In today's market, innovation is no longer just a choice: it is the speed at which an organization implements it that determines who leads the industry. In this article, we present the key takeaways from the recent interview with E.N.I.A by Sergio Ajani, Service & Solutions Design Director at Innovaway, on how to transform corporate infrastructure into an agile, AI-powered ecosystem.
A Culture of Service as a Foundational Pillar
Innovaway’s approach to the technology market stems from a deep-rooted "service culture." This culture stands in sharp contrast to a purely product-focused approach: it means taking ownership of the customer's problem and guiding them toward the optimal resolution. Over time, the service and solution portfolio has diversified to include technical support, application development, and BPO, yet the commitment to supporting the continuous growth of our clients' business remains unchanged.
A Strategic Approach to AI: Iterative Process and Quick Wins
When it comes to Artificial Intelligence, there is no one-size-fits-all answer or a single model that works for every organization. Integrating these complex tools requires practical, common-sense steps typical of project and change management.
- Adopting Artificial Intelligence requires, first and foremost, a strategic vision of what the company aims to achieve through this technology.
- A "big bang" approach is highly discouraged; the most effective methodology is iterative, progressing through steps based on so-called "quick wins."
- IT decision-makers must set short-term, measurable goals capable of generating an immediate positive impact upon which to build the overall strategy.
- AI models entail significant implementation and operational costs; therefore, their introduction must address real business needs rather than the simple desire to showcase a "badge" of innovation.
Data Governance: The Lifeblood of Artificial Intelligence
Artificial Intelligence cannot generate value if it draws from unstructured or unreliable data. Any implementation must adhere to the core principles of computer science: "garbage in, garbage out."
- Unlike traditional Data Warehouses or CRMs, which possess native tools to quickly identify anomalies, AI models mediate their output through complex reasoning that stems exclusively from the ingested data.
- Data quality management within the system becomes an essential requirement right from the initial stages of the process.
- It is necessary to implement an even more stringent data ingestion pipeline for these systems.
- Data collection and quality operations must be shifted as far upstream as possible in the corporate value chain, by reviewing internal IT processes.
- Maintaining core business operating systems within Excel files is a practice that must be phased out, as it violates security regulations and compromises infrastructure governance.
Security, Compliance, and the Risk of Shadow IT
Innovaway serves clients operating in critical sectors—such as finance, insurance, and Public Administration—which are subject to stringent regulations like the AI Act, NIS2, and DORA. In this scenario, compliance is not an obstacle, but an asset.
- The steps required to achieve an adequate security posture and regulatory compliance represent a true competitive advantage for companies, forcing them to analyze and optimize their internal processes.
- The use of AI, which is predominantly cloud-based, elevates the level of criticality for corporate security.
- "Golden data"—the most valuable data for the business and for training models—should not be allocated to environments that lack the company's direct control.
- To protect this information, companies can leverage encryption or create private clouds and edge architectures, keeping sensitive data "in-house."
- AI security concerns and operational costs are among the primary drivers of the current "cloud repatriation" phenomenon.
- An additional critical risk for companies is represented by "shadow IT"—namely, the use of unauthorized or untracked artificial intelligence tools by employees.
Change Management and Continuous Learning
The integration of Artificial Intelligence transforms workflows, making employee engagement and continuous training crucial elements for success.
- Employee onboarding is essential to foster the understanding that, although dynamics are shifting, human capital remains indispensable to the business.
- Investment in training must be substantial and ongoing.
- Upskilling programs must involve the entire organization, starting from the executive level down to operations personnel.
- Through a collaborative approach, it is important to analyze the benefits that new technologies can bring to individual departments and business units, while operating in strict compliance with privacy and security.
Leading the Ecosystem of the Future: Moving Beyond Mere Technological Adoption
For public and private organizations ready to embrace Artificial Intelligence, success does not lie in the mere replication of existing models, but in the ability to anticipate complexities and draw inspiration from market pioneers. Transforming the potential of AI into a real competitive advantage requires a long-term strategic vision and a partner capable of accelerating the time-to-value, while maximizing investment returns. Innovaway is not just a technological guide, but a true ecosystem of advanced expertise. We listen, design, and govern innovation alongside decision-makers, transforming today’s technological complexity into tomorrow’s digital success.
