The Vendor That Never Existed: The EnerSys Shell Company Case

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September 9, 2026
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Sheila Kaye Jameson was a logistics analyst at EnerSys Corporation in Reading, Pennsylvania, not an executive, not someone with unusual authority. Over roughly eleven years she embezzled approximately $1.8 million from her employer using a company she invented herself. She was sentenced to 48 months in federal prison and ordered to pay $1,864,024 in restitution to EnerSys and its insurer, plus $256,447 in back taxes to the IRS.

What happened

Jameson created a shell corporation called Aries Consulting Group. It did no work for EnerSys. It provided no services, delivered no goods, and had no legitimate business relationship with the company at all. What it had was a name, a bank account, and a place in EnerSys’s vendor records. Jameson used her position to submit invoices from Aries Consulting to EnerSys, and EnerSys paid them, for over a decade, on the strength of nothing more than an invoice arriving from a vendor that existed in the system.

She also failed to report any of the embezzled income on her federal tax returns, which added tax fraud charges on top of the mail fraud charge she ultimately pleaded guilty to.

Why the gap existed

This case is the purest version of a problem that shows up across every entry in this series so far: a system that verifies a vendor once, at onboarding, and then trusts that vendor’s invoices indefinitely without asking whether the underlying business relationship still makes sense, or ever made sense in the first place.

Eleven years is the number that should stop anyone reading this. Not eleven months, not two years. Eleven years of invoices from a company that never did a single hour of real work, moving through an accounts payable process that had every opportunity to ask “what does Aries Consulting actually do for us” and never did.

That question doesn’t get asked because vendor verification tends to be treated as a one-time gate. Pass it once at setup, and the vendor becomes permanently trusted infrastructure. Nobody re-examines a vendor relationship that has been running smoothly for years, precisely because it has been running smoothly for years. The absence of a problem gets read as evidence there isn’t one, when it might just mean nobody has looked.

Controls that would have caught it

A recurring vendor spend review is the most direct control here: a periodic requirement that every vendor above a spend threshold be re-justified with a current description of the services being provided and evidence that those services were actually delivered. Not a renewal of a contract. An active accounting for what the money is buying.

A second, more structural control targets exactly the gap Jameson exploited: any vendor whose only interaction with the company is invoicing, with no purchase orders, no receiving records, no contract on file, and no employee outside the person who onboarded them able to describe what the vendor does, should be flagged automatically for review regardless of how long the relationship has run. Tenure should never be treated as verification.

A third control, specific to logistics and operations roles with vendor-creation authority, is separating who can create a new vendor record from who can approve payments to that vendor. Jameson’s position gave her enough reach to do both. A system where those two functions sit with different people doesn’t stop a determined employee from ever attempting fraud, but it does stop one person from running the entire scheme alone for a decade without anyone else’s decision ever touching it.

An AI prompt example for ERP fraud detection

The pattern this scheme depended on, a vendor relationship with invoices but no other supporting business activity, is exactly the kind of thing worth checking continuously rather than during an occasional audit. Against the ERP’s vendor, purchasing, and receiving data, a controller could run something like:

“List all active vendors with total payments over $50,000 in the last three years that have no associated purchase orders and no receiving or goods-receipt records on file.”

A second query targets the tenure blind spot directly:

“Flag any vendor active for more than five years whose invoicing pattern has not been reviewed or re-verified since initial onboarding.”

Neither question is hard to answer once it’s asked. The entire eleven years this scheme ran is evidence that nobody was asking it.

The pattern for this series

Every case in this series follows the same shape: what happened, what shared assumption let it run, what a properly governed ERP control looks like, and one or two concrete AI prompts that turn a periodic audit question into something that can run continuously. The goal isn’t to suggest AI replaces the underlying data governance. It’s to show what becomes possible once that governance exists and someone actually asks it the right question.

Source disclaimer

The case details in this article are drawn from press releases published by the U.S. Attorney’s Office for the Eastern District of Pennsylvania, a public government source. All facts, figures, and quotations describing the case are sourced from those releases. The analysis of the control gap, the proposed detection controls, and the AI prompt examples are original commentary and are not part of the source material.

References

United States Attorney’s Office, Eastern District of Pennsylvania. “Corporate Employee Sentenced For Embezzlement And Tax Fraud.” Press release. https://www.justice.gov/usao-edpa/pr/corporate-employee-sentenced-embezzlement-and-tax-fraud

United States Attorney’s Office, Eastern District of Pennsylvania. “Corporate Employee Charged With Embezzlement And Tax Fraud.” Press release, June 28, 2012. https://www.justice.gov/archive/usao/pae/News/2012/June/jameson_release.htm

One Governance Gap, Two Fraud Channels: The MI5 Denny’s Case

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September 9, 2026
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In 2022, a Minnesota woman was sentenced to more than nine years in federal prison for embezzling over $881,000 from a Denny’s franchisee and a family-owned construction company. What makes this case worth a second look, beyond the last article’s Randstad payroll scheme, is that the same person ran two separate fraud channels through the same underlying weakness, and neither channel needed to be sophisticated to work for five years.

What happened

As Director of Operations for MI5, Inc., Kimberly Sue Peterson-Janovec had oversight of payroll, vendor billing, and cash deposits across eight restaurant locations. She used that access two ways. First, she submitted false requests for vendor payments, creating fake email accounts to impersonate vendor employees and generate fake correspondence supporting the payments, netting roughly $336,000. Second, and separately, she manipulated the payroll system to issue herself unauthorized pay using the names of employees who no longer worked for the company, netting another $20,000. On top of that, she was held responsible for an additional $181,000 in stolen cash deposits.

