With regulators and boards paying closer attention to where sensitive data sits, Fred Lherault, Field CTO EMEA/Emerging Markets at Pure Storage, outlines why hybrid strategies and selective cloud repatriation are likely to accelerate as AI scales.

After two years of accelerated AI experimentation, rising expectations, and rapid vendor expansion, I believe 2026 will mark an important inflection point for organisations building modern data infrastructure. Many enterprises are now moving past the initial hype cycle and focusing on what is required to operationalise AI reliably and at scale.

That shift is already visible across customers evaluating how AI will integrate into production workflows. If we extrapolate from these trends, several themes are likely to influence how organisations design their data pipelines, storage architectures, and cloud strategies in the year ahead. The following reflects my perspective on how these dynamics may unfold.

From hype to production: data readiness and inference become the priority

While some organisations are still convincing themselves how essential AI is, most are now realistic about what they do, and, crucially, do not deploy. The switch in focus from training to inference means that, without a robust inference platform, and the ability to get data ready for AI pipelines, organisations are set to fail.

As AI inference workloads become part of the production workflow, organisations will have to ensure their infrastructure supports not just fast access, but also high availability, security, and non-disruptive operations. Not doing this will be costly, both from a results perspective, and an operational one.

However, most organisations are still struggling with the data readiness challenge. Getting data AI-ready requires going through many phases, such as data ingestion, curation, transformation, vectorisation, indexing, and serving. Each of these phases can typically take days or weeks, and delay the point when the AI project’s results can be evaluated by the business.

Organisations who care about using AI with their own data will focus on streamlining and automating the whole data pipeline for AI – not just for faster initial results evaluation, but also for continuous ingestion of newly created data, and iteration.

This remains one of the most significant barriers to AI adoption. Enterprise data is often dispersed across legacy systems, cloud environments, and archives, which makes it difficult to access and prepare at the speed AI workflows require. In 2026, we can expect this challenge to become more pronounced as organisations look to extract value from all of their data, regardless of location. Manual preparation will not scale to meet these requirements. Automated pipelines, richer metadata, and integrated data platforms will become essential foundations for organisations aiming to use AI with continuous, repeatable outcomes.

AI and data sovereignty will reshape cloud strategy, and accelerate selective repatriation

The dual issues of AI and data sovereignty are driving concerns about where data is stored, and how organisations can maintain trust, and guarantee access in the event of any issues. In order to extract value from AI, it is critical for organisations to know where their most important data is, and that it is ready for use.

Concerns about data sovereignty are also driving more organisations to reconsider their cloud strategy. Rising geopolitical tensions and regulatory pressure will shape nations’ data centre strategies in 2026 in response. Governments, in particular, want to minimise the risk that access to data could be used as a threat or negotiating tactic. Organisations should be similarly wary, and prepare themselves.

We are already seeing early indicators of this shift. Boards and regulators are paying closer attention to where sensitive and strategically important data resides, driven, in part, by evolving regulatory frameworks such as GDPR, DORA, and guidance emerging from the EU AI Act. This scrutiny is prompting many organisations to reassess cloud strategies that once prioritised cost or convenience over sovereignty and resilience.

As a result, hybrid models are likely to expand, with more AI-critical datasets and workloads positioned closer to where they can be governed, audited, and controlled. This is not a retreat from the cloud, but a more deliberate, workload-specific leveraging of it.

KubeVirt will scale into mainstream production

The recent changes to VMware licensing that followed Broadcom’s acquisition have kickstarted a conversation around alternative approaches to virtualised workloads. KubeVirt, which allows management of virtual machines through Kubernetes, provides one such alternative—a platform that encompasses both virtualisation and containerisation needs—and I expect it will take off in 2026.

The KubeVirt offering has matured to the point where it is suitable for enterprise needs. For many, moving to another virtualisation provider is a huge upheaval, and, while it may eventually save money, it always comes with a set of limitations and constraints, especially when it comes to everything that surrounds the virtualisation platform (data protection, security, networking, and so on).

KubeVirt enables organisations to leverage the growing Kubernetes ecosystem to more quickly realise the value in a platform which provides the capabilities to manage, orchestrate, and monitor not just VMs, but also containers, regardless of how the proportion of those evolves over time.

