Assess
Use a best practice framework that starts with business value chain analysis to ensure success of AI programs.
Industry
Communications, Media and Information Services
Connect. Curate. Comprehend: Unlocking Value Beyond Boundaries
HIGHLIGHTS
- AI integration across large communications enterprise ecosystems and value chains will be essential to compete in an AI-driven world.
- More and more telecom companies are inherently designing products, systems and services integrated with AI-capabilities to become “AI-native” organizations. The telecom industry is at the cusp of moving from aggressive experimentation to thoughtful AI implementation.
- This approach can enable a pragmatic infusion of AI to drive core product differentiation, create new revenue-generating models, and deliver superior customer experiences with improved operational efficiencies.
Leveraging the power of “ Thoughtful AI implementation “ for communications, media and information services
Few technology advancements have sparked the public interest and imagination like Generative AI.
The latest advances in artificial intelligence (AI) show promise for business in terms of revenue growth and leading-edge innovation. In addition, AI offers enormous potential to create new jobs and enhance human capabilities, making AI a strategic imperative for businesses today. The new generation of digital services required by cross-industry enterprise ecosystems and value chains will be characterized by real-time behaviors and capacity to be fully integrated. This calls for the ability to manage higher speed of data traffic and greater computing resource requirements beyond the current levels offered by hybrid cloud and networks.
Competition is keen in Communications, Media and Information Services
The Communications, Media and Information Services (CMI) industry is at the crossroads of fierce competition.
Traditional revenue streams are plateauing or at decline, customer expectations are soaring, and investments in disruptive technologies like 5G, 6G, edge computing, etc. are demanding compelling business case and innovative solutions. In this dynamic landscape, CMI enterprises can leverage the power of AI to unlock potential value across products, services, and operations to become “AI-first” enterprises. At the same time, heeding market dynamics and economic circumstances, they are aiming for further operational efficiency and optimization.
The telecommunications industry is experiencing a massive transformation with increased adoption of AI-enabled technologies. Telcos are moving from network-driven to software-driven models, evolving toward becoming “techcos.” More and more telecom companies are inherently designing products, systems and services integrated with AI-capabilities to become “AI-native” organizations. This is a game changer in yielding core product differentiation, new revenue-generating models, and delivering superior customer experiences with improved operational efficiencies for telecom companies. There is a strategic shift in the architecture as the prospects and potential of adoption of AI (including GenAI) gets deeper into the core commerce business and infrastructure/network systems and operations as communications service providers (CSPs) move from digital telco service providers (DSPs) into the AI-driven and AI-native telco era.
The C-suite champions of telecom companies are laying down, or have laid down already, a whole-of-business AI strategy, where the business units integrate Data & AI for improved customer experience, network resilience, employee engagement, and financial outcomes, and most often, a central Data & AI function ensures strategic and cross-company AI opportunities achieve productive business outcomes. To enable this, the Data & AI function is empowered to redefine and reimagine how data and AI can be leveraged with multi-year investment, dedicated leadership, executive support, and company-wide consistency, balancing long-term benefits with short-term gains.
CMI leaders believe AI has the capability to deliver more highly personalized, proactive, and value-added experiences across the customer journey in a way that drives competitive differentiation and builds long-term loyalty. Most CMI executives expect AI’s impact on their business model will be greater than or equal to earlier disruptive technologies, and they’re optimistic about its potential, according to the recent TCS AI for Business Study. The democratization and availability of AI native resources is expected to improve GTM success rates, user experience, reduce spending on commercial off the shelf software/hardware (COTS) and operations.
AI, however, is not a plug-and-play technology with a one-size-fits-all strategy, as the TCS study shows. The findings from CMI executives reflect their varied approaches to AI — while some want to establish an enterprisewide AI strategy, perhaps upending current ways of working; others want to figure out how best to leverage AI to reap its benefit. About a fifth of the respondents say that they will continue to experiment with AI, and about a quarter plan to wait and see what their competitors do.
