Industrial Process

The modern accounting landscape is undergoing a structural transformation driven by artificial intelligence and the accelerating shift from realspace to cyberspace. In realspace, work depends on human memory, manual document handling, and tacit knowledge passed from person to person. Cyberspace operates differently: information becomes both machine‑interpretable and human-interpretable (e.g. not a copy), knowledge persists in shared graphs rather than individual minds, and intelligent agents amplify human capability by reducing the cost of forgetfulness.

This page explores how accounting; already a deterministic, mathematically coherent system; can be industrialized through digital models, semantic structures, and AI‑enabled workflows. By treating information as modular, reusable, and interpretable by both humans and machines, organizations can eliminate the “human bucket brigade” of document shuffling and embrace a new operating logic built for cyberspace rather than retrofitted from realspace.

Artificial intelligence is not an add‑on; it is the catalyst for a new cognitive architecture in which human‑machine teaming becomes central to professional practice. The result is a discipline that evolves from memory‑intensive labor into system‑design and logic‑management; an industrial process capable of scale, precision, and unprecedented clarity and quality.

* * *

Here are my thoughts. Links provide details. As pointed out, accounting is a closed deterministic system. Its logic is reproducible, auditable, and mathematically coherent. It is a mathematical substrate. This determinism, combined with its capacity for both precision and structured adaptability make accounting truly unique. These deliberately provided characteristics can be leveraged when putting together an industrial process to operationalize artificial intelligence within accounting information systems at scale.

The work of accountants, auditors, and analysts can be described as "arranging abstract symbols". Algorithms can be used to describe this work. Poka yoke is a Lean Six Sigma technique that can be used to "mistake proof" work processes. ValueFlows is a model for coordinating economic activity. ValueFlows and others use the Resources, Events, Agents (REA) model to describe the accounting process. The ISO/IEC Accounting and Economic Ontology includes REA. Data Centric Accounting (DCA) describes the facets of a business event.

The Structural Advantage

By separating the information and the “costume” that information wears (i.e. the document); representing the information as a graph which a computer based process can reliably interpret (i.e. treating these graphs as semantics-oriented structures known as models), organizations eliminate the traditional "human bucket brigade"; the fragile (and costly) practice of humans manually moving text from one document to another document. Instead, the model is the code.

Think a machine-interpretable graph which can then be projected into a human-readable document. A high level model of a report provides a "natural" or "neutral" human readable rendering of the information within that graph. This explains the natural/neutral rendering projection. And here is an example financial statement.

Services as Software

Service as Software (SaS) will be a new way to deliver professional services. Entire workflows will have significant reductions in the "friction" that exists today. Quality will skyrocket and attaining that level of quality will cost less. Intelligence and work capabilities of accountants, auditors, and analysts will be amplified. Teaming with smart software will be a force multiplier. Hybrid artificial intelligence, grounded in rules-based artificial intelligence and supplemented by probability-based artificial intelligence, and operated by a skilled, experienced accountant/auditor/analyst will make "the average accountant" above average. A super accountant/auditor/analyst. Human-machine teaming.

Think knowledge as a product.

Proficiency

Proficiency is the capability, skill, and knowledge that you might have for doing something. There are general levels of proficiency: literacy, fluency, mastery. Proficiency is a progression. Increasing efficiency or productivity without maintaining quality or increasing quality is useless. It really achieves nothing.

Digital proficiency today is critical. The landscape is changing; new maps are required.

Role of Standards

Standards tend to be under appreciated. OMG referrers to BPMN, DMN, and CMMN as the “triple crown” of business process management. As I understand it; BPMN allows you to document the "recipe" of a business process, DMN allows you to model your business decisions separate from processes, and CMMN which allows you to model manual processes; are all explicitly designed to be both machine-interpretable and semantics-oriented. They are a “complementary suite” of capabilities. They do not just provide visual shapes for human presentation; they define a precise, underlying mathematical and logical model that software engines can execute directly.

This video helps you understand how BPMN, DMN, and business process management engines work. See SmartDraw to see how BPMN diagrams are created.

