Organisations across all industries are strategically pivoting towards AI (artificial intelligence) to build resilient, AI-driven enterprises.
This is among the findings from IDC’s FutureScape report, outlining IDC’s top 10 predictions shaping the future of the IT industry.
This year’s predictions underscore the urgent need to accelerate the AI pivot, advocating for strategic, long-term investments in advanced AI-enabled capabilities. Over the past 18 months, organisations of all sizes and industries engaged in extensive hyper-experimentation with AI.
In 2025, IDC anticipates a shift from experimentation to reinvention. This shift will be driven by the introduction of AI agents, renovations in data, infrastructure, and cloud to deliver scalable ‘answers,’ and an enhanced focus on resilience through sound economics and pervasive cyber-recovery.
Supporting this transformation, IDC forecasts that worldwide spending on AI-supporting technologies will surpass $749-billion by 2028.
Notably, IDC reports 67% of the projected $227-billion 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.
“In the evolving landscape of AI, the future hinges on our ability to not just experiment, but to strategically pivot – transforming experimentation into sustainable innovation,” says Rick Villars, group vice-president: worldwide research at IDC. “As we embrace AI, we need to prioritize relevance, urgency, and resourcefulness to forge resilient enterprises that thrive in a data-driven world.”
IDC’s FutureScape 2025 research focuses on the external drivers poised to reshape the global business ecosystem over the next 12 to 24 months. It also examines the challenges that technology and IT teams will encounter as they define, build, and govern the technologies required to thrive in a digital-first world.
A closer look at IDC’s top 10 worldwide IT industry predictions reveals the following:
- AI Economics: In the coming year, CIOs will focus on documenting the extent of overall AI use, moving from AI experimentation to monetization. Laying a sound foundation to automatically measure and optimise AI-enabled applications will be imperative to overcome IT modernisation hurdles across all enterprises.
- AI Pivot Barriers: There are several factors that could hinder the success rates of GenAI implementations, the top limiting factors include developer shortages, high costs, inadequate infrastructure performance and poor IT/line-of-business coordination. IDC predicts that up to 30% of organisations will reconsider their GenAI investments if solutions to these barriers are not aligned with business strategy.
- Cyber-Resiliency: High visibility ransomware disruptions continue to make cyber-recovery and cyber-resiliency top agenda items for many enterprise IT teams. An organisation’s inability to adapt to changing threats and the expanded use of AI will hinder its capacity to meet AI-influenced business outcome expectations.
- Cloud Modernisation: Organisations that successfully modernize their cloud architectures will benefit from improved ROI, more cost-effective, operationally efficient and sustainable IT outcomes, and better workload and application performance.
- Data as Product: Data-as-a-product architecture will result in a significant breakdown in data silos and inefficiencies among large enterprises. A data as product approach is a way of producing and consuming data that makes processes repeatable and data-enabled outcomes more consistent and reliable.
- App Metamorphosis: The rise of copilots from the GenAI boom of 2022 is quickly giving way to AI agents – fully automated software components that are empowered to use knowledge and skills to assess situations and take action limited or no human intervention.
- Interference Delivery: As organisations continue to accelerate their adoption of GenAI and agentic workflows, inferencing workloads will increase dramatically. Looking towards the future, it is imperative to not get locked into a single inferencing option and instead develop a “multi-inferencing” operational strategy.
- Decarbonising AI Infrastructure: The potential rise in e-waste mirrors the rapid increase in AI investment across all enterprises. To address the environmental challenges of harnessing AI’s benefits, enterprises are turning to Sustainable AI Frameworks that focus on minimising the environmental impact of artificial intelligence by addressing key elements such as energy efficiency, resource optimisation and e-waste reduction.
- Unified Platforms for Composite AI: Enterprises are quickly learning that focusing on basic productivity AI and GenAI use cases will deliver limited business impact. AI success comes when technology underpinnings and workflows are in place to scale solutions up and across the organisation – creating a holistic, coordinated platform to ensure economies of scale in investments across an organisation.
- New Work Roles: The need to automate will create an AI-driven workplace transformation, evolving the employment journey life cycle. When questioned on preparedness to meet digital work transformation requirements, a total of 47% of IT and LOB leaders claimed they were significantly prepared and had made changes to work practices and policies, leveraging technology to support current and future business requirements.