AI adoption strategies vary in CMI
AI adoption strategies vary in CMI
For the telecommunications industry, the advent of GenAI and shift in the operating model from telcos to techcos provides interesting opportunities to reimagine the telco value chain creating core product differentiation, enabling creation of new products and services, and thus, new monetization avenues. These also help in delivering superior customer experience along with an increase in customer lifetime value (CLV) while driving efficiency and resiliency.
In fact, 50% of financially overperforming “Pacesetter” telcos in the study are exploring how to use AI (both GenAI and established AI technologies) to support greater personalization to generate customer loyalty.
With the adoption of AI in their core commercial business and network infrastructure, telcos are moving from being just DSPs to AI-driven enterprises. The softwarisation of networks enables the application of AI to:
- Infuse experiential intelligence in networks
- Dynamically improve network performance
- Allocate network resources based on customer demand and drive more intent-based networks, thereby creating core product differentiation
Real-world examples of autonomous networks leveraging AI
Verizon driving customer experience in the core network infused with AI-based intelligence for product differentiation
Comcast’s mission to leverage classic and GenAI to transform the cable business with a prime view into improving revenue. Applied AI infusing intelligence in core products such as connected homes, video entertainment, and residential connectivity products are a part of this initiative.
Similarly, AI has the potential to transform and reinvent the entire Media value chain from pre-production to distribution, and to other strategic areas such as product innovation, customer and content monetization, and experience management. For Media, personalization and targeted content recommendations using AI is one of the most valued use cases in driving better user engagement in streaming apps, news media (print and video) and social media. Companies like Disney are using AI to contextualize advertising for the Disney+ and Hulu streaming services. Foxtel in Australia, for instance, has been heavily investing in AI to lift sports fan engagement to the next level. Machine language models are able to identify when and how wickets fall in live cricket games, in real time, and letting fans know before it happens by recognizing patterns in player behavior based on past form and the live conditions of the pitch.
Large telcos with huge networks and loads of operational data are looking into powering GenAI models with their data to build Telco domain models for the industry. A global telco AI alliance (GTAA)was announced during MWC Barcelona 2024 where few telcos came together to establish a joint venture, through which the companies plan to develop Large Language Models (LLMs) specifically tailored to the needs of telecommunications companies. Meanwhile, Telcos are shifting from "CSP/operator-defined products" to "customer-driven products" in B2B2X.
Customer experience is a big focus area for telcos with use cases like:
1. Hyper-personalized product recommendations across multiple touchpoints
2. Bring more dynamism in pricing engines for real-time offer management
3. Drive more predictability in “win- back” campaigns and offer uptake maximization
4. Revenue assurance copilots
5. Preemptive churn analysis, proactive retentions & CLV maximization
6. Cognitive Business Network command centers driven through AI-led automation
Operators like Vodafone in the UK, EE (BT), Telstra and Telefonica are infusing core AI in driving customer experience in these aspects. According to the AI for Business Study, over a third (38%) of telco executives want customers to have more personalized, AI-driven interactions with their products and services .
Telcos are seeking greater personalization
Telcos are seeking greater personalization
Another emerging opportunity for telcos is the monetization of AI, allowing telcos to resell infra and cloud services. Telcos have a unique advantage – the ability to use their network resources to set up and sell AI digital factory services (infrastructure needed to support AI applications) to enterprises as well as small and medium businesses to meet the growing demand for sovereign cloud / sovereign infrastructure.
Insights from TCS’ research reveals that of respondents from CMI reported that they are already exploring AI’s potential to increase personalization and contextualization, providing vastly more targeted, relevant and rewarding audience interactions.
As Media and Entertainment (M&E) organizations are increasingly moving their media content workflows and advertising ops workflows to the cloud, and are also exploring remote production in the cloud. AI/ML will play a significant role in automating, infusing intelligence to and streamlining these workflows. AI will be a key lever in packaging, bundling, and presenting content in various desired formats to drive customer interest and revenue opportunities. GenAI could also be used to deliver media product innovation through market intelligence and feedback on uptake from audience on their experience and engagement with the products. International Broadcasting Convention’s (IBC’s) recent accelerator programs have been experimenting with AI applications in media in areas such as using Unreal Engine and AI for smart remote production for real-time animation, auto-dubbing and colour grading, metadata management such as facial recognition in sports, to name a few.
