In 2026, artificial intelligence dominates every conversation, every conference, and every corporate strategic plan. But those working in technology know that before AI there was big data, before that mobile, and tomorrow there will be something else.
The real question concerns a company’s ability to seize innovation when it arrives and integrate it into its processes without having to chase it. To do so, it must have built the right foundations, which today have a very specific name: cloud.
In this article, we explore the value of the cloud and the related journey as an enabler of innovation and as a strategic factor.
Key Points
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The Cloud Journey is a strategic investment that generates value across multiple dimensions, including the ability to leverage innovation efficiently.
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Choosing the cloud does not mean abandoning existing infrastructure. Hybrid and multicloud models create a balance between innovation, control, security, and compliance.
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The starting point is not technology but the use case: define what you want to achieve, assess your existing data assets, and choose the most suitable platform.
Integrating AI into business processes requires solid foundations
Today, innovating means generating concrete value with AI. From software development to customer service, up to supply chain management, artificial intelligence has become a primary competitive factor, and it is no surprise that many companies have launched AI projects with enthusiasm.
Sometimes, however, organizations have attempted to adopt AI without first building the foundations necessary to fully leverage its potential. The result has been promising PoCs that never made it into production, but above all, investments that did not generate the expected return.
For an AI application to truly work, the entire underlying ecosystem must be up to the task. Data is the essential starting point, but tools are also needed to develop and deploy applications, to train and monitor models over time, and to integrate them with enterprise systems. And then there is the entire ecosystem of observability, security, and compliance, which are essential for the modern enterprise.
The complexity is evident, and it is no coincidence that the cloud has established itself as a concrete response to all these challenges.
How the cloud enables innovation
Building an AI infrastructure without using the cloud is technically possible. The question, however, is not whether it can be done, but at what cost in terms of time, resources, and management complexity.
To provide a concrete example, consider a company that wants to implement a virtual assistant based on a large language model (LLM) for customer service. Using a traditional on-premise model, it would need to purchase appropriate hardware, install and configure the development environment, build the integration layer with existing systems, manage the data pipeline, and handle security, updates, and monitoring. When technology evolves — which in AI happens rapidly — the cycle starts again.
In a cloud environment, considering, for example, those of major hyperscalers, most of these building blocks are already available: data services, development environments, middleware, pre-trained models, and observability and governance tools. Organizations effectively enter an already structured ecosystem, configure the necessary components, and start working on the use case. This does not reduce the need for specialized skills, but it produces an efficiency effect that is difficult to ignore when considering time to market and return on investment.
The competitive advantage, therefore, does not lie in choosing between cloud and on-premises from an ideological perspective, but in the acceleration that the cloud enables: competitive timelines, costs aligned with actual usage, and the ability to scale without updating physical infrastructure.
Cloud journey as a strategic choice: hybrid and multicloud models
Undertaking a cloud journey aimed at leveraging the potential of innovation does not mean embracing a radical cloud-first model, nor abandoning what works in the existing infrastructure. It means making a strategic choice: structuring the technology platform in a way that allows it to accommodate innovation without having to chase it each time.
That said, the dichotomy between cloud and private infrastructure belongs to an outdated phase of the technological debate. The dominant model is no longer the pure cloud-first approach of the early days, but a hybrid and increasingly multicloud approach: architectures that integrate private components and public cloud resources, orchestrating services and resources from different providers within a single enterprise platform.
Multicloud, or rather hybrid multicloud, is not an ideological choice, but a concrete response to the need to leverage the most innovative services of each provider, distribute workloads intelligently, and maintain control over sensitive data and processes without giving up scalability.
These models introduce greater management complexity, but they also represent the most effective balance between scalability, continuous access to innovation, security, and compliance.
How to start an AI-oriented Cloud Journey
At this point, it is worth asking what it means to undertake a cloud journey to bring an AI application into production. The path follows a clear logic, and following it makes all the difference.
- As always, the starting point is not technology, but the problem to be solved, because a customer service system based on LLMs has very different infrastructure requirements compared to a predictive model for credit scoring. Defining the use case precisely is the prerequisite for everything that follows.
- The second step is to assess the existing data assets: where they are located, their condition, which contain sensitive information that must remain within a controlled perimeter, and which can instead move to the cloud without constraints. This analysis defines the cloud architecture, and which provider makes sense to work with based on the specific use case. Not all providers are equivalent: the platform must be chosen based on concrete needs.
- Before moving any data, however, it is essential to address data quality and all related processes. Moving incomplete, inconsistent, or poorly governed data to the cloud does not solve the problem — it simply shifts it.
- At this point, it makes sense to build a cloud-native environment for the development and deployment of the AI application, leveraging the ecosystem of building blocks or technologies (including open source) provided by cloud providers. The rule is to start with a pilot use case that is measurable, with clear KPIs and objectives.
Kirey, your partner for a tailored cloud journey
Supporting companies in their journey to the cloud is a well-established practice for Kirey. Over the years, we have gained extensive experience across real-world use cases, in different and often highly regulated contexts, and we are therefore able to design, implement, and manage tailored cloud architectures, finding the right balance each time between innovation, security, and compliance.
Customization, especially in this field, is a necessity because the outcomes companies expect from AI are deeply different, as are their starting contexts, levels of digital maturity, internal policies, and regulatory frameworks.
Contact us to design together the cloud journey that will allow your company to fully leverage the potential of innovation, today and tomorrow.
