{"id":491199,"date":"2025-12-09T09:33:53","date_gmt":"2025-12-09T09:33:53","guid":{"rendered":"https:\/\/www.mobileworldlive.com\/?post_type=commercialcontent&p=491199"},"modified":"2025-12-24T09:33:28","modified_gmt":"2025-12-24T09:33:28","slug":"telecoms-next-big-move-scaling-ai-beyond-the-pilot-phase","status":"publish","type":"commercialcontent","link":"https:\/\/www.mobileworldlive.com\/telecoms-next-big-move-scaling-ai-beyond-the-pilot-phase\/","title":{"rendered":"Telecom\u2019s Next Big Move: Scaling AI Beyond the Pilot Phase"},"content":{"rendered":"\n

PARTNER CONTENT: AI is no longer a future consideration in telecom. From agent enablement to cybersecurity, most organizations have already begun integrating AI into their operations. Yet the path from adoption to transformation is far from straightforward.<\/strong><\/p>\n\n\n\n

For many telecoms, AI still lives in isolated pilots – technically successful but disconnected from broader business strategy. The question now isn\u2019t if<\/em> AI should be pursued, but how<\/em> it should be embedded into the enterprise in a way that delivers sustained value.<\/p>\n\n\n\n

Scaling Is Still the Struggle<\/strong><\/p>\n\n\n\n

A recent AI roundtable Bounteous ran with senior telecom leaders surfaced a familiar issue: most AI programs are making progress, but few have scaled successfully.<\/p>\n\n\n\n

This finding aligns with recent research: 88% of telecom firms report expected better returns from AI initiatives. Yet only 12% are building engineered AI products. Most investment remains focused on embedded tools or team enablement – automating tasks, summarizing calls, or tagging sentiment.<\/p>\n\n\n\n

These wins are real, but they\u2019re not transformative. Without a strategy to scale, telecoms risk stopping at efficiency instead of driving growth.<\/p>\n\n\n\n

Why Pilots Stall<\/strong><\/p>\n\n\n\n

Telecom organizations face two core blockers to AI scale: fragmented ownership and lack of strategic alignment.<\/p>\n\n\n\n

Our research found 50% of AI responsibility still sits with the CIO, while only a small portion of firms have cross-functional governance in place. This siloed ownership model makes it difficult to align AI initiatives with broader business goals.<\/p>\n\n\n\n

At the same time, AI efforts are often disconnected from KPIs that matter at the enterprise level. When automation delivers cost savings but no visibility into customer impact or revenue outcomes, the business case weakens over time.<\/p>\n\n\n\n

\u201cWe\u2019re automating the edges,\u201d one executive said during the roundtable, \u201cbut not transforming the core.\u201d<\/p>\n\n\n\n

The Maturity Gap<\/strong><\/p>\n\n\n\n

Self-reported AI maturity varies widely across the sector. While 36% of telecom leaders consider their organizations \u201cexpert\u201d, 22% still place themselves at the beginner level – twice the cross-industry average.<\/p>\n\n\n\n

This divide reflects deeper operational challenges. In some firms, AI is fully embedded in workflows. In others, adoption remains inconsistent across teams or functions. Additionally, 15% of telecom leaders expressed low confidence in AI\u2019s impact, often citing limited training, poor cross-team collaboration, or concern about job displacement.<\/p>\n\n\n\n

These aren\u2019t technical problems – they\u2019re cultural and structural.<\/p>\n\n\n\n

What\u2019s Working, and What Comes Next<\/strong><\/p>\n\n\n\n

The most effective telecom AI use cases today are not flashy, but focused:<\/p>\n\n\n\n