It isn’t news that Machine Learning and Artificial Intelligence are profoundly changing many elements of our daily lives. From driving and routing assistance to social network feeds and home automation, most people are already using AI — perhaps without realizing it. But while consumer AI tops the news cycle, there is another AI revolution going on inside business operations, and Noodle.ai’s Enterprise Artificial Intelligence is at the vanguard.

Noodle.ai envisions a company of the future, where AI engines support all critical business functions — demand prediction, supply chain optimization, revenue management, customer experience, etc. “There are already promising AI providers for nearly every function inside the enterprise,” advises Steve Pratt, CEO of Noodle.ai, “but without thoughtful organization and implementation — without a cohesive AI strategy — companies will soon own an array of disparate siloed AI solutions.”

Enterprise Artificial Intelligence is the strategic and technical foundation that ensures AI investments made today grow in step with tomorrow’s AI advances. President and Chief Operating Officer Raj Joshi explains, “most of the companies we’re working with are focused on building their very first AI engines, but these learning algorithms mature quickly, and improvements are exponentially compounded when multiple AI engines are interconnected.”

After seeing the results from even a pilot AI implementation, clients grasp the value that a fully integrated network of AI engines represents. And, given the learning aspect of these algorithms, early-movers have a significant advantage.

While a true enterprise-wide AI is still some years out, Noodle.ai is preparing their client partners by building core Predictive Platforms, as well as the engine that keeps those platforms in concert. Some of the most dramatic improvements that Noodle.ai has already delivered are centered around four Predictive Platforms in particular:

• Demand Prediction & Pricing
• Logistics & Inventory Management
• Industrial Operations
• Multi-Channel Customer Interaction Optimization

Recent advances in hardware and learning algorithms, adept at transforming messy or unstructured data, have greatly improved the predictive power of historic data. Additionally, these advancements have enabled previously untapped external data sources lending additional predictive power. Predictive models that don’t take advantage of these advancements are easily an entire generation behind, in terms of sophistication.

While artificial intelligence and machine learning may sound intimidating, it’s not magic. “It’s just math,” is a phrase commonly heard around the Noodle.ai offices. Although, 80% of Noodle.ai employees have a Master’s degree and 30% have PhDs, so the math they’re referring to can start to look like magic.

“We recommend that executives across every company begin by educating themselves about AI — what it can and what it can’t do. Then start with a well-defined pilot project that provides a measurable return. Tackling small targeted AI implementations forces companies to understand what skills they have and what they need,” explains Joshi.

Bradley Stewart, CEO of XOJET, speaks enthusiastically about Noodle.ai’s data science methodology in this video, “it’s challenging our business to think about our business more critically.” XOJET had teamed up with Noodel.ai tobring advanced data science and predictive analytics to their core demand and fleet optimization challenges.

“The demand for these predictive services has only been accelerating,” reports Joshi, but Noodle.ai, led by former co-founders of Infosys Consulting, is ramping up quickly to meet the challenge.

Since Analytics India Magazine first reported on Noodle.ai’s Bangalore office inauguration just a few months ago, they’re closing in on 100 employees. At a recent campus recruitment event at IIT Madras, Chennai, India over 300 students registered for interviews. Noodle.ai is still several months from its first birthday, but they’ve already achieved all of their first year headcount and revenue goals.