The telecommunications industry is experiencing unprecedented automation. From Self-Organising Networks (SON) reducing manual interventions to AI-powered predictive maintenance preventing equipment failures, operators are successfully automating complex technical operations that once required constant human oversight.
Yet this automation surge is creating an unexpected challenge. The more standardised processes become automated, the harder it becomes for operators to differentiate themselves in competitive markets. When every mobile operator or service provider automates the same functions using similar AI solutions, commoditisation accelerates rather than competitive advantage emerging.
This is the AI paradox in telecom. While AI excels at optimising technical operations, it’s simultaneously making strategic business thinking more valuable, not less. The professionals who will thrive are not just the ones who can implement AI solutions, but those who can translate automated capabilities into sustainable competitive advantages that AI cannot replicate.
The Automation Reality: Impressive Results, Identical Approaches
AI implementations across mobile networks are delivering tangible commercial benefits. Recent deployments demonstrate the technology’s maturity in handling standardised, repetitive tasks.
Self-Organising Networks: Proven Commercial Impact
Nokia’s MantaRay Cognitive SON deployment with stc Group in Saudi Arabia represents what Nokia calls “a global first” in autonomous RAN (Radio Access Network) operations. During high-traffic periods with 40% increased network demand, the AI system processed over 10,000 autonomous actions (network operations actions), delivering a 30% increase in utilisation rates on loaded cells and 10% average improvement in user throughput. stc achieved a 13% reduction in energy consumption across their networks in 2023, with plans for AI-powered products across over 200 systems by 2025.
These results translate into clear financial benefits: reduced OPEX through fewer manual interventions, potential CAPEX deferral by extracting more capacity from existing infrastructure, and revenue protection by maintaining service quality during peak demand. Innovile reports that SON solutions can reduce dropped calls by up to 20%, whilst gaining up to 25% more network capacity and 30% more throughput.
Predictive Maintenance: From Reactive to Proactive
AI-powered predictive maintenance analyses time-series data from baseband units, power amplifiers, and cooling systems to predict hardware failures well in advance. This enables proactive intervention before failures occur, shifting operations from reactive “fix-and-repair” to predictive “prevent-and-optimise” models. The business impact extends beyond uptime: predictive maintenance cuts unscheduled repair costs, extends equipment lifespan, and reduces emergency technician dispatches.
Open RAN Intelligence: Breaking Vendor Dependencies
O-RAN’s RAN Intelligent Controller (RIC) hosts AI applications for real-time optimisation. Rakuten Mobile has achieved approximately 17% energy savings per cell in its live network using RIC-hosted automation applications, whilst NTT DoCoMo expects to lower its TCO by up to 30% and decrease power consumption at base stations (cell sites) by as much as 50% using Open RAN automation.
The Differentiation Crisis: When Everyone Automates the Same Things
These impressive results mask a fundamental competitive challenge. In mature markets with three or four mobile network operators (like the UK’s current landscape), automation is already underway. Mobile operators are implementing AI-driven solutions for network analytics, predictive maintenance, and customer service optimisation. As AI solutions continue to mature and vendors offer increasingly standardised capabilities across the industry, operators risk converging on identical approaches based on the same 3GPP standards and similar vendor solutions.
The Commoditisation Trap
Consider how pricing strategies operate in competitive telecom markets. When operators compete on traditional services (voice, text, and data), proposition teams constantly monitor competitor pricing, asking questions like ‘How much is the competitor selling 20GB for? We need to price ours similarly to stay competitive.’ This reactive pricing approach is easily automated through AI algorithms that track competitor rates and adjust pricing accordingly.
But here’s the problem. When every operator automates pricing decisions using similar competitive intelligence, they end up in pricing circles that benefit no one. Automated responses to automated competitor moves create a race to the bottom rather than sustainable differentiation.
The Standardisation Challenge
The convergence towards common standards amplifies this challenge. Unlike earlier generations where 3G had multiple competing technologies and 2G offered several technical approaches, 4G networks use only LTE technology, and 5G networks implement only New Radio (NR). When operators use the same technology based on identical 3GPP standards, often from similar vendors following the same specifications, differentiation opportunities shrink dramatically.
