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After twenty years designing systems for global enterprises, we've witnessed transformation after transformation promise breakthrough results, only to deliver short-term wins and long-term technical debt. The problem isn’t the technology — it’s the people and methodologies that surround them.
Some Enterprise AI approaches are repeating the same mistakes, while others are creating net-new issues.
Legacy ERP and HCM vendors using the traditional Integrator Model — led by global system integrators (GSIs) that still rely on the same systems-engineering and point-solution playbooks that powered the last decade of digital transformation.
Their approach delivers system engineers trained only in configuration who rewire business processes to fit how the solutions work —not how your business should actually be run. The historic data on this playbook speaks for itself, and now many companies are attempting to apply the same framework to building and deploying AI.
55–70% of ERP platforms fail to deliver intended business value (Gartner, 2025).
>50% of Oracle and SAP customers remain on-prem because heavy customizations make migfration nearly impossible (Gartner, 2024).
>50% of back-office workflows remain error-prone and overly customized (Gartner Research, 2024).
Customers are forced into “keep-the-lights-on” (KTLO) strategies and are now retraining functional resources on point solutions with basic AI integration.
Because these point-solution vendors focus too narrowly, their products fail to solve meaningful business problems or generate real ROI.
(Real example from a leading “AI” point solution.)
At the opposite end of the AI spectrum, many enterprises have turned to AI-native companies like OpenAI and Palantir that follow a forward-deployed engineering model, leveraging software engineers rather than system engineers to build AI-first systems for customers. Though technologically superior, their cost structures create a fundamental ROI mismatch for back-office processes:
OpenAI requires a minimum of $10M in engagements—but how many back-office problems are worth $10M to solve?
Palantir's fees, which range on average between $3-8M, may make sense only for mission-critical intelligence, not routine back-office processing.
The economics don't work: A 30% reduction in month-end close time might save $500K annually - a fantastic ROI at Dayos's price point, but a rounding error against a $10M investment.
This creates an automation desert: Thousands of valuable optimizations go unaddressed because they're too small for Palantir but too complex for point solutions. These companies leave money on the table - not because the problems can't be solved, but because their cost models make solving them unprofitable.
Consider the low-hanging fruit they ignore:
Automating three-way match exceptions ($200K annual savings)
Accelerating vendor onboarding ($300K in efficiency gains)
Reducing manual journal entries ($400K in reduced errors)
Streamlining expense report approvals ($250K in time savings)
Each delivers meaningful ROI, but none justifies a $3M Palantir deployment. This is where 95% of enterprise automation value actually lives - in the aggregate of hundreds of processes that individually save $100K-500K annually.
What is left is the majority of companies taking the Siloed Build Model — building alone in-house - despite MIT State of AI in Business 2025 study, which found that 95% of enterprise AI initiatives fail to yield ROI.
But how companies adopt AI is crucial. Purchasing AI tools from specialized vendors and building partnerships succeed about 67% of the time, while internal builds succeed only one-third as often. Alone, each company ends up rebuilding the same workflows in isolation, consuming budget without creating shared learning, reusable infrastructure, or network effects.
According to the study's research, seven of nine industries show no structural change despite massive AI investment. The study found that:
Only 5 percent of Internal enterprise AI projects ever reach production.
Enterprises that partner with external vendors see twice the success rate of internal builds.
And 70 percent of AI budgets are still spent on customer-facing use cases, even though the highest ROI lives in Finance, Procurement, and HR.
This “build-it-yourself” behavior isn’t strategic—it’s circumstantial. When no viable AI-first products exist, enterprises are forced to construct their own foundations, just as they once would have built custom ERP systems if off-the-shelf versions hadn’t existed. The result is duplication, not differentiation.
Despite the limitations of both approaches, by late 2026, most large enterprises will have already made long-term vendor commitments.
The difference between success and failure won’t be whether they deploy AI, but who they partner with and how those systems are engineered.
Dayos applies the forward-deployed engineering model inside the GSI ecosystem — directly into the back office. This approach combines the operational rigor of the Integrator Model with the adaptability of a modern software company.
Our forward-deployed engineers embed within each customer’s unique ERP or HCM data layer, using Dayos’ native integration framework to build adaptive automation that scales safely into production. Every 90-day pilot is engineered for measurable ROI, not experimentation, enabling enterprises to move beyond configuration and toward continuous optimization.
This contrast defines the new AI landscape — where Dayos emerges as the only system bridging operational depth with adaptive intelligence.
The competitive landscape reinforces this divide. Most enterprise players still fall into one of two camps:
Legacy ERP and ITSM platforms tied to GSIs and MSPs.
AI infrastructure companies focused on model delivery, not operational integration.
Dayos occupies a new category: the AI Operating System for business applications. Unlike AI infrastructure vendors, Dayos is purpose-built to run inside ERP and HCM environments, using forward-deployed engineering to make back-office systems truly AI-native.
This landscape illustrates why most enterprises remain split between speed and depth — and why a new model, the Forward-Deployed Framework, is needed to connect AI innovation with operational reality.
Taken together, these four models reveal how enterprise AI has evolved—but also why most organizations stop short of real transformation.
Systems Engineers are stable but static.
Point Solutions are fast but shallow.
FDEs are powerful but costly.
Adaptive Systems are promising but hard to implement in legacy environments.
The gap between ambition and execution remains wide—and that’s exactly the gap Dayos bridges.
Dayos’ forward-deployed engineers pair the rigor of a GSI delivery framework with the adaptability of a software company, ensuring every pilot produces measurable ROI and scales safely into production.
We deliver outcome-based results through 90-day pilots, each drawn from a library of proven use cases — finance close acceleration, HR data-quality correction, compensation planning, and vendor-risk automation.
Every pilot starts fast, scales safely, and delivers measurable ROI within a single quarter.
Customers don’t start from scratch; they start from a common problem— customized to their ERP or HCM data layer and monitored for continuous optimization.
The MIT study found that organizations using personal AI tools (the "shadow AI economy") achieve 90% adoption rates while official enterprise initiatives stall at 40%. That's exactly what Dayos operationalizes: bringing consumer-grade flexibility with enterprise-grade governance.
Second, we pick the right use cases, we believe backoffice automation delivers the highest ROI while simultaneously right-shoring operations that were outsourced over the past decade. The MIT study found that partnerships leveraging adaptive AI in back-office functions—like finance, procurement, and HR—delivered the highest ROI, with externally partnered solutions succeeding twice as often as internal builds. These gains came primarily from reduced BPO spend and accelerated operational cycles, not workforce cuts.
Many companies are also chasing the wrong use cases — automating marketing copy or chatbot scripts — instead of focusing on the high-value back-office functions that actually define how a business runs.
That’s why Dayos focuses squarely on the highest-value, hardest-to-replace work — the back-office processes where small improvements compound into massive savings, and where AI doesn’t just assist people, it augments entire systems.
The next wave of enterprise transformation won’t be led by vendors selling tools or consultants selling hours—it will be led by those engineering outcomes. Dayos exists to make that shift real. We’ve built a framework that connects AI innovation to operational ROI, where every deployment compounds value rather than resets it. Our 90-day pilots prove that enterprise AI can deliver measurable ROI fast, safely, and repeatedly.
Source Attribution: Data and insights in this article draw from MIT's "The GenAI Divide: State of AI in Business 2025" research study, which analyzed 300+ AI implementations across 52 organizations, conducted by Aditya Challapally, Chris Pease, Ramesh Raskar, and Pradyumna Chari through Project NANDA at MIT.