance, and business from solid education and then they need to learn how to inspect the reasoning, validate the evidence, identify missing context, and explain why they accepted, edited, or rejected an AI suggestion.

    A junior accountant may no longer spend three hours building the first draft of a working paper from scratch. Instead, they may spend one hour reviewing an AI-generated draft, checking source documents, documenting exceptions, and preparing a short conclusion for manager review. That can still be powerful training if the firm is deliberate about what the junior is learning.

    The review note of the future should not simply say, “Fix this calculation.” It should ask: Did you verify the source? Did you understand the assumption? Did you consider the prior-year treatment? Did you identify the exception? Did you explain why you accepted or rejected the AI suggestion?

    That is how judgement gets built in the next generation of CPAs. The learning moves from doing every step manually to understanding the work deeply enough to review, challenge, and improve it.

    Formal Education

    If the work changes, education has to change as well. Universities and colleges cannot prepare students for AI-supported accounting by simply adding one lecture on AI ethics or one assignment using a chatbot. AI has to be integrated into how accounting is taught. That requires a different kind of learning. Students should be given AI-generated workpapers and asked to find what is wrong. They should review AI-generated tax memos and identify missing facts. They should inspect bookkeeping-agent outputs and explain which items should be accepted, edited, rejected, or escalated. They should learn how to document their reasoning, not just arrive at an answer.

    CPA education in Canada is also moving in this direction. The new CPA Professional Program, launching in 2027, is being developed to align with Competency Map 2.0 and to equip CPAs to lead in a fast-moving world. CPA Ontario’s education partners have described the coming program as blending technical excellence, ethics, real-world experience, and future-focused skills that evolve with the demands of a world being reshaped by AI.

    Colleges and universities should also think carefully about the bookkeeping and accounting technician layer. If bookkeepers are going to become agent managers, accounting programs must teach more than debits, credits, and software navigation. They must teach data quality, workflow design, exception handling, source-document reliability, system controls, and AI supervision. A student should graduate knowing not only how to prepare a bank reconciliation, but also how to supervise an AI system that prepares one.

    How CPA Professionals Can Get Ready

    For current CPA professionals and firm leaders, the next step is not to wait until AI is perfect. It will not be perfect. The better approach is to start building controlled workflows that allow the firm to learn safely.

    Start with one workflow. Choose something repetitive, meaningful, and reviewable: transaction classification, bank reconciliation support, variance commentary, document intake, tax memo preparation, or month-end reporting. Then define what AI is allowed to draft, what a bookkeeper or technician must validate, what a junior accountant must review, and what requires CPA judgement.

    This is also where firms need clear governance. Deloitte’s poll of finance and accounting professionals found that trust was the leading barrier to agentic AI adoption, followed by integration into existing systems and lack of skilled personnel. The same poll found that 59.7% of respondents trusted AI agents to make decisions only within a defined framework, while judgement calls should continue to be made by people.

    That finding should not surprise anyone in the accounting profession. Trust is not created by saying the AI is accurate. Trust is created by workflow design, review discipline, access controls, source traceability, documentation, and accountability.

    In practical terms, firms should be able to answer a few basic questions before AI output is relied upon: Who approved the AI output? What data did the system use? What did the human reviewer change? What exceptions were escalated? Where is the audit trail? What work is the AI allowed to do, and what still requires professional review?

    The safest operating model is not “AI decides.” The safer model is: AI proposes, the professional decides. That model protects the firm, the client, and the profession. It also creates the right learning environment for staff. Junior professionals can see the AI output, review the evidence, make a recommendation, receive feedback, and improve. Bookkeepers can monitor agents and manage exceptions. CPAs can focus more of their time on judgement, advisory, assurance, client communication, and risk.

    Wolters Kluwer’s 2025 Future Ready Accountant Report found that AI adoption among firms rose from 9% in 2024 to 41% in 2025, with 77% planning to increase AI investment and 31% citing advanced technical skill development as a top staffing challenge. The message for CPA firms is clear: technology adoption and people development have to move together.

    Current CPA professionals can prepare by becoming more intentional about how they use AI. They should learn how AI tools produce outputs, where those tools fail, how to request source-backed reasoning, how to document review procedures, and how to explain AI-assisted conclusions to clients. They do not need to become software engineers. They do need to become better supervisors of technology.

    Conclusion: CPAs Owns the Final Judgement

    AI will change the accounting career ladder. It will change the work of bookkeepers, how junior staff learn, what universities and colleges need to teach, and how CPA firms design their operating model.

    But it will not remove the need for CPAs.

    In fact, as AI becomes more capable, the need for trusted professional judgement will increase. Clients will still need someone accountable. Firms will still need someone to sign off. Business owners will still need someone who understands the facts, the risks, the numbers, and the human context behind the decision.

    The firms that succeed will not be the ones that simply automate the most work. They will be the ones that build the strongest connection between AI capability and professional judgement. They will use AI to create capacity, but they will also build the review discipline, training model, governance structure, and education partnerships required to protect trust.

    That is the opportunity in front of the profession. CPA firms should not respond to AI with fear, blind adoption, or nostalgia for the old training model. They should respond by building a better model.

    AI can generate the first draft. The profession still owns the final judgement. And in the world of AI, that judgement becomes the premium.

    Auciera was built on this principle: AI handles the work that does not require judgement, so the CPA can focus entirely on the work that does.

    Author

    Andrew Ross CPA

    Andrew A. Ross, CPA, CMA

    Andrew Ross, CPA, CMA, is the Co-Founder and CEO of Auciera, an AI-native accounting platform built for accounting professionals and businesses that demand clarity, control, and confidence in their financial operations. Andrew brings over 25 years of accounting, tax, and financial management experience across public practice, consulting, and academia. He spent nearly a decade at two of the world’s leading professional services firms, serving as Senior Manager of Tax at EY and Performance Management Consultant at PwC, where he advised organizations on tax performance and enterprise financial decision-making. He has also held senior roles at Longview Solutions and MicroStrategy, giving him a deep understanding of how technology intersects with financial operations at scale. Since 2018, Andrew has served as a Professor of Accounting and Tax at Humber College, where he continues to shape the next generation of accounting professionals. His academic work reflects the same principle driving Auciera: that rigorous professional judgment and governance are non-negotiable, regardless of what tools are doing the work.