Two different fraud mechanisms, one root cause. Both vendor identity and employee identity were things the system trusted once established and never re-verified.

Why one control gap produced two exploits

Most ERP fraud writeups treat vendor fraud and payroll fraud as separate problems needing separate controls. They usually are separate controls in practice, but they share the same underlying assumption: once a master record exists, whether it is a vendor or an employee, the system treats it as valid until someone actively flags it. Nobody was asking the system to continuously re-verify “is this vendor real” or “is this employee still employed” on every transaction. Both checks happened, if at all, as periodic manual review rather than a standing rule enforced on every payment run.

That is the pattern worth generalizing. A fraud scheme doesn’t need two different weaknesses to run two different exploits. It needs one weak assumption that both processes happen to share.

Controls that would have caught it

Vendor side. A governed vendor master process should treat any new vendor contact channel, especially email domains that don’t match a registered business domain, as a flag requiring secondary approval before the first payment goes out. Cross-referencing vendor contact emails against internal employee email patterns is a cheap, high-value check most ERP implementations never configure, because it isn’t a default workflow. It has to be built.

Payroll side. Every pay run should cross-check active employee status at the moment of payment, not rely on a termination flag set once in HR and assumed to propagate. In practice, this means the HR and payroll integration needs to be a hard gate, not a soft sync, so a terminated worker record cannot appear as a valid payee in any pay run regardless of when the termination was recorded relative to payroll cutoff.

Both sides. The five-year duration of this scheme is the real tell. Neither vendor payments nor payroll runs were being reviewed for anomalies as a routine, systemic process. They were being trusted because they had always been trusted.

An AI prompt example for ERP fraud detection

This is where AI-assisted review earns its place, not replacing the controls above but catching what static rules miss because nobody thought to write the rule. Using a natural-language query layer against the ERP’s vendor and payroll data, a controller could run something like:

“Compare vendor contact email domains against our employee email domain. Flag any vendor created in the last 24 months where the contact email domain is unregistered, uses a free email provider, or closely resembles an employee’s name.”

And separately:

“List all payroll disbursements in the last fiscal year paid to employee IDs with a termination date recorded in HR prior to the pay period start date.”

Neither query requires new functionality. Both require someone to think to ask the question, which is exactly what a five-year undetected scheme tells you nobody was doing. The value of an AI layer here isn’t that it catches something a human couldn’t. It’s that it makes asking the question cheap enough to do routinely instead of only after something else triggers an audit.

The pattern for this series

Every case in this series will follow the same shape: what happened, what shared assumption let it run, what a properly governed ERP control looks like, and one or two concrete AI prompts that turn a periodic audit question into something that can run continuously. The goal isn’t to suggest AI replaces the underlying data governance. It’s to show what becomes possible once that governance exists and someone actually asks it the right question.

Source disclaimer

The case details in this article are drawn from a press release published by the U.S. Attorney’s Office for the District of Minnesota, a public government source, along with contemporaneous news coverage of the same case. All facts, figures, and quotations describing the case are sourced from those releases and reports. The analysis of the shared control gap, the proposed detection controls, and the AI prompt examples are original commentary and are not part of the source material.

References

United States Attorney’s Office, District of Minnesota. “Kenyon Bookkeeper Sentenced to More Than 9 Years Prison for $881,000 Employer Embezzlement and Tax Fraud Scheme.” Press release. https://www.justice.gov/usao-mn/pr/kenyon-bookkeeper-sentenced-more-9-years-prison-881000-employer-embezzlement-and-tax

Walsh, Paul. “Woman who embezzled $880,000 from Denny’s franchisee, Rochester company gets 9 years.” Minnesota Star Tribune, June 2022. https://www.startribune.com/9-1-4-years-in-prison-for-woman-who-embezzled-880k-from-dennys-franchisee-rochester-company/600184577

Autopsy #15: The Broken Windows Demo

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September 9, 2026
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Cause of death: the small stuff got waved off as cosmetic, and cosmetic was never what it was actually signaling.

Partway through the demo, something small is visibly off. A field labeled “Custmer Grp” with the typo still in it. A report total that’s correct but formatted with the wrong currency symbol. An old menu item labeled “Legacy Approval, do not use” still sitting in the navigation where everyone can see it. Someone in the room notices, points at the screen, and the presenter waves it off without breaking stride. “That’s cosmetic, we’ll clean that up before go live, doesn’t affect the numbers.” The room nods and moves on, because the thing being demonstrated, the actual functional capability, did in fact work correctly.

Nobody asks the question that actually matters here, which isn’t “does this affect the numbers.” It’s “what does leaving this visible tell the two hundred people who are about to start using this system every day.” That’s the autopsy. The defect itself was never the problem. What it signals is.

What actually happened

Broken windows theory, originally a criminology idea from the early nineteen eighties, argues that visible, unaddressed signs of disorder, a broken window left unrepaired, graffiti left up, don’t just reflect neglect, they actively invite more of it, because their presence signals that nobody is watching and nothing gets enforced. The mechanism isn’t about the broken window causing damage on its own. It’s about what an unrepaired window communicates to everyone who sees it about whether this place has an owner who cares.

The same mechanism runs through a go live environment with unusual precision. A typo in a field label, a leftover legacy menu item, a report that’s functionally correct but visually sloppy, none of these break anything on their own. What they do is tell the first cohort of real users, on day one, before anyone has formed a habit yet, that quality here is negotiable. A user who sees an obviously wrong field label on their first day draws a reasonable, entirely rational inference: if that got through, my own sloppy data entry probably will too. Nobody enforces the small stuff here. The inference isn’t about that one field. It’s about the whole system’s apparent tolerance for error, and it gets formed in the first few days, before the system has any track record to override it.