AI-First, Digital-First, Graph-First

An AI‑First or Digital‑First or Graph-First mindset begins with a genuine paradigm shift: you recognize that the landscape has fundamentally changed, and the old map no longer describes the territory. Instead of trying to retrofit yesterday’s operating logic, you adopt an entirely new logic -- one built around how cyberspace actually behaves, how digital systems truly operate, and how artificial intelligence creates, transforms, and accelerates value.

An AI-First or Digital-First or Graph-First paradigm flips the equation. Rather than struggling to figure out how to gat a computer to understand thousands of documents arbitrarily created by humans which make it extremely challenging to precisely understand; the new paradigm creates information in a form that is easy for artificial intelligence to understand, and then converts that into something humans can effectively understand which is significantly easier. This matters when a use case requires accuracy, reliability, precision, and no mistakes.

Artificial intelligence fundamentally reshapes the cost of human forgetfulness by dramatically reducing the penalty associated with imperfect recall. Intelligent software agents act as extensions of human memory and reasoning, using knowledge graphs as persistent, structured external memory. This shifts the cognitive landscape: remembering becomes less about internal storage and more about orchestrating reliable, machine-supported recall.

Accounting & Audit by Design (A&AD) framework is AI-First, Digital-First.

Refactoring

In accounting and auditing, the adoption of artificial intelligence and intelligent agents signals a profound cognitive reorganization. The way forward involves human-machine teaming.

Memory becomes a shared resource distributed across human and machine, dissolving traditional boundaries between what the professional must personally retain and what can be delegated to computational systems. Under this emerging paradigm, the core skill of the accountant or auditor is no longer defined by how much information they can hold or mentally manipulate. Instead, it is defined by how clearly they can think across multiple scales; conceptual, procedural, and systemic; when designing, validating, and navigating complex logical structures.

Because knowledge can be stored outside the brain of a human in the form of a knowledge graph; knowledge “ownership” also changes. In the past, when an employee acquires knowledge; that knowledge leaves an organization when the employee leaves the organization. Because of the knowledge graph, more knowledge can be retained by an organization, shared between different employees of an organization, and even shared with intelligent software agents. In addition, every employee can be as “smart” as the smartest employee contributing to the commonly shared knowledge graph.

Accounting and auditing thus evolve from memory-intensive disciplines into system‑design and logic‑management disciplines, where human expertise lies in structuring, interpreting, and governing the interplay between human judgment and machine intelligence.

The office of the CFO is going to be refactored. Processes will fundamentally change.

Knowledge

Knowledge is the most precious kind of information and consists not just of simple analysis, but of crucial insights that combine information with context. Knowledge is information that has been interpreted, connected to experience, and made actionable. Knowledge is interpreted information which is understood within some conceptual framework (a.k.a. shared definition, area of knowledge) giving the information meaning. Knowledge is a form of familiarity with information from some specific area or corpus.

Knowledge is stored in a knowledge organization system. A knowledge organization system provides a framework. Knowledge can be packaged as a product. Knowledge is governed.

Meaning and Understanding

Meaning is not something inherent in just words or objects. Meaning is consciously and deliberately produced through shared human systems, shaped by context, and sustained by communities of stakeholders.

Understanding is when something makes sense to you well enough that you can use it, explain it, or see how it fits into the context with or connects to other things you know. Meaning is the content. Understanding is the competence (skill and experience). Meaning is static. Understanding is dynamic.

Meaning is an encoding; understanding is an active process: interpreting, integrating, contextualizing, applying, reasoning. Meaning is the map. Understanding is the ability to navigate the terrain.

Meaning is intersubjective; it relates to a collective. Understanding is individual.

Meaning enables understanding. Understanding validates meaning. There is a feedback loop between meaning and understanding. Without stable meaning, understanding collapses into ambiguity. Without understanding, meaning is inert, no uptake.

Fragmentation

Epistemic traceability is the unbroken, verifiable chain of evidence, logic, and data structure that proves how a system or organization knows its assertions are true. Maximizing epistemic traceability minimizes epistemic risk.