    References

    1. CPA.com. “2025 AI in Accounting Report.” The report discusses AI-powered workflow automation, human-in-the-loop verification, agentic AI, bookkeeping automation, tax review, audit risk, advisory services, and the need for firms to build governance and staff capabilities. https://www.cpa.com/sites/cpa/files/2025-06/CPAcom-2025-AI-in-Accounting-Report.pdf
    2. U.S. Bureau of Labor Statistics. “Accountants and Auditors.” Occupational Outlook Handbook. The BLS projects 5% employment growth for accountants and auditors from 2024 to 2034, about 124,200 annual openings, and notes that automation is expected to make advisory and analytical duties more prominent rather than reduce overall demand. https://www.bls.gov/ooh/business-and-financial/accountants-and-auditors.htm
    3. AICPA & CIMA. “2025 Trends Report.” The report identifies trends in U.S. accounting graduations, hiring of new graduates, and firm expectations. It reports declining bachelor’s and master’s accounting completions but continued optimism among responding firms about hiring accounting graduates. https://www.aicpa-cima.com/professional-insights/download/2025-trends-report
    4. Employment and Social Development Canada. “Financial Auditors and Accountants – Canadian Occupational Projection System.” The projection identifies 83,100 job openings for financial auditors and accountants in Canada over the 2024-2033 period. https://occupations.esdc.gc.ca/sppc-cops/occupationsummarydetail.jsp?lang=eng&tid=16
    5. Journal of Accountancy. “How will accountants learn new skills when AI does the work?” The article directly addresses the apprenticeship challenge created when AI takes over repetitive training work and argues for conceptual mastery, human judgement, technological fluency, and adaptability. https://www.journalofaccountancy.com/issues/2026/mar/how-will-accountants-learn-new-skills-when-ai-does-the-work/
    6. Deloitte. “Trust Emerges as Main Barrier to Agentic AI Adoption in Finance and Accounting.” Deloitte’s poll found strong optimism around AI agents, but also identified trust, integration, and lack of skilled personnel as adoption barriers, with most respondents preferring AI decisions only within defined frameworks while judgement calls remain with people. https://www.deloitte.com/us/en/about/press-room/trust-main-barrier-to-agentic-ai-adoption-in-finance-and-accounting.html
    7. Wolters Kluwer. “2025 Future Ready Accountant Report.” The report found AI adoption among firms rose from 9% in 2024 to 41% in 2025, with 77% planning to increase AI investment and 31% citing advanced technical skill development as a top staffing challenge. https://www.wolterskluwer.com/en/news/wolters-kluwer-releases-its-2025-future-ready-accountant-report
    8. CPA Competency Map 2.0. The Canadian CPA Competency Map identifies data and data governance, real-time decision-making, technological innovation, automation, and evolving entry-level roles as major themes shaping the future of the profession. https://assets.cpaontario.ca/students/pdf/cpa-competency-map.pdf
    9. CPA Western School of Business. “Introducing the CPA Professional Program.” The Canadian CPA profession is updating certification to align with Competency Map 2.0, including refreshed education, examinations, and practical experience requirements. https://www.cpawsb.ca/cpa-professional-program/
    10. CPA Ontario Centre for Accounting and the Public Interest, Ivey Business School. “Begin your journey to a CPA designation.” The page describes the new CPA Professional Program as blending technical excellence, ethics, real-world experience, and future-focused skills for a world redefined by AI. https://www.ivey.uwo.ca/accountingcentre/for-students/prospective-students/

  • Autonomous AI in FP&A: Why Data Governance Determines Trust

    Autonomous AI Can Build the Forecast, But Only If Your Data Can Survive the Audit

    An interview on why data governance, auditability, and human accountability determine whether autonomous FP&A actually works.

    The tension in 2026: CFOs want speed and demand control

    By 2026, Artificial Intelligence (“AI”) is no longer a “future” topic in finance. Gartner reports that 59% of finance leaders say they use some form of AI in their finance function. CFOs also increasingly see Generative AI (“GenAI”) as useful for explaining forecast and budget variances, a practical, low-friction entry point into FP&A work.

    But the leap from “AI that explains” to “AI that decides and acts” is where many finance organizations stall. Deloitte’s CFO Signals shows finance leaders’ top concerns about enabling Generative AI include technical skills (65%), Generative AI fluency (53%), and risk of adoption (30%), a blend of capability gaps and risk aversion that becomes even sharper when autonomy enters the room.

    And then there’s the unglamorous blocker: data quality. Gartner notes poor data quality costs organizations at least $12.9 million per year on average, a reminder that AI doesn’t fix messy inputs; it can amplify them.

    Why Data Quality Is the Real Barrier to Autonomous AI

    Against that backdrop, I spoke with Robyn Halbot, an EPM planning architect who has built both ML forecasting tools and enterprise planning applications, about why “good data” is the real reason CFOs hesitate to trust autonomous systems, and what autonomous FP&A looks like when done safely.

    Robyn has delivered over forty planning and forecasting implementations and has seen firsthand why trust breaks down long before AI enters the process. Her view is simple: autonomy is not a feature. It’s an operating model that must be auditable.

    Interview: Building Trust in Autonomous AI for FP&A

    Andrew A. Ross:

    Robyn, before we get into Autonomous AI, give us your quick origin story. How does your background connect to what people now call “Autonomous AI”?

    Robyn Halbot:

    I’ve spent fifteen years implementing planning and forecasting systems, OneStream, Anaplan, Oracle EPBCS, Prophix, across banking, retail, manufacturing, insurance, public sector. Over forty projects, about half at enterprise scale. I’ve been the person in the room when a CFO asks why the numbers don’t tie, or why the forecast is three weeks late, or why nobody trusts the model.

    A few years ago, I co-founded a company that built ML-driven forecasting tools. We integrated macroeconomic indicators, ran sensitivity analyses, generated scenarios. The technology worked. But I couldn’t find product-market fit. I spent months pitching to CFOs and planning teams, and I kept running into the same walls: data quality issues, governance concerns, and honestly, a lack of clarity on what problem we were actually solving for them.

    That experience shaped how I think about AI in finance. The hardest part was never the algorithm. It was the data. It was the governance. It was understanding what finance leaders actually need versus what technologists think they should want.

    What’s being called “Autonomous AI” today is really an evolution of that same discipline. But the foundation is identical: if your data isn’t clean, if your definitions aren’t consistent, if your processes aren’t auditable, autonomy just scales your problems faster.

    That’s why Gartner’s stat about $12.9M average cost from poor data quality isn’t abstract. It’s the tax organizations pay before AI even enters the picture.

    Why Data Matters More Than the Model

    Andrew:

    Let’s go straight there. You’re saying the biggest lever isn’t the model, it’s the data?

    Robyn:

    In finance, absolutely. I’ve seen this play out dozens of times.

