How to prepare for the world of AI: telecom strategies for success

Success in the AI era will depend on how effectively telecom operators translate experimentation into enterprise-wide transformation.

In brief

• Telcos should prepare for multiple AI scenarios while building capabilities that remain valuable across potential futures. 
• Scaling AI requires more than successful pilots; it demands stronger data, technology, talent and operating foundations.
• Operators can create new value by focusing on areas where they hold durable advantages and clear routes to differentiation.

We recently sat with MIT professors Alex Pentland and Hossein Rahnama to discuss how telecommunications companies can improve operations by more effectively adopting AI into their enterprise. The following recaps their discussion.

As AI continues to transform the world, the telecommunications industry stands at a critical inflection point. Telcos have been among the most active adopters of AI, yet many are still struggling to develop compelling strategies that align AI initiatives with business goals and fully leverage AI’s potential to transform the enterprise.

Four futures of AI

AI has been moving faster than most businesses can adapt, shifting from generating text to producing code, images and videos in just a few years. While AI will likely play a major role in the future of enterprise, the way it scales — and the level of trust and regulation that follows — could diverge sharply. To prepare, telcos should build strategies that remain flexible across multiple plausible futures rather than betting on a single trajectory.

For one, governments may issue regulations that significantly curtail AI use and expansion. Another potential future could see large monopolies dominate the AI ecosystem, limiting opportunities for smaller companies. Alternatively, AI could transform entire industries, enabling startups with smaller teams to outperform legacy giants.

Either way, these potential scenarios highlight both the risks and opportunities that organizations may face as AI continues to evolve. They also point to the importance of building resiliency and flexibility into long-term plans.

Four futures

1. Constraint: In this potential future, organizations scale AI too quickly, resulting in visible and potentially catastrophic public failures that shatter public trust and lead to overregulation as businesses adopt risk-averse strategies.
2. Growth: This scenario follows a more optimistic path as AI improves steadily, demonstrates clear ROI, drives widespread enterprise adoption and dramatically boosts productivity while integrating humans into AI workflows.
3. Transform: In this scenario, AI drives a major breakthrough for a broad set of enterprises, in which startups with smaller staff outperform legacy giants, creating entirely new markets.
4. Collapse: In the fourth scenario, AI power coalesces around one or two monopolies, slowing innovation and inhibiting competition and market access except for a select few.

No matter which AI future unfolds, telcos will need to build repeatable capabilities that help their customers and their own organizations — starting with operational use cases that create measurable value. In the near term, many are using AI to:

• Modernize networks to enable predictive maintenance and enhance the customer experience through more efficient operations and reduced downtime. One global operator, for example, has deployed AI-powered anomaly detection to identify service issues in near real time, moving from reactive support toward more proactive service management and improving service availability across a broad international footprint.
• Integrate AI-driven analytics and automation to enable chatbots and virtual assistants, improving response times and personalization. Another telco improved first-time resolution and customer satisfaction by introducing a generative AI virtual assistant that can handle more complex customer interactions with greater fluency and personalization.
• Invest in 5G and adjacent technologies to enable real-time data processing, fraud detection and dynamic resource allocation. Others are using 4G- and 5G-based network application performance interfaces (APIs) to support fraud prevention and identity verification use cases, including SIM-swap detection, number verification, device location and key product characteristics (KPC) checks. This points to a broader opportunity for monetizing network intelligence in addition to improving connectivity.

To this point, most telcos have yet to translate early progress with AI pilots into enterprise-wide programs. To close that “pilot-to-scale” gap, telcos need to do more than choose the right use cases; they also need to strengthen the underlying technology, data, operating model and talent. That will also require overcoming several obstacles that prevent broader AI adoption, from improving network infrastructure to integrating data from disparate silos. Among the more common challenges are:

• Network infrastructures that are still lacking: Originally designed for traditional communication services, many telcos may lack the flexibility, scalability and real-time data processing capabilities required for advanced AI applications.
• Limited data availability and poor quality: Successful AI implementation in telcos requires access to large volumes of high-quality, accurate and timely data. Many telcos are still contending with fragmented data across multiple systems and formats, and inconsistent data quality can undermine AI model performance.
• Regulatory obstacles for using customer data: Concerns about data security, privacy breaches and regulatory compliance represent a significant hurdle to fully utilizing customer data for AI-driven insights and personalization. Given that they often have access to sensitive customer information, telecom operators must comply with strict data privacy regulations. 
• Outdated legacy systems: Many telecom organizations continue to operate with legacy IT systems that are not designed for interoperability or modern data analytics. This hinders seamless data sharing and integration and limits the scope and effectiveness of AI initiatives.
• Talent and skills shortfalls: Telcos face an increasing demand for people who are skilled in network and IT functions. As a result, many are finding that skills in areas such as cybersecurity, AI and machine learning are difficult to fill. In response, more telcos are focusing on upskilling, recruiting from tech partners and trying to buy those skills through acquisitions.

Transformation domains

To reposition themselves for an AI-driven future, telcos need to address four areas we refer to as transformation domains:

1. Fix the foundation
2. Automate the engine
3. Reinvent engagement
4. Capture new markets

Building a robust data strategy and mechanism for data collection represents a critical step for telcos seeking to fix the foundation. To fully leverage big data and apply real-time analytics, telcos need to reimagine data collection, management and governance while continuing to address concerns about data privacy and regulatory compliance. Among the other challenges telcos face in the data management space are:

Automating the engine through AI-ready technology and tools

Because of the immediacy of telco operations, many applications, such as network optimization or fraud detection, require near-instantaneous processing and decision-making. This puts significant demands on data infrastructure. To that end, telcos need to continue building on efforts to improve existing network infrastructure by prioritizing investments in AI-ready technologies such as advanced analytics platforms, machine learning frameworks and automation tools that can handle telco-specific data types and use cases. These investments include:

• Rolling out 5G networks and edge computing, which will further accelerate new AI-driven services such as real-time analytics, autonomous network management and enhanced Internet of Things (IoT) applications while reducing latency and bandwidth use.
• AI platforms and tools tailored for telecom needs, which will help with network traffic prediction, fraud detection and customer churn analysis.
• Adopting cloud platforms and hybrid cloud strategies, which will offer telcos scalable computing resources and advanced AI services; in addition, hybrid cloud approaches allow operators to balance data security, regulatory compliance and operational flexibility.

Positioning telcos for future growth

Establishing a strong data foundation and upgrading network infrastructure address two of the four transformation domains (fix the foundation and automate the engine). To complete the picture and position themselves to succeed in the future, telcos also need to focus on the remaining two domains:

Reinvent engagement: Transforming customer service from a cost center into a revenue driver by using proactive, brand-aligned AI agents for hyper-personalization will enable telcos to deepen customer engagement. A personalized customer value engine could lead to the creation of more personalized offers that reduce churn, generate new revenue and set the stage for sustainable growth.

Capture new markets: Telcos should consider taking advantage of a major new growth opportunity by providing value-added services for the AI economy and developing an “AI infrastructure monetization strategy” that allows them to move up the value stack by offering powerful GPU resources at the network edge. Because telcos can’t be all things AI, the goal should be to prioritize a small number of plays that build on clear structural advantages, such as location-specific edge footprints, sovereignty requirements and enterprise distribution. Aggressively moving forward in selected areas will enable them to capture a bigger share of the AI infrastructure market and avoid the trap of being a utility provider. This will represent a radically different approach, but the potential benefits may be worth the risks.

Developing AI talent and culture

Inadequate talent, skills and culture management were also identified as key issues for telcos in the EY report “How can telcos navigate a world of evolving risks?” Accordingly, telcos need to fully address the human element as they deploy AI in their organizations.

For example, while many telco employees are adept in skills necessary for AI, such as data science and machine learning, this is a critical time for telcos to perform a thorough analysis of their workforce and identify gaps that can be addressed by training and upskilling programs.

A key part of this effort will entail establishing cross-functional teams that combine domain experts, IT professionals and AI specialists who can align AI solutions with business objectives and operational realities.

Promoting a culture of innovation and continuous learning that encourages experimentation represents the final piece in creating an AI-ready workforce. To that end, telcos should establish pilot projects and knowledge-sharing efforts that extend this culture and reinforce a mindset that helps employees adopt new AI technologies in their daily workflows.

Address ethical and regulatory considerations

Building trust among stakeholders is a critical part of AI transformation for telcos and other companies. A key part of that effort is promoting and following through on the responsible use of AI, as well as taking steps to comply with evolving industry standards and legal requirements.