Disjointed tools do not enable traceability/trackability; they cause fragmentation. Information flow today works similar to a human bucket brigade.

Process

A process is an activity or set of activities that take an input or inputs and creates an output or outputs, i.e. some intended result. The input-process-output model or pattern (IPO Model) is a widely used approach for describing a process. A process is used to perform work.

Process knowledge and procedural knowledge represents the steps, parts, pieces, modules; the algorithms; that serve as a "composite set" of all the "information blocks" or "organisms" and the understanding, the "know-how" of the construction of accounting, reporting, audit, or analysis artifacts. Artifacts like an accounting system, a closing book for creating and organizing the working papers that support a financial statement, an audit bundle that supports the work of a third party or internal auditor, the financial statement itself, or the financial analysis models used to understand reported financial information.

Procedural knowledge, this "know how" or "practical knowledge" or "imperative knowledge" or "performance knowledge" is the knowledge exercised in the performance of some task. Procedural knowledge is different than descriptive knowledge which is also known as "declarative knowledge" or "business rules" or "knowing-that" which involves knowledge of specific propositions such as "Assets = Liabilities + Equity" or basically facts expressed using declarative statements. Procedural knowledge involves one's ability to "know how" to do something such as create a "proof of cash" or a "lead schedule" or how to put together a balance sheet.

That process knowledge and procedural knowledge is also all the "business rules" (a.k.a. assertions, constraints, restrictions) that apply to how all the information in those information blocks is assembled and are used to test that assembly of information block organisms to understand the epistemic risk that exists that you got something wrong. Knowledge is more than just the "information blocks" that make up the "closing book" or "audit bundle"; knowledge is also the "know how" related to how to put those "information blocks" together.

Process knowledge relates to how things get built in an industrial setting (process, projects, workflows). The information artifacts used by accountants, auditors, and analysts need to be industrial strength. The creation of accounting, audit, and analysis artifacts are industrial processes, they are construction processes.

Not all knowledge is equal.

Explicit knowledge is knowledge that is easy to articulate, write down, and share. Explicit knowledge is objective, documented, and easily shared information and tends to be found in manuals, reports, documents, and books. Explicit knowledge is formalized and codified.
Implicit knowledge is the application of explicit knowledge. Skills that are transferable from one job to another are one example of implicit knowledge.
Tacit knowledge is gained from unique personal experience that is more difficult to express and tends to be unwritten. Tacit knowledge tends to be more nuanced, experience-based information like intuition or a learned skill that is hard to articulate and understand. Tacit knowledge tends to be important subtleties and nuances that takes deep understanding to get right.
Common knowledge which is knowledge us humans takes for granted but computer know nothing about. Remember, computer are dumb beasts. Everything has to be spelled out for them in detail for them to be helpful and reliable.

Epistemology is the study of the nature and sources of knowledge. Epistemology is the blueprint, the engineering, and the inspection process which is used to answer questions like is the accounting, audit, or analyst artifact that I am working on complete? Was the financial reporting framework followed when the artifact was constructed? Can we trust the artifact?

Risk

Epistemic risk relates to the epistemology, that blueprint, being wrong. Should we take the risk and submit the artifact the regulator? Epistemology is less about what you know, and more about how you know what you know, and whether you should trust it. Epistemology is about assessing your control mechanisms. Boundaries, fences, guardrails.

Accountants and auditors are experts in assessing this epistemic risk. The problem is that (a) there is far to much human involvement of very expensive humans so costs tend to be higher than desirable, (b) humans are, well, human and they make mistakes, and (c) the only tool accountants have are the electronic spreadsheet, the "Swiss army knife" of the accountant, or work very similar to the electronic spreadsheet because they are document or document-like and non standard.