KubeVirt’s momentum reflects a broader shift in how organisations want to operate their infrastructure. As containerisation becomes standard and AI workloads scale, many teams are looking for a unified operational model that reduces complexity, and avoids long-term platform lock-in. Consolidating virtual machines and containers under a single control plane aligns with this direction.

If adoption increases as predicted, storage and data services will evolve in parallel, with greater demand for persistent, low-latency, Kubernetes-native storage that can support mixed-workload environments.

2026 will be about discipline, not disruption

If the past two years have been defined by rapid disruption, driven largely by AI, 2026 is likely to be a year where organisations prioritise the operational foundation required for long-term success. Enterprises will:

• Move from AI experimentation to consistent, production-grade inference models
• Modernise data pipelines to support continuous data readiness
• Reassess cloud strategies with a sharper focus on sovereignty, governance, and resilience
• Evaluate VMware alternatives, such as KubeVirt, which support a unified approach to virtual machines and containers

The organisations able to take these shifts in their stride will be best placed for success in 2026.

This article is part of our DCR Predicts 2026 series. The series will officially end on Monday, February 2 with a special bonus prediction.

James Lucas, CEO at CirrusHQ, argues that cloud autonomy and ‘choice by default’ will accelerate as organisations push back on lock-in, cost shocks, and rigid contracts.

Over the last 12 months, we’ve seen more organisations recognise the value of the cloud. For us, there’s been a significant uptick in public sector organisations taking a cloud-native approach – something I expect will continue at pace into 2026.

As organisations realise the benefits of the cloud through smaller projects aligned with best practice, it’s encouraging to see them consider future migrations and deployments. But there are other developments I foresee over the next year.

Cloud autonomy will become a reality

Gone are the days when organisations wanted the security of a lengthy contract with a single vendor. Legacy vendor lock-in in the cloud remains a challenge for many – and we’ve seen a sharp rise in organisations being hit with significant cost hikes and lengthy contract extensions. Increasingly, they’re breaking away from the status quo and demanding cloud infrastructure that gives them the flexibility their business requires.

How organisations want to work with vendors has evolved significantly since many of those contracts were first signed. With cost and commitment under greater scrutiny, I expect more organisations will recognise the value of cloud marketplaces in 2026.

Marketplaces can give organisations the autonomy to pick and choose the services and tools they need, when they need them – without the pain of restriction. And when no one knows what might be around the corner from a macroeconomic or geopolitical perspective, organisations will increasingly seek to maintain control over the business operations that are within their power.

Shadow IT vs data sovereignty

Hyperscalers are creating and launching sovereign cloud offerings to guarantee where customer data is stored and processed. But organisations using cloud services must also ensure shadow IT doesn’t undermine sovereignty efforts or increase non-compliance. Enterprises need to take this seriously in 2026.

Many IT environments can benefit from stronger best practice – regardless of whether an organisation is pursuing something as complex as sovereign cloud. Much like the adage “if you don’t test your backups, you don’t have any,” in 2026 organisations should recognise that if they don’t have automated, detailed reporting on policy compliance, then they effectively don’t have it at all.

Without automated oversight, IT estates can become unwieldy, unmanageable, and non-compliant – and often end up duplicating work and data. By automating the detection of non-compliant activity, organisations can adopt a ‘shift left’ approach: addressing issues earlier in the process and keeping the environment secure and manageable.

AI and the cloud will be co-dependent

Unsurprisingly, AI will remain top of mind for organisations over the coming year. While many will look to AI to drive transformation, it will require a solid data foundation to thrive.

As we saw recently at AWS re:Invent, as the cloud enters a new phase of maturity in 2026, major platform investments will likely focus on three areas: advanced AI, data consolidation, and financial control.

From what we’re seeing in the wider market, cloud platforms will make AI development more dependable by automatically managing steps, fixing errors, and tracking complex jobs – dramatically improving the stability of AI tools and long-running workloads.

For those concerned about AI’s environmental impact over the coming year and beyond, the answer isn’t halting progress. It’s treating climate, power, and water considerations as measurable factors to be managed alongside performance and cost. Thoughtful choices around architecture, suppliers, and workload optimisation can help ensure AI delivers value while aligning with sustainability goals.