    You can have the most sophisticated forecasting engine in the world, but if your product hierarchy changed mid-year and nobody updated the mappings, you’re forecasting against the wrong structure. If customer IDs don’t reconcile between your CRM and your ERP, your revenue attribution is fiction. If cost center ownership is ambiguous, your expense forecasts will get overridden by every department head who doesn’t trust them.

    These aren’t edge cases. This is the norm in most organizations I work with.

    Autonomous AI doesn’t fix broken master data. It doesn’t resolve inconsistent revenue recognition timing. It doesn’t reconcile your GL to your planning model. What it does, if the foundation is weak, is produce confident-sounding answers built on unreliable inputs. And that’s actually worse than no automation at all, because now you’ve got machine-generated fiction that looks authoritative.

    The CFOs who get this right invest in the boring fundamentals first: common definitions, documented lineage, reconciliation controls, change visibility. It’s not glamorous, but it’s what makes AI trustworthy.

    Why CFOs Hesitate to Trust Autonomous AI

    Andrew:

    CFOs are identifying what AI in finance can do, but Autonomous AI building the plan still makes them nervous. Why?

    Robyn:

    Because forecasting isn’t just a math problem, it’s an accountability problem.

    When a planning analyst builds a forecast, they own it. They can explain why they assumed 3% growth instead of 5%. They can point to the customer conversations, the pipeline data, the operational constraints that informed their judgment. When the CFO presents that forecast to the board, there’s a human chain of reasoning they can trace.

    When an AI drafts a variance explanation, that’s helpful, it’s saving time on routine analysis. But when an AI builds the plan and recommends headcount cuts or capex deferrals, the CFO needs to answer much harder questions: What data did it use? What assumptions did it make? How did it weigh trade-offs? Can I defend this to the audit committee?

    Deloitte’s survey captures this tension. It’s not that CFOs don’t want automation, they do. It’s that they’re uncomfortable with untraceable automation. They need to maintain human ownership of the assumptions, even if AI is doing the computational heavy lifting.

    The organizations that succeed with autonomous AI in FP&A will be the ones that design for transparency from day one, not the ones that bolt on explainability after the fact.

    The Autonomous FP&A Maturity Model

    Andrew:

    What’s the most realistic role for Autonomous AI in Financial Planning and Analysis in 2026?

    Robyn:

    The realistic path is graduated autonomy with clear human checkpoints.

    I think of it as a ladder:

    Level 1 – Assist: This is where most organizations are today. AI summarizes results, drafts variance narratives, answers ad-hoc questions about the data. Gartner’s finding that GenAI’s most immediate impact is explaining forecast variances fits squarely here. It’s valuable, it’s low-risk, and it builds organizational comfort.

    Level 2 – Execute bounded workflows: AI refreshes the weekly forecast automatically. It reconciles actuals to plan and flags exceptions. It runs standard scenario packs and produces management reporting with consistent commentary. Humans set the parameters; AI executes within those guardrails.

    Level 3 – Propose decisions with evidence: AI recommends actions, staffing adjustments, pricing changes, investment timing, with confidence scores and supporting data. But a human reviews, approves, and owns the decision. This is where adoption slows, because accountability is real.

    Gartner predicts 40% of enterprise applications will embed task-specific AI agents by end of 2026. That’s significant. But I’d expect most finance organizations to be operating at Level 1 or 2, with selective experiments at Level 3 in lower-stakes domains.

    The jump to full autonomy, AI making consequential decisions without human approval, is further out for finance than the hype suggests.

    Governance, Risk, and Why AI Projects Fail

    Andrew:

    So, what’s the catch? Why isn’t every CFO rushing to Level 3?

    Robyn:

    Because autonomy without governance gets killed, and it should.

    Reuters reported that Gartner expects over 40% of agentic AI projects to be canceled by 2027 due to high costs, unclear ROI, and overstated capabilities. That’s not pessimism; that’s pattern recognition. We’ve seen this cycle before with RPA, with predictive analytics, with blockchain in finance. The technology works in demos. It fails in production when organizations haven’t done the foundational work.

    CFOs are right to be cautious. They’re looking for a clear business case with realistic value, not vendor slideware. They want to see governance and controls that can survive scrutiny, from internal audit, from regulators, from the board.

    And regulators are paying attention. The UK’s FCA has already flagged new risks tied to speed and autonomy in financial services, emphasizing that accountability still rests with humans under existing rules. Even if you’re not a bank, CFOs hear the same message: autonomy increases the governance burden, it doesn’t eliminate it.

    The winners in 2026 won’t be the organizations with the flashiest AI demos. They’ll be the ones who can prove their AI is behaving correctly, and explain exactly how it reached its conclusions.

    A Practical Framework for Trusting Autonomous AI

    Andrew:

    If a CFO asked you, “How do I trust an Autonomous AI forecasting solution?” what would your response be?

    Robyn:

    I’d give them a framework that makes AI earn trust incrementally:

    1. Data readiness gates (before the model runs):

    • Automated reconciliations between source systems and the planning model
    • Completeness checks that flag missing or stale data
    • Change visibility, if a hierarchy changed, if an assumption was overridden, it’s logged

    2. Forecast defensibility:

    • Back-testing against baseline methods (does AI actually beat a simple trend?)
    • Confidence intervals and error decomposition (where is the model uncertain?)
    • Explicit, documented assumptions, not just outputs, but the reasoning chain

    3. Agent guardrails:

    • Read-only mode first, then bounded execution based on approvals
    • Workflow gates for any material changes
    • Full audit trail of prompts, inputs, and outputs
    • Kill switch and rollback capabilities

    4. Operating model:

    • Clear ownership, someone is accountable for AI outputs, not “the algorithm”
    • Training and fluency building (Deloitte’s survey shows skills gaps are a real barrier)
    • Regular review cycles where humans validate AI recommendations against judgment

    If an organization does these four things, they can move up the autonomy ladder with confidence. If they skip them, they’re building on sand.

    What Autonomous AI in FP&A Will Look Like in 2026

    Andrew:

    Last question, what’s your prediction for Autonomous AI in FP&A, specifically in 2026?

    Robyn:

    In 2026, the AI solutions that win won’t be the ones with the most agents or the flashiest demos. They’ll be the ones that operationalize trust:

    • Strong data foundations so the forecast isn’t fiction
    • Practical AI in FP&A starting with variance explanations, reporting automation, scenario generation
    • Governance that scales with autonomy, audit trails, approval workflows, human accountability
    • Proven ROI on specific use cases, not vague promises about transformation

    Autonomous AI won’t replace CFO judgment in 2026. But it will increasingly compress cycle times, expand scenario coverage, and standardize decision support, for organizations whose data can survive scrutiny and whose controls can survive autonomy.

