We can safely say that “most” businesses have implemented AI one way or another. However, research from MIT shows that 95% of organizations see no tangible results from their AI investments.
This is a huge issue. Companies are drowning in data but are stuck with disconnected software, data silos, and manual processes, which, in today’s world, is ineffective, slow, and simply not enough.
There is, however, a better way emerging. The solution is using AI as an Operational Intelligence Layer, which is a unified decision-making ‘brain’ that connects departments and enables real-time, autonomous decisions across the enterprise.
The Silo Problem
Most enterprises still operate in silos. Pricing systems don’t talk to inventory or scheduling, and marketing forecasts don’t sync with supply chain plans. It’s like rowing a boat from one side; you won’t move in the intended direction.
These fragmented workflows mean that each department makes an isolated choice. Often, these choices are executed with incomplete information, resulting in a less cohesive strategy, duplicated effort, and decisions that fail to support broader business objectives.
As you can imagine, the cost of such disconnection decision-making is high. Teams spend hours “firefighting” issues caused by another department, opportunities get missed due to insufficient data availability, and so forth.
Then, bolt an AI into this disconnected process… It simply doesn’t work. The problem remains the same. That’s why only 5% of organizations see results from their AI investment.
What is an Operational Intelligence Layer?
An Operational Intelligence Layer is essentially a centralized AI “brain” spanning across business functions. Therefore, instead of separate systems making disconnected decisions, this layer ingests data from all departments and external market signals.
Fetcherr’s Large Market Model (LMM) is an example of such a layer. It’s a generative AI system that plugs into pricing, inventory, capacity planning, scheduling, and more, through one unified platform, providing somewhat of a crystal ball for modern businesses.
The LMM Concept
At its core, the LMM is an AI decision engine that bridges silos and connects departments through one intelligence layer. It’s not a chatbot or a dashboard. It’s more like a unified AI brain that guides operations.
Traditional enterprise AI operations tend to analyze historical data and spit out narrow reports or recommendations. An LMM, on the other hand, forecasts, optimizes, and acts in real time as the data is fed into the system.
For example, Fetcherr’s LMM doesn’t just review and report based on numbers; it uses real-world market behavior and makes live decisions with proper context. This means that all departments, like pricing, inventory, controllers, etc., are drawing from the same AI brain that considers the full picture.
This mirrors how we as humans think and make decisions. We form decisions based on the information available to us and constantly update those decisions as new signals emerge. An LMM applies this same adaptive logic just at an organizational and much faster level.
Goals-Based Optimization
A key feature that sets LMMs apart is goal-based optimization. The business designs its primary objectives, like maximizing revenue, growing market share, or improving operational efficiency, and the AI relentlessly drives towards these goals.
The LMM ingests massive amounts of data from internal systems and external sources. It then simulates millions of “what if” scenarios in a virtual market. Based on the goal and data provided, it’ll perform the right actions for the best possible outcome.
This goal-driven loop repeats continuously. As a result, decisions are always up-to-date with the latest data and performed based on the overarching goals of the organization.
From Reactive to Proactive Business Decisions
An operational AI layer fundamentally changes how decisions are made. It shifts teams from a reactive posture to a proactive mode.
The Old Way
In the traditional setup, decision-making is largely manual and hindsight-driven. Often, analysts pore over reports, Excel sheets, and disconnected dashboards to try to connect the dots.
Taking airlines as an example (though the patterns hold in retail, finance, etc.), pricing managers used to set fares by hand, using an RMS (Revenue Management System). This used static fare “buckets” and rules that update only occasionally. Revenue managers may then adjust inventory or prices based on lagging forecasts.
With such a system, a price change could take hours or days through this old system. Worse, human error could also appear. For airlines that want to maximize revenue, such processes make booking optimization slip through the cracks.
The New Way
With an AI layer like LMM in place, decision-making becomes real-time, continuous, and data driven. The AI system monitors all relevant data 24/7, similar to a super-analyst working all hours of the day, and reacts based on the business's goals and the data.
