Innovation In Telecommunications

Most companies don't have an API problem. They have an API discovery problem. How to address it? Your APIs already run on AWS, Azure, or other gateways. They work fine. The real challenge? Nobody can find them, understand them, or adopt them easily. Every API integration requires multiple calls and months of dev work. Here's what typically happens: • APIs scattered across Postman, GitHub, and multiple gateways • Documentation is outdated or buried in Confluence • Internal teams asking, "Wait, do we have an API for that?" • Potential partners are unable to onboard themselves • Compliance and governance nightmares    Sound familiar? This is where a proper developer portal changes everything. Not another gateway. Not more infrastructure. Just one unified portal where all your APIs live, are documented, and ready to use. This is exactly what Digitalapi.ai, partner of this post, does: 1) Auto-discovery across your entire stack Connect your AWS gateways, Postman workspaces, and GitHub repos. AI automatically finds, catalogs, and documents every API. No manual work needed. 2) AI-powered documentation that never gets stale Every endpoint update is instantly reflected in your docs. Internal teams and external partners always see the current state, eliminating the number 1 reason integrations fail. 3) Built-in governance and compliance Automatic checks ensure your APIs meet security standards and compliance requirements. No more manual audits or spreadsheet tracking. You know something is wrong the moment an issue is introduced. 4) Branded portal for 3rd party adoption Open your APIs to external developers through a professional, branded portal. They can discover, test, and integrate, all self-service. That means so many fewer calls! 5) Monetization built in Turn API access into revenue with subscription tiers, usage-based pricing, and automated billing. Your APIs become a business channel, not just a technical feature. Just like it always should have been. The result? • Internal teams find and use existing APIs instead of rebuilding them • Partners onboard themselves without bothering your engineering team • New revenue streams from API subscriptions • Faster integrations = faster partnerships = faster growth Your API already exists. Make it discoverable, governable, and monetizable. Check out http://www.DigitalAPI.ai and see how a proper dev portal transforms scattered APIs into a growth engine. Did you ever struggle with an API integration? Let me know in the comments :) #productmanagement #api #apistrategy

Telcos, Welcome to Your New Customers: AI Agents The iPhone marked a before and after in telecom. Networks engineered for voice collapsed under video demand. Operators spent billions on spectrum, radios, and fibre backhaul, but ARPU sank from $22.39 in 2009 to $13.56 by 2019 and another 20 percent by 2023. The value was captured by Apple, Google, and digital platforms, not the carriers who carried the load. A second shock is arriving with AI agents. These are not IoT devices with dumb SIMs but autonomous pieces of software, often cloud-based, that authenticate, negotiate, and transact thousands of times per second. Their arrival reshapes every part of the telco business. Networks shift from managing downstream video streams to orchestrating upstream biometric data, inference payloads, and relentless bursts of signalling. Edge compute becomes the new backbone, replacing CDNs as the critical layer of performance. Operations and BSS no longer revolve around monthly bundles but around real-time billing, event-based charging, and automatic SLA credits. The customer journey breaks apart: the “user” is no longer a human who can be persuaded by advertising or loyalty points, but an algorithm that selects providers based only on latency, trust, and price. Commercial logic pivots from ARPU to RPI, revenue per thousand verified interactions, with identity and determinism becoming the true products. Even the ecosystem map shifts: just as Apple and Google seized the interface in the smartphone era, hyperscalers are already racing to build agent marketplaces. SoftBank has announced plans to deploy one billion AI agents across its companies, and forecasts put the telecom opportunity at $188 billion by 2034. Nobody willl invite Telcos to the party. We will need to claim our role this time, or once again build the infrastructure while someone else takes the economics. Full analysis here: https://lnkd.in/gvkTKqzx