Small defects waved off in a demo compound specifically because they arrive stacked. One typo alone probably wouldn’t shift anyone’s behavior. A demo that’s waved off three or four small things in a row, cosmetic each time, individually defensible each time, has quietly established a pattern before go live even happens.

Why it works on smart people

Triage is a genuinely good practice, and drawing a line between functional bugs and cosmetic ones is a reasonable way to prioritize limited time before a deadline. The instinct to wave off a typo isn’t wrong on its own terms. Where it goes wrong is in applying an engineering severity framework, does this affect the calculation, to a question that was never actually about calculation. Users don’t experience a system the way an engineer categorizes a bug ticket. They experience it as a single continuous impression of whether the place is cared for, and that impression doesn’t sort itself into functional and cosmetic buckets the way a backlog does.

There’s also a time pressure effect specific to go live weekends. By the time anyone’s looking at a stray legacy menu item or a mislabeled field, the team is usually exhausted, focused on the handful of things that could actually cause a financial misstatement or a blocked transaction, and a cosmetic issue genuinely does rank lower by every reasonable prioritization framework available in that moment. The framework is correct. It’s just answering a different question than the one that actually determines early adoption behavior.

The actual damage

This is the one that shows up as a slow erosion in data quality that nobody can point to a single cause for. Three months after go live, free text fields that were supposed to use standardized values are full of inconsistent entries. A workaround process has formed around the one screen that still has a visibly awkward layout, because early users concluded, correctly, that nobody was watching that screen closely. None of this traces back cleanly to the typo in the field label from the go live demo, but the typo, and the two or three cosmetic issues that sat alongside it, unaddressed, in the very first days anyone used the system, set the tone that made the rest of it feel acceptable.

The remediation cost here is unusually high relative to how small the original defects were, because by the time the erosion is visible enough to act on, it’s a data quality and behavior problem spread across the user base, not a five minute fix to a field label. Fixing the typo now costs the same five minutes it always would have. Fixing six months of inconsistent data entry that grew out of the signal the typo sent does not.

The fix, if you’re the one presenting, or the one buying

Fix the visible small stuff before go live specifically because of what it signals, not because of whether it moves a number. If a genuine deadline forces a trade off, say so honestly and specifically to the people who’ll be using the system, “we know this field label is wrong, it’ll be fixed by Friday, here’s how to report anything else you notice,” rather than letting it sit silently and be discovered on its own. The difference between an acknowledged, tracked flaw and a silent, ignored one is exactly the difference between an owner who’s watching and one who isn’t, and users read that difference correctly, every time.

The typo was never going to break a journal entry. What it broke, quietly, was the first impression of whether anyone was going to notice if they didn’t get their own entries right either.

Standardization is exactly the governance broken windows theory argues for, and it’s worth asking what happens when it erodes at scale, not just in one field label. In Is Vibe Coding the End of Standardized ERP?, I look at what happens when every business starts generating its own bespoke operating logic with no shared standard to enforce consistency. A thousand small ungoverned deviations, each individually reasonable, is the broken windows problem running at the scale of an entire industry instead of one field label.

The Specialist Knows Its Job. The Generalist Knows Your System.

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September 4, 2026
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Enterprise software has spent forty years arguing about the same question in different clothes. Should a function do one thing correctly, or should a tool be given enough authority to figure things out on its own. Agentic AI has not settled that argument. It has just given it a new vocabulary.

Look at the two ends of what is shipping right now. SAP’s Joule agents, and most of the enterprise agent catalog around them, are built narrow. Each one has a defined job, a defined sequence, and a defined set of systems it is allowed to touch. An invoice reconciliation agent reconciles invoices. It does not decide to also check vendor master data for anomalies unless that step was explicitly wired into its process. Microsoft’s Cowork points at the opposite pole. It is handed a single broad tool, effectively full reach into a Dynamics 365 Finance and Operations environment, and told to figure out how to get a stated outcome done. Nobody hand built a sequence for it. It builds its own.

Both are called agents. They are not the same kind of thing, and the difference matters more than the marketing suggests.

What a specialized agent actually is

A specialized agent is closer to a very well automated macro than it is to a colleague. It has a fixed task, a fixed order of operations, and a boundary around what data and systems it can reach. This is not a limitation bolted on as an afterthought. It is the entire design philosophy. You get predictability in exchange for narrowness. When the invoice reconciliation agent runs, you know what steps it took, in what order, against what data, because those steps were specified in advance. If it fails, it fails in a way you can trace to a specific step that did not resolve.

That traceability is not incidental in a finance system. It is the whole point. An auditor does not want to hear that an agent figured out how to close the period. They want a sequence of steps that can be replayed, checked, and defended.

What a generalized agent actually is

A generalized agent like Cowork inverts that trade. Instead of many narrow tools each doing one job, it gets one wide tool and the discretion to decide, at run time, what sequence of actions gets it from the stated goal to a finished result. Ask it to reconcile a vendor’s account for the quarter and it will decide for itself which records to pull, which discrepancies are worth flagging, and in what order to check them. Two runs against the same request, on the same data, are not guaranteed to take the same path to get there.

That is not a bug in the current generation of these tools. It is the feature being sold. A generalized agent does not need someone to have anticipated every scenario in advance and built a process for it. It reasons its way through situations nobody wrote a workflow for. The cost of that flexibility is that the path it takes on any given run is not fully knowable ahead of time, and reproducing it exactly on demand is harder than reproducing a fixed sequence.

