So, Daniel sent us a follow-up that is honestly kind of hilarious in retrospect. He wrote in and said, "We did an intriguing episode about the state of ERP in 2006. Sadly, my prompt was missing a '2'! I meant 2026. But the previous episode is a fascinating historical snippet. In 2026, where is ERP and how has AI influenced things?"
Herman Poppleberry here, and I have to say, I am actually glad we did that 2006 deep dive first. It provides the perfect foil for what we’re seeing today. If 2006 was the era of the "system of record"—basically a digital filing cabinet where humans manually typed in what happened yesterday—then 2026 is the era of the "autonomous core." We’ve moved from recording history to predicting and executing the future.
It’s a massive jump. I mean, imagine an ERP system today that doesn't just send you an alert saying a shipment is delayed. Instead, it’s already identified the delay, scouted three alternative suppliers, checked their current stock via API, recalculated the landed cost impact on your quarterly margin, and drafted a revised production schedule. It’s just sitting there waiting for you to hit "approve," or in some cases, it’s already done it.
That is the "agentic" shift we’ve been tracking all year. And by the way, for the tech nerds listening, today’s episode is actually being powered by Google Gemini 3 Flash. It’s writing our script, which is fitting since we’re talking about the models that are essentially the brain surgery happening inside these massive enterprise suites. The fifty-billion-dollar ERP market is undergoing its biggest transformation since the Y2K bug, but this time it’s not about fixing code—it’s about replacing human manual labor with autonomous agents.
It’s wild because in 2006, the "Big Bang" implementation was the scary monster under the bed. You’d spend eighteen months and fifty million dollars just to get the thing to talk to your warehouse. You had these massive teams of consultants living in hotel rooms for two years just to map out a single procurement process. Now, in 2026, we’re seeing companies drop AI-native modules into their stack and get them running in days. But before we get into the "how," let’s talk about the "what." What does ERP even mean in 2026? Because the boundaries between the back office, the supply chain, and the customer seem to have completely dissolved.
You’re spot on. The old silos are dead. In 2026, ERP has become a "system of intelligence." The architectural shift is the real story here. We’ve moved away from those monolithic, heavy suites—think the old SAP R/3 where you needed a specialized degree just to navigate the menus—to what we call composable microservices with an AI orchestration layer.
Right, so instead of one giant "God-box" software, you have these specialized bits that all talk to each other through a central AI brain. It's like moving from a giant, heavy ocean liner to a fleet of fast, coordinated drones.
Well—I shouldn't say exactly, I should say, the mechanism is that the AI acts as the connective tissue. Take SAP’s Joule agent, for instance. They launched a major version in late 2025, and by now in mid-2026, it’s capable of executing multi-step procurement workflows entirely on its own. If you’re a procurement manager, you can set a policy that says "Joule, you have autonomy for any purchase order under fifty thousand dollars, provided the supplier has a high reliability rating." The AI then handles the negotiation, the matching of the invoice, and the payment.
But how does it actually negotiate? Is it just looking at a price list, or is it actually "haggling" with another AI?
It’s both. It’s analyzing volume discounts, historical lead times, and even the supplier’s current financial health. If the supplier's AI knows they have an overstock of raw materials, your ERP’s agent will detect that signal and squeeze for a better margin. It’s a high-speed digital poker game played at the millisecond level.
That "under fifty thousand" threshold is the key, isn't it? It’s the "trust but verify" model. But I’m curious about the technical "how" here. We’re not just talking about basic "if-this-then-that" logic. This is transformer-based, right?
It is. The heavy lifting is being done by transformer models that use attention mechanisms to look at historical patterns. But they aren't just looking at your internal data anymore. These 2026 systems are pulling in external telemetry—weather patterns, geopolitical unrest, shipping lane congestion. When a transformer model analyzes these variables, it can forecast inventory needs fourteen days out with something like ninety-four percent accuracy. That’s a level of precision that was science fiction in 2006.
I remember in that 2006 discussion, we talked about "batch processing." Like, you’d run a report overnight and hope the printer didn't jam so you could see your numbers in the morning. Now, it’s all in-memory, real-time streaming. But I wonder about the implementation side. You mentioned it doesn't take eighteen months anymore. How does generative AI actually speed up the setup? Is it just writing the code for the connectors?
