The Artificial intelligence It is no longer a futuristic technology, it has become a fundamental pillar for business transformation. Organizations of all sizes and sectors are using AI to gain efficiency, improve customer experience, and accelerate their innovation. However, implementing this technology is not a simple path. From data quality to proper infrastructure, each stage of the process poses unique challenges that need to be addressed strategically.
Hewlett Packard Enterprise offers a comprehensive approach to help companies realize the potential of AI at every phase of their development, regardless of their level of technological maturity.
A journey divided into four key stages
Transformation with AI is a journey that can be divided into four essential stages, depending on the maturity level of each organization:
1. Identification of use cases and analysis of available data
The first step in this journey is to understand where and how AI can be applied to generate value. At this stage, organizations should ask themselves:
- What critical business problems can be solved with AI?
- What data is necessary and what is available?
One of the biggest challenges here is working with quality data. Although many companies have large volumes of data, it is often dispersed, poorly structured or incomplete. HPE recommends implementing strong data management strategies, such as governance, consolidation on flexible platforms, and use of scalable storage, to ensure that data is useful and available at the right time.
2. Experimentation and proofs of concept (PoC)
Once use cases are identified, companies move toward experimentation. Proofs of concept are essential to validate the viability of AI models in controlled environments. However, this step also has its challenges:
- What infrastructure is needed to run complex AI models?
- How to ensure that testing reflects the real impact of AI?
HPE addresses these challenges with solutions such as its Global Center of Excellence in AI and Data, located in Las Rozas, Madrid, which centralizes talent, resources and technological infrastructure to help companies in this stage. Furthermore, the use of advanced techniques such as fine-tuning (specific settings of pre-trained models) and RAG (generation augmented by recovery), which optimize the results of the models by integrating them with specific and updated databases.
3. Deployment to production
Taking AI models to production is one of the most critical moments in this journey. Even if a proof of concept has been successful, the real-world environment presents additional challenges, such as scalability, latency, and integration with existing systems.
Here, inference computing plays a key role. This process allows AI models to make decisions in real time, which is essential in applications such as virtual assistants, recommendation engines, and real-time data analysis. HPE offers optimized hardware solutions, such as servers ProLiant DL380a Gen11 y Compute DL384 Gen12which guarantee exceptional performance for models with up to 500 billion parameters.
4. AI lifecycle maintenance
Implementation is not the end of the road. AI must continually evolve to remain useful and relevant. This involves monitoring models, retraining them with new data and optimizing the technological infrastructure.
In this context, MLOps (Machine Learning Operations) emerges as an indispensable methodology to manage the life cycle of models. Inspired by DevOps, this practice automates key tasks such as continuous deployment and data integration, improving collaboration between teams and ensuring the scalability of AI solutions.
Beyond technology: impact on industries
The versatility of AI allows its application in diverse sectors, each with its own challenges and opportunities:
- Health: AI powers personalized diagnoses, reduces administrative burden for physicians, and optimizes patient care.
- Finance: From fraud detection to process automation, AI generates significant savings and improves customer experience.
- Retail: Improve the consumer experience with personalized recommendations and optimize the supply chain.
- Energy: It facilitates predictive maintenance and efficient resource management, contributing to sustainability.
HPE Private Cloud AI: the ultimate boost
To overcome common barriers, such as data sovereignty and security, HPE has developed Private Cloud AIa solution that combines private infrastructure with pre-configured tools, allowing companies to deploy AI quickly and securely. This platform integrates advanced self-service capabilities, accelerating AI projects from pilots to full-scale deployments.
It includes pre-configured tools and libraries that streamline AI development and deployment, reducing the time it takes to get new projects up and running. The platform offers a self-service interface powered by HPE GreenLake, making it easy to access and manage AI resources without additional complexity for IT teams.
Are you ready to lead with AI?
Artificial Intelligence is no longer optional. It is a transformative tool that is redefining the standards of business competitiveness. With the support of HPE, organizations have the resources, expertise and infrastructure necessary to integrate AI into their operations and lead in their industries.
Discover how to transform your company thanks to AI. Download the full document here and start building the future today.
