Technology & Software

Industry: Technology & Software

The global technology and software industry is in the midst of a platform transition comparable in scale to the shift from on-premise to cloud computing — as agentic AI capabilities, sovereign cloud infrastructure requirements, and a fundamentally reshaped enterprise security model driven by AI-era threats collectively redefine how software is built, deployed, and secured.

Agentic AI: the new application layer

As discussed in the Business Services category, agentic AI — AI systems capable of autonomously planning and executing multi-step tasks using external tools and APIs — represents one of the most significant shifts in enterprise software in years, with the agentic AI and automation market growing at a 32.6% CAGR toward USD 227.8 billion by 2031. For the technology and software industry specifically, this shift has profound implications for software architecture and competitive dynamics: traditional SaaS applications, designed around human users navigating interfaces, are being reimagined with agent-accessible APIs and "agent experience" (AX) design considerations alongside traditional UX. Foundation model providers (OpenAI, Anthropic, Google) are positioning their agent frameworks and APIs as new platform layers that other software companies build upon, similar to how cloud infrastructure providers became a foundational layer for the previous software generation — creating both opportunity (for software companies that build effective agentic capabilities into their products) and competitive threat (for software companies whose core value proposition can be replicated by a sufficiently capable general-purpose agent operating their existing interfaces).

Sovereign AI cloud: a new infrastructure category

The global sovereign AI cloud infrastructure market for telecom and enterprise applications reflects a growing requirement, particularly among governments and regulated industries, for AI infrastructure (compute, data storage, and increasingly the AI models themselves) that operates within national borders and under national jurisdiction — driven by data sovereignty regulations, national security considerations regarding AI capability dependency on foreign providers, and in some cases explicit industrial policy goals to develop domestic AI infrastructure capability. This has created opportunity for telecommunications companies (which already operate significant data center infrastructure and have existing government and enterprise relationships) to expand into AI infrastructure provision, often in partnership with global cloud providers or AI model providers under arrangements that satisfy data sovereignty requirements while leveraging external technical expertise. The Gulf states, India, and several European countries have been particularly active in developing sovereign AI infrastructure initiatives, reflecting both the strategic importance increasingly placed on AI capability and concerns about dependency on US or Chinese AI infrastructure providers.

Zero-trust security: the response to AI-era threats

Cybersecurity architecture continues to shift toward zero-trust models — which assume no implicit trust based on network location and instead require continuous verification for every access request — driven both by the continued prevalence of sophisticated attacks that bypass traditional perimeter-based security, and increasingly by AI-specific security considerations. The global zero-trust network architecture market for telecommunications reflects this shift within an industry that operates particularly sensitive infrastructure; more broadly, zero-trust adoption across enterprises continues to be driven by remote/hybrid work's permanent normalization (which eliminated the traditional "trusted internal network" assumption underlying perimeter security models) and by the emergence of AI agents themselves as a new category of "identity" requiring access management — as enterprises deploy AI agents with access to internal systems and data, managing what those agents can access, under what authorization, and with what audit trail has become a significant new dimension of enterprise security architecture, sometimes termed "non-human identity management."

Cloud infrastructure: AI-driven capacity dynamics

As discussed extensively in the Energy & Utilities and Construction & Real Estate categories, AI workloads have fundamentally reshaped cloud infrastructure economics and capacity planning, with the major hyperscale cloud providers (AWS, Microsoft Azure, Google Cloud) all reporting capital expenditure levels for data center and AI infrastructure that represent step-changes from historical patterns. This has intensified competitive dynamics around AI-specific cloud services — managed AI model hosting, fine-tuning infrastructure, and increasingly, agentic AI orchestration platforms — as a key battleground for enterprise cloud spend, even as core infrastructure-as-a-service categories (compute, storage, networking) continue to see more modest growth rates than the AI-specific service categories layered on top of them.

Open source AI and the model ecosystem

The AI model ecosystem continues to feature both proprietary frontier models (from OpenAI, Anthropic, Google, and others) and an increasingly capable open-weight model ecosystem (including models from Meta, Mistral, and various Chinese AI labs including DeepSeek and Alibaba's Qwen series), with open-weight models increasingly competitive with proprietary models for many enterprise use cases, particularly where data residency requirements favor self-hosted deployment. This dynamic has significant implications for enterprise AI strategy, as organizations weigh the operational simplicity of API-based access to proprietary frontier models against the data control, customization flexibility, and potential cost advantages of self-hosted open-weight models — a decision matrix that connects directly to the sovereign AI infrastructure trends discussed above.

Regional dynamics

The United States continues to be home to the majority of frontier AI model development and the largest cloud infrastructure providers, though China's AI ecosystem has demonstrated increasingly competitive open-weight model development despite export control restrictions on advanced AI training hardware, raising ongoing questions about the long-term efficacy of hardware-based restrictions on AI capability development. India's technology sector continues to be a significant participant in the global AI ecosystem both as a major market for AI infrastructure and services, and as a source of AI talent and, increasingly, AI application development serving both domestic and global markets.

Research intelligence sought by technology and software enterprise buyers

Buyers of technology and software market research typically require: agentic AI platform competitive landscape and enterprise adoption tracking; sovereign AI cloud infrastructure market sizing by region and provider; zero-trust and non-human identity management vendor analysis; cloud infrastructure capacity and capital expenditure forecasting tied to AI demand; and open-weight versus proprietary AI model adoption analysis by enterprise use case.

All technology and software market research reports on this platform are produced by human analysts drawing on primary data from company financial disclosures, cloud provider capacity announcements, AI model benchmark data, and enterprise technology adoption surveys.