It’s more than that. It’s natural language configuration. In the old days, a consultant had to sit with a business owner, write a three-hundred-page requirements document, and then try to map those requirements to specific "T-codes" or menus in the software. It was a game of telephone where things always got lost in translation. In 2026, you basically "talk" to the ERP. You tell it, "Our fiscal year starts in July, we use LIFO accounting for these three warehouses, and we need a three-way match on all hardware procurement." The AI then configures the underlying microservices to match that business logic.
That’s a massive blow to the traditional ERP consultancy business. I can’t imagine the big firms are happy about "implementation" moving from a two-year billing cycle to a two-week onboarding process. What are they even doing with all those junior associates now?
They’re pivoting to "AI Alignment." Instead of configuring buttons, they’re auditing the AI’s logic. But it’s a crisis for the "legacy consultant," for sure. There’s a case study from early 2026 involving a mid-market manufacturing company. They were looking at a legacy rip-and-replace that was quoted at eight months. Instead, they went with a NetSuite AI-native module. They had the core procurement and inventory systems live in eleven days. Eleven days! The AI literally ingested their old messy spreadsheets, cleaned the data, mapped the fields, and built the workflows.
Eleven days is insane. It’s basically the "SaaS-ification" of the enterprise soul. But let's look at the big players. You’ve got Oracle, SAP, Microsoft, and Workday. They’re all fighting this "Agent War" right now. Oracle seems to be going for this "Process-Native" strategy. What does that actually look like in practice?
Oracle’s play is fascinating. They’ve built something called the AI Agent Studio inside their Fusion Cloud. Their pitch is: "Don't just use our agents; build your own." They want companies to create bespoke agents that understand their specific "secret sauce." For example, a specialized chemical company might build an agent that understands the specific shelf-life degradation of their polymers, something a generic AI wouldn't know. And to win market share, they’ve started embedding these AI features into their standard licenses for free. They’re betting that if they make the AI the "air you breathe" in the system, you’ll never leave.
It’s the "free sample" that eventually becomes the only way you know how to work. And then you have Microsoft, who is just shoving Copilot into every single corner of Dynamics 365. I saw a demo where someone was in a Teams chat, and they just asked the bot, "Hey, why is our margin down on the blue widgets this week?" and the ERP back-end did a multi-dimensional variance analysis and spat out a chart in the chat thread. No one had to "log in" to the ERP.
That’s the "Death of the Dashboard" we’re seeing. In 2006, the "Executive Dashboard" was the holy grail—a bunch of colorful pie charts that were usually two days out of date. In 2026, dashboards are seen as a failure of the system. If you have to look at a dashboard to find a problem, the AI didn't do its job. The AI should have found the problem, fixed it, and sent you a summary of the resolution. Think of it like a self-driving car. You don't need a dashboard that tells you the oil pressure is low; you just need the car to pull into the service station and tell you it’s being handled.
It’s the difference between a map and a GPS that just says "turn left." But let’s talk about the risks. If I’m a CFO and I’ve got an autonomous agent from SAP—let’s call him Joule—running around my supply chain, how do I know he isn't hallucinating a "great deal" on raw materials that actually bankrupts us? Or worse, how do I audit a decision made by a neural network?
This is the billion-dollar question of 2026: Explainability. We’ve had to develop entirely new audit trails. In 2006, an audit trail was just a log: "User Corn changed price from ten to twelve at four PM." In 2026, the audit trail has to include the "prompt context" and the data weights the AI used. Oracle and SAP have both introduced "Reasoning Logs." When an agent makes a decision, it generates a human-readable justification: "I increased the order for lithium-ion cells by twenty percent because I detected a strike threat at the primary port and the transformer model predicted a price surge of fifteen percent in the next thirty days."
So it’s basically the AI showing its work. I guess that helps with the "human-in-the-loop" requirement for high-value decisions. But what about the data itself? We’ve talked before about "garbage in, garbage out." If a company has twenty years of "spaghetti code" and messy data from their 2006-era systems, can a 2026 AI even handle that?
That’s why there’s this massive "Clean Core" movement right now. Companies are realizing that AI struggles with the highly non-standard, customized mess of the mid-2000s. To get the benefits of the autonomous ERP, you have to strip away those decades of custom "Z-programs" and go back to a standard, clean data model. It’s like a digital detox so the AI can actually see what’s going on. If your data is a mess of conflicting definitions for what a "customer" is, the AI is going to give you conflicting answers.
It’s ironic. We spent twenty years making these systems "unique" to our business, and now we’re spending millions to make them "standard" again so the robot can understand us. It’s like everyone realized that being "special" just meant being "broken."
