Many tier 1 telcos have begun to implement AI initiatives to build and operate their network, sell more efficiently and improve the customer support experience.
Based on our industry interviews, we expect increasing competitive intensity in the telco market to drive increased adoption of AI beyond the largest incumbents and into RSPs
Many large telcos are facing a myriad of headwinds: declining bandwidth prices, a more agile and less loyal consumer base and significant margin transfer to content providers. Many incumbents also face the issue of legacy systems that do not have the capability to deliver services to the next generation of telecommunication networks and an increasingly demanding customer base. Over the past 10 years, global companies have seen an average decline in margin from 35% to 33%, with net income dropping by 25%.
One of the biggest constraints facing telco companies is their ability to process and interpret data and use business intelligence to quickly respond to customer and market demands. Network, customer and market data could represent tens of millions of data points in a given day. While advances in Big Data provide the processing capabilities to store and process the data, analysis relies on manual interpretation from the network, marketing, and sales teams.
Over the past few years, artificial intelligence (AI) systems have been adopted to derive insights from these masses of data, and as the development of AI progressed and algorithms and techniques improved, the scope of AI implementation has expanded.
Deloitte research recently showed that telcos, in comparison to other industries, have invested more in AI and seen greater returns for their investment. The high ROI for AI projects therefore comes with the promise to reverse the downward trend in margin and income.
The first step is to identify key use cases that can add value. This paper aims to describe use cases that have been implemented in industry, to determine the impact of these efforts and therefore highlight key areas to focus on where building out AI capacity.
Artificial Intelligence is hitting critical mass in Telco
Global telco service, software and hardware providers are building out capabilities
Network, customer service, and sales and retention are the most common applications of AI in telco
Moving from business intelligence to machine learning
Network design and operational use cases
Customer experience AI use cases
AI for Sales and Churn Prevention use cases
Factors driving AI adoption within an organisation
Providing clear business cases with cost estimates and benefit targets
Building a clear team structure with defined funding sources
Alignment to corporate and organisational strategy
Taking a fast-follow approach by leveraging global service providers
How ready is your organisation to make AI a leading part of its strategic agenda?
Are you using AI to support your corporate strategy?
Are you extracting deep, actionable insights from your customer data?
Are you struggling to efficiently scale your operational capacity?
Are you maximising your return on infrastructure deployment?
Have you chosen the right team structure to ensure wide scale adoption of AI use cases?
Conclusion: Adoption of AI is already table stakes
Appendix 1: Primer on ‘artificial intelligence’
What do we mean by ‘Artificial Intelligence’?
The history of AI
Narrow AI versus general AI
AI: Sorting the hype from the reality
List of charts/tables
Figure 1. ROI vs investment for various industries
Figure 2. Example mobile heatmap of the received customer network quality
Figure 3. Conceptual diagram for the self-healing network
Figure 4. Example of a CVC upload and download traffic
Figure 5. Examples of Telstra and Optus chat bots
Figure 6. Conceptual diagram for building propensity models
Figure 7. Timeline of AI reaching human-equivalent performance across narrow tasks
Figure 8. Annual AI-related published per year
Figure 9. Comparative number of research papers published on deep learning, by country
Figure 10. AI expert predictions on timeline for matching human performance
Figure 11. Perceptions of emerging technology in banking among CIOs