AI for digital industries: navigating enterprise needs, investments and supplier decisions
This report is available to those subscribed to the IoT & Enterprise module.
Digital transformation of vertical sectors is accelerating, with AI being a top area of investment. GSMA Intelligence surveyed nearly 4,200 enterprises across 21 countries and 10 vertical sectors to gain relevant insights into their digital transformation across a range of technologies. This report analyses the key findings and implications for AI.
These insights can help suppliers of AI technologies enhance their B2B strategies and messaging, improve their competitive position, target new services and markets, identify most suitable partners, and redefine their budget and allocation of resources. For end users of AI technologies, these insights can help enhance benchmarking activities, supplier decisions, services and products and spending decisions.
Related research
The rise of digital industries: navigating enterprise needs, investments and supplier decisions
Digital transformation of enterprises across vertical sectors is accelerating. During 2024-2030, enterprises will spend 10% of their revenues on digital transformation, and this provides new B2B opportunities to technology suppliers, including operators. The rise of digital industries is not one size fits all though. As sectors have different needs and priorities, getting insights directly from enterprises is key to formulate the right B2B strategies and capitalise on the digital transformation opportunity.
Telco AI: State of the Market, Q2 2025
If 2024 was about establishing the strategic rationale for AI, 2025 is about assessing progress. To this end, GSMA Intelligence has evolved this research series to establish a baseline that can be tracked over time for the industry, its supply chain and partnership ecosystem. As well as recent developments from the telco AI space and the Telco AI tracker, this edition provides a deep dive of AI at the edge, looking at the strategic value of different AI inference options.
AI inference in practice: new intelligence from the hospital floor
Enterprises need to understand that where inferencing happens – whether at the user edge (device and enterprise/on-premises) or network edge (far edge, near edge and telco private cloud) – will have major implications for application performance, data sovereignty, resilience and energy efficiency. This analysis focuses on how running AI workloads on the enterprise edge (on-premises) can deliver improved outcomes.
Authors
How to access this report
Annual subscription: Subscribe to our research modules for comprehensive access to more than 200 reports per year.
Enquire about subscriptionContact our research team
Get in touch with us to find out more about our research topics and analysis.
Contact our research teamMedia
To cite our research, please see our citation policy in our Terms of Use, or contact our Media team for more information.
Learn moreRelated research
The rise of digital industries: navigating enterprise needs, investments and supplier decisions
Digital transformation of enterprises across vertical sectors is accelerating. During 2024-2030, enterprises will spend 10% of their revenues on digital transformation, and this provides new B2B opportunities to technology suppliers, including operators. The rise of digital industries is not one size fits all though. As sectors have different needs and priorities, getting insights directly from enterprises is key to formulate the right B2B strategies and capitalise on the digital transformation opportunity.
Telco AI: State of the Market, Q2 2025
If 2024 was about establishing the strategic rationale for AI, 2025 is about assessing progress. To this end, GSMA Intelligence has evolved this research series to establish a baseline that can be tracked over time for the industry, its supply chain and partnership ecosystem. As well as recent developments from the telco AI space and the Telco AI tracker, this edition provides a deep dive of AI at the edge, looking at the strategic value of different AI inference options.
AI inference in practice: new intelligence from the hospital floor
Enterprises need to understand that where inferencing happens – whether at the user edge (device and enterprise/on-premises) or network edge (far edge, near edge and telco private cloud) – will have major implications for application performance, data sovereignty, resilience and energy efficiency. This analysis focuses on how running AI workloads on the enterprise edge (on-premises) can deliver improved outcomes.
- 200 reports a year
- 50 million data points
- Over 350 metrics