If every operator automates SON optimisation, implements similar predictive maintenance, and uses comparable RAN intelligence, where does competitive advantage actually come from? Technical excellence becomes a basic requirement rather than a competitive advantage.
The Automation Convergence Impact
As AI solutions mature, vendors increasingly offer similar capabilities across the industry. The Network Data Analytics Function (NWDAF) introduced in 3GPP Release 15 enables advanced analytics in 5G core architecture with standardised interfaces. When NWDAF continuously monitors network slice performance and provides real-time utilisation KPIs to functions like the Policy Control Function (PCF), every operator implementing this standard gets similar analytical capabilities.
This creates a competitive paradox: the more successfully the industry automates operational efficiency, the more operators need strategic differentiation that automation cannot provide.
Why Strategic Business Thinking Becomes More Valuable, Not Less
The solution isn’t to avoid automation. Operational AI delivers measurable OPEX savings and service quality improvements that competitive markets demand. The solution is recognising that strategic thinking becomes more valuable precisely because it cannot be automated.
No One-Size-Fits-All Strategic Solutions
If there were universal answers to strategic challenges, AI could indeed provide them. But if every operator used the same AI-generated business strategies, competitive advantage would disappear entirely. Strategic thinking requires nuanced decisions that must be different from competitors to create sustainable value.
Take Self-Organising Networks as an example. Deploying SON in highly mature markets with advanced automation already in place might deliver minimal incremental benefits. But deploying the same SON technology in developing regions with frequent manual interventions could drive substantial OPEX savings. The calculation extends beyond technical metrics: labour costs, infrastructure maturity, ARPU levels, and competitive dynamics all influence whether automation investments create strategic value.
The Business Model Innovation Challenge
The telecommunications industry faces increasing competition as software development barriers continue to lower thanks to AI-assisted coding and cloud infrastructure. From WhatsApp reducing SMS revenue to unified communications and collaboration apps like Zoom, Teams, and Slack displacing traditional business phone systems, the evolution from circuit-switched to IP-based calling enabled software providers to create the first competitive layer above traditional networks. Companies like Airalo and Holafly represent a second wave of this evolution, creating software solutions that reduce the need for customers to directly engage with traditional operators for connectivity services. As AI makes software development even easier, this trend will likely accelerate further.
This represents a critical shift from competing for individual revenue streams to facing potential challenges in maintaining direct customer relationships. Mobile operators have seen pressure on SMS revenue from apps like WhatsApp, international calling revenue from IP-based services, and now data revenue faces similar challenges as software-based eSIM platforms like Airalo and Holafly purchase wholesale data from mobile operators to offer travellers multi-operator access. The key challenge isn’t the eSIM technology itself (mobile operators offer eSIMs too), but that customers are obtaining eSIMs from these third-party platforms instead of directly from mobile operators. The wholesale model isn’t new – mobile operators already sell wholesale capacity to traditional MVNOs, often with bandwidth restrictions and performance limitations during peak hours. However, these software-based eSIM providers offer a compelling advantage: they can work with multiple operators simultaneously, allowing customers to access the best coverage and data performance in any given area rather than being limited to a single operator’s network quality. This convenience factor and superior connectivity experience creates an additional layer between operators and end customers, where direct customer relationships risk being replaced by wholesale supply arrangements. If this trend accelerates, telecommunications networks could face the challenge of becoming primarily backend infrastructure providers with reduced direct customer connections, requiring new approaches to business models and competitive positioning.
Why Strategic Decisions Require Human Insight
Strategic success ultimately depends on convincing humans (CEOs, SVPs, and directors etc.) to approve investments and strategic directions. These decisions aren’t purely analytical; they’re influenced by organisational priorities, timing, competitive pressures, and strategic context that change constantly. The business model disruptions we’ve discussed, from AI-driven software-based service competition to changing customer relationship dynamics make this human element even more critical.
Effective business model development requires reading organisational dynamics that AI cannot access. If decision-makers express concerns about capital availability, you emphasise CAPEX savings over OPEX benefits. If the company prioritises cloud-first transformation, you reframe solutions using as-a-service models. If customer churn threatens revenue, you highlight retention benefits even if they’re not the strongest financial metrics. When competitive threats emerge from new business models, you must adapt your strategic messaging to address specific stakeholder concerns about market positioning and competitive response.