The real axis underneath the label

Specialized versus generalized describes the shape of the agent. It does not describe the thing that actually matters to a finance or operations leader deciding whether to trust one of these near a ledger. That deeper axis is deterministic versus probabilistic.

A deterministic process, whether run by a person, a script, or a narrow agent, produces the same output from the same input every time. A three way match either passes or it does not, based on rules that do not change between Tuesday and Thursday. A probabilistic process, which is what a large language model doing open ended reasoning actually is underneath the interface, produces an output that is likely to be correct given the input, not guaranteed to be correct. Run it twice and you may get two answers that are both individually defensible and are not identical.

Specialized agents tend to sit closer to the deterministic end, because their designers constrained the decision space in advance and left little room for the model to improvise. Generalized agents tend to sit closer to the probabilistic end, because improvisation across an open ended tool is the entire value proposition. But the two axes are not the same axis wearing different names, and conflating them is where a lot of the current enterprise anxiety about agents actually comes from. A specialized agent can still make a probabilistic judgment call inside one of its fixed steps, such as classifying a transaction description as likely fraudulent. A generalized agent can still land on a fully deterministic sub task, such as pulling a specific ledger balance, where there is only one correct answer and no room to improvise.

Why D365 F&O is the place this gets tested for real

Dynamics 365 Finance and Operations is a useful proving ground precisely because it already lives with both philosophies side by side. Its native controls, workflow approvals, posting rules, three way match, are deterministic by construction. They were built that way before anyone was talking about agents, because a ledger that produces a different answer on a re-run is not a ledger anyone can close a period against.

Layer Copilot style features and Cowork on top of that and you get a system where the system of record stays deterministic while the layer working on top of it is free to reason probabilistically about what needs attention. That is not a contradiction. It is the correct division of labor. The ledger should never guess. The agent looking for the discrepancy that the ledger’s own rules were not written to catch is allowed to guess, because guessing well is precisely what it is there to do.

The mistake worth watching for is treating a generalized, probabilistic agent as if it were a specialized, deterministic one because it happens to be plugged into the same system. Giving Cowork the same blind trust you would give a fixed three way match rule is a category error. One was built to never deviate. The other was built to deviate on purpose, whenever deviating produces a better answer.

Where this actually lands

Neither shape is the correct answer in general. A specialized agent is the right tool when the process is well understood, the stakes of an unreviewed error are high, and an auditor will eventually ask for the sequence of steps. A generalized agent is the right tool when the problem is not well understood in advance, when the value comes from adapting to a situation nobody scripted for, and when a human is still positioned to review the output before it becomes a transaction that cannot be undone.

The organizations getting this right are not the ones picking a side. They are the ones that have stopped asking whether an agent is good, and started asking where on both axes, specialized to generalized and deterministic to probabilistic, a given task actually belongs. Get that placement wrong in either direction and you either waste a general reasoning tool on a task a five line business rule already solved, or you hand a fixed script a problem it was never built to handle and call the resulting failure a bug instead of what it actually is, a design mismatch.

The Contoso Convergence, Episode 14: Go Live Weekend

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September 4, 2026
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An Advanced Dungeons & Dynamics 365 session. Episode 14 of 14. Season finale.

Previously on: Episode 13, The Audit Trail, the party disclosed the tax overcorrection to Feld immediately rather than waiting, and Feld pulled in audit and legal, then asked the party what go live actually needed from him personally.

Fourteen episodes. A shadow ledger, a silent integration, a rebate accrual eighteen months out of date, a chart of accounts stitched together by three different consultants, a demo that was finally allowed to break, and a tax override that outlived the bug it was built to fix. Every one of those problems is either resolved or being actively managed in the open. This is the weekend all of it gets tested at once.

The Session

GM: Cutover weekend. Every system down, every fix live, every character sheet about to be tested for real.

Marge: Status check. Thorne?

Thorne: Ledger’s clean. Rebate automation posted its first real accrual an hour ago. Correct, to the cent.

Sable: Chart of accounts holds. TEMP_DO_NOT_USE is finally retired, we mapped every report around it first.

Vex: Tax override removed. Legal signed off on the disclosure. Integration retry logic held through a full order cycle.

Ai. Cassiopeia: Power user proficiency scores, current state: seven of eight above target. Denise is mentoring the eighth.

Marge: Feld?

Feld: Board’s aware. Audit’s engaged. For the first time in this whole project, there’s nothing left that we’re hiding from ourselves.

GM: Monday morning. The first live order comes through. Every person who ever touched this engagement is watching the same screen.

Thorne: It’s processing.

Sable: It’s processing clean.

Marge: Third time’s the go live.

The order posts. No exceptions. No shadow ledger. No suspense account. Somewhere, Priya, back from a very long vacation, gets a message with just one word in it: “Live.”

Vex: (watching the screen) Fourteen episodes for one clean transaction.

Sable: It’s never really been about the one transaction.

Ai. Cassiopeia: Correct. It is about the fact that the next one, and the one after that, will hold the same way, for reasons everyone in this room can now actually explain.

Thorne: (quietly) That’s the part I’ll remember. Not that it worked. That we know why it worked.

Marge: (to the room) Good work, everyone. Truly.

Feld: (standing at the back of the room, having watched the whole thing) I’ve sat through two go lives that felt like holding your breath and hoping. This one felt like watching something that was actually built to hold.

Oskar: (from near the door, smiling for the first time in fourteen episodes) It’s a good feeling.

