More than half (56%) say they’ve realised neither revenue nor cost benefits from their AI investments, per the PwC’s 29th Global CEO Survey based on responses from 4,454 chief executives across 95 countries and territories. Companies’ capital spending on AI is projected to increase, with Wall Street analysts estimating $527 billion in 2026, up from $465 billion earlier. Investors are being more selective about AI stocks, and earnings reports have led to upward revisions for hyperscaler AI companies’ spending.
New Career Trajectories
While this typically raises questions over ROI from these mega billions, it also throws up opportunities for data science and management professionals with competencies that facilitate the alignment of scalable AI initiatives with overarching business strategies can catalyze transformative career trajectories. These enterprise challenges directly translate to high-demand skills for data science and management students: building scalable MLOps pipelines, aligning AI with business strategy, and leading ethical deployments. Students mastering these can deliver the “enterprise-scale” foundations CEOs crave, securing premium careers in a market favoring AI-fluent talent amid reinvention.
Not Just Another IT Refresh
“We’re seeing organizations treat AI infrastructure like a typical IT refresh, but that’s where they get stuck,” said Robert Zabel, Associate Partner and Research Lead at the IBV and co-author of the study, in an interview with IBM Think.
An MIT Sloan Management Review’s 2025/2026 AI trends support PwC’s early-returns lag, noting cultural barriers block data-driven cultures despite GenAI hype (92% cite change management issues). Agentic AI promises and unstructured data focus align with CEOs’ transformation anxieties, urging measurement of GenAI pilots. World Economic Forum data bolsters dynamism: AI-exposed jobs see 66% faster skill shifts, with 92% of GenAI users reporting productivity gains.
When an organization attempts to scale AI production within the parameters of a traditional planning model, the IBM reports points out a number of problems can arise, including data gaps, governance risks and the infrastructure’s inability to handle AI workloads. Organizations can’t predict when a model’s processing and resource needs will suddenly increase, and data requirements can shift overnight, illuminating the need for a hybrid infrastructure for AI.
Trust by Design
The IBM report research suggests organizations use a “trust-by-design” approach, which embeds governance into an enterprise’s AI infrastructure from the very start.
“The governance gap is real,” said Zabel. “Executives keep telling us how critical it is, but when we look under the hood, most organizations are flying blind on AI risk management.”
PwC’s work with organisations confirms mounting evidence that isolated, tactical AI projects often don’t deliver measurable value. Tangible returns come from enterprise-scale deployment consistent with company business strategy. This, in turn, demands strong AI foundations, including a technology environment that enables AI integration, a clearly defined road map for AI initiatives, formalised responsible AI and risk processes, and an organisational culture that enables AI adoption.
Those Who Sees Returns
Data from this year’s PwC survey shows that companies in the vanguard—the one in eight achieving both additional revenues and lower costs from AI—are furthest ahead in building these foundations. They’re also applying AI more extensively across different areas of the business. For example, 44% of those in the vanguard have applied AI to their products, services, and experiences, compared to only 17% for other companies.
As CEOs worry over AI foundations; it’s time from data science and management students to respond with hybrid prowess—analytics + engineering + governance. Data science students see PwC’s “pilot failures” as calls for production-grade expertise, not model tweaks. Our curricula must blend tech with leadership judgment, by fostering “system thinkers” for products/services integration to prepare future-ready talent.
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