Enterprise Drivers for the New AI-Centric World (Part 3)

Part 3: The Enterprise Edge in an AI-Centric World

Strategic Decisions Come from Somewhere — the 8 Main Reasons for Enterprise AI

Having established the broad context of the AI-centric world and its transformative impact across industries, we now turn our attention to the specific factors driving AI adoption in enterprises. The urgency of these drivers is underscored by recent market analysis showing that by 2030, activities accounting for up to 30 percent of hours currently worked across the US economy could be automated — a trend accelerated by generative AI according to McKinsey. Further, McKinsey estimates the top potential revenue impact of generative AI across industry sectors.

Figure 1 — Source: McKinsey & Company, The economic potential of generative AI

GTC 2025

These eight main drivers form the foundation of strategic decision-making around AI implementation and directly influence the choices organisations make in building and adapting their AI infrastructure. As we explore each driver, keep in mind how they connect to the broader trends and challenges outlined in the Introduction. Understanding these motivations will provide essential context for the detailed discussions of AI infrastructure, data management, and technological shifts that follow in subsequent chapters.

As artificial intelligence (AI) transitions from an emerging technology to a recognised transformative force, our market analysis team has observed enterprises across industries increasingly recognising its potential to revolutionise their operations and competitive landscape. Working closely with technology vendors and enterprise clients, we’ve gained unique insight into how organizations are approaching this transformation. This chapter explores the key drivers motivating businesses to adopt AI technologies, providing context for the technical discussions that follow in this report.

Understanding these eight drivers is essential for business leaders and technologists alike. They not only shape the strategic decisions behind AI implementation but also influence the specific infrastructure and operational choices organisations make in their AI journey. As we explore each driver, we’ll see how they interconnect and collectively contribute to the AI-centric world.

1 — Operational Efficiency and Automation

One of the primary drivers for AI adoption is the promise of enhanced operational efficiency through automation.

1. Process Automation: AI technologies, particularly Robotic Process Automation (RPA) and intelligent bots, are transforming traditionally labour-intensive tasks. For instance, in customer support, AI-powered chatbots can handle a significant portion of customer inquiries, reducing response times and freeing human agents to focus on more complex issues. McKinsey estimates that AI and related technologies have the potential to automate activities that currently occupy 60–70% of employees’ time.
2. Predictive Maintenance: In manufacturing and industrial sectors, AI-driven predictive maintenance is revolutionising equipment management. By analysing sensor data and historical performance, AI systems can forecast equipment failures before they occur. This proactive approach significantly reduces downtime and maintenance costs. For example, a study by Deloittefound that predictive maintenance can reduce breakdowns by 70% and lower maintenance costs by 25%.
3. Resource Optimisation: AI excels at optimising resource allocation across various business functions. In supply chain management, AI algorithms can optimise inventory levels, reducing carrying costs while ensuring product availability. In the retail and hospitality sectors, AI-powered systems optimise staffing schedules based on predicted customer demand, leading to improved service levels and reduced labour costs.

2 — Scalability and Flexibility

The ability to scale AI solutions and adapt to changing business needs is a crucial driver for enterprise adoption.

1. Cloud and Hybrid AI Models: Cloud-based and hybrid infrastructure models offer the scalability and flexibility that modern enterprises require. These models allow organisations to rapidly scale their AI workloads up or down based on demand, without the need for significant upfront investment in hardware. The trade-offs include cloud vendor lock-in and escalating and opaque usage bills.

Enterprises are increasingly adopting hybrid cloud models to manage AI workloads effectively while maintaining flexibility. As noted by Jim Stathopoulos, CIO of Sun Country Airlines, the fast-changing landscape of data and AI calls for a hybrid approach to balance experimentation, security, and cost control. Hybrid cloud infrastructures are becoming the preferred choice to ensure scalability while protecting sensitive data and managing resource allocation efficiently.

2. Modular AI Solutions: Enterprises are increasingly drawn to modular AI solutions that can be customised and expanded over time. This approach allows businesses to start with specific use cases and gradually expand their AI capabilities as needs evolve and expertise grows. Modular solutions also facilitate easier integration with existing systems and processes, reducing disruption during implementation.