Well, the "specialness" now happens at the prompt level and the agent logic level, not in the underlying database schema. Look at Unilever. In Q1 of this year, they deployed an Oracle AI-native module specifically for purchase order processing. They reported a seventy-three percent reduction in manual touchpoints. That’s not because they had "special" code; it’s because the AI was smart enough to handle the exceptions that used to require a human to pick up the phone. It's about being "standard" in the plumbing but "custom" in the brain.
Seventy-three percent is a staggering number for a company that size. That’s thousands of man-hours redirected to... well, I hope something more productive than data entry. But it brings up a second-order effect: what happens to the people? If the "Legacy Consultant" is dead, and the "Data Entry Clerk" is gone, who is running the ERP in 2026?
The new role is the "Business Architect" or the "AI Orchestrator." These are people who understand the business logic—the "why" of the company—and their job is to "teach" the AI. They aren't clicking buttons; they’re defining parameters. They’re saying, "In a supply chain crisis, prioritize the healthcare clients over the retail clients," and then the AI executes that across ten thousand orders. It requires a much higher level of strategic thinking. You're no longer the person who knows which menu the 'Submit' button is in; you're the person who knows how the business should react to a 10% spike in energy costs.
It’s moving from being the pianist to being the conductor. You’re not hitting the keys; you’re making sure the whole orchestra stays in sync.
And the "orchestra" is getting bigger. One of the most interesting things in 2026 is that ERP isn't just about internal data anymore. Sustainability—ESG reporting—is now a core module. In 2006, "Green" was a PR buzzword. Today, because of regulations like the EU’s CSRD, companies have to track the carbon footprint of every single SKU in real-time. You literally cannot do that without AI agents scouring your Tier 3 and Tier 4 suppliers' data.
That sounds like a nightmare to do manually. "Excuse me, Mr. Third-Party Plastic Supplier, what was the carbon emission of the truck that delivered the pellets to your factory?" The AI just pings their system, scrapes the data, and rolls it up into a report. Does it actually work that smoothly in practice?
Mostly. The friction comes when a supplier doesn't have an AI-ready API. In those cases, the 2026 ERP systems actually have "Agentic Outreach." The AI will literally draft an email to the supplier, follow up if they don't respond, and then use OCR to read the PDF they eventually send back. It's like having a tireless intern who never sleeps and speaks every language.
And because the systems are now API-first and cloud-native, they can actually "talk" to each other across company boundaries. We’re starting to see the very beginning of "Inter-ERP Negotiation." Your ERP’s procurement agent talks directly to the supplier’s ERP’s sales agent. They negotiate the price based on real-time inventory levels on both sides, and the deal is closed before a human even knows there was a shortage.
It’s a complete shift in the power dynamic. In 2006, the person with the best relationship with the supplier won. In 2026, the company with the best-trained procurement agent wins.
Okay, that is getting into "The Matrix" territory for business. If the machines are talking to each other and making deals, when do we get to go to the beach? Or does this just mean we’re going to be working at a higher "velocity of nonsense" where everything happens so fast that humans are just trying to keep up with the status updates?
It’s definitely a velocity shift. But the "nonsense" part is what the AI is supposed to filter out. The goal of the 2026 ERP is "Exception Management." You only care about the things that the AI couldn't solve. If everything is running according to plan, the system should be invisible. It’s like the "Invisible Machine" we’ve talked about before—the better it works, the less you notice it. You only get a notification when there's a problem that requires "human judgment"—which usually means something that involves ethics, brand reputation, or extreme high-stakes risk.
I love that. The ultimate success of an ERP is that nobody mentions the word "ERP" during the board meeting. They just talk about "margins" and "growth." But let’s get practical for a second. If someone is listening to this and they’re still stuck in a "System of Record" mindset—maybe they’re still running that 2006 version of SAP because "if it ain't broke, don't fix it"—what is their first move in 2026?
Don't do a full-suite rip-and-replace. That’s the ghost of 2006 talking. The 2026 approach is "Modular AI." You pick one high-friction area—maybe it’s your accounts payable, or your demand forecasting—and you drop in an AI-native service that sits on top of your legacy data. You let the AI prove its value by handling the "messy" parts. Once you see that ninety-four percent accuracy in your inventory forecasts, the rest of the organization will be begging to move to the "Autonomous Core."
So it’s a "Trojan Horse" strategy. You bring in a small, smart tool, and it eventually takes over the whole city because it’s just better at the job.