    And honestly? I’ve started to wonder if we’re even asking the right question. Everyone’s focused on whether AI can build a more accurate forecast. But in my experience, the CFOs I work with don’t actually need a more precise number, they need help thinking through what happens when assumptions change. That’s a different problem than “predict the outcome better.” It’s about scenarios, trade-offs, strategic alignment. I’m exploring that more in some upcoming writing, because I think it changes what “valuable AI in FP&A” means.

    But that’s a bigger conversation. For now, the foundation is clear: get your data right, get your governance right, and let AI earn trust one use case at a time.

    Editorial Perspective: Autonomous AI Is a Multiplier, Not a Replacement

    If you read Robyn’s answers too quickly, you might walk away thinking this is another “AI is coming” article. It isn’t. It’s a warning label, and a roadmap.

    The hype narrative says autonomous AI will build the plan faster than humans ever could. Robyn’s narrative is more useful: autonomy is only as strong as the data discipline and governance beneath it. That’s not a philosophical point. Gartner has long argued that poor data quality imposes enormous costs on organizations (often cited at $12.9M per year on average). In FP&A, the damage isn’t only financial, it’s decision-quality. Bad hierarchies, mismatched customer IDs, inconsistent revenue timing. Those aren’t “analytics issues”, they are forecast killers. Autonomous AI solutions don’t make those problems disappear. They scale them.

    This is why CFO hesitation isn’t irrational conservatism, it’s professional responsibility. When an AI writes a variance explanation, the CFO can treat it like a draft. When an autonomous AI solution proposes headcount actions, pricing shifts, or a new capital plan, the CFO must answer a harder question: “Can I defend this decision, and explain exactly how it was produced?” Deloitte’s CFO Signals highlights that leaders see barriers in skills, fluency, and adoption risk, signals that even willing teams perceive autonomy as an operating-model shift, not a software upgrade.

    In 2026, the most important finance capability may not be “who has the best model,” but “who can prove the model is behaving.” Autonomous AI is on a fast path into enterprise platforms; Gartner predicts a sharp jump in enterprise applications integrating task-specific AI agents by the end of 2026. That means CFOs won’t be able to ignore autonomy; they’ll need to shape it. And shaping it starts with insisting on the basics, as Robyn described: data lineage, reconciliation gates, back-testing, confidence reporting, explicit assumptions, approval workflows, and a real audit trail.

    There’s also a quiet realism in the timing. A lot of AI initiatives will fail, not because autonomy is impossible, but because organizations will confuse demos with durable operating systems. Reuters reported Gartner expects over 40% of agentic AI projects to be canceled by 2027 due to high costs, unclear value, and “agent washing.” Finance leaders should treat that as a competitive advantage opportunity: the winners will be the ones who implement Autonomous AI with measurable ROI and controllable risk.

    So, the editorial view is simple: autonomous AI is not replacing the CFO’s judgment in 2026. It’s compressing the planning cycle, expanding scenario coverage, and standardizing decision support, for organizations whose data can survive scrutiny and whose governance can survive autonomy. If you want Robyn’s interview to land with credibility, let the piece end on that point: Autonomous AI isn’t a magic wand. It’s a multiplier. If the foundation is strong, it multiplies speed and insight. If the foundation is weak, it multiplies error, at machine scale.

    Many of these ideas reflect broader shifts in how accounting systems themselves are being redesigned for autonomy.

    This Editorial Opinion reflects the perspective of the Auciera team based on ongoing conversations with accounting professionals and regulators.

    About the Participants

    Andrew A. Ross, CPA, CMA

    Andrew Ross is the CEO and co-founder of Auciera. He is a CPA with experience in accounting, technology, and advisory services, and focuses on how system design, data architecture, and automation can improve the delivery of professional accounting work.

    Robyn Halbot, MBA, BSc, PMI-ACP

    Robyn Halbot is Principal at AshPoint Solutions and has fifteen years of experience as an EPM Planning Architect, having delivered over forty planning and forecasting implementations across OneStreamXF, Anaplan, Oracle EPBCS, and Prophix for organizations in banking, retail, manufacturing, insurance, and the public sector. She previously co-founded an ML-based forecasting startup that explored integrating macroeconomic indicators with financial data for scenario analysis. Robyn is currently building LLM applications for EPM workflows and focuses on the intersection of enterprise performance management, AI, and practical automation.

    References and Research Sources

    Deloitte. (2024). CFO Signals™ 1Q 2024: What North America’s top finance executives are thinking, and doing (with a focus on Generative AI in the finance organization and the enterprise).

    Gartner. (n.d.). Data quality: Why it matters and how to achieve it. (Includes Gartner research citing an average annual cost of at least $12.9M from poor data quality.)

    Gartner. (2024, June 27). Gartner survey shows 66% of finance leaders think generative AI will have most immediate impact on explaining forecast and budget variances.

    Gartner. (2025, August 26). Gartner predicts 40% of enterprise apps will feature task-specific AI agents by 2026, up from less than 5% in 2025.

    Gartner. (2025, November 18). Gartner survey shows finance AI adoption remains steady in 2025.

    Reuters. (2025, June 25). Over 40% of agentic AI projects will be scrapped by 2027, Gartner says.

    Reuters. (2025, December 17). Agentic AI race by British banks raises new risks for regulator.

    Further Reading on Autonomous AI and AI-Native Accounting

    For readers interested in exploring how these themes connect to modern accounting system design and AI-native financial workflows, the following resources provide additional context.

  • AI Will Change Small Business. CPAs Will Shape What Happens Next.

    AI Will Change Small Business. CPAs Will Shape What Happens Next.

    AI Will Reset the Playing Field, and CPAs Can Help Write the Game Plan

    Written by Andrew Ross

    I have been engaging many CPAs in the conversation about how AI will impact business, and specifically the accounting function. As everyone is trying to get their head around this AI thing, there is one consistent thought relating to small business; this has the potential to provide small businesses with tools that scale at an unprecedented pace, if the small business owner can figure it out. This is a big “IF,” considering most larger businesses are struggling to implement value-added AI into their operations.

    For small businesses and entrepreneurs, this is their moment. Leveraging leveraging AI, especially generative AI, as it has shifted a “someday transformation” into an everyday capability that can be easily leveraged inside a small business. That change is bigger than the technology itself: it changes who can adopt, how fast they can adopt, and who benefits first. This is a rare opportunity where small and nimble organizations can close the distance on much larger competitors, not because they can outspend them, but because they can out-learn, out-adapt, and out-deliver to create an AI-supported organization.

