In the fractured and uncertain world of 2026 and beyond, enterprises are being exposed to new risks and opportunities that were not envisaged even just a few years ago. Is it enough just to continue along the digital transformation journey, or is a rethink needed? In this Viewpoint, we make the case for CIOs to adopt a more radical approach. We outline five complementary strategies aimed at leveraging technology to improve productivity, efficiency, and resilience, turning technological disruption into enduring advantage.
It has now been more than 40 years since the term “VUCA” (volatility, uncertainty, complexity, ambiguity) was introduced by the US Army War College to describe an increasingly uncertain environment. So it’s fair to say that by now, most businesses “eat VUCA for breakfast.” The new kid on the block, “BANI” (brittle, anxious, nonlinear, incomprehensible), appeared five years ago. It has some supporters — although “incomprehensible” is not such a helpful concept when you’re strategizing.
Whatever you call it, in the last few years, it’s become clear that we’re moving to a very different sort of normal. We now have a more fractured world, with converging crises around resources, climate, society, and geopolitics. Events and their impacts are rapidly amplified — and sometimes distorted — through ubiquitous digital connectivity. Floods of information and noise saturate communications channels. Exponentially developing technology is upending assumptions about how business can, and should, operate.
What does this mean for business strategy in 2026 and, in particular, for the CIO leading enterprise IT? Is it enough to continue broadly as before along the digital transformation journey, or is it time for a rethink?
In this Viewpoint, we make the case for a more fundamental rewiring of the organization around two complementary aims: productivity and resilience.
The most important global trends in the post-VUCA world point to five clear strategic priorities for CIOs (see Figure 1):
While the US economy continues to defy expectations in 2025, Europe has experienced stagnant growth amid rising tariffs, with many businesses seeing a squeeze on profits. Views on the prospects for 2026 are divided: some forecast continued growth, while a significant number predict a downturn. The geopolitical environment remains volatile.
In this climate, driving greater efficiency and productivity is one of the most powerful ways to hedge against economic disruption. AI has long been touted as a key enabler of this transformation, but for most businesses, the promised benefits have not materialized. According to MIT’s “State of AI in Business 2025” report (based on data from the first half of 2025), around 95% of enterprise AI initiatives failed to generate measurable impact. This has led to what the authors refer to as the “GenAI divide” — the gap between companies that have fully embedded AI into their operations and those that remain stuck at the pilot stage, using isolated AI applications as mere “assistants” rather than fundamentally changing business workflows.
Fortunately, recent advancements in agentic AI present new opportunities to bridge this divide in 2026. Agentic AI can perceive, reason, and act autonomously, learning continuously from contextual data. It can be seamlessly integrated into enterprise architecture, offering the potential to replace “assistive” use cases with autonomous systems that can act across interconnected functions, from supply chain planning to customer support. Unlike the previous generation of robotic process automation, which was rule-based, agentic AI is context-driven, grounded in real-time data, and capable of dynamically interpreting and adapting to complex workflows. The potential for improving productivity and efficiency is huge.
Most CIOs already have agentic AI on their radar and are allocating resources. However, simply adopting isolated pilot approaches — as many did with generative AI (GenAI) tools in the past few years — risks repeating the same mistake of not delivering bottom-line impact quickly enough. To capture agentic AI’s full productivity potential, organizations must adopt a value-driven approach: identify where autonomous systems can deliver the greatest measurable ROI and then rewire process architecture to realize their full benefits at scale.
Often, this will mean leveraging the embedded agentic capabilities of existing enterprise platforms rather than developing custom solutions from scratch. Relying on existing ecosystem vendors’ built-in solutions, such as SAP, Salesforce, or Microsoft, will allow companies to accelerate development, reduce integration complexity, and ensure interoperability across the value chain.
Technological development today is moving too fast for most enterprises to keep up. In addition to breakthroughs in AI, developments in cloud infrastructure, robotics, and cybersecurity are reshaping the digital landscape faster than ever before. Expert skills and capabilities are scarce, making it nearly impossible to stay ahead just through recruitment, in-house skills development, and corporate R&D.
The concept of building an ecosystem through partnerships, acquisitions, and academic collaborations is not new. Yet most enterprises still have a long way to go in nurturing their innovation ecosystems. For example, last year in the UK, fewer than 40% of businesses collaborated with other organizations on innovation, according to Innovate UK’s “The State of Innovation 2024” report. Moving from owning technology to orchestrating it across an ecosystem has become critically important. Collaborations with AI research labs, startups, and hyperscaler ecosystems enable enterprises to pilot emerging technologies at speed, without bearing the full R&D burden. Partnerships with universities and research centers can provide privileged access to early-stage innovations and emerging talent pools.
