At the beginning of 2026, AI-assisted development has moved beyond being a tooling topic to become a lever of systemic competitiveness. The rapid rise of Anthropic with Claude and OpenAI with GPT-5.2 has clarified the landscape: technical teams now operate with de facto standards. Environments such as Cursor or Windsurf are redefining individual and collective productivity by drastically reducing the marginal cost of code.
For telecom players, the question is therefore no longer which tool to choose, but how to integrate these capabilities into the organization. What happens to the IT value chain when an assisted developer produces two to three times more? How should internal teams be resized, build-vs-buy decisions arbitrated, or the modernization of network IT, OSS, and BSS accelerated? And more importantly: who truly controls the business context injected into these models?
The shift is strategic. Operators capable of industrializing these tools on complex projects—network orchestration, APIs, edge computing, cybersecurity—will gain velocity, while others will accumulate organizational debt. Conversely, unguided adoption exposes companies to risks: technological dependency, loss of know-how, or dilution of technical accountability.
Four strategic paths emerge
Faced with this imperative, telecom operators have several strategic options, not mutually exclusive but with very different implications:
Option 1: Vertically integrated proprietary models
Rather than enduring standardization around general-purpose models, some operators may choose to develop or refine AI models specialized in telecom domains: 3GPP standards, network protocols, RF optimization, equipment configuration. This approach—potentially shared among major players (Orange, Deutsche Telekom, Verizon, NTT)—could create a durable competitive advantage for complex, domain-specific use cases. Initial costs are high, but technological control and differentiation are maximized.
Option 2: Differentiated strategic partnerships
Instead of remaining simple users, large operators could negotiate privileged agreements with Anthropic, OpenAI, or other major players. These partnerships could include access to sector-specific training data, on-premise deployment capabilities for sensitive contexts, co-development of domain-specific features, and sovereignty guarantees. This path transforms dependency into a structured strategic relationship while preserving access to the market’s best general capabilities.
Option 3: Governed internal platformization
Building a shared AI development platform across all teams (IT, network, product) with integrated governance, rather than allowing fragmented individual usage. This approach involves standardizing tools, centralizing business contexts, establishing security and compliance guardrails, and systematically measuring gains. Some Asian operators, such as NTT or SK Telecom, already appear to be moving in this direction, creating true internal “AI development factories.”
Option 4: Monetization and external platformization
Turning internal capability into a B2B offering: an AI-assisted development platform specialized for telecoms, aimed at equipment vendors, MVNOs, integrators, or adjacent verticals (energy, transport, smart cities). Operators hold unique datasets (network data, performance metrics, behavioral insights) and have already solved complex use cases. This strategy converts a cost center into a revenue stream and positions the operator as an ecosystem enabler.
A matter of positioning, not just adoption
These four options are not mutually exclusive. An operator may combine internal platformization (Option 3) with strategic partnerships (Option 2), or develop proprietary models (Option 1) while preparing an external offering (Option 4). The right mix depends on ambition, scale, geographic positioning, and technological maturity.
One thing is certain: in 2026, AI-assisted development is no longer optional. It is a strategic choice, on par with cloud adoption or network virtualization in previous years. Telecom operators that define their trajectory early—and invest accordingly—will set the market’s pace. The others will be forced to follow the rhythm imposed by hyperscalers and AI-native newcomers.
The window for action is now measured in quarters, not years.