The Telecom Industry in Transformation: Reflecting on three key challenges: Digitalisation and evolving consumer needs are transforming many sectors, with the telecom industry being no exception. In response to this dynamic landscape, I would like to share three technology challenges the telco industry must engage with over the coming years:   1) EMBRACING THE CLOUD: The development of cloud-native services for telecom functions such as voice and data is a huge challenge. This involves refactoring our traditional network hardware and monolithic telephony systems, moving everything into the cloud, and changing to devops working models. The payoff? Flexibility, faster service updates, resiliance, and the facilitation of personalised interaction options for our clients. Yet, we must overcome many transformation hurdles. The implementation of virtualisation and automation technologies requires a complete update of our network architecture, new product versions from our vendors, as well as a lot of skill and competency changes for our employees.   2) NAVIGATING THE AI WAVE The advent of #GenAI provides the telecom industry with an array of tools and services. AI can enhance efficiency across numerous areas from chatbots, AI-assisted call center agents, hyper-personalized marketing strategies, to optimized network maintenance. However, beyond efficiency, AI also holds the potential to introduce innovative services benefiting the end customer. Trust, privacy, and transparent handling of customer data are key to the acceptance of these new features.   3) ENSURING TRUST AND SECURITY The potentially most significant challenge ahead is maintaining robust security and customer trust. With hundredthousands of cyber attacks per month on our own Swisscom infrastructure and projected global damage from cyberattacks reaching USD 10 trillion per annum by 2025, security is paramount. In the future, trust-based innovation will be the competitive edge for telecoms and IT service providers. Earning trust is an ongoing, hard-pressed task that cannot be simply bought or created through marketing campaigns.   Achieving these challenges will require one crucial element - our employees. Developing the right skill set and a supportive corporate culture is key to handling such transformative pressures.   What challenges do you see for the telecom industry? How are these mirrored in your field? Looking forward to hearing your thoughts. Swisscom #TelecomIndustry #Transformation #CloudTechnology #CyberSecurity #InnovatorsOfTrust 

Telco–GSI strategic partnerships are the blueprint for telco innovation and growth via the channel in the AI-era. Telco services delivered by GSIs will hit $43.2 billion in 2025, growing 4.8% y/y according to Canalys (part of Omdia) analysts Devan Adams and Peter Bryant. Driving this growth are enterprise edge services with 19.3% (telco) and 17.2% (GSI) five-year CAGRs. These strong alliances play a vital role in midmarket and enterprise service deliveries across key growth areas like 5G, cloud, edge computing, and AI (generative and agentic AI). Co-development of industry-specific solutions along with joint innovation labs for 5G, edge, and AI use cases, combined with collaborative GTM strategies are driving growth. While telcos deliver core connectivity, edge compute, and managed services, GSIs provide cloud migration, OSS/BSS modernization, additional IT managed services and AI. The blueprint: —> To accelerate growth (which has been lagging), telcos must use key learnings and best practices from their GSI partnerships across their entire channel ecosystem: —> Co-innovating and co-creation with a wide aperture of partners to build differentiated solutions —> Co-selling to expand reach and win complex enterprise deals. Telcos need to step up their marketplace game with over $460 billion of enterprise commitments there for the taking. —> Co-marketing to amplify brand and solution awareness Follow (or meet with) Devan and Peter to get access to this research and dig a level deeper!

🚀 Agentic AI and Reinforcement Learning in Telecom Network Optimization and Automation 🌐 The telecom industry is on the brink of a major transformation with Agentic AI and Reinforcement Learning (RL) driving innovation in network optimization and automation. Here’s a quick overview of these groundbreaking concepts and their role in shaping the future of telecom: 1. Agentic AI: The Next Frontier Agentic AI represents a leap forward in AI evolution—autonomous systems capable of sensing, reasoning, and acting upon their environment. According to the World Economic Forum, AI agents are transitioning from simple rule-based systems to complex autonomous entities. Core features of AI agents include: 🌟 Advanced language and multimodal models 🔗 Memory management for context awareness 🧠 Decision-making and planning capabilities 📈 Continuous learning mechanisms 🔧 Tool integration for enhanced functionalities 2. Reinforcement Learning (RL): Learning Through Experience RL enables AI agents to learn optimal strategies through trial and error, making it a cornerstone of autonomous decision-making. Core components: 🎯 Agent: Learns and acts to achieve goals 🌍 Environment: The context for operation 🔄 Actions, States, Rewards: Feedback-driven adaptation 📜 Policy: Strategies for success Key techniques include Deep RL, Q-Learning, and RL with Human Feedback (RLHF), paving the way for smarter, adaptable systems. 3. Agentic AI + RL: A Powerful Synergy RL is the backbone of agentic AI, enabling: 📘 Learning frameworks for autonomous decisions 🛠️ Goal-oriented behavior with strategy optimization 🤝 Integration with advanced AI models for collaboration and reasoning 4. Solving Telecom RAN Optimization Problems The Radio Access Network (RAN) faces challenges in performance, resource allocation, and dynamic environments. AI and RL offer solutions like: 🔧 Automated parameter tuning 🔀 Dynamic resource allocation 🔮 Predictive maintenance ⏱️ Real-time optimization 5. Automating Telecom RAN with Agentic AI & RL Self-Organizing Networks (SON) exemplify automation driven by Agentic AI and RL. Key areas include: Self-Configuration: Automated setup of network elements Self-Optimization: Mobility, load balancing, and capacity improvements Self-Healing: Fault detection and recovery Implementation highlights: 🤖 Deep Q-Networks for real-time decisions 🌐 Multi-agent systems for coordination 🔄 Continuous learning for performance enhancement Agentic AI and RL are more than just technologies—they’re catalysts for the intelligent, autonomous future of telecom. With these advancements, we can achieve unparalleled efficiency, adaptability, and customer satisfaction in AI-RAN network management. #Telecom #AI #ReinforcementLearning #AgenticAI #NetworkOptimization #Automation #AIRAN #AIforRAN