Real-world examples
A compelling use case for the M&E industry is automated content generation using natural language processing (NLP) and computer vision algorithms, which generates both text and visuals. GenAI adds to opportunity areas in content creation, curation and personalized delivery through creativity-infused automation in use cases ranging from script writing, creating content summaries in any format from anywhere any-time, multi-lingual and multi-contextual localizations, content enrichment and moderation, sentiment analysis and monetization. While there is lot of debate on creative human capabilities versus AI, judicious use of GenAI in augmenting human creativity helps M&E companies to deepen content engagement and delivery. In fact, according to the AI for Business Study CMI report*, 64% of CMI executives say human creativity will be their company’s competitive advantage.
An image depicting most CMI
An image depicting most CMI
NBC Universal is tapping into AI to automate buying across linear and streaming TV. The company is launching a new AI-infused product called One Platform Total Audience or AI-powered Total Audience, which automates planning across its linear channels and streaming platforms via a single audience-based buy—and potentially give its legacy networks a boost in the process.
Accelerating the journey to AI maturity
AI native enterprises can chart their path toward maturity while identifying value stream interventions, AI hotspots and a way to interlace reusable AI capabilities with existing transformation journeys across the sector. As the study shows, CMI executives are prioritizing AI productivity gains but, at the same time, over a quarter of respondents are starting to view AI more as a quality-enabler than as a productivity-enhancer. In fact, improving quality along with driving innovation may prove to be where companies see the most value from AI over the long term. Whatever their business priority, their success will depend on their ability to transform into AI-native enterprises.
To succeed in an AI-driven world, CSPs need a holistic approach towards building their AI adoption strategy that draws upon the strengths of other initiatives and disciplines such as augmented SRE, DevOps, Hybrid FinOps and applied in the context of autonomous business and integrate it while shaping the North Star architecture and the capability evolution roadmap for program shaping and planning.
TCS recommendations
Design
Design a pathway to production for AI use cases, including creating a model that encompasses the larger ecosystem of partners, suppliers and customers while enabling human engagement.
Skills
Assess the skills and role shifts that will be required to adopt AI solutions at scale.
Success metrics
With AI implementations, organizations need to move beyond existing metrics to identify the right performance indicators to measure their impact on their business.
Governance
All GenAI use cases must be designed and instrumented with Responsible AI principles at the core ensuring accountability, transparency, fairness, reliability, safety, security, privacy and inclusiveness. Organizations must build with production, not proof of concepts (POCs), in mind. Keeping control on costs from inception is important. Building repeatability into the process and using reference business architecture will enable speed and certainty of implementation, end-to-end observability, and value management.
Scale
Prioritize enterprise readiness for scaling up successful pilot programs.
Success in the controlled environment of a pilot/POC exercise is no guarantee of success in scaled productionization. Invest in initiatives to prepare your data, tooling, architectures, and people for enterprise-wide AI adoption.
About the study
TCS’ Thought Leadership Institute conducted a double-blind study of approximately 1,272 senior executives with P&L responsibilities in 12 industry sectors across Asia, Europe, Nordics, LATAM, North America, and the United Kingdom and Ireland, with respondents having annual revenue of $5 billion to $100 billion. The communications, media and information services industry findings are based on feedback from 86 survey respondents.
This global study examines how CEOs, LOBs, directors, and business line managers prepare their businesses to be AI-ready, focusing on aspects such as their strategy around operations, talent, and future implementation plans. The study provides best practice recommendations based on these insights.
REFERENCES
*TCS AI for Business Study – Communications, Media & Information Services Report p6
1. Verizon unveils new AI tools to transform customer experience | News Release | Verizon
2. Here's how AI is transforming Disney, Spotify, and TikTok (yahoo.com)
3. VOXI by Vodafone launches generative AI chatbot to enhance customer experience
4. Telefónica launches Aura and leads the integration of artificial intelligence (telefonica.com)