This contextual sensitivity comes from experience, intuition, and understanding stakeholder psychology. In an industry facing increasing competitive complexity, these capabilities require human strategic thinking rather than computational analysis that cannot account for the nuanced organisational and market dynamics that influence strategic decisions.
The Strategic Thinking That AI Cannot Replicate
The division between AI capabilities and human requirements becomes clear when examining business strategy development:
What AI Can Handle:
- Monitor competitor pricing and suggest automated responses
- Calculate NPV, IRR, and payback periods for defined scenarios
- Process financial projections and sensitivity analysis
- Optimise network performance within established parameters
- Generate reports based on predefined metrics
What Requires Human Strategic Thinking:
- Understand market dynamics and competitive positioning beyond price
- Determine whether benefits represent OPEX savings, CAPEX deferral, indirect revenue protection, or new revenue opportunities
- Adapt business strategies to organisational priorities and stakeholder concerns
- Navigate regulatory environments and competitive factors unique to specific markets
- Create differentiated value propositions that competitors cannot easily replicate
- Connect operational AI capabilities with strategic business model innovation
The Market Context Challenge
Strategic thinking must account for variables that change faster than AI models can adapt. ARPU variations across markets, regulatory differences, competitive landscapes, and customer behaviour patterns all influence whether identical technical solutions create business value.
In low-ARPU markets, revenue protection through churn reduction might carry less weight than direct cost savings. In high-ARPU environments, customer experience differentiation becomes critical for competitive positioning. Even with standardised network technologies, local market dynamics require strategic adaptation that AI cannot provide.
Building Competitive Advantage in an AI-Enhanced Industry
The future belongs to operators who can combine operational AI efficiency and creativity with strategic business thinking that creates sustainable differentiation. This requires bridging technical automation capabilities with business model innovation that competitors cannot easily match.
The Strategic Integration Challenge
As the telecommunications industry implements more AI-driven automation, the complexity of connecting technical capabilities with commercial opportunities increases rather than decreases. Network slicing, edge computing, and IoT enablement create new revenue possibilities, but realising these opportunities requires strategic thinking about customer needs, pricing models, and competitive positioning.
Success requires professionals who can translate automated network improvements into compelling commercial propositions whilst understanding the technical foundation well enough to avoid unrealistic promises or missed opportunities.
The Differentiation Imperative
In competitive markets where technical capabilities converge, strategic differentiation becomes the primary value creator. This means developing business strategies that leverage automated operational efficiency whilst creating customer value that competitors cannot easily replicate through their own automation initiatives.
The question isn’t whether AI will automate more telecom operations (it will). The question is whether professionals can develop the strategic thinking capabilities that become more valuable as automation expands.
The Strategic Advantage of Human-AI Collaboration
The winning combination isn’t human versus AI, but human strategy directing AI execution. Operational AI delivers measurable efficiency gains, but strategic success requires human oversight for market positioning, competitive differentiation, and business model innovation.
Those who master this combination (understanding what AI can automate whilst developing the strategic thinking that AI cannot replicate) will find themselves increasingly valuable in an industry where both technical excellence and commercial acumen determine success.
As AI transforms telecom operations, the professionals who thrive will be those who can navigate the complex intersection between automated technical capabilities and strategic business differentiation. Success depends on connecting operational efficiency with strategic value creation that competitors cannot easily match.
This requires strategic thinking that can translate technical capabilities into sustainable competitive advantages whilst adapting to market dynamics, stakeholder priorities, and competitive pressures. The challenge isn’t learning about AI automation itself, but developing the strategic thinking skills that remain uniquely human.
That’s exactly why I created the Business Case Builder and Business Case Fundamentals courses. These are designed for product, strategy, and technology teams who need to build credible, market-ready business cases that stand up to executive scrutiny whilst creating differentiated value propositions. Using real-world telecom examples, the training walks you through connecting technical understanding with strategic business thinking, providing practical frameworks you can apply immediately.