Let’s go back to the airline example. With an AI revenue management system, it can forecast demand and control how many seats to offer at each price point automatically. It can take data from demand, external market factors, and internal systems to provide predictive pricing.
Now, decisions that used to take teams and analysts days are now done instantly. With great accuracy, also. Such systems offer 95%+ pricing accuracy with continuous adjustments. However, AI isn’t here to replace humans. Instead, it’s here to enhance human judgment.
This now turns a human's role of ‘doing’ into ‘steering’, setting goals, defining boundaries, and making strategic decisions while AI handles execution at scale.
Azul Airlines Case Study
Azul Airlines is one of Latin America’s largest carriers with about 200 aircraft and 30 million passengers per year. Azul partnered with Fetcherr to transform its revenue operations using the LMM-based Generative Pricing Engine (GPE).
Implementation
When Azul began this journey in 2022, it lacked a modern pricing platform. Despite their organization's size, it was still using legacy tools that were slow and siloed.
The airline’s revenue management team was already high-performing. Therefore, adopting an AI “co-pilot” required a bit of a mind shift.
They started with a small subset of routes. This allowed them to test the AI suggestions and file pricing decisions. The team still closely monitored results to “review” the system.
Naturally, as the system demonstrated accuracy, their confidence grew. This then resulted in Azul expanding the use of AI pricing across more of its routes.
Results
After GPE implementation, they experienced real-time business optimization.
- 70% on Auto-Pilot: Today, over 70% of Azul’s flight network is managed with AI-driven pricing on auto-pilot. The LMM now handles the majority of pricing decisions automatically.
- 3 Million Decisions Per Year: The system now generates 3+ million fare recommendations annually, taking millions of micro-decisions off analysts.
- Zero Filing Errors: By handing off fare updates to the AI, Azul eliminated manual tasks and now has a 100% accuracy in publishing prices.
- 2,000 Hours Given Back: Automation freed up more than 2,000 hours per year for Azul’s revenue management team.
- Instant Revenue Uplift: As soon as Fetcherr’s LMM was deployed, Azul experienced a revenue increase immediately.
In practical terms, this meant better load factors (more seats filled at optimal prices). It also allowed them to have predictive business intelligence working for them 24/7.
AI Transparency, Explainability, and Privacy in Business
Leaders and regulators need confidence that AI systems are making rational, fair decisions without introducing any risks. This makes AI transparency, explainability, and privacy very important.
Glass Box, Not Black Box
AI enterprise integration tends to fail because it operates as a “black box”. When decision logic cannot be explained, stakeholders quickly run away.
Fetcherr avoids this by following a glass box approach. Every LMM decision is explainable, auditable, and traceable. Users can effortlessly see what data points and market signals drove a specific action.
Having such transparency enables governance, tuning, and regulatory compliance of the system, which generally builds trust amongst stakeholders.
Privacy by Design
Trust also depends on data privacy. Therefore, Fetcherr’s mission while designing this LMM was to ensure it optimized decisions using comprehensive market analysis, not individual user behavior.
How it works is by responding to macro supply and demand dynamics without profiling individuals. As a result, user information is kept safe. This privacy-first design protects customer fairness, avoids regulatory risks, and simplifies compliance with frameworks like GDPR.
Conclusion
As the data shows, most enterprise AI pilots fail to offer real-world value. This is why many look at it as “just another tool”. However, implemented properly, it can be a core infrastructure for business.
A unified AI decision platform is what’s going to drive businesses. By using powerful LMMs, you’re able to use multiple data sources, both internally and externally, to make decisions based on data, your customers, and the market, to drive performance on all business metrics.
Many organizations are understandably cautious about AI adoption given the high failure rate. However, as Azul’s journey shows, when approached correctly with unified intelligence, gradual implementation, and human oversight, AI can deliver measurable operational improvements.
Azul’s case study is just a single example. While airlines like Azul are proving the model, the same approach works for all industries, regardless of whether it’s retail, logistics, hospitality, or finance.
It’s normal to be skeptical. It’s new. But it is and will be an an architurcutre that both small and large organizations will use to drive their growth and innovation. For more information on how you can get started today, contact us.