Ultimately, success in 2026 won’t just be measured by migration speed. It will be measured by whether organisations can combine the foundational stability of the cloud with proactive compliance – so technology decisions are considered, deliberate, and future-proof.

That means getting cloud systems ready to operate more efficiently and intelligently. Making the cloud work harder and deliver maximum value for the business is clearly the direction we’re headed – and it’s a positive shift I fully support.

This article is part of our DCR Predicts 2026 series. Check back every day this week for a new prediction, as we count down the final days of January.

BS Teh, Chief Commercial Officer at Seagate Technology, outlines the security, edge and cost pressures pushing organisations beyond cloud-first.

The speed at which data is generated, used and stored today is unprecedented, and it continues to grow. In 2026, this trend will accelerate further, placing even greater demands on businesses.

Globally, this is reshaping not only the IT landscape but also the way companies innovate. Data has long been the foundation for innovation: it enables the development of new business models, the automation of processes, and the customisation of products to meet individual customer needs.

Teams are increasingly data-driven, using intelligent analytics to make faster, more informed decisions. At the same time, new forms of collaboration are emerging, powered by AI tools that consolidate knowledge and foster creative exchange.

In the age of AI, the value of data is more evident than ever: it is the most important asset in the digital economy. AI algorithms rely on analysing large and diverse datasets to identify patterns, generate forecasts and create value.

The better companies capture, structure and store their data, the more effectively they can leverage AI’s potential. This means businesses capable of managing and storing large, complex datasets efficiently gain critical competitive advantages. Those able to handle data securely, flexibly and sustainably are laying the foundation for innovation, agility and long-term success.

As a result, the data storage industry is at a turning point. It must not only keep up with exponential data growth, but also deliver solutions that meet demands for sustainability, scalability and cost efficiency. This transformation is largely driven by rapid advances in AI, which generate and process ever-growing volumes of data and set new requirements for storage infrastructure.

Hybrid strategies for the next generation of the data economy

The role of AI as a growth driver and ‘data multiplier’ is undeniable. AI has made data the most valuable asset in the digital economy, prompting a fundamental shift in enterprise computing – one that is already shaping data centre planning and investment today, and will continue to do so in 2026.

Nearly 75% of business leaders are moving from a ‘cloud-first’ approach to a hybrid model that combines public cloud, private infrastructure and edge computing. The reasoning is clear: companies want to enhance security, enable real-time edge applications and reduce costs, while meeting the growing demands of AI-driven workloads.

The conclusion is simple: all data has value today. Unlocking that value requires a smarter, hybrid approach to IT infrastructure and storage—one that meets both today’s and tomorrow’s needs.

Generative AI accelerates the content explosion

Another key driver of the data explosion is GenAI, which is fuelling a boom in digital content creation. GenAI is democratising content production: employees across departments can now generate text, images and videos within minutes. This fundamentally changes workflows and introduces a new, data-driven reality for businesses.

The impact is already clear. Nearly three-quarters of businesses report that GenAI enables employees outside traditional creative roles to create content independently – for example, in sales, HR or product management.

This results not only in more content, but also in new formats that were previously too costly or time-consuming to produce, such as personalised videos, training materials or marketing assets. Over two-thirds of companies report an overall increase in content files, with faster production speeds and greater variety.

Many now create multiple versions of the same content to target audiences more precisely. At the same time, average file sizes are growing, and nearly half of companies are storing larger volumes of similar or redundant files, further increasing storage demands.

To keep up, many companies plan to retain their data for longer and are increasingly adopting data-tiering and archiving strategies. While a majority have already expanded or modernised their storage infrastructure, only one-third feel fully prepared for the demands of GenAI workloads today.

By 2026, it will become clear which companies have set the right course for sustainable data management, and which risk being overwhelmed by the content explosion.

Future-proof storage strategies will determine success in 2026

The content explosion driven by GenAI is both an opportunity and a challenge. Companies that align their storage strategies accordingly will benefit twice: they will unlock the full potential of AI-generated content while maintaining control over their data assets.

Data is becoming a strategic resource, as AI is transforming creativity, productivity and entire industries at an unprecedented pace. Businesses should treat every single byte of data as valuable, because it truly is.

This article is part of our DCR Predicts 2026 series. Check back next week for our final week with a new prediction every day.