Telecoms and Banks Connect - Tapping into Transactions and Tech to Grow Revenue 💡 This surge of digital financial services presents an urgently needed opportunity for the telecommunications industry to extend into new markets and generate new additional revenue streams. Some communications service providers (CSPs) have transformed landscapes with mobile money in emerging markets, including countries across Africa. Others are exploring an Economy of Things, where IoT devices complete transactions. But CSPs cannot deliver these solutions alone. They must partner with banks, which can help ensure mobile transactions are seamless, secure, and successful. The growth of technologies such as cloud and open APIs clears the way for banks and CSPs to collaborate and create real-time financial solutions. 4 pillars of Embedded Finance to build new revenue-generating solutions: 👨💻 Embedded Payments - Embedded payments allows customers to complete transactions seamlessly without leaving a platform's website or mobile application. CSPs can partner with consumer electronics retailers to offer rebates to their customers. 📱 Embedded Banking - Embedded banking solutions are integrated into non financial applications and platforms and enable businesses to provide slimmed down banking services to customers in a single client experience. A popular example is Lyft Direct, which offers a checking account and linked debit card exclusively to Lyft drivers. 💰 Embedded Lending - Embedded lending solutions are designed to offer consumers more seamless access to financial products and services that enable a purchase through apps, website, or in store. One example: BNPL options from providers such as Klarna or Clearpay (Afterpay). 💳 Hosted payment solutions - The services permit a company to have a fully integrated card-acquiring solution. Payment solutions in this space offer business management tools to help small businesses get up and running as well as take payments for their services. An example is Toast, which provides a single platform combining many of the systems needed to run a restaurant, including point-of-sale, payment processing, and online ordering. With jointly delivered financial services, CSPs and banks can place themselves at the core of revolutionary cross-industry solutions, leveraging their strengths to extend their reach to more customers. Combining data insights is one nexus. CSPs have network and call-detail information, customer service usage and payment histories, plus detail from billions of IoT devices. Banks have information on consumers' buying behaviors, spending patterns, credit scores, loan details, and more. Robust analyses of these unique types of data can uncover customer struggles, needs, and opportunities to spark imaginative service ideas. Source: IBM x GSMA x J.P. Morgan - https://bit.ly/49HCq2A #Innovation #Fintech #Banking #Telecoms #OpenBanking #EmbeddedFinance #API #FinancialServices #Payments #Lending #Data 

BSNL LTD turns profitable after 17 years - A masterclass in business resilience. Once written off as outdated, BSNL just reported a ₹262 crore profit in Q3 FY2025, its first since 2007. This isn’t just a telecom turnaround, it’s a lesson in survival, strategy, and resilience for any business fighting to stay relevant. Key Takeaways from BSNL’s Revival: 💵 Tough Decisions Pay Off - BSNL was drowning under a massive wage bill. - The government’s ₹3.23 lakh crore revival package included a Voluntary Retirement Scheme (VRS), reducing its workforce by 44%. - Wage bill slashed by ₹7,000 crore annually, proving that hard choices can create leaner, more agile organizations. 🎭 Strategic Partnerships Drive Growth - BSNL was late to 4G & 5G, but instead of building from scratch, it partnered with TCS for its 4G rollout. - Lesson? The right partnerships can accelerate innovation and cut costs. 🔎 Find & Own Your Niche - Private telecom giants fought over urban markets, while BSNL doubled down on rural India (90% village penetration). - Lesson? Underserved markets can be goldmines, find yours and dominate it. 💰 Monetize Existing Assets - BSNL leveraged its vast land bank & infrastructure to generate additional revenue. - Lesson? Businesses often overlook hidden assets, sometimes, what you need is already within reach. 🚀 The Road Ahead - Challenges remain, BSNL still lags in 5G rollout and carries a ₹33,000 crore debt. - Profitability is just the beginning; long-term success requires constant adaptation. 💡 BSNL’s journey is proof that resilience, strategic pivots, and smart decision-making can turn things around. What’s your take on BSNL’s revival?

