Penteo has published its Data & AI Report 2025 – Universe of integrators, which concludes that data has become the main strategic asset for the adoption of artificial intelligence, driving a profound transformation in architectures, governance models and the roles played by integrators in Spain.
The Penteo study identifies the emergence of the “next-generation data and AI integrator,” capable of uniting consulting, platforms, governance, modernization, advanced analytics and generative AI with a focus on business value, scalability and accountability.
Key points
Artificial intelligence is transforming data into the strategic core of the company, not only as technological support but as a driver of new business models. Unified platforms, distributed architectures and industrialization of generative AI set the agenda, while data quality and governance emerge as critical barriers to mass adoption of AI at scale.
• Data is the new core of business architecture
Organizations are moving towards modern data platforms, distributed data mesh models, greater automation and convergence between operational, analytical and AI data. The priority is to create solid foundations to deploy AI use cases at scale.
• Generative AI reconfigures priorities and requires data maturity
The industrialization of AI requires quality, governed and traceable data, as well as architectures that allow models to be trained and deployed safely. Demands on vectors, MLOps, MLaaS, GPUs and model orchestration are growing.
• Data governance and quality: critical barriers
Structural challenges persist: legacy data, silos, shortage of expert profiles, low analytical culture, and difficulties in ensuring quality, lineage and security in hybrid environments. Companies are looking for pragmatic and accelerating approaches to improve governance.
• Technological complexity on the rise
Classic data warehouses coexist with lakehouse platforms, distributed architectures, internal marketplaces, catalogs, vector stores, real-time pipelines and model orchestration. This raises the need for automation, AIOps, MLOps and DataOps.
• Digital sovereignty and compliance
Regulated sectors demand data residency, traceability, responsible AI, explainability and compliance with future European AI regulation, promoting sovereign data and AI solutions.
• ESG and data for sustainability
Impact measurement and ESG reporting automation drive new data platforms and AI use cases to calculate footprint, improve efficiency, and meet regulatory requirements.
Market evolution
Spanish organizations face the challenge of turning data into a real competitive advantage, and to do so they need integrators capable of uniting strategic vision, technological platforms and responsibility in the use of AI.
• From “enabling data” to “transformative data and AI”
Data stops being a technical enabler and becomes a driver of AI-based business models, from intelligent automation to sector assistants, prescriptive analytics or specialized generative platforms.
• Unified platforms and data as a product
Lakehouse, data fabric and data mesh adoption increases, with greater domain autonomy, lineage automation, decentralized governance and intelligent catalogs.
• AI at scale and MLOps as standard
The industrialization of AI—including generative—requires complete life cycles, monitoring, automated pipelines, and continuous operation of models. The most advanced companies are pivoting towards AI as a corporate capability.
The emergence of the “next-generation Data & AI integrator”
New generation integrators are capable of deploying complete solutions under increasing demands for security, digital sovereignty and regulatory compliance, in alliance with the main manufacturers in the market.
AI is transforming data into the strategic core of the company, not only as technological support but as a driver of new business models
Leading integrators combine strategic consulting, data platform design, data governance, engineering, advanced analytics, MLOps, generative AI and security, with proprietary methodologies and accelerators.
Differentiating capabilities include:
- Data mesh/fabric/lakehouse;
- Data architecture and strategy;
- Advanced engineering and automation;
- MLOps/LLMOps;
- Generative AI platforms;
- Smart catalogs and advanced governance;
- Security and privacy;
- Sovereign data spaces;
- Accelerators and own assets.