Modular Building Blocks (e.g. Information Legos)

But what if there were a better way? I have mentioned the notion of the "information block" (i.e. core pattern). This notion of the information block is a structural insight, exploitable regularity. Allow me to elaborate. I am going to use a common analogy to help explain the notion of the information block, the Lego analogy. Lego blocks or bricks have the following fundamental characteristics:

Modularity: Legos are modular. They are small modular pieces which are interchangeable with one another. There are different "modules"; bricks of different sizes, specialized pieces like wheels and windows.
Standardization: Every Lego brick has the same little bumps (studs) on top and holes on the bottom (tubes). Because these "interfaces" are always the same, any two pieces can snap together, no matter what they look like. The connectors are standard and you change things without breaking the system. Legos build in 1958 will connect to Legos built in 2025. Only 18 in a million Legos are rejected for quality reasons; that is how good Lego's brick making process has gotten.
Portability: Because Legos are standard, they are portable. For example, you can take your Legos to a friend's house and her Lego set will fit into your Lego set.
Reusability: Because Legos are standard and portable, they have reusability. With Legos, you don't need to reinvent the wheel every time you want to do something. For example, if you are building a castle and you want a tower in your castle; you don't need to reinvent the tower making process. You just use the same bricks and same approach you used to build the wall.
Scalability: With Legos, you can start small and scale. With Legos you can start by building a house and then brick-by-brick turn the house into a city. You don't need a new "system" to get bigger; you just add more blocks.
Versatile: Legos are versatile. There is not only one type of Lego brick, there are many different types. And you can build pretty much anything with Legos.
Precision components: Certain Lego pieces, like wheels, are critical for specific functions and these precision components can be created and provided within the Lego system.

There are two aspects to Legos that might seem like negative qualities. The first is the notion of "glue". You don't glue Legos together. In our system there is the notion of "glue"; that glue is logic. The second notion is that of "decay". Lego bricks don't change over time, certain systems, like a closing book or audit working papers, do change. This is not an issue for us as our system has the notion of "extensibility" and/or "customizability". Our system is "flexible" or "elastic" and we have a way to control that flexibility/elasticity.

Imagine the notion of an "information block". Imagine that the information block is interpretable by both a human and a machine. Imagine that the meaning expressed by that information block is interpretable by both a human and a machine; one version interpretable by both in a global context (i.e. not specific to one software application). This is achieved by automatically generating a human interpretable projection from the machine interpretable information. Imagine being able to "snap" together information blocks into a process. Imagine information blocks as being like a Lego. Imagine that human powered "bucket brigade" assisted by Lego-like "information blocks" that "snap" together using logical glue.

Digital Information Organism

These Lego-like information blocks are really "organisms" of information as defined by Atomic Design Methodology. When these Lego-like information block organisms are assembled to create a "closing book" (a.k.a. closing binder) or "audit bundle" (a.k.a. internal or independent audit working papers) or "digital financial statement" or "financial analysis model"; that set of information blocks becomes knowledge.

Here is a description of the digital information organism. Until now, it has not been possible to use such a global open industry standard modular, model-driven, semantic oriented, artificial intelligence enabling mechanism because such a mechanism did not exist. But now the mechanism does exist.

It is the process of creating that "closing book" or that "audit bundle"; the choice of which information block "organisms" to include and how those "organisms" are assembled; it is in that process and in the procedural knowledge that we understand how, the "know-how", the final creation is made. It is in that know-how that we trust the information blocks and trust the process and procedures used to create those information block Lego-like organisms.

What if you could take these Lego-like information block organisms and the "know-how" or procedural knowledge and the "knowing that" of declarative business rules and give all that to a machine, like the mindful machine for accountancy, because you could articulate that information using a global open industry standard and then get a machine and human to work together collaboratively to perform work?

This is an informatics problem and an industrial engineering problem.

Industrial Engineering, Digital Livability

Informatics relates to the intersection of information, people, and technology and the practical application of computational systems; understanding how people will "live" in the digital realm within some specific area of knowledge that makes sense to users of that technology. Informatics is the conscious management of information, knowledge, and accumulated knowledge. Informatics spans the knowledge accumulation process of an

individual member (learning)
organization (institutionalized knowledge)
area of knowledge (professional knowledge; subject matter)

Informatics has theories, principles, frameworks, and strategies that can be applied to solve information management problems. Informatics is about harnessing the power and possibility of digital technology to transform data and information into knowledge that people use every day. Informatics is about understanding how people will “live” in the digital realm with an elegance of design that makes sense to users of a particular technology. Informatics is about delivering the best user experience possible.