Precisely. And you have to audit your "decision points." Look at your current workflows and ask: "Where is a human just acting as a rubber stamp?" If a human is just looking at two numbers and saying "yep, they match," that is a prime candidate for an AI agent. In 2026, human intuition should be reserved for strategy and crisis, not for matching an invoice to a purchase order. Think about your warehouse staff. Instead of them hunting for a pallet based on a printed sheet, they have AR glasses that highlight the exact bin, while the ERP has already pre-calculated the most efficient walking path based on the next five orders.
It’s about dignifying human work, in a way. Taking the robotic parts out of the human’s day and giving them back to the actual robots. But I have to ask—what about the "black box" problem? If the AI is making all these micro-decisions, and something goes wrong, who gets fired? The CEO? The Head of IT? Or the AI vendor?
We're seeing the rise of "Algorithmic Liability Insurance" in 2026. Companies are literally taking out policies against their ERP agents making bad calls. But the real answer is that the "AI Orchestrator" role I mentioned carries the responsibility. You are responsible for the guardrails you set. If you tell the AI to "maximize profit at all costs" and it decides to stop paying your suppliers to save cash, that's a human failure in policy setting, not a machine failure.
That’s a sobering thought. The machines are only as ethical as the parameters we give them.
That’s the optimistic view, and I think it’s the right one. The ROI isn't just in "saving money" on headcount; it’s in the agility. When the next global disruption hits—and we know it will—the companies with an autonomous ERP will pivot in seconds, while the ones stuck in 2006 will still be trying to run their "overnight batch reports" to figure out what went wrong. Imagine a scenario like the 2021 Suez Canal blockage. In 2006, it took weeks for companies to understand the impact on their inventory. In 2026, the ERP agents had already rerouted shipments and adjusted customer delivery dates before the news even hit the mainstream cycle.
It’s the difference between being a sailboat and a speedboat. One reacts to the wind; the other powers through it.
And with the compute power we have now—thanks to platforms like Modal, which, by the way, provides the GPU credits that power this very show—running these transformer models locally on your enterprise data is actually feasible. You don't have to send your most sensitive "secret sauce" to a public cloud if you don't want to. You can run your "Agentic Core" in a private, secure environment. This has been a huge hurdle for industries like defense or high-end pharmaceuticals, but the 2026 tech stack has finally solved the "privacy vs. intelligence" trade-off.
That’s a huge point for the "Pro-Privacy" and "Pro-Security" crowd. You get the AI brains without the AI exposure. You're basically building a "Private Brain" for your company that knows everything about you but tells nothing to the outside world.
It’s a brave new world for the back office, Corn. From the "Missing 2" in Daniel's prompt to the reality of 2026, it’s been a wild twenty-year journey. We’ve gone from manual keying to autonomous orchestration. I suspect by 2030, we won't even call it "ERP" anymore. It’ll just be the "Operating System of the Business."
"Business OS." I like it. Simple, clean, and hopefully doesn't require a six-month training course just to find the "print" button—not that anyone prints anything in 2026 anyway. I mean, do people even have printers in their offices now?
Only as museum pieces or doorstops. If you’re printing in 2026, you’ve got bigger problems than your ERP. You're basically living in the stone age.
Fair point. Well, I think we’ve thoroughly unpacked the "2026 reality" for Daniel. It’s a lot more exciting than the 2006 "historical snippet," though I still have a soft spot for those old server rooms with the raised floors and the humming air conditioning. There was something very "physical" about the old way of doing things.
Nostalgia is fine, but I’ll take the ninety-four percent accuracy and the eleven-day implementation any day. I don't miss the 2:00 AM phone calls because a batch job failed and the ledger wouldn't close.
Spoken like a true nerd, Herman Poppleberry.
Guilty as charged. But let's be honest, even you don't miss those green-screen terminal interfaces.
No, I definitely don't. I'll take the natural language "Business OS" every time.
Alright, I think that’s a wrap on the great ERP "Missing 2" saga. We've come a long way from digital filing cabinets.
Thanks as always to our producer, Hilbert Flumingtop, for keeping the gears turning behind the scenes. He's the only one here who still uses a physical keyboard, I think.
And a huge thanks to Modal for the GPU credits. This has been My Weird Prompts. If you’re enjoying the show, a quick review on your podcast app really does help us reach more people who want to dive deep into the weird world of AI and business. Tell your friends, tell your boss, tell your autonomous procurement agent.
We’ll see you in the next prompt.
Goodbye.
Peace.