    To be clear, large organizations will absolutely win too, but many will move slower than their budgets suggest, because AI isn’t a departmental upgrade. It’s a cross-functional operating change that touches data, controls, workflows, risk, and people. And in big enterprises, “everything changes” moves at enterprise speed. Getting their “people” on board to drive the change will be one of the biggest obstacles.

    Small businesses do not need to “boil the ocean” to compete. They need a game plan: start now witheasy-to-use packaged solutions, understand how to work with AI and leverage it, build the muscle of operating in an AI-supported environment, and expand deliberately, fromback office to core operations and customer-facing applications.

    This article lays out the case for why the timing matters, why the big players often stall, and a practical phased plan to change to an AI-supported organization. Whether you’re a CPA sitting in the CFO chair or advising clients in practice, you are uniquely positioned to be the change agent to guide a small business on this journey.

    Why AI Is Different This Time

    Most of us have lived through technology cycles that promised revolution and delivered incremental value. As an example, ERP implementations that ran long, the internet craze that took longer to realize value, and automation that saved minutes but didn’t change the business model.

    Generative AI is different because it targets a layer of work that sits between strategy and execution: drafting, summarizing, searching, reconciling, analyzing, explaining, creating first versions, and translating information into action. It’s not only automating tasks, it’s accelerating knowledge work.

    The scale of this potential impact is why you’re seeing so much urgency. McKinsey’s research estimated generative AI could add the equivalent of $2.6T to $4.4T annually across dozens of use cases, with a meaningful share coming from functions that exist in every business (e.g. customer operations, marketing & sales, software engineering, accounting & finance).

    And we’re seeing early evidence that, at the task level, AI can create real time savings, especially when used intensively, not occasionally. A St. Louis Fed analysis (based on survey data) found an average self-reported time savings of 5.4% of work hours among users in one wave, and it discusses how specific time savings could translate into measurable productivity gains as adoption becomes formal and workflows change.

    A CPA’s caution is warranted here: time savings do not automatically become profitable. The goal is to avoid simply moving the effort from one step in the workflow to another step later in the workflow. The point is not that AI guarantees productivity; it’s that it creates a new lever for productivity that doesn’t exist at this price and accessibility level.

    The Canadian Reality: Adoption Is Accelerating From a Low Base

    In Canada, businesses have traditionally been risk-averse and slow to adopt; for AI, the adoption curve is now visible in official data.

    Statistics Canada reported that 12.2% of businesses said they used AI to produce goods or deliver services over the previous 12 months (Q2 2025), up from 6.1% a year earlier.

    Looking forward, Statistics Canada reported that 14.5% of businesses planned to adopt AI over the next 12 months (Q3 2025), while roughly two-thirds reported no plans and a meaningful minority were uncertain.

    That pattern, rapidgrowth from a small base, creates the competitive window. Early adopters are learning faster than the rest of the market. And learning speed matters right now.

    Why Big Companies Move Slower Than You Think

    When I compare notes with CPAs inside larger organizations, I hear a consistent reality: plenty of interest, plenty of pilots, and a lot of friction moving from experimentation to scaled value.

    Harvard Business Review has captured the organizational nature of the barrier: it’s often not that the models don’t work; it’s that people, process design, and governance stop the value from showing up.

    From a finance-and-controls lens, the blockers are predictable:

    1) Integration and workflow change

    AI delivers value when it redefines the workflows, not bolted on. Redefining workflows means reworking ERP/CRM processes, approvals, documentation, and control environments. That’s slow in enterprises.

    2) Data and governance become the bottleneck

    Deloitte’s research emphasizes that as AI moves toward deployment and scale, governance becomes the difference between accelerating and stalling, and that leaders start asking about ROI, safe/ethical practices, workforce readiness, and operational readiness.

    3) Risk appetite and brand exposure are higher

    Large brands have more to lose from customer-facing errors, privacy incidents, or “hallucinated” misinformation.

    4) Measurement discipline is uneven

    Gartner’s research highlights the maturity gap: 45% of leaders in high AI-maturity organizations said their AI initiatives remain in production for three years or more, versus 20% in low-maturity organizations, and it flags data quality/availability as a top challenge.

    5) Change fatigue is real

    In many large organizations, AI is competing with cybersecurity programs, ERP modernization, cost transformations, regulatory change, and operating model shifts. AI becomes “one more transformation,” even though it touches everything and it is a foundational change.

    None of this is a knock on enterprise leaders, many are doing excellent work. It’s simply a timing observation: the bigger the ship, the longer the turn. Small businesses can run shorter learning loops.

    The Small Business Advantage: Packaged AI Has Democratized Adoption

    Here’s the twist I keep seeing in owner-managed businesses: AI adoption is happening even when owners don’t label it “AI.”

    BDC reported a striking gap: when entrepreneurs were asked if they used AI, 39% said yes, but when shown a list of AI-enabled tools, that number jumped to 66%. BDC also reported that 27% of entrepreneurs didn’t realize they were using AI.

    Using the old “build vs. buy” argument, many small businesses will look for a defined and proven solution that requires little to no development or supporting infrastructure where AI solutions deliver value almost immediately. Packaged AI solutions lower three classic barriers for small business:

    • Cost: AI features are increasingly bundled into subscriptions.
    • Capability: you don’t need a data science team to start.
    • Speed: implementations can happen in days, not weeks or months.

    But it also creates a new risk: adoption without a plan. Tools get turned on, staff experiment, a few wins show up, and then something goes wrong (bad output, privacy concern, client complaint, unexpected usage costs) and leadership reacts by banning or freezing everything. This is where CPA professionals can be a competitive advantage. We’re trained to bring discipline: define outcomes, establish controls, document processes, and measure value.

    I suggest a straightforward plan or playbook to peers (and clients), and I anchor it on a simple progression:

    • Back-office foundation (low risk, repetitive work, measurable ROI)
    • Core operations (more integration, more differentiation)
    • Front office and sales support (highest upside and highest risk)

    The goal isn’t to avoid front office. The goal is to earn the right to go there by learning how to govern AI in lower-risk workflows first.

    The Playbook for AI in a Small Business

    Phase 1: Back-Office Foundation (Low Risk, Fast Learning, Measurable ROI)

    Back office is the ideal training ground: defined data, repeatable steps, clear success metrics, and tight feedback loops. The easiest use cases I hear repeatedly in conversations with CPAs and operators involve replacing or augmenting bookkeeping software to help with:

    • Vendor invoice capture/coding assistance;
    • Duplicate detection and anomaly flags;
    • Vendor onboarding checklists;
    • Drafting collection emails with approved tone/escalation;
    • Summarizing customer history before calls;
    • Variance explanations (with links to specific causes);
    • Flux analysis assistance;
    • Policy memo drafting and documentation updates;
    • Alerts that proactively provide awareness to trends and changes;
    • Scenario analysis to anticipate impact of changes: turning numbers into a coherent first-draft story;
    • Finance/HR internal FAQs;
    • Staffing   support; and
    • Drafting SOPs and checklists.

