Applications of AI in technology, media and telecommunications
In technology, media, and telecommunications (TMT), AI adoption and maturity vary significantly by sector.
Telecom companies tend to be the furthest along at embracing AI, thanks to the sector’s longstanding focus on operational efficiency and customer acquisition/retention. AI technologies are already in widespread use both for customer-facing activities such as contact centers and customer engagement, and for back-office activities such as manufacturing and logistics.
Looking ahead, the sector’s successful track record with AI in these areas is encouraging telecom companies to expand their AI efforts into new areas. One area that will likely be a particularly strong focus within the next few years is using AI for predictive analytics that can turn telecom companies’ wealth of customer data into valuable insights that can further boost acquisition and retention.
Many legacy technology companies have been slower to embrace AI. However, digital natives such as Google, Amazon, and Facebook are using AI in very sophisticated ways, particularly in their commercial products and services.
of TMT companies expect AI to have a significant impact on product offerings over the next five years.
is the expected CAGR for AI growth in telecom by 2022.
Facing the top obstacles
AI adoption and maturity levels are significantly lower at other types of technology companies, with many companies insisting on seeing sector-specific use cases and proven results before scaling up their AI programs and investments.
Many existing AI efforts in the sector are limited to scattered experiments and small-scale pilots, without an overarching strategy for harnessing the full power of AI and digital data. As more organizations shift their AI workloads to a cloud environment, data integration challenges are intensifying. Some of the most common barriers to access third-party data sources include dealing with disparate data that exists on different systems and merging data from diverse sources. For all these efforts, the right talent and expertise can be critical. Often, AI/ML initiatives fail primarily due to lack of expertise, besides other major factors that include unavailability of production-ready data and an integrated development environment.
Data governance is also a hot-button issue, as many TMT organizations still lack a formal data governance framework and a dedicated budget to address the issue. A shortage of specialists and difficulty in building a comprehensive data strategy are among the top challenges impeding data governance efforts.
Top challenges facing TMT AI adopters:
Where are the opportunities for TMT companies?
The impacts of the COVID-19 lockdown have accelerated interest and investment in AI and digital transformation, particularly for common AI applications such as robotic process automation (RPA), as well as more advanced use cases such as smart factories and digital supply chains.
In the media sector, most of the focus for AI has been on personalizing content and customer engagement — and this trend could increase in the future. During the COVID pandemic, many media companies enjoyed a sharp rise in subscriptions and revenue, and as the crisis subsides and people start returning to their normal lives, there will be a scramble to retain as many customers as possible. Success will likely hinge on providing consumers with the best possible experience and content, which can create an even greater need for AI-driven personalization.
of TMT organizations believe that AI-powered transformation will happen over the next three years.
of TMT organizations achieved ROI of above 20% due to AI investments.
Understanding what can be achieved by AI today
Explore five use cases depicting how TMT-related businesses are harnessing the power of AI to revolutionize their business and the industry.
Factories and supply chains that think and feel
Smart Factory and Digital Supply Network
Use AI to optimize the contract manufacturing process through micro services, and to accelerate demand planning, improve demand signals, and tightly integrate cross-functional supply chain processes.
Direct Consumer Engagement
Use AI to automate engagement and communication with customers, predict customer behaviors and next best actions, and increase personalization.
Digital Contact Center
Use AI technologies such as natural language processing and machine learning to build Voice Virtual Assistants that are more efficient, engaging, and human-like.
Detect Fake Media Content
Use advanced AI technologies to detect ‘deepfakes’ and fake media content by identifying subtle content anomalies
Turning customer data into cash
Customer Data Monetization
Use AI to extract and monetize insights from the vast amounts of customer data now being generated by digital systems.
Navigating the future of AI in the TMT industry
In the face of competitive pressure, TMT-related organizations have made AI critical to their business strategy and are using a wide range of practices to ensure the success of their efforts; however, they are still grappling with issues related to implementation, data, and managing costs.
Explore our five emerging AI use cases in the TMT industry to uncover future-driven opportunities to getting the execution right from a technology and organizational standpoint:
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