What actually changed between attempt three and the first two

Nothing about Contoso’s underlying complexity got simpler over the course of this campaign. The chart of accounts is still layered with history. The rebate program is still complicated. International tax rules are still international tax rules. What changed wasn’t the difficulty of the problem. What changed was whether the people closest to each problem felt safe enough, and equipped enough, to name it clearly the moment they found it.

Every failure in this campaign, the shadow ledger, the silent integration, the eighteen month manual accrual, the two year old tax override, the training program measuring attendance instead of competence, shares the same shape underneath the technical details. Something wasn’t working, someone noticed, and the information didn’t travel to whoever needed it, either because there was no channel for it, or because the channel that existed had already taught people that raising it wasn’t worth the trouble. Priya’s spreadsheet, Oskar’s silence, Denise’s unheard flag in month two, Feld’s own two years of unspoken knowledge, all of it is the same failure wearing different clothes.

What actually got fixed on this go live wasn’t the ledger. It was that. A demo that was allowed to break. A sponsor who learned that honesty wouldn’t blow up the room. A frontline analyst who finally got asked a follow up question instead of being scored and moved past. None of that shows up on a go live checklist, and all of it is the actual reason the third attempt held when the first two didn’t.

The pattern isn’t over

Every dungeon in this campaign, the shadow ledger, the forgetting curve, the golden path demo, the burden rate blind spot, is a real failure mode from real ERP engagements, just given a party and some dice. If any of these fourteen episodes made you wince in recognition, that was always the point.

  • Read the framework this whole campaign is built on: Gamifying the Enterprise: Game Mechanics for Continuous Proficiency, available now on Amazon: https://www.amazon.com/dp/B0GY3VWLVX
  • Get the full AD&D365 configuration guides, the Bare Bones Configuration Guides, Fifth Edition, all seven volumes, and start turning your own team’s onboarding into something closer to episode eleven than episode one: adnd365.com/start

Thanks for following the Contoso Convergence from the docks of Waterdeep all the way to a clean Monday morning transaction. If there’s an appetite for it, there’s more than one way this world could keep going.

The Contoso Convergence, Episode 13: The Audit Trail

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September 3, 2026
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An Advanced Dungeons & Dynamics 365 session. Episode 13 of 14.

Previously on: Episode 12, The Customization Nobody Remembers, Vex found a two year old tax override still running on top of a platform fix that made it unnecessary eighteen months ago, which meant international tax had likely been calculated twice for a very long time.

Two weeks to go live, and the party finally has to decide what to do with a discovery that’s bigger than a bug fix. This episode is about the moment a technical finding becomes an ethical one.

The Session

Vex: The override was patching a tax bug. The bug was fixed in a platform update eighteen months ago. Nobody removed the patch.

Sable: So for eighteen months, international tax has been calculated twice. Once correctly by the platform, once again by the leftover override.

Thorne: Every international order in that window needs review.

Ai. Cassiopeia: I can generate the affected transaction list. It is not small.

Marge: Feld needs to know today. Not after go live.

The party requests an unscheduled meeting. Feld reads the summary standing up, without sitting down first, which everyone in the room understands as a bad sign before he says a word.

Feld: (reading the list) This goes to audit and legal before it goes anywhere else.

Thorne: That’s the right call. It’s not a great one. But it’s the right one.

Feld: (setting the list down carefully) Two go live attempts failed because people were afraid to bring me news like this. You just did it anyway.

Marge: That’s the job.

Feld: I know it is. I’m still noticing that it happened.

Vex: There’s a smaller silver lining, if it helps. The overcorrection was consistent and directional. It should make the reconciliation methodical rather than exploratory. We know exactly what we’re looking for.

Ai. Cassiopeia: I concur. This is not a needle in a haystack. It is a known pattern applied against a known window.

Feld: (a short, tired exhale that’s almost a laugh) After everything else this project has thrown at me, “at least it’s a known pattern” is genuinely reassuring. That says something about the last four months.

Sable: It says the bar moved. Not that this is small.

Feld: Understood. Bring in whoever you need. What does the timeline look like with audit and legal both in the loop?

Feld authorizes disclosure, pulls in audit, and, for the first time in the engagement, asks the party what go live actually needs from him personally.

The moment disclosure stops being optional

Every episode up to this one has been about finding a problem and deciding how to fix it. This one is different, because the discovery crosses a line where “fix it quietly and move on” was never actually on the table. A tax miscalculation across a known window of international orders isn’t a configuration issue the team gets to resolve on its own judgment. It has legal and regulatory weight that belongs to people outside the war room, and pretending otherwise, even with good intentions, would have been a much bigger risk than the original bug.

What’s worth noticing is how unremarkable the decision to disclose actually was, once it got made. Marge’s line, “Feld needs to know today, not after go live,” isn’t dramatic. It’s the obvious, boring, correct call, and it’s only notable at all because of everything the party has learned about this project over twelve episodes: two prior go live attempts where the vague language of “data quality issues” let real problems go unnamed. Feld himself sitting through disclosures he could have made and didn’t. The default on this project, before this episode, was to let uncomfortable news dissolve into something softer before it reached the person who needed to hear it clearly.

Feld’s response is the real payoff of everything episode seven and eight built. He doesn’t get defensive. He doesn’t ask if it can wait. He reads the finding standing up, makes the harder call immediately, and then does something a lot of sponsors never manage: he notices out loud that the team told him the truth without being forced to, and says so, rather than treating honesty as the baseline he was owed all along.

What happens next

Audit and legal are engaged, the timeline is adjusted to account for it, and the party heads into the final stretch with every major fire from the last twelve episodes either resolved or actively being managed in the open. Cutover weekend arrives, and every fix built over the course of this campaign gets tested at once, live, in front of the whole company.