    Statistics Canada’s expected-use analysis showed notable planned interest in AI applications such as virtual agents/chatbots. The key for Phase 1 is to use those capabilities internally first (e.g., finance helpdesk, policy Q&A) before exposing them to customers, and maintain governance with a human-review approach.

    How to run Phase 1 like a professional, not like a tech hobby, would be to create a 4–8 week sprint. In Week 1, identify the solution to tackle first and define clear outcomes to achieve with measurable performance metrics, such as time saved, exceptions generated, rework rates, error rates, and/or cycle time. Then in Week 2, choose one solution that you expect to deliver the outcomes you are looking for. Then in Weeks 3 and 4, pilot the solution with guardrails and limit the scope to minimize the impact and time commitments, and always have a human review for anything that could impact external communications, customer communications or interactions, and external reporting of results. Then in Weeks 5 and 6, measure the outcomes using the performance metrics established at the beginning of this initiative.

    The “CPA edge” in Phase 1 is making experiments operationally credible using documented purpose, defined inputs, defined review steps, and measurable outcomes.

    Next the organization will move into Phase 2, which will focus on core operations with differentiation, not just efficiency. After the back office learns to deploy AI solutions with discipline, move into workflows that affect operational execution. Examples of Phase 2 initiatives would be procurement support (vendor comparison summaries, RFP drafting, verified), an operational documentation workflow (practitioner notes, client information gathering and verification), compliance and quality documentation (SOP drafting, automating internal controls), or inventory and scheduling narrative support (where data exists). This is where AI solutions becomes more than cost savings; it becomes speed and consistency of execution, which is exactly how smaller firms compete against larger ones.

    A recurring theme I hear from CFO peers: “We used AI, but nothing changed.” That’s the warning sign. Real value comes when you redesign the operating model so the organization can handle more volume or complexity with the same headcount and maintain control and quality.

    The last phase, Phase 3, will focus on front office & sales support, which will provide the highest value; however, it also carries the highest risk. This is where the upside is obvious: faster responses, better proposals, more personalized outreach, improved lead qualification, and better customer service coverage. Using AI solutions to address these workflows can carry risk spikes with accuracy risk (hallucinations), brand voice risk (tone mismatches, inappropriate phrasing), and/or privacy and consent risk (customer data, personal info). This phase will require a more disciplined approach, and small businesses will need to start with a proposal/business case, have defined workflows and objection handling guides, ensure there is a human review step in any public-facing communication, and a human approval step before any action is taken that could reach a customer, vendor or employee. Only move into customer-facing automation when you can demonstrate:

    • Monitoring,
    • Escalation paths,
    • Clear accountability for outputs, and
    • Privacy safeguards consistent with regulator expectations.

    Canada’s Privacy Commissioner has published principles for responsible, trustworthy, privacy-protective generative AI, emphasizing transparency, safeguards, documentation, assessments, and auditing. Those principles map well to how CPAs already think: document, test, evidence, monitor.

    Through the first phase, a small business will start developing an AI-supported operating model and this is what will turn into their advantage. They will start building what I refer to as their 6 Building Blocks. They are:

    • Develop a simple scoring model to prioritize an initiative by understanding the value, risk, ease, and data readiness.
    • Document their data discipline by defining what can be used where and establishing “no-go” categories (client confidential data, personal information, unreleased financials) for public tools.
    • Establish acceptable AI use policy that is short, practical, and enforceable, including confidentiality rules, and citation/verification expectations.
    • Define and document the accountable role for each workflow and add a review checklist that establishes the accuracy, completeness, tone, source verification, and record retention.
    • Each AI solution will have defined monitoring and continuous improvement, where exceptions are tracked, user feedback incorporated, and prompts and chats are tracked and logged in a secure manner for review/audit purposes.
    • Establish clear guidelines for customer, vendor and employee information that cover data retention, how information is used with models, security controls and audit logs, and how privacy and access are managed.

    Closing

    Small businesses will need a guide on this journey. Their guide will help manage the uncertainty with AI and assist them in establishing the AI governance needed at each phase, allowing the business to start early, start small, and build capability.

    AI adoption is accelerating in Canada from a low base, and the tooling has become accessible through packaged solutions. At the same time, many larger organizations are still wrestling with the integration, governance, and measurement requirements to scale. That gap creates an opening for small and nimble organizations to compete, and for CPAs to lead.

    Now is the time for small business. And this is the moment for CPAs to move from “AI curiosity” to “AI execution with trust.”

    This Editorial Opinion reflects the perspective of the Auciera team based on ongoing conversations with accounting professionals and regulators.

    Additional Note: Structure Guidance

    If you want structured guidance that scales beyond a one-page policy, there are credible frameworks:

    NIST AI Risk Management Framework (AI RMF 1.0) for mapping and managing AI risks.

    ISO/IEC 42001 (2023), an AI management systems standard that provides a structured approach to managing AI responsibly.

    Small business doesn’t need enterprise bureaucracy, but it does need enough structure to avoid accidental risk and to capture value consistently.

    If you do this well, the business doesn’t just “use AI.” It becomes capable of using AI safely, repeatedly, and profitably, which is the real competitive advantage.

    Additional Note: Canada’s Regulatory Horizon

    CFOs ask me: “What’s the law in Canada right now?” The practical answer in early 2026: the expectations are moving even where legislation is unsettled.

    Bill C-27 (which would have enacted major privacy reforms and the proposed Artificial Intelligence and Data Act) died on the Order Paper when Parliament was prorogued on January 6, 2025, according to detailed legal timelines and analysis.
    LEGISinfo provides the bill’s history and status within that parliamentary session.

    But the bigger point for small business is this: don’t wait for perfect certainty. Build governance that will survive changes:

    • privacy-by-design,
    • documented purpose and data handling,
    • human oversight,
    • monitoring and evidence.

    If you can demonstrate disciplined use, you’ll be far more resilient to regulatory and client expectations, especially in sectors where trust is the product.

    About Andrew A. Ross, CPA, CMA

    Andrew Ross is the CEO and co-founder of Auciera, an AI-native accounting platform designed to help finance teams operate with greater intelligence, automation, and control. He works closely with finance leaders to understand how autonomous AI is reshaping the operating model of the enterprise and writes on the future of financial systems, data architecture, and organizational readiness in an era of machine-driven execution.