Similar to how a chef transforms a recipe using kitchen equipment into an unforgettable meal; informatics transforms the use of information and knowledge into a successful experience. Similar to an architect that transforms a building into a livable space by placing doors, windows, and utilities with functionality and ease; informatics improves “digital livability”.

Industrial engineering is an engineering profession that is concerned with the optimization of complex processes, systems, or organizations by developing, improving and implementing integrated systems of people, money, knowledge, information and equipment. Industrial engineering is central to manufacturing operations. Industrial engineers understand tools such as Lean Six Sigma. Lean Six Sigma involves systematically removing operational waste, reducing process variation, and managing process quality. Industrial engineers understand Lean Six Sigma techniques such as how to use mistake proofing tools like poka yoke.

Closing Book, Audit Bundle

Creating the final financial statement product and the supporting detailed information of the "closing book" and "audit bundle" and coordinating all that work has characteristics of a "job shop" and an "assembly line". There are tasks and processes, the work performed, are algorithms that are sometimes performed by humans and sometimes performed by machines (i.e. sometimes algorithm steps can be automated and sometimes they cannot).

Algorithms are a well defined sequence of instructions or steps. Algorithms are always unambiguous and are used as specifications for performing a task or process.

What if there really was a better way? What if we rethink things like the financial statement, the "closing book", the "audit bundle". What if we created a new vision. What if there was a universal global open industry standard for accounting and audit working papers. What if those accounting and audit working papers really did "snap" together like those Lego-like information block organisms; think information Legos.

What if we could create industrial strength canonical best-practice based accounting artifacts and share them effectively. What if we could create a tool that supplements the capacities of the electronic spreadsheet.

What if we could create a self correcting virtuous cycle using feedback loops?

Smart Software

Up until now it was impossible to do better. The beloved electronic spreadsheet, the accountant's "Swiss Army knife" is struggling to meet the real needs of accountants. The truth is that the electronic spreadsheet was a "stepping stone"; not the "be-all, end-all" tool that some think that it is. The electronic spreadsheet is not going away; but we accountants will have new tools to help us perform much of our work.

Why can we do better now? The environment has changed. Fifty years ago we did not have the internet; but we do now. Twenty five years ago we did not have global open industry standard structured information exchange formats; today we do (XBRL, RDF, LPG). We had rule-based artificial intelligence fifty hears ago, but it was too hard to use. Probability-based artificial intelligence did not exist until about ten years ago. The notion of a knowledge graph is about ten years old. How do you put all these pieces together?

Human-Machine Teaming

What is new is that now machines can help humans more. We can now effectively "team" a human with a machine. We can create what I refer to as that "mindful machine". What if we really did create a "mindful machine for accountancy". Another name for this is a knowledge based system.

What if we did put all these pieces together and created new procedures and documented that procedural knowledge such that it was understandable by both humans and also by the machines. What if we could capture important institutional knowledge such that the knowledge is retained within an organization rather than that knowledge leave the organization whenever an employee left the organization. What if we could communicate more clearly. What if we could reduce the threat of inaccuracy or eliminate inaccuracy altogether?

In an industrial setting, how to create a "closing book" or "audit bundle" is procedural knowledge, the "know how". That procedural knowledge has both explicit aspects which are documented in manuals and other such documentation, but much of that knowledge is also implicit and tacit and exists only in the heads of your most talented, skilled, and experienced employees. These experienced employees carry this tacit information in their heads; the process steps, conditions, judgment calls, and other such things that make these complicated procedures work. This important procedural knowledge exists in experienced hands and heads of employees, as documentation in margin notes of outdated manuals, and in the institutional memory of workers who may retire (are retiring), or move on to other work at any time. Common knowledge is well understood by humans, but machines are oblivious to this common knowledge.