In the context of AI, modular hybrid models offer the best of both worlds — flexibility in scaling workloads on the public cloud while securely handling sensitive data in private cloud environments. According to IDC, hybrid cloud infrastructure is expected to dominate enterprise decision-making for AI workloads as businesses seek to optimize both scalability and security.

3 — Cost Efficiency and Total Cost of Ownership (TCO)

While AI implementation can require significant investment, the long-term cost benefits are a major driver for adoption.

1. Reduction in Labor Costs: By automating routine tasks, AI helps reduce reliance on manual labour for low-value activities. A report by Accenturesuggests that AI could increase labour productivity by up to 40% by 2035, enabling people to make more efficient use of their time.

2. TCO Optimisation: Enterprises are increasingly focused on optimising the total cost of ownership for their AI initiatives. This involves considering not just the initial implementation costs, but also ongoing operational expenses. Solutions that leverage optimised hardware (such as NVIDIA GPUs) and strategic partnerships (e.g., with storage solution providers like VAST) can help enterprises achieve better TCO. For instance, NVIDIA reports that its DGX systems can reduce AI training time by up to 60%, translating to significant cost savings in GPU usage and energy consumption.

Additionally, as AI workloads are resource-intensive, many companies are rethinking cloud strategies to balance cost predictability with performance. Enterprises are increasingly adopting hybrid cloud models to control escalating costs. According to IDC, spending on private, dedicated cloud services is expected to reach $20.4 billion during 2024 and more than double by 2027, driven in part by the need to manage AI-related expenses effectively.

3. AI-as-a-Service Models: The rise of AI-as-a-Service offerings is attracting enterprises looking to minimise upfront costs and operational burdens. These managed services, such as those offered in Deloitte’s “Silicon to Services” package, provide AI capabilities with predictable, subscription-based pricing. This model is particularly appealing to organisations that lack the in-house expertise to develop and maintain complex AI systems.

4 — Data-Driven Decision-Making and Real-Time Insights

In today’s data-rich business environment, the ability to derive actionable insights quickly and accurately is a significant driver for AI adoption.

1. Enhanced Business Intelligence (BI): AI systems enable real-time data processing and analytics, providing businesses with actionable insights that were previously unattainable. In the financial sector, AI-powered BI tools can analyse market trends, customer behaviour, and risk factors in real-time, enabling more informed investment decisions. Morgan Stanley’s AI assistant, which helps wealth managers quickly synthesise vast amounts of internal knowledge, exemplifies this trend.
2. Personalisation and Customer Engagement: AI’s ability to process and analyse large volumes of customer data in real-time enables unprecedented levels of personalisation. In the retail sector, companies like Stitch Fix use AI to provide personalised product recommendations, significantly enhancing customer engagement and driving sales. The global AI in retail market is expected to grow from $5 billion in 2021 to $31.2 billion by 2028, according to Grand View Research.
3. Retrieval-Augmented Generation (RAG): Enterprises are increasingly seeking real-time RAG capabilities, which enhance traditional AI models by integrating live, context-specific data. This ensures more accurate and up-to-date insights, particularly crucial in dynamic business environments where conditions change rapidly.

However, many enterprises are now adopting private clouds to ensure that real-time AI data is secure. Somerset Capital Group, for example, has shifted its critical AI workloads to a private cloud, ensuring sensitive data is not exposed to public cloud infrastructures where it might inadvertently be integrated into external models. This shift in cloud strategy highlights the growing concern over AI data security in real-time applications.

5 — Compliance, Security, and Data Sovereignty

As regulatory requirements around data privacy and security become more stringent globally, compliance has become a critical driver for AI adoption strategies.

1. Navigating Regulatory Requirements: AI solutions that help enterprises adhere to complex regulations such as GDPR in Europe or HIPAA in U.S. healthcare are in high demand. These solutions must ensure data protection while still enabling the powerful analytics capabilities that AI offers.
2. Data Sovereignty and Locality: With data sovereignty laws becoming more prevalent, enterprises require AI infrastructure that supports hybrid deployment models. These allow organisations to keep data within national borders, addressing regulatory requirements and avoiding compliance issues. According to Gartner, by 2024, 35% of publicly listed companies will have hybrid and multi-cloud deploymentsto address data residency and compliance requirements.
3. AI Governance and Transparency: There’s a growing need for AI models that are not only effective but also explainable and transparent. This is crucial for meeting ethical standards and regulatory compliance requirements, particularly in sectors like finance and healthcare where decision-making processes must be auditable.