    References

    Statistics Canada. Analysis on expected use of artificial intelligence by businesses in Canada, third quarter of 2025.

    Business Development Bank of Canada (BDC). New BDC study reveals 27% of Canadian entrepreneurs don’t know they’re using artificial intelligence (AI).

    McKinsey & Company. The economic potential of generative AI: The next productivity frontier.

    Federal Reserve Bank of St. Louis. The impact of generative AI on work productivity (Feb 2025).

    Deloitte. The State of AI in the Enterprise (2026 report series page).

    Gartner. Survey finds 45% of organizations with high AI maturity keep AI projects operational for at least three years (June 30, 2025).

    Harvard Business Review. Overcoming the Organizational Barriers to AI Adoption (Nov–Dec 2025).

    NIST. AI Risk Management Framework (AI RMF 1.0).

    ISO. ISO/IEC 42001:2023, Artificial intelligence management system (overview page).

    Office of the Privacy Commissioner of Canada. Principles for responsible, trustworthy and privacy-protective generative AI technologies.

    Parliament of Canada (LEGISinfo). Bill C-27 (44-1): Digital Charter Implementation Act, 2022 (bill history and status).

    Gowling WLG. Bill C-27 timeline of developments (updated Jan 2025).

    Torys LLP. Looking ahead: the Canadian privacy and AI landscape without Bill C-27 (Jan 16, 2025).

    OECD. The adoption of Artificial Intelligence (AI) by Small and Medium-sized Enterprises (SMEs): A comparative study (G7 context).

    AICPA & CIMA (CPA Canada/AICPA series landing/download page). Artificial intelligence and assurance series resources.

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  • Autonomous AI Will Replace ERP as the System of Work

    Autonomous AI Will Replace ERP as the System of Work

    Executive Brief: Autonomous AI is rapidly becoming the system of work, forcing CFOs to rethink ERP-centric operating models before their next platform decision.

    Why Finance Leaders Must Rethink the ERP-Centric Operating Model

    For decades, ERP has been the operational backbone of the enterprise, the system where transactions are recorded, processes enforced, and audit trails preserved. CFOs know that changing ERP is never “just technology.” It is a multi-year commitment that reshapes data, policy, and how people operate.

    Now, a new execution layer is emerging, powered by autonomous AI. The question facing CFOs is no longer which ERP to deploy, but whether ERP will remain the place where work actually happens.

    That mental model is colliding with a platform shift that will not wait for ERP timelines.

    The next era will not be defined by “ERP plus some AI agents.” It will be defined by AI-native autonomous accounting platforms, systems of agents that can interpret intent, orchestrate workflows across multiple applications, execute tasks, continuously learn from outcomes, and improve. In this future, traditional ERP solutions will quickly recede into the background as a system of record, while the AI layer becomes the system of work, the place where decisions are initiated, actions are executed, and exceptions are managed.

    This shift is arriving fast. Gartner predicts that 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% at the time of its forecast. This is not a marginal feature upgrade; it is a change in how enterprise software operates and how employees experience “work.”

    For CFOs considering an ERP replacement or major upgrade, the implication is stark: you may be investing in the wrong platform as the front door to your business. Even if your ERP remains critical in the back-end, autonomous AI will increasingly replace it as the primary interface and execution layer. “Replacement” doesn’t mean ripping out the ledger overnight.

    When CFOs hear “ERP will be replaced,” many imagine a big-bang cutover. That is not how platform transitions usually happen. Replacement in the enterprise tends to be functional: the part of the stack that users touch, and that drives daily execution, moves to a new layer first. The legacy platform remains underneath until it is mostly invisible.

    ERP historically served three roles:

    1. System of record: the authoritative store for transactions and master data
    2. System of workflow: approvals, reconciliations, exceptions, and controls
    3. System of experience: screens, forms, reports, and navigation that people use to do work

    Autonomous AI initially target the second and third roles. AI Agents are changing the operating reality from “users navigate modules” to “users state outcomes, agents execute.” McKinsey has described the emerging “divide” between the surge in AI investment and the underinvestment in the ERP capabilities needed to enable AI at scale, signaling that agents are becoming the orchestration layer, and ERP must adapt to support them.

    Industry coverage is making the same point in plain language: AI is already stripping away the repetitive work associated with ERP usage and shifting how employees interact with the underlying systems. This is the early shape of replacement: ERP persists, but it no longer runs the work in the way it once did.

    The pace of change CFOs can’t ignore

    ERP programs move on a multi-year clock. AI moves on a quarterly, and sometimes monthly, or even weekly clock.

    The market is rapidly standardizing on “agentic” patterns in enterprise software. Microsoft’s Dynamics 365 roadmap, for example, is explicitly organized around agents for ERP processes (time, expense, approvals, reconciliations, supplier communications), reflecting a model where agents do the work and humans supervise outcomes. When major vendors position agent execution as the new default, CFOs should read that as a platform shift, not a product feature.

    At the same time, the data foundation required to make AI work is different from the data foundation required to run traditional enterprise reporting. Gartner warns that organizations that don’t address “AI-ready data” will endanger AI success, and predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data. That is a CFO message as much as a CIO message: the constraint is increasingly the operating model and data readiness, not whether you selected the “best” module set.

    In other words, the ERP timeline may be measured in years, while your competitors’ AI execution layer improves every quarter, every month. That gap becomes a strategic disadvantage, not a technology inconvenience.

    Autonomous AI will change the Finance Function

    Autonomous AI platforms reshape finance along three dimensions: execution, control, and competitive advantage.

    Execution moves from transaction processing to intent-driven orchestration.
    Instead of “open the AP module, review the invoices, validate the detail, send for approval, resolve exceptions,” teams increasingly operate by declaring intent instead.

    “Review invoices above $25K, identify unusual terms or pricing, confirm approvals, and summarize exceptions for my sign-off.”

    Agents then execute across the ERP platform, procurement, contract repositories, and vendor portals, logging actions and producing an auditable trail. As more enterprise applications integrate AI capabilities, this becomes the default workflow style, not an innovation lab experiment.

    Controls will evolve from process policing to outcome governance; where traditional controls assume humans follow steps inside a system. Autonomous execution requires controls that govern what agents are allowed to do, under what thresholds, with what evidence. That means:

    • materiality-based human sign-off rules;
    • automated validation and reconciliation checks;
    • monitoring for recurring error patterns and drift; and
    • evidence capture that auditors can rely on.

    The control conversation becomes less about “did users follow the right screens?” and more about “did the system enforce the right policy, record the evidence, and escalate the right exceptions?”

