Why Most AI Investments Will Look Like ERP Projects by 2028

 

Right now, enterprise AI feels like the iPhone moment. A demo lands, a room gasps, someone signs a budget, and everyone pictures a clean, magical product that just works.

Here's my prediction. By 2028, most of those investments won't be remembered as the iPhone. They'll be remembered as SAP.

If you were in a boardroom in the late 1990s or 2000s, you know exactly what that means, and your stomach probably just dropped. ERP was going to unify the company, kill the silos, and give leadership one version of the truth. Some of it delivered. A lot of it ran years long, cost multiples of the estimate, and ended in a system nobody quite loved and everyone learned to work around. The software was never the hard part. The company was.

AI is walking into the same story. Not because the technology is weak, but because of what always happens when powerful technology meets a large organization: the innovation becomes operations.

Why ERP is the right rhyme, and the iPhone is the wrong one

The iPhone was a product you bought and used. ERP was a program you implemented. That difference is everything.

A product pays off the moment you unbox it. A program only pays off after you've rebuilt your processes, retrained your people, cleaned your data, and rewired how decisions get made. The technology is maybe a fifth of the work. The other four-fifths is organizational surgery.

Enterprise AI is a program, not a product. The chatbot demo is the product. Getting it to actually change how your claims get processed, your contracts get reviewed, or your supply chain gets planned, safely and at scale and with someone accountable, is an implementation. And implementations rhyme. The technology works. The organization doesn't. That was the real story of ERP, and it's about to be the real story of AI.

This isn't even the first time. ERP, then CRM, then cloud, then the data-warehouse wave: each arrived as innovation and left as governance. Salesforce didn't fix bad sales processes; it exposed them. "Lift and shift" to the cloud didn't cut costs until companies rebuilt what they moved. Every wave starts as a breakthrough and matures into an operating-model change. AI is simply reaching that stage faster than anyone expected.

The specific ways it will rhyme

Budgets balloon and timelines slip. The demo cost a rounding error; the deployment will not. Wiring AI into real workflows means data pipelines, access controls, evaluation, monitoring, and endless edge cases the demo never showed. ERP taught us the pilot is the cheap 10%. AI is teaching the same lesson to anyone paying attention.

A consultant economy appears overnight. ERP built Accenture and Deloitte as we know them. The same thing is happening now: the "AI transformation practice" is being staffed as you read this. The irony your CIO will feel personally: after a decade of selling "disruption," they'll end up hiring the exact roles that made ERP work: enterprise architects, business analysts, data stewards, integration specialists, program managers, change managers. The revolution will be delivered by the people who ran the last one.

Customization eats the promise. The pitch is a general model that does everything. The reality is that your data, your compliance rules, and your processes are unlike anyone else's, so every serious deployment becomes a custom build. The gap between "standard package" and "our special requirements" that made ERP balloon is waiting for AI, in the same place.

The value is in the redesign, not the tool. This is the deepest rhyme. ERP only paid off for companies that used it as a reason to simplify how they actually worked. The ones who bolted it onto their existing mess just automated the mess, expensively. AI is merciless about this. Drop it on a broken process and you get a faster broken process. Bad knowledge management becomes bad RAG. Bad documentation becomes confident hallucination. Bad workflows become automated chaos. The model doesn't fix the rot; it scales it. Most of the return will come from the redesign the AI forces, not the AI itself.

And then it becomes table stakes. The part executives least want to hear. ERP stopped being an advantage the moment everyone had it; it went from competitive edge to the cost of staying in business. Enterprise AI is on the same curve, only faster. The general capability you're paying a premium for today will be a commodity your competitors also have by 2028. The advantage was never the software. It was what only you could do with it.

Where the analogy breaks, to be fair

It isn't a perfect match, and I'd be doing bad analysis if I pretended it was.

AI is cheaper to start with, adopts bottom-up before any big program begins, and iterates in weeks where ERP iterated in years. A team can get real value from an off-the-shelf tool tomorrow, with no eighteen-month rollout. That's genuinely different, and it's good.

But that changes the entry, not the endgame. Easy pilots are exactly what will lull companies into underestimating the enterprise-scale version, which is where the ERP dynamics come roaring back. Cheap to start is not the same as cheap to deploy across a regulated, political, legacy-bound organization.

