Applying AI for improving prediction and business outcomes will transform decision making in most job functions of telecommunication operators in the next decade.

AI tools used in the right context will improve customer care, network operations and planning, fraud detection, and personalized marketing. The abundance of high-quality data, advances in computational processing, and sophisticated machine learning models available in the open source community is reducing both the cost and accuracy of applying AI compared to conventional hard coded methods. Attempting to deploy AI, even 5 years ago, was not economically feasible because of limited data sets, higher cost computing, and inferior ML models to conventional statistical regression techniques. In short, it was difficult to justify the economic benefits. That has all changed and the technology is actively being deployed in many sectors outside of telecom for natural language translation, facial recognition, advance driver assistance systems, and industrial automation.

Multitudes of use cases

The use cases are abundant from improving customer care to avoiding fraudulent transactions and this will drive investments. In the automation and assurance domain we expect to see a 20-fold improvement in isolating faults and service impacting events.

The value of AI is that it uses data to discover patterns and then predict outcomes more reliably than current methods. The power of AI is that it is constantly improving its learning algorithm using a technique called back propagation that changes weights in the hidden layer to achieve higher levels of accuracy.

Business outcomes drive success

It is critical to balance the cost of data acquisition with the accuracy and business outcome that you want to achieve. Other considerations in any AI driven project is the use of data that may violate data privacy and regulatory laws on the use of personal data. AI will be useful for some task but not relevant for other task where a limited data set is available or historical context of the problem is largely unknown. Organizations applying AI for specific functions will need training data to train the AI tool, input data to test and run the AI system, and feedback data for improving the accuracy of the AI tool. The context and type of data will determine the value and its use in specific use cases noted above.

We consider AI in its current state as having the potential to become a high value tool used for specific task to solve difficult business problems. It is important to look at AI in the context of the business problem and the results that you want to achieve that yield faster, cheaper and more accurate results that current workflow task or traditional IT tools. The executives and implementer’s of AI must balance the long-term strategy, organizational impact of AI on existing jobs, data privacy laws, and the economics of AI in improving decision making for all aspects of its business.

Let’s meet up in Barcelona

Suppliers in the telecom market applying AI techniques include Ciena Blue Planet, Nokia, Amdocs, Spirent, and others. If you have interesting deployments to discuss contact me directly or lets meet at MWC Barcelona.