While agility in machine learning deployment models will not be exactly like the agile software methodologies that many CSPs are familiar with, there are many common elements. The main difference is that machine learning agile methodologies are data-driven. To close that gap, good agile project management practices need to be customized and applied in a machine learning context for the organization. Often there is a disconnect between what the business needs versus what is able to be produced by machine learning engineers using the data and time available. How do you deploy models when the business needs them in two weeks, yet fine-tuning your model will take three to six months?

When you are dealing with millions of customers and vast amounts of data, as is common for many CSPs, the challenge becomes greater. Machine learning in production is becoming less about algorithms and more about the data workflows surrounding them—how to train machine learning models in the lab, deploy them into production, monitor and evaluate their performance, and improve them. If data flows are long, expensive or manual, they pose a big problem. In these cases, the strategy needs to be rethought and alternative solutions need to be considered to simplify the data flows. 

Many CSPs question whether they should have one centralized AI division for their organization or let each group use their own strengths. The simple truth is that most large organizations are not ready to have one artificial intelligence division to address all internal and external organizational needs on day one. It is still too early to have a single AI foundation. To illustrate this point, let’s use the example of one major U.S.-based telecom company. It has three AI divisions: one focused on operations and customer care; another focused on global supply chain strategy, which makes sure that products reach customers and that sourcing and procurement are effective; and a third focused on big data and artificial intelligence systems that create new data products. While these divisions remain separate, they do have a common organization that addresses data management, data governance, data warehousing, and data lakes, and common analytical and AI technologies. The goal of this common organization is to facilitate cross-functional, cross-organizational projects in a large enterprise. However, for smaller service providers, a centralized AI function may be a viable option.

AI in the Back Office

Business Support Systems (BSS) are foundational systems in any telecom company. They assist with taking orders, addressing payment issues, and tracking revenues, among other task and support processes such as product management, order management, revenue management, and customer management.

To improve the customer experience, most service providers are implementing new omni-channel and self-service capabilities. In these new revenue areas, service providers require support from their BSS platforms. The digital world is also changing the way service providers manage, sell, and support their core services. More service providers are adopting a digital-first paradigm, which puts the emphasis on automation.

Many traditional telco transactions like provisioning can occur without human intervention and in real or near-real time. Telecommunication companies are also aiming for more personalized interactions with their customers by using big data and analytics to tailor marketing and upselling efforts to specific customers and segments.

When planned and executed properly, AI has tremendous potential for service provider organizations. To create success, it’s best to stick to a tried-and-true methodology:

1. Look at the big picture
2. Define a use case for AI (Choose a use case which has high business impact and relatively low complexity)
3. Obtain the data to support the use case
4. Discover and visualize the data to gain insights
5. Prepare the data for machine learning
6. Select a model and train it
7. Fine-tune your model
8. Present your solution
9. Launch, monitor and maintain your system.

Simply put, success starts by focusing more on the use case, the user experience, and getting the right set of data than worrying about the latest and greatest AI algorithms.