Faster business growth requires much more reasoning and much more effort.

2X to 10X growth requires multiplying our efforts on thinking, creativity and on actions. For example, in sales and marketing, we need to be much more creative and require much more hustle just to get our voice heard. Consumer attention span is at an all time low and we need to cut through the noise harder than ever before.

Good news, however is that AI is getting mature. It can think, it can be creative. It can hustle. It can not only detect distinctive growth patterns and anomalies but also assist in automating many of our mandate labor intensive tasks. All the reasoning and hustle that were human tasks so far - are being assisted by AI now.

Growth Analytics and ability to find growth silver-bullets (that has the potential to grow a business by 2X to 10X) is now possible with the recent advances in AI.

Technological Evolution

It all started with algorithms and data science getting popular. Predictive Analytics (Machine Learning, Prescriptive analytics, data science or whatever you may prefer to call it) has helped many companies solve some of the complex problems and resulted in millions of dollars in savings. Data Analytics - data modelling and actionable insights were its biggest strength. Then came the Deep Learning approach. Ability for a computer to comprehend and express itself became the new norm. Deep learning outperformed standard machine learning algorithms for complex scenarios.

However, both Predictive Analytics and Deep learning were limited in their ability to solve some of the complex Growth Scenarios. See Predictive Analytics is dead. So, we needed to go beyond standard data science and that is the genesis of JetFerry.

The growth problems are hard and AI is evolving. For example, understanding customer sentiment is utmost important in predicting growth. Customers are speaking, we are limited by our ability to listen. Customer sentiment data is available in text and audio formats. However, a combination of current Data Science combined with state of the art NLP (Natural Language processing) simply lacks in its ability to extract sentiment to required precision. We definitely need to look beyond. Computing needs to involve from interpreting rules and logic to understanding context - which is hard.