https://www.pexels.com/photo/17483870/
Executives keep asking the same question: how fast can a current product get an AI feature? Now, that urgency is fed by headlines and budgets, with IDC estimating that global enterprises will spend about $307 billion on AI services and platforms in 2025, rising toward $632 billion by 2028. Many teams respond by spinning up yet another microservice, wiring it to a large language model, and calling it progress, often after a late-night briefing with a partner that offers artificial intelligence and machine learning development.
Yet the harder truth is that most AI and ML development fails when it is treated as a decorative layer on top of legacy workflows. McKinsey’s State of AI survey shows that while adoption keeps growing, most organizations still struggle to move beyond pilots into real, scaled impact. The gap is rarely about model quality. It is about architecture. An application built without AI in mind cannot simply be patched with one more microservice and expected to behave like an AI-native product.
The microservices fallacy: when “adding AI” is already too late
Microservices were meant to give teams speed and independence. They still have their place. The problem comes when the same mental model is used for artificial intelligence and machine learning development. The pattern is familiar: keep the legacy core intact, expose a few APIs, stand up an “AI service,” and route some requests through it. On paper, the diagram looks clean. In production, it often quietly decays.
An AI-bolted-on service depends on data that was never designed for learning or feedback. Events arrive late or not at all. Labels sit in separate systems. Product teams cannot trace model outputs back to specific decisions. When something goes wrong, no one can confidently say whether the issue came from data quality, model drift, or a downstream business rule that overrides the prediction.
This is exactly the sort of weakness that shows up in surveys where boards ask why AI spending is rising while impact is flat. McKinsey’s 2025 work highlights that high performers treat AI as part of the operating model, with redesigned workflows and clear rules for when humans review model outputs, rather than as a separate “AI layer” parked at the edge of the stack. A microservice on the side cannot fix a process that still starts and ends in spreadsheets and email.
There is another, quieter cost. An AI add-on rarely collects the right feedback signals. Users may see recommendations or summaries, but their reactions are not captured in a structured way. Without that loop, AI and ML development become a one-off project. Models freeze. Behavior stops improving. The organization is left with a static feature carrying an “AI” label, while competitors move to systems that actually learn.
What AI-native architecture really looks like
An AI-native application starts from a different question: “If data and models are central, how should this product behave from the first click?” This sounds abstract, yet the consequences are very concrete.
In an AI-native design, the primary flows are built around decision points where a model can make, support, or suggest a choice. Data schemas are designed to capture not only what happened, but what could have happened and how users responded. Event streams are first-class, not an afterthought. Logging, monitoring, and feedback are treated as product features, not internal plumbing.
Deloitte’s Tech Trends report describes how advanced organizations are weaving AI into core trading, CRM, HR, and finance platforms, so that the “system of record” and the “system of intelligence” are the same thing rather than loosely coupled layers. That is the essence of AI-native architecture: the application and the model share the same heartbeat.
In practice, an AI-native application usually:
- Treats data as a long-lived product with clear ownership, quality rules, and access patterns
- Builds a real-time or near-real-time feedback loop into every critical decision
- Separates low-risk automated actions from high-risk ones that require human review, with clear escalation paths
- Bakes monitoring of model performance, drift, and business impact into the same dashboards leaders already use
In this kind of setup, ML and AI development is not bolt-on. It is the central discipline that shapes the product’s structure. A provider like N-iX, when engaged early, can help teams design event streams, feature stores, and decision points together rather than dropping a model into a finished application.
How to move from AI-bolted-on to AI-native
For many organizations, the question is not whether to start from scratch, but how to move from the current tangle of services toward AI-native systems without losing business continuity. That transition needs calm, deliberate steps.
First, pick one high-value journey where the absence of AI-native design hurts the most. It might be claims routing, fraud investigation, dynamic pricing, or sales assistance. Map the current path in detail. Where does data appear? Where does it disappear? Where do humans improvise with spreadsheets because the system does not quite fit reality? This is where artificial intelligence and machine learning development should begin, not with a demo that impresses in isolation.
Second, redesign that journey around data and models. Define which decisions will be supported by predictions, what inputs they require, and what feedback the system should capture after each decision. This is a good point to bring in an external partner experienced in artificial intelligence and machine learning development, but the ownership of the process should stay inside the business unit that lives with the results every day.
Third, adjust the technical architecture so that learning becomes routine. That means event-driven flows rather than nightly batch transfers, feature definitions shared across teams, and evaluation pipelines that compare current behavior with past baselines. Most of the projected $307 billion AI spend is coming from enterprises embedding AI into core operations, not from isolated experiments. The same logic should guide internal investment: prioritize areas where embedding AI into the main application will change how work is done, not just how screens look.
Finally, protect the human side. AI-native does not mean human-free. It means humans are given clear roles: supervising high-risk decisions, refining prompts and policies, supplying domain knowledge, and interpreting edge cases. Organizations like N-iX increasingly combine AI and ML development with change management and training, precisely because models without adapted teams remain underused.
A short conclusion
The temptation to “add some AI” as one more microservice will stay strong. Diagrams that show a tidy new service box are comforting. Yet current evidence from McKinsey, Deloitte, IDC, and others points in a different direction. The real gains come when data, models, and workflows are designed as one coherent whole.
AI-native architecture asks for more work upfront. It asks leaders to accept that some systems are not worth patching and must be rethought. Yet it also offers something the bolt-on approach never will: a product that learns every day, an organization that understands why its models behave as they do, and a path where artificial intelligence and machine learning development becomes a core business skill instead of a passing experiment.
The Microservices Fallacy: Why Your Next Application Must Be AI-Native