Part 1: The Enterprise Edge in an AI-Centric World
The emergence of an AI-centric world is radically reshaping the global technological and business landscape in real-time, creating both opportunities and challenges for enterprises across all sectors. IDC projects global AI spending to reach $632 billion by 2028, with a compound annual growth rate (CAGR) of 29.0% from 2024–2028. This report, created by the Stelia inhouse market analysis team, provides comprehensive insight into the emerging AI-centric world and its implications for business operations, innovation, and strategy.
13 Key Insights:
- Economic Impact: AI is experiencing unprecedented growth. McKinsey estimates that AI technologies could generate $17.1 trillion to $25.6 trillion in economic value globally each year. PWC estimates in their 2024 AI report “Sizing the Prize” that the potential impact of AI on the global economy will add an uplift of $15.7 Trillion to the global economy by 2030, effectively adding another India and China to the global economy. Figure 1 below illustrates this.
Figure 1 — Source: PwC Sizing the prize 
2. Generative AI Growth: Generative AI is seeing explosive adoption, with IDC forecasting its spending to grow at a CAGR of 59.2%, reaching $202 billion by 2028 and representing 32% of overall AI spending. This underscores the transformative potential of this technology across various industries.
3. AI Maturity: The ServiceNow ‘Enterprise AI Maturity Index 2024’ reveals varying levels of AI adoption across enterprises. “Pacesetters” — organisations scoring 50 or more on a 100-point scale — are leading the way, with 33% of them leveraging AI for transformation and innovation, compared to just 14% of other companies.
4. Technological Shift: A transition from CPU-centric to GPU-centric computing is underway, driven by the demands of AI workloads. This shift necessitates substantial changes in enterprise IT strategies, including adopting high-bandwidth, low-latency network infrastructures to support distributed GPU clusters. Enterprises must reevaluate hardware investments and network designs to accommodate this.
Figure 2 — Source: 10-Qs Filings
5. Data Proliferation: IDC projects global data volumes to grow at an annual rate of 2.7X until 2027, reaching 291 Zettabytes. AI workloads will increasingly consume storage capacity, driving data center storage from 10.1 zettabytes in 2023 to 21.0 zettabytes by 2027. Likewise with network capacity, with estimates suggesting that by 2030, 75% of all network application traffic will involve AI content generation, curation, or processing.
6. Infrastructure Challenges: The growth in AI computation demands is straining existing enterprise IT ecosystems. Issues such as data gravity, integration, scalability, and performance optimisation are becoming critical factors in successful AI implementation. IDC reports that software will represent over half of AI spending, with hardware as the second-largest category, highlighting the need for robust AI-ready infrastructure.
7. Workforce Transformation: AI and related technologies have the potential to automate work activities absorbing 60–70% of employees’ time today. This shift is driving demand for new roles such as AI configurators, data scientists, and machine learning engineers. Organisations are responding with a mix of external hiring and internal upskilling initiatives.
8. Data Sovereignty and Compliance: Global AI deployments are complicated by data sovereignty requirements. There’s a growing need to clarify what types of data fall under sovereignty rules, especially as AI models trained on global datasets and data in motion challenge traditional notions of data sovereignty.
9. Ecosystem Development and Innovative Solutions:
- Emergence of AI Availability Zones: Innovative approaches from companies like Stelia include AI Availability Zones or rings being developed to optimise AI wide area network infrastructure. These zones enable GPU-to-GPU workflows within a “metro zone” of a few hundred kilometres, improving data transfer stability and performance.
- Investments in Network Infrastructure: Companies such as Flexential, EU Networks, Zayo, and Light Source Communications are investing heavily in fibre networks to meet the burgeoning enterprise networking demands of AI. Lumen’s $5 billion deal to connect hyperscalers with AI connectivity exemplifies the industry’s commitment to enhancing private infrastructure.
- Collaborations and Partnerships: Enterprises are exploring strategic partnerships with technology vendors, cloud service providers, and network operators to accelerate AI adoption and infrastructure development, recognising that collaboration is key to overcoming complex challenges.
10. ROI and Investment: On average, companies are allocating 15% of their technology budgets to AI capabilities. According to ServiceNow, 65% of surveyed organisations are achieving positive ROI from their AI investments, with 23% reporting significant returns (over 15%).