Machine Interpretable Know How

Without the institutionalized knowledge in the form of formalized knowledge representations of both the explicit knowledge but also the tacit knowledge, implicit knowledge, and common knowledge. The absence of cohesive, formalized knowledge representations of explicit, implicit, tacit, and common process knowledge has significant consequences.

When procedural knowledge cannot be accessed or reused by machines and people, organizations face increased compliance risks, higher error rates during procedure execution, and substantial friction in training and onboarding new employees. The challenge of capturing processes as formalized procedures intensifies as organizations deploy artificial intelligence systems that require that rich procedural context to function effectively.

This is not about yet another incremental change to existing legacy systems (i.e. that kludge). Humans are sometimes underrated. Elon Musk admitted that Tesla made a significant mistake and tried to over automate. Another common mistake is to automate bad processes. Automation works best when you follow the following fundamental rule: Add automation incrementally and only automate processes that are already working smoothly. Another mistake companies make is to think that AI is going to fix all your data problems. The AI Ladder points out that 81% of business professionals don't understand AI correctly, how bad data (which most organizations have) is a nonstarter, and the lack of the right skills on part of both business professionals and information technology professionals is problematic.

Rethinking Accounting, Reporting, Audit, Analysis

Rethinking accounting, reporting, audit, and analysis means going further than simply automation. Here are some good ideas from the article The Future of Audit: What Will Audit Firms Look Like in 2030?

"Re-thinking the audit means going further than automation. It means evolving checklists into dynamic procedures that flex to client risk. It means using client data to expand coverage intelligently, not just pick random samples faster. It means building workpapers that link directly to schedules so tie-outs highlight themselves. It means pulling project management out of scattered spreadsheets and portals into a single system."

"The shift is about changing both the unit of work and the unit of value. The unit of work moves from people following steps to systems executing policies. The unit of value moves from hours to outcomes: coverage, assurance, readiness. People still sit at the center but their focus shifts to judgment, handling exceptions, advising clients, and telling the story behind the results."

"That’s what sets up the next step: imagining the audit not as a chain of manual steps, but as a system, a machine that brings together data, policies, and people in a completely different way."

The beloved electronic spreadsheet, the "Swiss Army knife" is showing its limits. The electronic spreadsheet will always be a useful tool accountants, auditors, and analysts will use; but it is only one useful tool. New tools are necessary for our new challenges. The same technology that is making information more complex, increasing the volume of information flow, and increasing the pace of information flow can, if configured correctly, also be used to solve those problems.

The consultancy Gartner estimates that currently the typical Fortune 1000 company used more than 800 electronic spreadsheets to prepare its financial statements for regulatory reporting. These spreadsheets tend to not be interlinked at all, or if there is interlinking it is brittle. Add to that the fact that 94% of business operational spreadsheets are known to have critical errors. This information is so important that the Sarbanes Oxley Act was created to address the symptoms of this problem by forcing public companies to document their systems. But Sarbanes Oxley does nothing to address the conditions which causes the symptoms. To address the conditions, a new approach is called for.

Industrialization

To industrialize something means to turn it into a routinized, repeatable, scalable, reliable process that can be done the same way every time. That includes:

Standardization: You define the steps clearly so the work is done the same way each time.
Repeatability: Anyone (or any machine) can follow the steps and get the same result.
Scale: You can do it not just once, but hundreds or thousands of times.
Efficiency: You remove unnecessary variation, waste, or improvisation.
Transfer from “craft” to “system”: Before industrialization: Work depends on individual skill, judgment, or memory. After industrialization: Work depends on a documented, controlled process.

A simple metaphor. If you cook a meal from memory, that’s craft. If you write a recipe that anyone can follow, that’s standardization. If you build a kitchen that can produce 500 identical meals a day, that’s industrialization.

Why this matters conceptually; Industrialization is essentially the move from:

tacit knowledge understood by an individual to explicit procedure “baked” into a process
individual variation into a controlled process
one off performance by a person to routinized, systematized production

This problem can, and will, be solved. Consider the Accounting & Audit by Design (A&AD) framework as you think about all this. AI-First, Digital-First, Graph-First.

Additional Information:

Search This Blog

Seattle Method