Next episode: Episode 14, Go Live Weekend, the campaign finale (coming soon)

If your team has ever found something that needed to go to legal or audit before it went anywhere else, you know exactly how heavy that meeting feels. Gamifying the Enterprise: Game Mechanics for Continuous Proficiency is available now on Amazon: https://www.amazon.com/dp/B0GY3VWLVX

And if you want a framework built around surfacing findings like this one clearly and early, instead of letting them soften into something vaguer, start here: adnd365.com/start

The $286,000 Ghost on the Payroll: The Randstad Case

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September 3, 2026
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In 2009, a former branch manager at Randstad was sentenced to five years and one month in federal prison for wire fraud and aggravated identity theft. The scheme ran for three years before anyone caught it. The method behind it was almost boring, which is exactly why it worked.

What happened

Cynthia Whitehead used the real names of former Randstad employees, people who had already left the company, to generate wage payments through the payroll system. She then kept those “wages” for herself. From 2004 to 2007 she embezzled more than a quarter of a million dollars this way. She pleaded guilty on July 8, 2009, and was ordered to pay $286,388.37 in restitution on top of the prison sentence.

Nobody defeated a clever fraud control here. The scheme worked because a former employee’s identity remained valid and payable inside the system long after that person had stopped working there.

Why the gap existed

The failure point sits exactly at the boundary between two systems that were supposed to agree with each other: HR, which knew someone had left, and payroll, which kept generating payments as if they hadn’t. That boundary is where this kind of fraud always hides. It isn’t defeating a rule. It’s exploiting the fact that two systems of record were allowed to disagree for as long as nobody checked.

A termination event should be the kind of thing that instantly and automatically closes off every downstream ability to pay that person again. In practice, in a lot of ERP implementations, it’s a status field that gets updated in one module and trusted to eventually matter everywhere else. Three years of undetected fraud is what “eventually” costs.

Controls that would have caught it

The control here isn’t exotic. A payroll run should not be able to issue a payment to any employee ID with an active termination date, full stop, checked at the moment of disbursement rather than assumed from a status flag set once and never re-verified. In practice, this means the integration between the HR module and the payroll module needs to function as a hard gate on every pay run, not a background sync that payroll happens to respect most of the time.

The harder part isn’t designing that rule. It’s noticing that it needs to be designed at all, because on paper the termination process already “worked.” The employee’s status did get updated. Nobody had built the check that made that status update actually block a payment.

An AI prompt example for ERP fraud detection

This is a case where an AI-assisted review layer earns its keep, not by replacing the underlying control but by making it cheap enough to run constantly instead of only during an annual audit. Against the ERP’s HR and payroll data, a controller could run something like:

“List every payroll disbursement in the last three fiscal years paid to an employee ID with a termination date recorded prior to the pay period start date.”

A second, complementary query catches slower variants of the same pattern:

“Flag any employee ID that received a payment more than 30 days after their most recent termination or leave-of-absence date recorded in HR.”

Neither prompt requires new system functionality. Both require someone to think to ask the question on a recurring basis rather than only after a tip or an audit forces the issue. That is the actual lesson of this case. The rule that would have stopped it wasn’t complicated. It just wasn’t being asked.

The pattern for this series

This is the first case in a series looking at real, documented ERP fraud schemes: what happened, what shared assumption in the underlying system let it run, what a properly governed control looks like, and a concrete AI prompt that turns a periodic audit question into something that can run continuously. The goal is never to suggest AI replaces the governance. It’s to show what becomes possible once the governance exists and someone actually asks it the right question.

Source disclaimer

The case details in this article are drawn from a press release published by the U.S. Attorney’s Office for the Northern District of Georgia, a public government source. All facts, figures, and quotations describing the case are sourced from that release. The analysis of the control gap, the proposed detection controls, and the AI prompt examples are original commentary and are not part of the source material.

References

United States Attorney’s Office, Northern District of Georgia. “Former Randstad Branch Manager Sentenced to Federal Prison for Embezzlement.” Press release, September 16, 2009. https://www.justice.gov/archive/usao/gan/press/2009/09-16-09c.pdf

Autopsy #13: The Competitive Bake-off Demo

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September 3, 2026
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Cause of death: the competitor being demoed against would not have recognized itself.

Midway through the pitch, the slide changes to a comparison. Two columns, sometimes three. Your product on one side, the competitor on the other, a row of checkmarks and X marks running down the middle. The competitor’s column is thinner than it should be. A feature they shipped eighteen months ago is marked absent. A limitation they fixed two releases back is presented as current. The pricing comparison uses the competitor’s list price against your negotiated discount. By the end of the slide, the choice looks obvious, because one column was built by a team with every incentive to make it look that way, and the other was built by nobody in the room.

Nobody in the room raises a hand and asks when this comparison was last verified. That’s the autopsy. A competitive comparison built entirely by one side, unchecked by the other, isn’t a comparison. It’s a résumé written by an opponent’s rival, and it gets read with the same scrutiny either way, which is to say, functionally none.

What actually happened

Every competitor’s product roadmap moves faster than any comparison sheet does. A gap that was accurate when a sales engineer built the deck eighteen months ago has, more often than the room assumes, already been closed, sometimes through a native feature, sometimes through a partner integration or a marketplace add-on that isn’t visible from the outside but is entirely visible to the competitor’s actual customers. The comparison sheet is a snapshot with no expiration date printed on it, presented as though it were current.

