Mobility powers AI-driven enterprise transformation

AI cannot succeed in isolation

Enterprises now require greater agility, scalability and mobility to support distributed operations and real-time data processing. AI is rapidly becoming the engine of this transformation, but success depends on a modern digital foundation where mobile, cloud and AI together automate core operational tasks and free capacity for innovation and growth. This will create reusable capabilities that scale across use cases to improve efficiency, enhance experiences and accelerate innovation. Research commissioned by Ericsson^[1] confirms that AI is viewed as essential for digital transformation, and its full value can be realized only when underpinned by robust mobile and cloud capabilities. However, it also reveals an execution gap: While almost 9 in 10 organizations see AI as critical, only a minority have successfully scaled it across their operations.

AI ambitions exceed enterprises’ digital readiness

In our study, 37 percent of enterprises report accelerating their digital transformation with positive momentum. AI is no longer confined to IT – it is now embedded across business functions, increasingly running core operational tasks. Yet, a critical gap exists. While 88 percent of enterprises expect their AI solutions to depend on real-time data, adoption of the enabling technologies remains low: Only 8 percent have fully scaled AI across multiple business areas or adopted cloud-based technologies and solutions, while just 18 percent have widely adopted cellular technologies and solutions that provide secure, reliable, real-time mobile connectivity.

This disconnect between AI ambition and technology adoption represents a clear opportunity. Service providers are well positioned to close the gap – by offering solutions that deliver the secure, always-on enterprise connectivity that AI-driven operations demand.

Mobility: Powering enterprise AI

Enterprises increasingly rely on distributed data sources – mobile devices, industrial sensors, vehicles and field equipment – to feed analytics and models that inform operations, customer engagement and new services. AI platforms, data lakes and analytics engines are often distributed across cloud, on-prem and the edge, and the demands of these use cases reshape enterprises’ network requirements. Mobile connectivity enables seamless data flow and unified workflows, evolving mobility from a basic utility into an imperative, strategic asset that underpins safety, productivity and new revenue streams.

Realizing value from AI requires the alignment of network services with business processes and measurable outcomes. This represents a pragmatic opportunity for service providers; to move from being pure infrastructure vendors, to become strategic partners, by packaging connectivity, edge compute, security and managed services for enterprises as part of their AI roadmaps to meet specific transformation objectives.

Mobile connectivity and cloud provide the real-time foundational capabilities enterprises require to scale AI securely and reliably. On the mobile side, enterprises in this study rank security (78 percent), reliability (72 percent) and bandwidth (67 percent) as the top enablers, highlighting the need for protected, dependable, high-throughput data flows. Mobile delivers the real-time connectivity that AI depends on. Of the surveyed enterprises, 80 percent have plans to increase their mobile investments.

Moreover, 75 percent identified 5 use case categories where they believe AI will have a significant business impact:

• Tracking and monitoring – continuous sensor and location telemetry for assets, goods and customers, needing scalable connectivity and edge processing.
• Connected operations – remote control, autonomous mobile robots (AMRs) and industrial control requiring private networks, low-latency edge compute and secure orchestration.
• Enterprise collaboration – real-time, secure Unified Communication and interoperable workspaces needing QoS, managed end points.
• Customer engagement – omnichannel, video and conversational AI with edge inference.
• Digital devices – kiosks, wearables and vehicles requiring device lifecycle, SIM/eSIM and security.

Why mobile connectivity matters most

Although AI attracts headlines and cloud receives significant architectural attention, it is the mobile part of the foundation that ultimately determines whether enterprise AI-led digital transformation will be successful. Global coverage, multi-operator support and adherence to global standards ensure AI workloads scale instantly across locations and business units without bottlenecks.

First, mobile networks provide the reach and coverage needed to connect distributed operations. A single enterprise may span multiple locations and include mobile fleets, each with different performance, security and regulatory requirements. 5G, private networks and wireless WAN (WWAN) can be tailored per site and per use case, while being managed as part of a unified enterprise architecture.

Second, 5G’s inherent low latency, high reliability and robust security make it possible to deploy AI and automation in scenarios where delays or outages are unacceptable, such as safety-critical environments, high-value production lines or real-time customer interactions. This is particularly relevant as new applications, including AI computer vision, AR-assisted operations and physical AI (which enables machines to perceive and interact with the physical world in real time) will drive rapid uplink growth, placing new demands on mobile networks that were historically optimized for downlink.

Third, mobile networks are increasingly becoming programmable platforms. With 5G standalone and network APIs, service providers can expose differentiated connectivity characteristics – such as assured throughput, low jitter or location awareness – directly to enterprise developers and ISVs. This enables them to design AI-powered applications that dynamically request the network performance they need. Network-powered solutions that combat fraud are the first use cases to come to market for service providers‘ monetization, as this is an urgent enterprise digital transformation need.

From connectivity provider to strategic AI partner

Our research highlights that enterprises increasingly see mobility, AI and cloud as the foundation for digital transformation, and plan to step up investments in mobile-enabled initiatives over the coming years. At the same time, many organizations struggle to move beyond isolated AI proofs of concept, citing gaps in connectivity, data readiness and operational integration as major barriers. This highlights that service providers could evolve their role to provide not only high-performance public 5G, but become orchestrators of the AI–mobile–cloud digital foundation for enterprises, for example as a managed service provider delivering outcomes instead of only connectivity. If they evolve their networks towards AI-native, cloud-native architectures that can handle the complexity of AI workloads, service providers can help enterprises unlock the full value of AI-led digital transformation.

As AI use cases are increasingly adopted, and enterprises demand more agility and intelligence, the importance of getting the mobile foundation right will only increase. Therefore, service providers need to evolve networks into AI- and cloud-ready platforms, build differentiated enterprise offerings around private and public 5G, and position mobile connectivity as the backbone of AI-driven digital transformation.