What to actually do with this

If your AI program is being budgeted like a software purchase, it will fail like an ERP project. Budget it as the organizational change it really is. Assume the model is the cheap part and the change management is the expensive part, and staff for that.

Expect the pilot magic to die on contact with the org, and plan for that death instead of being surprised by it. Pick the processes you're genuinely willing to redesign, not the ones you just want to sprinkle AI onto. And stop treating the general capability as your moat, because it won't be one for long. Your moat is your data, your workflow, and the specific redesign a competitor can't copy.

The companies that came out of the ERP era ahead weren't the ones with the biggest implementation. They were the ones who used a painful technology program as an excuse to become a simpler, sharper business.

So the question worth sitting with, before the next AI budget gets signed: are you buying a product, or signing up for an implementation? Because by 2028, almost everyone will discover it was the second one.

LLMs aren't Uber; they're MoviePass

 

Every investor wants AI to be Uber. Burn cash now, win the market, raise prices later, print money. The losses are an investment in dominance.

It's the wrong story. The honest one is MoviePass and the difference is the whole ballgame if you're betting your costs on cheap AI staying cheap.

Uber's losses had an exit. MoviePass's didn't.

Uber bled billions on purpose. But notice why the bleeding stopped. Once it owned a city - enough drivers, enough riders, a habit - serving one more ride barely cost anything while its pricing power climbed. The subsidy bought a position that got cheaper to hold. Network effects did the work. There was a door marked "profit" at the end.

MoviePass sold unlimited movies for $9.95 a month. The flaw was brutal: every time you used it, they paid the theater near full price for your seat. The more you loved it, the more they lost on you. Scale never fixed the math - it multiplied the loss. Your best customers were your worst customers. It grew itself to death in about a year.

One subsidy had an exit. The other had a cliff.

The line that separates them: for Uber, usage trended toward revenue. For MoviePass, usage was cost.

So which one is an LLM?

Sit with that line, because LLMs fall on the wrong side of it.

Every prompt burns real compute, every single time. Your heaviest users - the ones running agents, generating all day, building their whole workflow on it - cost the provider the most. Using it more makes the math worse, not better. That's the MoviePass shape exactly: usage is cost, not revenue, and the people who love it most bleed you fastest.

Uber's cost per ride fell toward zero as it scaled. An LLM's cost per query is inference, and inference doesn't drop because you signed up more customers. It climbs with how hard each one leans on you. Models do get cheaper per token over time - but agents now spend tokens fifty at a time, and the frontier keeps moving to bigger, costlier models everyone then expects as standard. The cost floor keeps walking forward.

There's no geographic density to defend, either. Weights leak. Open models catch up. The "unlimited intelligence for $20 a month" plan is running the MoviePass playbook with better branding.

You can already watch the same death spiral

This is the part that should make the analogy land. MoviePass didn't die in one step. It died in a sequence - and AI is visibly walking the same one.

MoviePass tried to hold the unlimited promise, then started carving it up: blackout dates on the movies you actually wanted, peak-hour restrictions, "fair use" caps, verification hoops, and finally a hard quota that replaced "unlimited" entirely. Each step quietly admitted the model didn't work.

Now look at AI in 2026. The best models drifting out of the flat subscription and behind higher tiers or API metering. Usage windows and rolling caps. Slower models on the cheap plan, the good one upstream. Limits that tighten right as you grow dependent. None of that is failure of the technology; it's the same forced retreat from "unlimited," just run earlier and more gracefully than MoviePass managed. We're several steps into a seven-step story whose last step, for MoviePass, was the lights going out.

Where the analogy breaks (and it does)

I'd be doing the same lazy thing I'm criticizing if I pretended it's a clean match. Two real differences:

AI has a moat MoviePass never had: switching costs. Once your data, prompts, tools, and your team's habits live inside one provider, you don't wander off for a dollar. That's far stickier than a movie subscription.

And providers have an escape MoviePass fumbled. MoviePass metered late and clumsily, and customers revolted. AI companies are metering early and smoothly, while the product is still loved. Same move, just run better.