The pricing rows compound the distortion in a specific, predictable direction. List price for the competitor, negotiated or promotional price for the vendor doing the presenting, is close to the default comparison method, and it isn’t dishonest in the narrow sense, both numbers are real, but it compares two different stages of the same negotiation, one that hasn’t happened yet against one that has. A prospect who hasn’t negotiated with either vendor is looking at the least favorable version of one price and the most favorable version of the other.

Feature checkmarks suffer the same collapse the Security Theater Demo’s checklist suffered. “Supports multi-currency” or “has a mobile app” is either true or false on the slide, with no room to indicate that one vendor’s version is a fully native capability and the other’s is a third-party add-on with a separate contract, a separate support line, and a separate bill.

Why it works on smart people

A side-by-side comparison has the visual grammar of objectivity. Two columns, symmetric formatting, a neutral-looking checkmark or X, all of it borrows the credibility of a spec sheet or a lab test, something that feels like it was measured rather than argued. The room’s guard is lower here than almost anywhere else in the demo, precisely because the format looks like data rather than persuasion, even though the party assembling the data has an obvious and undisclosed conflict of interest in every cell.

There’s also a research-avoidance effect. Verifying even a handful of rows against the competitor’s actual current documentation, or a call with their team, takes real time that a sales cycle often doesn’t budget for, and the comparison slide offers a shortcut that feels like due diligence without requiring any. Accepting the slide as accurate is the path of least resistance, and it’s a path built specifically to be the easiest one to take.

The actual damage

This is the one that produces a decision built on a comparison that was already stale, sometimes badly, by the time the contract was signed. A capability marked absent for the competitor that had actually shipped means the losing vendor was disqualified on a false premise, and the winning vendor’s genuine advantages, whatever they actually were, never had to be defended on their own merits because the comparison did the work for them.

Once implementation starts, the gap between the comparison sheet and reality becomes the buyer’s problem, not the vendor’s. If the deciding factor was a feature gap that didn’t actually exist, the buyer is now living with a vendor selected on the wrong basis, discovering the real trade-offs only after the switching costs have become substantial.

The fix, if you’re the one presenting, or the one buying

If you’re presenting, date every comparison row and be specific about the source, and if there’s a real chance a competitor has closed a gap since, say so rather than let a stale advantage stand unchallenged. If you’re buying, treat any vendor-supplied comparison as a hypothesis to verify, not a finding to accept. Take the three or four rows that would actually change your decision and check them directly against the competitor’s current documentation or a live conversation with their team, rather than checking all thirty rows equally or none of them at all.

A comparison chart built by one side and verified by neither side is not a comparison. It’s an argument wearing a spreadsheet’s clothes, and it deserves exactly the scrutiny an argument gets, not the trust a measurement earns.

The Waterdeep Trading Company runs on the same principle at its literal gates. In Receiving Controls That Stop Bad Goods, nothing becomes sellable stock on the strength of a seal or a claimed count alone. It gets checked at the gate, every time, regardless of who’s vouching for it. A competitive comparison deserves the same standing policy: verify before it enters the decision, not after.

The Contoso Convergence, Episode 12: The Customization Nobody Remembers

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August 31, 2026
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An Advanced Dungeons & Dynamics 365 session. Episode 12 of 14.

Previously on: Episode 11, Character Sheets, Denise’s caution turned out to be expertise nobody had asked about, and Vex offered to dig into the field she’d learned to distrust, the one that was wrong about one time in six.

Two weeks to go live. Everything on the ledger side finally looks green. This episode, that calm gets interrupted by something nobody was even looking for.

The Session

Vex: (deep in the code) Found something. There’s an override on tax calc. It only fires on international orders.

Thorne: Since when?

Vex: Git blame says two years ago. Author’s account is deactivated.

Sable: Do we know why it exists?

Vex: No comments. No ticket reference. Just a hardcoded exception for a specific country tax code.

Ai. Cassiopeia: I searched historical support tickets. There is a closed ticket from that period referencing an audit finding. No resolution notes attached.

Marge: Two weeks to go live and we’ve found a silent override to statutory tax logic with no documentation and no author.

Thorne: We can’t ship blind on tax. Not this close to the finish line, not this category.

Vex: I can reverse engineer what it’s compensating for. Give me two days.

Vex disappears into the code for the better part of the next two days, surfacing occasionally for coffee and increasingly specific questions.

Vex: (day one, afternoon) This override is doing something very deliberate. It’s not sloppy code. Whoever wrote this understood the tax engine at a level most consultants never bother to reach.

Sable: That’s not reassuring. That’s more unsettling.

Vex: (day two, morning) I think I found the audit finding it was responding to. There’s a gap between how the platform’s native tax engine handled a specific cross border scenario two years ago, and how the actual tax authority required it to be handled. Someone found that gap during an audit, panicked, and built this override to close it fast.

Thorne: Did it work?

Vex: (pulling up a comparison, going quiet) I need another hour before I answer that honestly.

Two days later, Vex has the answer, and it’s not what anyone expected.

Vex: The platform fixed the underlying bug in an update about eighteen months ago. This override is still running. On top of the fix.

The patch that outlived the problem it solved

An override built under audit pressure, with no documentation and a deactivated author, is exactly the kind of artifact that should terrify anyone doing a go live readiness review. But the actual failure here isn’t the override’s existence. Overrides built in a hurry, in response to a real and urgent finding, happen constantly, and they’re often the right call in the moment. The failure is that nobody built in a mechanism to revisit it once the underlying platform caught up.