11. Cross-Sector Impact: The Transition to an AI-centric world is driving transformations beyond the tech sector. IDC projects that the financial services industry will account for over 20% of all AI spending, while Business and Personal Services will see the fastest AI spending growth at 32.8% CAGR. Banking, high tech, pharmaceutical and healthcare are among the industries that could see the biggest impact as a percentage of their revenues. McKinsey illustrates this in their 2024 report ‘The economic potential of generative AI’, on industry impact.
Figure 3 — Source: McKinsey & Company, The economic potential of generative AI
12. Geographic Distribution: AI-driven economic growth is expected to be especially pronounced in China and North America, with China projected to see a 26% boost to its GDP by 2030 and North America a 14.5% increase. Together, these regions are anticipated to contribute $10.7 trillion, representing nearly 70% of AI’s global economic impact, according to PwC. Reflecting this leadership, AI adoption and investment show significant regional disparities. IDC forecasts that AI spending in the United States alone will reach $336 billion by 2028, making up over half of global AI expenditures and reinforcing the U.S.’s leading role in AI development and integration.
13. Future Trends: Emerging developments include industry-specific AI chipsets, potential breakthroughs in quantum computing, and new long-haul transport market technologies to address AI networking challenges.
Industry Outlook:
As AI continues to develop at a breakneck pace, it will become an integral part of all enterprise operations across all sectors. Through Stelia’s extensive partnerships with global Cloud Service Providers and technology vendors, we observe that the ability to simply integrate AI capabilities into business processes, breaking down silos between geographies, departments and even between organisations, will be a key differentiator for successful companies.
The Stelia market analysis team, working closely with enterprise clients across sectors, has identified that the future of AI in enterprise is not about adopting or managing “shadow AI” as a quick fix, but about reimagining products, services, business models and operations to fully leverage AI’s potential. This transformation, as evidenced by our cross-industry implementation experience, will require new approaches to data management, skills development, and even organisational structures.
Next Steps for Enterprise Leaders:
The transition to an AI-centric world presents both opportunities and challenges. Based on our continuous analysis of successful AI implementations and ongoing dialogue with technology leaders, enterprise leaders should consider the following actions:
- Assess current AI maturity using frameworks like the Enterprise AI Maturity Index and identify key areas for integration and improvement.
- Develop strategies for data management and mobility to support AI operations, considering both performance requirements and regulatory compliance.
- Invest in skills development to build AI capabilities within the organisation, focusing on roles like AI configurators, data scientists, and machine learning engineers.
- Explore partnerships and ecosystems that can accelerate AI adoption, including specialised infrastructure providers and AI solution vendors.
- Stay informed about evolving AI technologies, including generative AI and potential quantum computing breakthroughs, and their potential applications in your industry.
- Establish clear metrics for measuring AI ROI and regularly assess the impact of AI investments on business outcomes.
- Consider industry-specific AI applications and use cases, particularly in high-growth areas like financial services and business services.
- Plan for significant infrastructure investments, balancing software and hardware needs to support AI initiatives.
By taking a proactive and strategic approach to AI adoption and integration, enterprises willing to embrace change can position themselves to thrive in the emerging AI-centric business landscape. Our extensive ecosystem of partners, spanning cloud providers, technology vendors, and enterprise clients, consistently demonstrates how this drives innovation, efficiency, and competitive advantage.
The following chapters explore deeper into the primary facets of this AI-centric world, beginning with an exploration of how we arrived at this inflection point in technological evolution. As we examine the drivers, challenges, and opportunities presented by AI, keep in mind the key insights and action items outlined above. They will serve as a framework for understanding the detailed discussions that follow.
This article is part of a larger report on AI’s transformative impact on enterprises, infrastructure, and global competitiveness. The full 9 chapter report, “The Enterprise Edge in an AI-Centric World – An Executive Field Guide for 2025” explores the key challenges and opportunities shaping AI adoption. Each chapter provides deep insights into critical aspects of AI deployment, from power constraints and data mobility to automation and geopolitical strategy. Each section, offers actionable recommendations for enterprises, policymakers, and AI infrastructure providers navigating the future of AI.