6 — Competitive Advantage and Market Differentiation

AI is increasingly seen as a strategic tool for creating significant competitive advantage, enabling enterprises to innovate, differentiate their offerings, and respond rapidly to market changes.

1. Product and Service Innovation: AI enables businesses to develop new products and services faster. In the automotive industry, AI is driving innovation in autonomous vehicles. Tesla, for instance, uses AI for its Autopilot system, giving it a significant edge in the race towards fully autonomous driving.
2. Operational Agility: AI solutions that offer real-time decision-making capabilities allow companies to react swiftly to market changes, customer needs, or operational issues. This agility is particularly valuable in fast-moving sectors like e-commerce and financial trading.
3. Customer Experience Differentiation: By leveraging AI for personalisation and predictive analytics, companies can provide superior customer experiences. In the banking sector, AI-powered chatbots and virtual assistants are becoming commonplace, offering 24/7 customer support and personalised financial advice.

7 — Strategic Partnerships and Ecosystem Integration

The complexity of AI implementation often requires collaboration with technology providers and specialists, driving enterprises to seek strategic partnerships.

1. Technology Collaboration: Partnerships with technology leaders like NVIDIA for hardware or VAST for optimised storage solutions or Stelia for data mobility provide enterprises with the technical resources needed to scale AI efficiently. These collaborations can significantly reduce the time and risk associated with AI implementation.
2. Managed Service Offerings: Collaborations with consulting firms like Deloitte that provide managed AI and infrastructure services allow enterprises to leverage expertise without having to build in-house capabilities. This reduces implementation complexity and accelerates time to value.
3. Ecosystem Integration: Enterprises increasingly value AI solutions that can connect simply with existing software, cloud platforms, and hardware infrastructure. This integration ensures minimal disruption and smoother deployment, a crucial factor for businesses looking to maintain operational continuity while adopting AI.

8 — Innovation and Long-Term Digital Transformation

AI implementation is often a core component of broader digital transformation strategies aimed at positioning enterprises for long-term success.

1. R&D and AI Incubation: Many enterprises are investing in AI not just for immediate gains but also for long-term innovation. AI incubators and accelerators provide companies with environments to experiment with and develop new AI models and applications safely. For example, Volkswagen’sAI labs focus on developing AI solutions for various aspects of automotive manufacturing and autonomous driving.
2. Sustainability and Green AI Initiatives: As organisations focus on sustainability, AI solutions that optimise energy usage or enhance efficiency in supply chains and production processes are becoming increasingly attractive. According to a PWC report, AI could help reduce global greenhouse gas emissions by 4% by 2030.
3. Future-Proofing Operations: Beyond immediate needs, enterprises are driven by the need to future-proof their operations. Scalable AI infrastructure that can adapt to future technological advancements or business expansions is crucial for ensuring that today’s AI investments remain valuable in the long term.

Challenges and Considerations

While the drivers for AI adoption are compelling, enterprises must also navigate several challenges:

• Talent Shortage: The demand for AI skills often outstrips supply. According to a 2021 survey by O’Reilly, 52% of organisations cited a lack of skilled people as a significant barrier to AI adoption.
• Data Quality and Availability: AI models are only as good as the data they’re trained on. Ensuring high-quality, unbiased data sets can be a significant challenge.
• Integration with Legacy Systems: Many enterprises struggle to integrate AI solutions with existing legacy IT infrastructure.
• Ethical Considerations: As AI becomes more prevalent in decision-making processes, ensuring fairness and avoiding bias become critical considerations.

Balancing these challenges with the potential benefits requires careful strategic planning and a clear understanding of direct business outcomes and realistic capabilities.

Linking IT Spend with Business Outcomes

The drivers motivating enterprises to adopt AI are diverse and interconnected, ranging from operational efficiency and cost savings to strategic innovation and competitive differentiation.

The choices made in terms of hardware, software, and networking solutions should ultimately serve broader business objectives. By aligning technical decisions with strategic drivers, enterprises can maximise the value of their AI investments and position themselves for success in an increasingly AI-driven business landscape.

The next chapter will explore the global AI landscape, providing context on how these drivers are shaping AI adoption across different regions and industries.

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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.