Recently Artificial Intelligence (AI) has become a central topic in every tech discussion. Not a day goes by without someone calling it an “epochal revolution” or a “point of no return” for businesses. The reason for this rhetoric isn’t complex: it’s a fact that when we discuss what’s happening in the ICT market, we are facing true transformative phenomena that go far beyond simple evolution. The growing availability of increasingly sophisticated computational resources, combined with the democratization of AI tools, allows virtually anyone to adopt technologies that, in the past, would have been called “disruptive.” The latter term has lost some of its power and precision due to the overuse it’s seen in recent years.
Regardless of what you call it, the fact remains that AI has the potential to transform, even radically, every single business process, whether internal to the organization or external, involving customers and suppliers.
However, the actual adoption of AI in enterprise contexts reveals a more complex and nuanced narrative. While AI is unanimously recognized and perceived as an essential lever to fuel competitiveness, promote innovation, and optimize business processes, its real, tangible impact has, in many cases, been less than initial expectations.
This gap between perceived potential and actual results can be attributed to a multitude of factors, including the complexity of integrating with legacy systems, the lack of specialized internal skills, challenges related to data quality and availability, and the difficulty in measuring ROI in contexts not directly tied to productivity.
The Paradox of Enterprise AI Success
A recent MIT report, cited in Forbes, revealed a surprising fact: only 5% of AI projects in large companies can be considered a full success. This means that only a small fraction manages to live up to initial promises in terms of cost reduction, efficiency gains, or new revenue generation.
This doesn’t mean that AI doesn’t work, but that the distance between experimentation and tangible value is still wide. Perhaps, as the article suggests, we are measuring the results with the wrong criteria. For example, the MIT study doesn’t consider all the AI tools used daily by those working in large enterprises and the resulting benefits the company as a whole gains. But one fact remains: the majority of projects fail to get past the pilot phase or don’t yield benefits proportional to the investment.
Why the Model Isn’t Enough
One of the most common mistakes is thinking that you can just “connect an AI model to your data” to get results. In reality, a model, no matter how advanced, is just one node in a much longer chain. Without adequate infrastructure to collect, enrich, transform, and govern data, the model remains a laboratory exercise.
It’s like trying to build a skyscraper starting from the penthouse: without solid foundations and without systems to provide power, water, and services, the top floor is useless.
The Importance of a Holistic Approach
To transform data into value, an end-to-end approach is needed, one that can cover the entire information lifecycle, which resembles a true journey:
- Data Acquisition: Data doesn’t just “appear.” It comes from heterogeneous sources: legacy systems, cloud-native applications, IoT sensors. In the very first phase, it must be collected in a structured way. Technologies like Waterstream—which pairs MQTT and Apache Kafka—allow for real-time management of event streams of even billions of records.
- Transformation and Enrichment: The second stage consists of the transformation and enrichment phases. Raw data has little value if it isn’t normalized, aggregated, and enriched. This is where streaming pipelines and data engineering tools come into play, which convert data into an asset ready for analysis (e.g., Apache Flink).
- ML/LLMOps: Using AI models doesn’t mean mechanically launching an application after preparing it. It requires an ML/LLMOps approach—a model’s life doesn’t end at the time of training. It needs to be deployed in production, integrated into business processes, monitored to detect drift, updated, and retested. This requires highly specialized tools (like the Radicalbit Platform) to create an observability and governance infrastructure, without which the risks of error increase and business trust wanes.
- Business Logic and Digital Services: This is the final point: automated decisions, smarter digital services, and personalized customer experiences. This is where AI must demonstrate its value, delivering concrete and measurable benefits.
At Bitrock, we define this vision as a reference architecture: an integrated set of frameworks, tools, and methodologies that enable the latent potential of data to be unleashed and brought to business applications.
The Culture of Complexity
Success doesn’t just depend on technology; it also depends on corporate culture. Organizations that see AI as an isolated project, perhaps confined to the IT department, will find it difficult to turn it into value. Those that consider it part of a broader strategy—one that combines governance, skills, processes, and infrastructure—have a much higher chance of success.
Therefore, you need to embrace complexity, not fear it. An end-to-end approach not only allows for better risk management but also accelerates the transition from proof of concept to a production-grade solution.
Conclusion
The key message is simple: AI is not an end, but a means. It’s a crucial point in the data journey, which begins at the moment of their creation and ends only when they become decisions or services capable of generating value.
Companies that adopt a holistic vision, supported by integrated architectures and a governance-oriented culture, will be the ones capable of turning the promise of AI into reality.
At Bitrock, we have all the skills to accompany clients on this journey. Find out more about our services and get in touch with our professionals for a dedicated consultation.
