Recently, the International Data Corporation (IDC) released its much-anticipated global IT industry predictions for 2025 and beyond. The IDC FutureScape report outlines 10 key predictions that will shape the future of the IT industry, highlighting how organizations across all sectors will strategically pivot toward AI (artificial intelligence) to build resilient, AI-driven enterprises.
This year’s predictions emphasize the urgent need to accelerate AI transformation, advocating for strategic, long-term investments in advanced AI-enabled capabilities. Over the past 18 months, organizations of all sizes and across various industries have conducted extensive experiments with AI. By 2025, we expect a shift from experimentation to reformation. This transformation will be driven by innovations in AI agents, data, infrastructure, and cloud solutions to deliver scalable "answers," along with an enhanced focus on resilience through sound economics and universal network recovery. To support this shift, IDC forecasts that global spending on AI-enabling technologies will surpass $749 billion by 2028. Notably, the report reveals that $227 billion of AI spending in 2025 will come from enterprises embedding AI capabilities into their core business operations—surpassing investments in leading cloud and digital service providers.
Rick Villars, Group Vice President for Worldwide Research at IDC, stated that in the evolving AI landscape, the future hinges not only on our ability to experiment but also on our capacity to strategically adapt, turning experiments into sustainable innovations. As we embrace AI, prioritizing relevance, urgency, and resourcefulness is crucial to building resilient enterprises capable of thriving in a data-driven world.
The IDC FutureScape 2025 study focuses on external forces that will reshape global business ecosystems over the next 12 to 24 months. It also examines the challenges technology and IT teams will face in defining, building, and managing the technology needed to thrive in a digital-first world.
1. AI Economics: Over the next year, CIOs will focus on documenting the extent of AI adoption, shifting from experimentation to monetization. Overcoming IT modernization barriers will require a strong foundation for automated measurement and optimization of AI-enabled applications.
2.AI Transformation Barriers: Several factors could hinder the success of GenAI implementation. Key constraints include developer shortages, high costs, inadequate infrastructure performance, and poor IT/business alignment. IDC predicts that up to 30% of organizations will reconsider their GenAI investments if solutions to these obstacles fail to align with business strategies.
3. Cyber Resilience: Highly visible ransomware disruptions continue to make cyber recovery and resilience a top priority for many IT teams. Organizations that fail to adapt to evolving threats and widespread AI adoption will struggle to meet AI-driven business outcome expectations.
4. Cloud Modernization: Organizations that successfully modernize their cloud architectures will reap benefits such as higher ROI, cost efficiency, operational effectiveness, sustainable IT outcomes, and improved workload and application performance.
5. Data as a Product: The "data-as-a-product" framework will significantly reduce inefficiencies and eliminate data silos in large enterprises. This approach ensures repeatable processes and more consistent, reliable data-supported outcomes.
6. Application Transformation: The copilots that emerged from the GenAI hype in 2022 are quickly giving way to AI agents—fully automated software components capable of assessing situations and taking actions with minimal or no human intervention.
7. Inference Delivery: As organizations accelerate the adoption of GenAI and agent workflows, inference workloads will increase dramatically. Developing a "multi-inference" operational strategy will be critical to avoid dependency on a single inference solution.
8. Decarbonizing AI Infrastructure: The potential growth of electronic waste reflects the rapid increase in AI investments across industries. To address environmental challenges while leveraging AI, organizations are turning to sustainable AI frameworks focused on energy efficiency, resource optimization, and reducing electronic waste.
9. Unified Platforms for Composite AI: Enterprises will soon realize that focusing solely on basic productivity AI and GenAI use cases offers limited impact. AI success requires a comprehensive, coordinated platform that scales solutions across the organization, ensuring economies of scale.
10. New Job Roles: Automation will drive AI-powered workplace transformation, reshaping the employment journey lifecycle. When asked if they were prepared to meet the demands of digital work transformation, 47% of IT and LOB leaders stated they had already adapted work practices and policies to leverage technology for current and future business needs.
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