To that end, telcos will need to embed ethical AI use and transparency into every facet of the deployment. Embracing transparent decision-making processes will play a critical role in maintaining public confidence. 

Regulatory requirements are also evolving across jurisdictions and regions. Telcos must stay up to date on new measures as they are rolled out and seek to influence them where possible. 

Establishing trust with customers and stakeholders through proactive communication and ethical data handling will go a long way toward fostering a sense of trust among customers, partners and regulators regarding AI initiatives.

Collaborating with ecosystem partners

The EY report on how telcos can navigate a world of evolving risks flagged ineffective engagement with external ecosystems as a major challenge. Connectivity alone is not a sufficient differentiator for telcos. Strong ecosystem relationships are not just a way to maintain partnerships; they represent a critical way to capture growth in areas where demand is shifting, especially enterprise markets that now prioritize cybersecurity, data sovereignty and integrated digital infrastructure.

A stronger ecosystem will also help telcos speed up innovation by enabling them to work with leading cloud, FinTech and industry specialists and expand revenue beyond basic B2B and B2C service propositions. Telcos should also look into forming strategic alliances with diverse partners to accelerate access to cutting-edge AI solutions and research. On a regular basis, they should perform periodic reviews of these relationships to verify strategic alignment across partner models and identify opportunities such as infrastructure joint ventures (JVs) that can expand value creation.

For example, telcos may want to broaden their ecosystems by offering new products or services in the B2B market, such as real-time translation services or other features that incentivize collaboration with startups by enabling them to deliver services across networks. To make this work, telcos may want to consider adopting a venture capital-style approach — investing in a wide range of companies, with the understanding that one out of 10 or 20 may yield a meaningful return on investment.

In addition, telcos should join collaborative industry groups to help shape AI standards, foster knowledge exchange and establish programs that enhance competitiveness at the cutting edge of innovation. Engaging in joint innovation projects will also enable telcos to pool resources and expertise, driving faster development and broader adoption of impactful AI technologies. Establishing these ties will not necessarily drive growth on its own, but it can help telcos address ongoing knowledge challenges, improve utilization and position them for more selective and potentially lucrative partnership-level monetization.

Measuring success and scaling AI initiatives

Developing relevant metrics will also play a critical part in building an AI-driven enterprise. Telcos should define measures that map to the transformation domains — for example, network resilience and outage reduction (automate the engine), data quality and reuse (fix the foundation), customer experience improvements (reinvent engagement) and new-product velocity and revenue mix (capture new markets). Tracking network availability and uptime, as well as latency for more timely data transmission, will also keep the organization grounded in achieving key business goals.

Other key metrics include time to market for new products and services, along with customer outcomes such as churn rate and customer satisfaction. Tracking these alongside unit costs to serve and AI adoption/usage (where applicable) helps telcos understand whether AI investments are improving experience while sustaining profitability.

As they develop more effective metrics, telcos will need to implement pilot programs and phased rollouts to test AI solutions before moving to a full-scale deployment, enabling them to scale AI solutions across the organization.

AI-led delivery, measured across every dimension that matters

Embracing a new approach to the future

As AI embeds itself deeper into the fabric of every enterprise, telcos need to be ready to reinvent their approach to developing new technology. To thrive in the AI era, success will depend on investing in sector-specific knowledge to identify and unlock trapped value, distinguishing between true regulatory constraints and outdated processes, and fostering a culture where systems are continuously renewed. This will require alignment between industry experts and engineers, ongoing critical examination of legacy habits and an adaptive approach to software development that anticipates change. Embracing these principles will enable telcos to build resilient, innovative organizations ready to compete in a rapidly evolving landscape.

To seize a leadership role in an AI-driven future, telcos will need to take decisive action while staying focused on where they can win. That means prioritizing AI plays anchored in structural advantages and closing the pilot-to-scale gap by executing across the four transformation domains: fixing the foundation, automating the engine, reinventing engagement and capturing new markets.

By taking these steps, telcos can strengthen competitiveness and relevance as AI reshapes customer expectations and enterprise technology stacks. The path forward will not be easy, but a focused strategy — combined with the right foundations, capabilities, talent and ecosystem partnerships — will help telcos move beyond connectivity and build a differentiated role in the AI economy.