How MNOs Can Escape Legacy BSS Constraints and Monetize AI Faster

“While the network performance improved significantly, the revenue models remained stubbornly tied to legacy consumption-based billing. Operators have spent years optimizing for internal efficiency — making the network “faster” and “denser” — but they have largely failed to identify and sell new, high-margin, revenue-generating services that consumers and enterprises actually recognize.” – ZK Research

We are starting to see GenAI and Agentic AI show up in MNO network operations upgrades but they have not yet had the impact on legacy systems, particularly the billing side of the house.  Due to legacy systems that have siloed billing and customer data, the ability to apply agentic AI and GenAI integrations system-wide have been slowed, impacting MNO customer monetization efforts needed to stay competitive.

MNOs have discovered that AI implementations are not as easy as advertised.  Whether that is utilizing AI in the network, AI in operations, or AI in customer monetization.  This is especially true when looking all data systems holistically - the process of overhauling legacy systems with an integrated AI refresh can be daunting.  Effective AI implementations require more than just “bolting on” the newest, hottest AI solution into a telco’s systems.  Legacy systems data silos prevent the AI implementations necessary for the real-time personalization critical to deliver timely, customized offers in real time.

If MNOs don’t find solutions, start-up digital telco MVNO brands will launch their own competitive offers on cloud-based, digital only BSS platforms to leapfrog the MNO and cut into their market share. However, there is a path for MNOs to use the same cloud-based, all-digital BSS platform to counter MVNOs and beat them at their own game.

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Legacy Data Systems Fail With AI

“You have these somewhat older telco infrastructures that really can’t support the burden that AI places on the network.” – Kate Johnson (Lumen CEO)  While Kate is talking primarily about physical network limitations, this also applies to the hardware and software infrastructure outside the core network as the legacy MNO BSS struggles with AI integrations as well.

• Infrastructure Incompatibility: Legacy MNO infrastructure began in an environment prior to AI where network operations, billing accuracy, and regulatory compliance reigned supreme.  While that make the network and operational systems functional and reliable, that infrastructure model starts to fail when you try to implement AI over the top.  MNO data centers, with legacy servers hosting aging proprietary, overly mod-ed software systems, aren’t designed to handle the extreme power consumption, cooling requirements, and speed needed to support AI integration requirements.

• Different Data Flow Models: Telco networks are also optimized to push data traffic in one direction: to the user.  AI demands a different approach that requires feedback loops with a more balanced traffic flow, both from users as well as to.  User behavior must be incorporated into decision-making AI elements at high speeds, in real-time.  Legacy systems, with a hub-and-spoke architecture, just aren’t set up to do the edge-level computing that is required to avoid decision making delays and data gridlock.

• Data Silos: MNO systems are often a mix of proprietarily developed in-house systems, built on outdated software technologies, and often with endless software customizations to tailor otherwise flexible systems into inflexible “black box” systems that don’t work well with newer technologies like GenAI and Agentic AI. These "black boxes" are difficult to update, and have data locked down in isolated, unstructured silos that AI training tools are not able to access in real-time.

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Legacy Thinking and Legacy Rollouts Stall Innovation

• Organizational Risk Aversion: Unlike tech-native companies where risk-taking and an organizational imperative for change and growth is built in, MNOs have a hierarchical staffing structure and corporate mindset that often rewards taking a “safe” approach as opposed to the speedy, instant decision-making model inherent in AI processing. A Designit global survey showed that 43% of telecom professionals believe rolling out AI too quickly is a major mistake, and 32% treat it only as a cost-cutting tool, stalling innovation.

• 5G Rollout Malaise: 5G implementations and network slicing promised to unlock massive business customer spend, in manufacturing, healthcare, and logistics...but that didn’t end up materializing.  
  
  Both capital expenditures and operating expenses will likely be very high with the deployment of 5G standalone networks and their fully virtualized, cloud-native architectures. Against these large capital outlays, returns have been anemic across all generations, ranging from 1.5% to 4.5% of return on assets. - PWC  
  
  However, the technical complexity of implementing game-changing technology, coupled with lack of demonstrated “proof of concept” success to support their own capital investment commitment and the risk of betting their business success or failure on untested technology, caused many industry leaders to stay on the sidelines.   Lack of 5G monetization success is causing MNO leaders to drag their heels on high-stakes AI implementations.

• Talent Shortages: There is a critical shortage of personnel with both deep infrastructure expertise and modern AI skills within the telecom industry, leading to a slow pace of adoption.
  
  Most telecom companies are facing a significant shortage of personnel with the necessary skills and expertise in artificial intelligence, underscoring a critical talent gap in the industry. This shortage is a major hurdle as the demand for AI-driven solutions continues to grow, requiring professionals who are not only proficient in AI technologies but also capable of integrating these solutions into existing telecom infrastructures. – KED Consulting

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Reimagine Telco by Reimagining Your BSS

While in-house MNO systems struggle with AI integration, cloud-based, AI-aligned BSS environments do now exist to harness the power of AI - for more powerful customer connection and persuasive offers.

Without the constraint of legacy system design and optimization for a world that is fading away, a cloud-based, AI-optimized BSS can deliver powerful suggestions and offers tied to a customer’s buyer profile based on their specific activity and purchase behavior.

In this environment, customer service agents would be able to ask questions of the AI-powered BSS, much like asking a question of Chat GPT, achieving a more personalized and effective offer than could be suggested by an individual CSA without the tools, or operating in a legacy MNO billing and customer service environment.

As with the benefit to human operators in delivering a superior service and better offers to customers and prospects, these cloud-based, AI-tuned BSSs are designed to work with Agentic AI agents, multiplying the ability to deliver real-time, 24-7 365 responses without the need for expanded staffing.

Following the lead of many MVNOs in harnessing the power of the cloud and now AI tools, MNOs no longer need to be tied to the shortcomings of their legacy BSS.

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‍Enter MCP Enablement

Telcos and BSS providers have all been working towards AI integration. However, these integrations have often been siloed in test cases that aren’t able to roll out with scale where they’d be able to make significant improvements that deliver monetization benefits.

That’s where MCP can come in.  Model Context Protocol (MCP) is “an open-source standard for connecting AI applications to external systems.”  It allows GenAI (ChatGPT) and Agentic AI (Claude) tools to work between AI applications and a telco’s various data sources.

Implementing MCP over the BSS platform is a key link to allow hyperpersonalization for monetization offers to customers as well as ensuring smooth facilitation of AI across any business element the BSS touches.

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‍Interim Monetization

While MNOs have been “network focused,” improving their networks with 5G launches, monetization efforts have been sluggish, optimizing network efficiency but neglecting the creation of new, high-margin services that customers are willing to pay for.  Beyond the legacy network problems that make implementing AI solutions at all levels problematic, the institutional mindset and risk of making a career-killing decision can cause organizations to sit on the sidelines while their competitors, especially MVNOs, take the necessary risks and gain market share rewards.

But MNO leaders don’t need to take an all-or-nothing approach to their monetization efforts with 5G and AI.  Like MVNO’s MNOs can launch cloud-native digital telco brands to bring in customers, reduce churn, and operate as a sandbox testing site for monetization initiatives, all without risking their legacy systems and core customer base.

triPica has launched many successful digital brands in record time and is implementing MCP for AI agents to allow AI agent communication directly with the BSS and APIs.  Find out if a cloud-based, AI-optimized BSS implementation aligns with your MNO digital brand launch journey.