    The competitive edge is no longer primarily determined by who has the newest ERP UI. It will be determined by who has the most effective autonomous workflows, close acceleration, anomaly detection, cash application, procurement compliance, contract intelligence, and forecasting that continuously improves. This is the layer where cycle time shrinks and decision quality improves, and it is increasingly delivered by agents operating across systems.

    The CFO trap: Investing in ERP for a World That Is Ending

    Many CFO-led ERP decisions still optimize for familiar criteria: implementation certainty, integrator reputation, module coverage, and “AI capabilities” as a checklist. Those criteria matter, but they are no longer sufficient.

    Why? Because if autonomous AI becomes the primary platform through which work is done, then the front-end value of ERP shrinks over time. ERP becomes a back-end substrate that agents query and update, while the AI layer becomes the tool employees live in.

    This creates a new kind of ERP risk: you can successfully complete a major ERP program and still fall behind competitors who adopted autonomous finance execution earlier, because they made the organizational changes (data readiness, governance, skills, decision rights) that allow AI to compound.

    The winners will be the organizations that treat autonomous AI as a platform and build for continuous capability upgrades, rather than waiting for ERP go-live to “start AI.”

    The CFO playbook for the autonomous AI platform shift

    If autonomous AI is replacing ERP as the system of work, what should CFOs do today, especially if an ERP replacement or upgrade is already on the table?

    1) Reframe the transformation

    ERP as the back end, autonomy as the operating layer.
    ERP will remain important for integrity, compliance, and financial reporting for now. But your business should be designed so that autonomous workflows can sit above it and expand over time. This framing prevents a multi-year ERP plan from becoming a multi-year delay in competitive execution.

    2) Add a non-negotiable requirement: “AI agentic extensibility.”

    Your ERP decision must be evaluated based on how easily agents can securely interact with it: APIs, event streams, permissions, evidence capture, and auditability. The agent layer will evolve quickly; your ability to plug it into your systems safely will determine speed-to-value. Gartner’s forecast on agent integration should be read as a timeline constraint: this will be mainstream well before most ERP programs stabilize.

    3) Build “AI-ready data” as a finance agenda, not an IT side project

    If AI-ready data is the reason many AI initiatives fail, finance must co-own the solution, master data discipline, policy definitions, control mappings, and data lineage that auditors trust. Gartner’s warning about AI-ready data and project abandonment through 2026 is the kind of risk statement CFOs should take seriously.

    4) Govern autonomy like you govern financial controls

    Autonomous AI introduces delegated execution. That demands governance: what agents are authorized to do, who approves changes, how exceptions are handled, and how evidence is captured. Treat it like a finance control framework, with clear ownership and escalation.

    This approach should be playbook right now, but this is only the first shift. Most CFOs will accept the near-term view: “ERP becomes the data structure in the back end while AI runs the workflows.” That is directionally correct.

    But CFOs should hold a longer-term possibility in mind: it is only a matter of time before AI development engines find more efficient ways to store and structure enterprise data than today’s ERP-centric schemas.

    We are already seeing the data architecture evolve to support generative AI, such as the use of vector databases and new data processing pipelines alongside traditional warehouses and systems of record.

    This doesn’t mean the general ledger disappears. It means the enterprise’s primary data substrate may shift toward AI-ready architectures that better support unstructured data, semantic retrieval, and agent reasoning, while still reconciling back to auditable financial truth.

    In this platform transition, the interface will change first, then workflows, then data architectures. CFOs should plan with that sequence in mind.

    CFOs must lead the organizational shift, not just the software shift

    Autonomous AI platforms are on track to become the dominant system of work across the enterprise, with ERP increasingly becoming the back-end system of record. Gartner’s projection that agent integration will be widespread by the end of 2026 makes the timeline explicit.

    For CFOs, the decision is no longer “Which ERP do we pick?” It is:

    Will we redesign our organization, data, controls, decision rights, and workforce capability, to operate in an autonomous AI execution era?

    Because if you modernize ERP without modernizing the organization for autonomous AI, you risk building your next operating model around a platform the market is already moving beyond. And that is how competitive gaps become lasting: not because the ledger is wrong, but because the business cannot move at the speed the era demands.

    The next generation of finance platforms will not be ERP systems augmented with AI. They will be AI-native operating layers built for autonomous execution from day one.

    Forward-looking finance organizations are already evaluating AI-native platforms designed for autonomous execution rather than ERP-era workflows.

    About Andrew Ross

    Andrew Ross is the CEO and co-founder of Auciera, an AI-native accounting platform designed to help finance teams operate with greater intelligence, automation, and control. He works closely with finance leaders to understand how autonomous AI is reshaping the operating model of the enterprise and writes on the future of financial systems, data architecture, and organizational readiness in an era of machine-driven execution.

    References

    Bauer, F. (2025, July 14). Getting an ERP transformation back on track. McKinsey & Company. https://www.mckinsey.com.br/capabilities/tech-and-ai/our-insights/getting-an-erp-transformation-back-on-track

    Deloitte. (n.d.). Your guide to a successful ERP journey. Deloitte Canada. Retrieved February 5, 2026, from https://www.deloitte.com/ca/en/services/consulting/perspectives/successful-erp-journey.html

    Gartner. (2025, February 26). Lack of AI-ready data puts AI projects at risk. https://www.gartner.com/en/newsroom/press-releases/2025-02-26-lack-of-ai-ready-data-puts-ai-projects-at-risk

    Gartner. (2025, August 26). Gartner predicts 40% of enterprise apps will feature task-specific AI agents by 2026, up from less than 5% in 2025. https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025

    Gross, G. (2025, August 14). What parts of ERP will be left after AI takes over? CIO. https://www.cio.com/article/4033751/what-parts-of-erp-will-be-left-after-ai-takes-over.html

    Jensen, B., Deano, D., Allison, M., & Bin Asad, T. (2026, January 9). Bridging the great AI agent and ERP divide to unlock value at scale. McKinsey & Company. https://www.mckinsey.com/capabilities/mckinsey-technology/our-insights/bridging-the-great-ai-agent-and-erp-divide-to-unlock-value-at-scale

    Microsoft. (2025, May 9). A new era in business processes: AI agents for ERP. Microsoft Dynamics 365 Blog. https://www.microsoft.com/en-us/dynamics-365/blog/business-leader/2025/05/09/a-new-era-in-business-processes-ai-agents-for-erp/

    Shaikh, A., Soller, H., Młodziejewska, M., & Gibbs, M. (2024, October 3). Revisiting data architecture for next-gen data products. McKinsey & Company. https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/revisiting-data-architecture-for-next-gen-data-products

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