That’s a subtly different problem than most of what the party has found so far. The shadow ledger, the TEMP dimension, the silent integration, all of those were things nobody built correctly the first time. This one was built correctly, for the moment it was built in, by someone who clearly understood the tax engine well enough to solve a genuinely hard problem under real pressure. The failure happened later, quietly, when the platform vendor shipped a fix and nothing in Contoso’s environment ever checked whether the original workaround was still needed.

This is the maintenance half of technical debt that almost nobody plans for. Everyone budgets for building the workaround. Almost nobody budgets for the moment, months or years later, when the reason for the workaround quietly disappears and the workaround itself doesn’t. A patch with no expiration date and no owner just keeps running, doing whatever it was built to do, whether or not that’s still the right thing to do.

What happens next

The platform fix and the old override are now both active, which means international tax has likely been calculated twice for a very long time. The party has two weeks to go live and a discovery that reaches back much further than two weeks, into territory that’s going to need more than an engineering fix.

Next episode: Episode 13, The Audit Trail (coming soon)

If your environment has a workaround built in response to a problem that might already be solved somewhere upstream, this episode is worth sitting with. Gamifying the Enterprise: Game Mechanics for Continuous Proficiency is available now on Amazon: https://www.amazon.com/dp/B0GY3VWLVX

And if you want a framework built around giving every workaround an owner and an expiration date, start here: adnd365.com/start

Time and Tokens

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August 31, 2026
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There used to be a saying in the business world: everything can be done with time and Excel. Give a competent analyst enough hours and a blank spreadsheet, and there was no forecast, no reconciliation, no reporting problem that couldn’t eventually be solved. Excel wasn’t fast. It wasn’t elegant. But it was infinitely general purpose, and it always did exactly what you told it to do, cell by cell, every single time.

The saying is starting to change. People are now saying everything can be done with time and tokens.

It’s worth taking that seriously, because it’s not just a joke about AI hype. Something real shifted underneath it.

What Excel actually required

The old saying hid a dependency that everyone quietly understood. Excel could do anything, but only if a human already knew what “anything” looked like. Someone had to understand the business problem, decide what the model should compute, structure the formulas, and catch the errors when a dragged cell reference pointed at the wrong row. The tool was general. The thinking was not automated at all. Time, in that saying, mostly meant the time it took a skilled person to build and rebuild the model until it matched reality.

That is why “time and Excel” was a saying about human effort as much as it was about software. The spreadsheet was a canvas. The intelligence was still entirely yours.

What tokens change

A large language model flips part of that arrangement. You can now describe a vague, half formed problem in plain language and get something usable back without doing the structural thinking first. The model proposes the categories, drafts the formulas, writes the summary, or builds the first pass at a plan. Tokens, in the new saying, stand in for both the words you feed the model and the compute it burns iterating toward an answer.

This is a genuine capability shift, not just a rebrand of the old joke. With Excel, the bottleneck was almost always model construction. With a language model, the bottleneck moves toward specification and verification. You spend less time building the machine and more time describing what you want and checking whether what came back is actually right.

That is a real change in where human effort goes. It is not a small one.

Where the analogy breaks

It would be tidy to say tokens simply replaced Excel, but that’s not quite what’s happening, and the difference matters more the higher the stakes get.

Excel’s defining property was determinism. A formula produces the same output from the same inputs every time, and you can trace exactly why. That traceability is not a nice to have in finance, operations, or compliance work. It is the entire point. An auditor does not want to hear that the number is “probably right.” They want to see the formula.

Tokens do not offer that guarantee. A language model produces a plausible answer, and plausible is doing a lot of work in that sentence. Ask the same question twice and you may get two different, both reasonable sounding, results. For a first draft, a brainstorm, or a rough model, that is a feature. For a general ledger reconciliation, it is a liability.

So the honest version of the new saying is not that tokens replaced Excel. It is that tokens increasingly generate the Excel. The model drafts the formula, proposes the pivot, writes the first pass at the model, and a deterministic engine, spreadsheet or database or ERP calculation, still does the actual execution. The two are stacking, not swapping.

The bottleneck didn’t disappear, it moved

The old saying implied that with enough time, Excel could solve anything, and the effort lived in building the model correctly. The new saying implies that with enough tokens, a model can solve anything, and the effort lives somewhere else now: in asking the question well and in checking the answer carefully.

That is arguably a harder skill to teach than spreadsheet formulas ever were. A bad formula usually breaks visibly. A bad prompt often produces something that looks complete and confident and is wrong in a way that takes real domain knowledge to catch. The scarce resource used to be knowing how to build the model. Increasingly, the scarce resource is knowing enough about the problem to tell a good answer from a good sounding one.

Why this should sound familiar

Anyone who has sat through an ERP vendor demo has already seen a preview of this exact pattern. The demo always ends the same way: with the confident implication that the software understands your business and can just handle it, no configuration debt, no data cleanup, no edge cases. Excel never made that promise. It never pretended to understand your problem. It just calculated exactly what you told it to.

Tokens, by contrast, come wrapped in the same confident tone as the demo. The output reads fluently. It sounds like understanding. Whether it actually reflects your business, your chart of accounts, your specific edge case, is a separate question that the fluency does nothing to answer.

That is the real risk in the new saying, and it has nothing to do with the technology’s capability. It is the same trap the old vendor demos set: mistaking a confident answer for a correct one. Excel forced you to see your assumptions in the formula bar. Tokens can hide them in a paragraph that sounds like it already checked.

The updated version

Everything can still be done with time and Excel. That has not stopped being true. What’s changed is that tokens now do a lot of the early modeling work that used to eat the time. The saying isn’t wrong to update. It’s just missing a clause.

Everything can be done with time and tokens, as long as someone still knows enough to check the work.