So LLMs won't "die" like MoviePass. But the flat price will, the same way unlimited movies did.

What a leader actually does about it

This isn't "AI is a bubble." The technology is real and permanent. The pricing is the temporary part. Three moves:

Budget for the real cost, not the coupon. If your business case only works at today's subscription rate, you don't have a business case. You have a free trial. Model what happens when your most valuable, heaviest use becomes your most expensive.

Don't get hooked on one provider's cheap tier. The more generous it feels now, the more certain it is to tighten. Build so you can swap models without rebuilding the house.

And separate your real moat from the discount you're enjoying. The discount ends. Your data, your workflow, the thing a competitor can't copy - that's what deserves the investment.

MoviePass taught a generation a hard lesson: when the thing you love is sold below what it costs to make, the love is the problem. AI is more durable than that. But the price on your invoice today is a story about someone else's funding round - not about what this actually costs.

Plan for the bill, not the trial.

Perspective on SaaS providers

 

The narrative that “AI will make SaaS obsolete” misses the real shift. AI isn’t replacing SaaS, it’s rewriting its economics. Let’s examine it in little bit detail.

• From Features to Outcomes: Traditional SaaS sold tools you operate. AI-native platforms deliver autonomous execution. Agents don’t just assist—they generate leads, draft outreach, optimize campaigns, and run workflows end-to-end. Value shifts from “what the software does” to “what it delivers.”.

•  Pricing Models Are Resetting: Per-seat licensing loses relevance when one AI agent replaces multiple users. Expect hybrid structures (subscription + usage + outcome-based) and tighter pressure on revenue predictability.

• Rise of AI-native competitors: There will be two classes of SaaS providers. Incumbents – tweaking the existing offerings to accommodate AI and new players – developing AI native systems from scratch. If incumbent do not overhaul SaaS architecture in big way, they may become glory of past.

•  The “Build In-House” Mirage: AI slashes dev costs, tempting mid-market teams to ditch vendors. But TCO, compliance, security, and ongoing maintenance will likely push many back to established SaaS ecosystems.
•  Consolidation & Verticalization: AI will compress “SaaS sprawl.” Horizontal platforms face margin pressure, while vertical/specialized providers with proprietary data and deep workflow integration will strengthen.
•  GTM Still Dominates Cost Structure: AI accelerates engineering, but sales, marketing, and enterprise trust-building remain the largest cost centers. Code is cheap. Distribution and adoption are hard.
•  Agent Reality Check: Non-deterministic outputs and evaluation complexity mean AI is currently a powerful automation layer—not a full replacement for mission-critical systems. Governance and quality control remain non-negotiable.

 In conclusion, large incumbents will survive, but “seat growth” will slow, pricing power will compress, and AI-native challengers will redefine categories. The new competitive moat isn’t features or code, it’s workflow ownership, domain expertise, and outcome accountability..

What do you think!!!

What if your company's entire tech stack ran on one operating system and that OS is Microsoft?

 

That's not hypothetical. It's exactly what Microsoft is building.

I've been studying Microsoft's AI strategy, and the scope of their vertical integration is striking. They now control every layer of the AI value chain from GPU clusters in Azure data centers all the way to the Copilot sitting inside your Word document.

 

 Three things that stand out:

1. The multi-model bet is smart. By offering OpenAI, Phi, Mistral, Meta, and Cohere in one platform, Microsoft isn't picking a winner: they're building the marketplace. Customers choose Microsoft wins either way.

2. Data gravity is the real moat. Fabric + Synapse + Azure Data Lake create tight coupling between enterprise data and AI tooling. Once your data pipelines are inside the Microsoft ecosystem, switching costs become enormous.

3. Copilots aren't a feature; they're the product. Microsoft is effectively selling AI coworkers embedded in software people already use daily: Word, Excel, GitHub, Dynamics, Bing. Distribution wins.

 

What this means for professionals today: The "build vs buy" AI question is increasingly becoming "stay in the Microsoft stack or opt out entirely." The ecosystem depth is genuinely impressive but so is the lock-in risk.

Is your organization leaning into the Microsoft AI stack or deliberately diversifying away from it? I'd love to hear your take.