Transforming Domain AI Experience With Zero Code, Disrupting the Legacy Enterprise Software Ecosystem
Enterprise AI projects fail, not from lack of ambition, but from forcing large language models into workflows they were never designed for.
90% of AI projects fail. That single statistic captures the huge gap between the promise of artificial intelligence in the enterprise and the reality that most organisations are living through today.
Despite the hype and investment, the vast majority of companies attempting to adopt AI are not seeing results. The reasons are consistent, systemic, and solvable, but only if the industry is willing to challenge the foundations of how enterprise AI is being built and delivered today.
Why AI Projects Keep Failing
The failure is not random. Companies are struggling with AI because they lack the internal expertise to execute it properly. Without the right talent, teams make the wrong choices from the start, selecting the wrong workflows to automate, working from incomplete or poorly structured data, and attempting to retrofit or fine-tune large language models onto small, specific use cases that never warranted that level of complexity in the first place.
Large language models were not built for businesses in the first place. They were not designed around departmental workflows, domain-specific outcomes, or the practical constraints of how organisations actually operate. Attempting to bend them into that shape is expensive, slow, and rarely successful.
Another critical failure point is that business users are entirely excluded from the process. AI experimentation in most companies sits exclusively with technical teams. Data scientists, engineers, and specialists who are far removed from the day-to-day workflows and decisions that AI is supposed to improve. When the people who understand the problems have no ability to participate in building the solutions, the results are predictably misaligned.
The Compute Problem Nobody Is Talking About
Retrofitting large language models for small, narrow use cases is not just strategically wrong, it is computationally wasteful. Using generative AI across every workflow regardless of whether it is appropriate consumes enormous compute resources and energy for no justified reason.
Many of the outcomes that companies are trying to achieve through large models can be accomplished using smaller, more targeted AI models and other AI techniques that require a fraction of the compute.
The assumption that generative AI is the right answer everywhere is one of the most costly mistakes the enterprise AI space is making right now. Matching the right model size and technique to the actual complexity of the use case is the smarter path.
No Visible Outcomes, No Real ROI
Beyond the wrong model choices and the compute bloat, companies are also starting from the wrong data. Without a pre-trained platform, without business workflows that are already loaded and understood, and without domain and industry context built in, every project starts from scratch.
That means long-running proof-of-concepts that stretch on for months and years without delivering visible outcomes or measurable benefits. Teams invest significant time and resources into experiments that never reach production, and business stakeholders lose confidence in AI as a genuine lever for transformation.
The absence of zero-code capability makes this worse. When business users have no way to experiment and innovate without writing code or depending on technical manpower, the pace of iteration grinds to a halt. AI remains an experiment in a lab rather than a tool in the hands of the people who need it most.
Meanwhile, legacy software continues to carry a high total cost of ownership that is simply not justified in the new AI world. It is not agile enough for a business landscape that is changing rapidly, and it was not built for the kind of AI-native experiences that organisations now need to remain competitive.
Use Cases Across Industries
The promise of zero-code domain AI becomes tangible when viewed through the lens of specific industries where the gap between potential and delivery has been widest.
In healthcare, clinical teams can deploy AI to streamline patient triage, flag anomalies in diagnostic data, and automate documentation that currently consumes hours of physician time each day. Without needing to involve data science teams, hospital administrators can configure workflows that surface relevant patient history at the point of care, reducing errors and improving outcomes without a single line of code written.
In manufacturing, floor supervisors can build AI-driven quality control checks, predictive maintenance triggers, and supply chain alerts tailored to their specific equipment and processes. Rather than waiting months for an IT-led implementation, operational teams can iterate in real time, catching defects earlier and reducing unplanned downtime at a fraction of the cost of legacy monitoring systems. Across every sector, the pattern is the same, the organisations that win will be those that put AI in the hands of the people who understand the work.
This realization was the foundation of TheNoah.ai, the first AI native bottoms up, fully-pre-trained, zero-code full stack AI Platform-as-a-Service (PaaS) in the world, with 50+ verticals, 1000s of use cases, data, models, agents, insights and experiences pre loaded ready to use from day one in minutes and hours at a fraction of a cost, manpower and time, fully disrupting the legacy software in the companies making them redundant.
The Noah.ai is being built with an open platform to empower organizations of any scale to start using AI with no technical requirements, no high-cost proof-of-concepts, and no lengthy experimentation processes, yet realise AI benefits and outcomes instantly in minutes.
The world needs to move away from legacy software and toward genuine AI experiences, and that shift has to happen without requiring companies to become AI companies themselves. Business users in organisations of any size, whether small, medium, or large, need to be able to innovate rapidly, experiment freely, and deploy AI into their workflows without coding, without months of setup, and without armies of specialists.