In 2026, CIOs need to take innovation ecosystem development more seriously. This means adopting agile ways of working, building digital platforms that enable seamless integration, and cultivating a culture that promotes creativity, co-creation, and openness to trial and error. It also requires slimming down the core technology stack and integrating best-of-breed solutions across modular interfaces. Only then will innovation velocity be able to match the pace of market disruption.
Compute capacity has become a key geopolitical battleground. Governments are investing heavily in building domestic capabilities while tightening export controls. A few large players, split between the US, the Netherlands, and Taiwan, dominate the upstream part of the AI value chain. Further down the stack, sovereign and regional ecosystems are emerging for cloud, model, and application layers. The era of global cooperation and mutual interdependence has all but disappeared.
For enterprises, this means that digital sovereignty and resilience have risen to the top of the agenda. Architectures developed over decades, based on global optimization for cost-efficiency, interoperability, and hyperscaler scale, need to be revisited.
For 2026, CIOs must ensure that their IT systems are resilient enough to withstand shocks, whether from supply chain choke points, regulatory changes, or geopolitical conflicts. While the gradual repatriation of digital and cloud services within regional or national borders is one approach, achieving full data sovereignty is unrealistic. Instead, enterprises should pursue a hybrid approach, strengthening control over data and operations while maintaining flexibility and innovation. For example, hybrid and multi-cloud models allow CIOs to diversify risk and avoid vendor lock-in. Sovereign clouds ensure compliance with local data regulations and national security requirements (e.g., General Data Protection Act [GDPR], Digital Operational Resilience Act [DORA], Network and Information Security Directive [NIS2]). Edge computing brings processing closer to users and devices, reducing latency and increasing resilience in case of network disruptions.
Strengthening IT resilience also requires rethinking the operating model. This may involve rebalancing global coordination with local autonomy. One approach is to define strategy, governance, and common platforms centrally while empowering regional hubs to adapt IT delivery to local regulatory requirements, infrastructure availability, and business needs.
Energy market volatility, GPU scarcity, and hyperscaler pricing models are exposing the fragility of current IT economics. As businesses increasingly move toward agentic and multi-agent AI, these models typically consume orders of magnitude more energy than simple query-based AI. Yet electricity grid strains are already acute in some regions due to current data center loadings. Huge investments in data center capacity for AI (likely to exceed US $300 billion in 2025) will drive upward pressure on pricing for users. With the dependence on a small number of hyperscalers high, the true cost of AI access is not yet being reflected in pricing models. How all this will unfold as new, more complex AI applications become increasingly embedded in critical business processes remains uncertain (see Figure 2).
Across large enterprises, IT costs typically represent 3%-6% of total revenue, although they can reach 8%-10% in highly digital sectors such as financial services and telcos. Infrastructure costs have surged, with cloud expenditure climbing to nearly 15% of IT spend.
This figure is expected to rise still further with the explosion of workloads in data analytics, software as a service (SaaS), and AI. Repatriation of cloud and data infrastructure to address sovereignty concerns adds yet another cost driver. Overall, IT spend is expected to grow by 7%-10% in the coming year. Without commensurate gains in efficiency, this growth could become unsustainable.
Given the projected rise in IT costs and the uncertainties surrounding AI pricing, CIOs in 2026 will need to focus intently on controlling and reducing costs. Key strategies include applying best financial operations (FinOps) practices for optimizing AI costs and applying rigorous monitoring and usage guardrails that align computing consumption with business value. Shifting toward smaller, task-specific AI models rather than general large language models is also key. These models can offer superior speed, contextual accuracy, and lower compute requirements. If run locally, they can also improve data control and reduce latency. Traditional IT efficiency practices, such as archiving low-value data in deep tiers, reassessing SaaS subscription packages, and decommissioning or rightsizing idle compute instances, remain important.
So far, we’ve focused on what CIOs can do to optimize the bottom line. But evolving technology is increasingly enabling corporate IT to drive topline growth as well. Data, intelligence, platforms, and new AI-enabled business models offer opportunities to generate fresh revenue streams, optimize pricing, and personalize customer engagement at scale. This provides an additional route to increase resilience, going beyond just efficiency and productivity.
CIOs should actively pursue the monetization of IT capabilities by considering three primary routes:
The rise of AI is accelerating what was already a trend toward IT monetization. The boundaries between IT, operations, and commercial strategy are blurring. Organizations that treat IT and AI as a profit center and a productivity tool are redefining how value is captured and scaled.
In the world of 2026 and beyond, enterprises must ensure they can leverage AI and other rapidly developing technologies to improve productivity, efficiency, and resilience. By doing so, they will ensure sustained competitiveness in the face of shocks and disruptions. Rewiring for efficiency, strengthening ecosystems, building resilient architectures, enforcing cost discipline, and monetizing IT capabilities are complementary strategies that will help create an intelligent and balanced enterprise that can thrive in the years ahead and turn technological disruption into enduring advantage.
By Michael Majster, Joeri Samyn, Matteo Piscopo
cloud technology axon
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