This article is based on analysis originally published by cloudcomputing-news.net, drawing on Gartner market research data from mid-2019.
The Macro Shift in Enterprise IT Spending
Enterprise IT budgets are under pressure from two converging forces: the accelerating migration from traditional, capital-intensive infrastructure to flexible hybrid multi-cloud architectures, and the deepening integration of artificial intelligence into core business systems. Together, these trends are forcing technology product managers and CIOs to fundamentally rethink how and where they allocate technology investment.
According to Gartner’s 2019 global IT spending forecast, worldwide IT expenditure was projected to reach $3.79 trillion for the year—a modest increase of just 1.1 percent over 2018. Currency volatility driven by a strengthening US dollar, trade tensions, and uncertain macroeconomic conditions contributed to a downward revision of that forecast from earlier in the year.
“Currency headwinds fuelled by the strengthening US dollar have caused us to revise our 2019 IT spending forecast down from the previous quarter. Through the remainder of 2019, the US dollar is expected to trend stronger, while enduring tremendous volatility due to uncertain economic and political environments and trade wars.”
John-David Lovelock, Vice President, Gartner
IT Infrastructure Market Development
Not all segments of IT spending are moving in the same direction. The data centre systems segment was expected to experience the sharpest decline in 2019—a drop of approximately 2.8 percent—driven primarily by falling average selling prices (ASPs) in the server market. Component cost adjustments and improved hardware efficiency were major contributors to that compression.
In contrast, the enterprise software market was forecast to grow to $427 billion in 2019, up roughly 7.1 percent from $399 billion in 2018. The primary driver of this growth is the ongoing migration of enterprise workloads from traditional, on-premises software to cloud-based alternatives. Application software has seen the most significant cloud shift to date.
Looking beyond application software, Gartner anticipated increased near-term growth in the infrastructure software segment, particularly in:
- Integration Platform as a Service (iPaaS): Middleware and integration tooling delivered as cloud services, enabling organizations to connect disparate on-premises and cloud applications without managing underlying infrastructure.
- Application Platform as a Service (aPaaS): Cloud-hosted environments for developing, running, and managing applications, reducing the operational overhead traditionally associated with platform management.
For technology product managers, this environment requires a more deliberate portfolio strategy: balancing investments in high-growth cloud and software categories against the natural decline in hardware-centric markets. According to Gartner’s assessment, product managers who take a long-term view of portfolio decisions made in 2019 will be better positioned for success through 2020 and beyond.
Outlook for AI Applications and Spending Growth
Artificial intelligence is increasingly cited as a disruptive force reshaping IT investment priorities—but Gartner’s analysis makes an important distinction that is often overlooked in market commentary: AI is not a standalone product category. It is a set of techniques and a computer engineering discipline that is being embedded into existing products, services, and development pipelines across virtually every industry.
“The choices CIOs make about technology investments are essential to the success of a digital business. Disruptive emerging technologies, such as artificial intelligence, will reshape business models as well as the economics of public- and private-sector enterprises. AI is having a major effect on IT spending, although its role is often misunderstood.”
John-David Lovelock, Vice President, Gartner
This framing has practical consequences for how IT leaders should evaluate AI-related spending. Rather than tracking a discrete “AI budget line,” organizations should expect AI capabilities to appear as enhancements within their existing software, cloud platform, and infrastructure investments. Examples include:
- Cloud platform AI services: Major cloud providers embed machine learning APIs, natural language processing, and computer vision directly into their platform offerings, making AI accessible without specialized infrastructure.
- Enterprise software augmentation: CRM, ERP, and ITSM vendors are integrating AI-driven features—predictive analytics, anomaly detection, intelligent automation—into their core products.
- DevOps and operations tooling: AIOps platforms use machine learning to correlate events, predict incidents, and reduce alert noise, embedding AI into the operational workflow rather than treating it as a separate initiative.
Strategic Implications for CIOs and Technology Leaders
The combined effect of cloud migration and AI integration is pushing IT investment decisions in a consistent direction: away from owned, depreciating hardware assets and toward recurring service-based expenditure that can scale with business demand. For CIOs and IT strategy teams, several practical considerations follow from this:
- Rebalance CapEx vs. OpEx: Continued cloud adoption shifts capital expenditure to operational expenditure. Budget models, approval processes, and financial governance frameworks need to account for this structural change.
- Evaluate AI as a platform feature, not a project: Rather than funding standalone AI initiatives, assess how cloud vendors and software providers are embedding AI capabilities into platforms already in use. This often delivers value faster and with lower implementation risk.
- Prioritize integration investment: As multi-cloud and hybrid architectures mature, integration complexity grows. iPaaS and aPaaS investments that reduce integration friction can unlock value across the broader portfolio.
- Plan for currency and macroeconomic risk: Global IT procurement is exposed to foreign exchange volatility. Multi-year cloud agreements and vendor negotiations should factor in currency hedging considerations where applicable.
- Take a multi-year view of portfolio decisions: Near-term market softness in hardware segments does not eliminate the need for infrastructure investment. Strategic leaders plan for the 2-to-3-year horizon, not just the current fiscal year.
Summary
The convergence of cloud adoption and AI integration is not a temporary market fluctuation—it represents a structural shift in how IT value is created and delivered. Organizations that recognize AI as a cross-cutting discipline embedded in cloud platforms and enterprise software, rather than an isolated product category, will make more coherent investment decisions. Meanwhile, the continued decline of traditional data centre hardware spending and the growth of enterprise software and cloud services reflect a market that has already begun to price in this new reality. For DevOps teams, platform engineers, and technology strategists, aligning investment priorities with these structural trends is increasingly a competitive necessity.