I am traveling to Dallas today for TM Forum, a leading global industry event for #telecommunications providers, vendors, and analysts. #TMForum is where #telcos set the agenda for future-ready architectures, AI-powered operations, and the modernization of OSS and BSS. It feels like the perfect moment to share my latest blog post: Telecom OSS Modernization with Data Streaming: From Legacy Burden to Cloud-Native Agility Operational Support Systems (#OSS) have always been the nervous system of telecom operations. They activate services, assure quality, and manage the underlying network. In today’s world of 5G, multi-cloud, and #AI, this role has never been more critical. Yet most operators remain trapped in a legacy environment that slows down innovation and drives up costs. My new article explains how a #DataStreaming platform with #ApacheKafka and #ApacheFlink helps telcos evolve OSS step by step into a real-time, cloud-native architecture. Key topics include: - OSS as the central nervous system of the telco stack - The pain of legacy OSS and why modernization is overdue - How Kafka and Flink act as the event-driven backbone for modernization - OSS and BSS integration for seamless #OTT services - The role of data streaming in AI-driven and agentic operations - Lessons learned from EchoStar / Dish Wireless’ greenfield 5G OSS/BSS approach Data streaming turns OSS into a growth engine. It reduces OPEX, accelerates time-to-market, and builds the foundation for predictive and agentic AI. Read the full blog here: https://lnkd.in/esZbhmWZ How do you see telcos balancing the need to modernize OSS incrementally with the pressure to deliver new 5G and AI-driven services immediately?

🚀 How Machine Learning Helps Telecom Networks Self-Optimize What if your network could predict traffic surges and adjust its own resources before users even notice a slowdown? With AI and machine learning, that’s exactly what’s happening in telecom today. Let’s break down how it works: 1️⃣ Data Collection: The Foundation Telecom operators continuously gather network data across: ✔ Different regions ✔ Cities & neighborhoods ✔ Individual cell towers This helps track traffic flow and identify normal usage patterns. 2️⃣ Detecting Anomalies in Real Time ML models compare live data against historical trends. A sudden spike in usage? → Could be a major event, festival, or unexpected demand. → The system flags it before performance drops. 3️⃣ Smart, Automated Adjustments Once an anomaly is detected, the system recommends (or even automates) actions like: 📶 Adding bandwidth ⚙️ Optimizing software resources 🔧 Tweaking network settings 4️⃣ Continuous Learning = Smarter Networks The system learns from every event: ✔ Were predictions accurate? ✔ Did adjustments work? ✔ How can it improve next time? The result? A proactive network that: ✅ Prevents congestion ✅ Enhances user experience ✅ Optimizes costs & efficiency Key Takeaways 🔹 ML turns raw data into actionable insights 🔹 AI-driven recommendations reduce downtime 🔹 Self-improving systems = future-proof networks To learn about AI & 5G, visit - https://lnkd.in/eT-ZZyrP #AI #Telecom #MachineLearning #Networks #Innovation #Tech

What’s the hidden cost of “waiting until next quarter” to fix your telco’s data stack? For telcos, the delay is rarely just technical. It’s strategic. Every month spent wrestling with siloed systems, fragmented governance, and architectural debt compounds your risk — and drains opportunity. Learn more about it here – https://lnkd.in/d8PyHj-2 That’s the central theme of Witboost’s latest whitepaper on Digital Transformation in Telecommunications, which I had a chance to review this week. It unpacks the 7 persistent challenges that telecom operators face — and why the status quo isn’t just inefficient, it’s unsustainable: - Network downtime costing $1.2M/hour - Redundant data initiatives increasing OpEx - Misaligned IT, data, and business teams stalling execution - Inability to use even 10% of the data they generate But what makes this paper powerful isn’t just the diagnosis — it’s the playbook for action. Here are three ideas that stood out to me: 1️⃣ From Centralized Governance to Computational Governance Legacy governance assumes a central authority can review everything. But that doesn’t scale. Computational governance applies policies at runtime, creating real-time compliance and freeing up teams to move faster. 2️⃣ Decentralization with Accountability Telcos must move toward domain-based decentralization. That doesn’t mean chaos — it means data product teams owning quality, access, and policy. This creates natural boundaries with clear responsibility. 3️⃣ Transformation via Use Case Pathways The report argues that “big bang” transformations rarely succeed. Instead, telcos should start with high-impact use cases (like churn reduction, AI-driven NOC analytics, or API monetization) and build maturity over 18+ months. The best part? It provides a maturity model and a realistic 3-phase roadmap—from laying the foundation to scaling and optimizing. This is essential reading for: CDOs and Chief Transformation Officers Heads of Architecture, AI, or Data Engineering Anyone leading platform modernization or customer experience in telecom 📘 Link to download the report: https://lnkd.in/d8PyHj-2 I’d love to hear: What’s one roadblock your org keeps running into when it comes to scaling data use in telco?