The distance between how far artificial intelligence (AI) techniques and technologies are from development to mainstream adoption is a key reference point for planning upcoming product portfolios and roadmaps in the telecoms industry.
Peter Liu, research vice president at Gartner says, product and services leaders must have a solid understanding of not only the realistic adoption times and a strong prediction on the subsequent impact of AI technologies on the industry.
In the latest Gartner analysis on AI and its impact on the telecom industry, we have identified the latest overarching themes and trends in the space.
Conversational AI takes hold
Understanding the interests of the customer and delivering an above and beyond customer experience is paramount to the telecoms industry. Telecoms businesses need to improve the methods in which they deal with customers on a day-to-day basis, with the aim of enhancing experience, improving care quality and overall creating a much deeper level of customer understanding.
Successful customer management includes not only caring for existing customers, but predicting retention rates, forecasting future buyers and estimating when they might buy, who they might choose and what they may be willing to pay. Using this information, the most successful businesses will then offer existing and future customers, what they want, when they want it.
Implementing technology to do this can be complex, when you take into account the volume of data and context required to enable businesses to arrive at actionable conclusions. We believe that conversational AI technologies such as chatbots and virtual assistants are driving forward this new way of interacting with customers.
Chatbots and virtual assistants are able to continuously translate customer interactions into actionable insights through machine learning (ML), augmented analytics and natural language processing (NLP).
Intelligence augmentation to empower decision-making
Augmented intelligence, which can provide executives with sophisticated models as a basis for short and long-term decision making, is starting to gain traction in the telecoms industry. Where real life experts may have made company decisions alone in the past, new AI technology is trying to replicate the knowledge and reasoning methods of these experts. The AI can then offer assessment and recommendations for any given problem.
This is especially interesting in the telecoms industry where speed and accurate decision-making is so often vital.
AI for operational efficiency
The second trend is leveraging AI to achieve operational efficiency and automation in the telecoms industry. As new technologies and services like 5G and various network slicing software are deployed, communications service providers (CSPs) networks have become more complex.
To address this complexity, CSPs need to increase the intelligence of short and long-term network operations and planning. Relying on traditional human intervention here is not enough intelligent and real-time closed-loop automation can have a hugely positive impact.
AI can monitor patterns by analysing large volumes of network data in real-time and detect/predict events/act on them automatically. This not only ensures a robust and automatic network operation but also runs on lower operational costs and minimises the risk of human error.
Edge AI is encroaching on the cloud
Finally, the last trend is around edge AI. The velocity, variety and volume of data captured by endpoint devices are facilitating a rapid expansion in demand for advanced AI within various ‘edges’. The increasing use of edge devices to aggregate sensor data is spurring the growth of niche edge AI vendors who are focused on improving the management of on-site data, and the insights gleaned from this.
Edge AI also offers immediate financial impact such as reducing cloud, network transport and storage costs. In response, we have noticed that cloud service providers are extending their reach into customer premises and private clouds.
We expect other providers such as communications platform-as-a-service (CPaas) and content delivery network (CDN) vendors also entering the market to provide their version of near-and far-edge value.
Product and services leaders need to structure AI solution proposals by viewing CSPs’ digital transformation through the following lenses: improving customer experience, operational excellence and sales/marketing activity. In addition, decide where to use various types of AI in network operation and management segments and identify how human skills and AI can work together.
The author is Peter Liu, research vice president, Gartner.
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