Artificial Intelligence (AI) has unquestionably taken a central position in contemporary debate, dominating both specialized narratives and discussions related to everyday consumer products. We find ourselves in an era characterized by intense media saturation which, while amplifying awareness of this technology, in some circumstances risks generating a superficiality that trivializes its profound transformative scope.
It is in this scenario that a tendency is observed in multiple business contexts to perceive AI primarily as an innovation achievement: a mere element to be included in one’s business strategy to affirm a presumed avant-garde spirit. This approach, however, proves partial and fundamentally flawed. If AI is considered an end rather than a strategic means, there is a serious risk of compromising the opportunity to achieve concrete, measurable, and sustainable benefits in the long term.
It is therefore essential to shift the focus from the hype to its pragmatic application to unlock its real value. In other words, to consider AI not as a goal, but as a means. As such, its concrete value emerges only when it is applied and realized at an operational level to solve specific business problems. This is the core of Applied AI: transforming innovation into business impact, making processes smarter, more agile, and future-ready. In this article, we want to explore this concept and the path that could lead to the maturity of Artificial Intelligence: from generic AI to a true strategy based on full-stack AI, governed by the principle of Reasonable AI.
Applied AI: The New Paradigm for IT Leaders
The concept of Applied AI marks a fundamental turning point: the focus shifts from innovation for its own sake to its practical utility within company processes. Talking about Applied AI means recognizing that Artificial Intelligence must be considered as an element of the available technology stack, on par with an advanced database or a cloud infrastructure. It is not an isolated object, but an integrated component. To generate concrete value – which translates into efficiency, cost reduction, and service acceleration – AI must therefore be:
- Contextualized: Integrated within a well-defined business process (from supply chain to customer service).
- Sustained: Supported by a robust technological and operational infrastructure.
- Purpose-Driven: Aimed at achieving a measurable business objective.
The path towards maturity marks the point where there will almost no longer be talk of Artificial Intelligence in a broad sense, but simply of applications that, internally, leverage probabilistic engines or advanced algorithms.
Full-stack AI
As seen previously, AI is not a self-sufficient element: its capabilities and the results obtained depend entirely on the technological substratum that supports it. An approach that we could call full-stack AI. It is not enough to have a high-performing model: an entire infrastructure is needed that guarantees the quality and availability of data.
The concept of Applied AI therefore implies the need to have quality data correctly acquired, processed, recorded, and cataloged. A robust supporting architecture must also be available: a series of applications that could be defined as related to traditional IT, but which are essential for powering and hosting AI models. Finally, the presence of automation and governance systems necessary to manage the software lifecycle is essential to ensure that models are continuously and reliably trained, implemented, and monitored.
The AI Gateway as a control and governance tool for “Reasonable AI”
Nel momento in cui l’Intelligenza Artificiale inizia a generare valore, soprattutto all’interno di contesti enterprise complessi, emerge una chiaraThe moment Artificial Intelligence begins to generate value, especially within complex enterprise contexts, a clear need emerges: the need for a control layer. While Responsible AI focuses more on ethics and compliance, at Bitrock we have begun to adopt and implement a new concept, namely that of Reasonable AI. Reasonable AI is not just about ethical or legal compliance, but about control, with common sense, over our advanced applications. The risks that threaten the business, in fact, can often lead to unpredictable inefficiencies, such as the uncontrolled increase in costs.
Let’s consider a tangible example for decision-makers: in a company with thousands of employees using applications based on Generative AI models, every question asked via a prompt has a certain cost, even if limited. If thousands of people ask the same information daily, the company ends up paying thousands of times for the same answer. This is an unreasonable and unsustainable cost.
To face this challenge, an AI Gateway can be used: a tool that acts as a semantic control layer for Generative AI within complex organizations. The Radicalbit AI Gateway, specifically, ensures reasonableness by acting on two crucial fronts:
- Cost Optimization: The Gateway uses a semantic engine to intercept questions, recognize if an answer has already been generated, and serve a cached response. Instead of paying for thousands of queries, the company effectively pays only once, applying a principle of elementary efficiency.
- Enforcing and Compliance: The platform allows the company to enforce internal policies, ensuring that the use of AI is in line with regulations (such as GDPR) and codes of conduct, providing a centralized and auditable control point.
Through the AI Gateway, true Reasonable AI can therefore be achieved, guaranteeing the adoption of an Artificial Intelligence that, while powerful, is managed in a secure, economical, and sustainable way for the company.
Conclusions
The evolutionary path of Artificial Intelligence within the enterprise context culminates with its systematic integration and proactive governance. The future of AI no longer lies in isolated experiments, but in its operationalization and measurable alignment with business objectives. To capitalize on the wave of Generative AI and other forms of Artificial Intelligence, tech and business leaders must therefore converge on a strategy based on two inseparable principles.
Firstly, Applied AI must be recognized as a true architectural necessity. It is not an optional add-on, but an imperative that requires the implementation of a solid infrastructure based on the concept of full-stack AI. This ensures that AI can operate scalably and reliably, moving from theory to fluid integration into critical production processes.
Secondly, Reasonable AI serves as an indispensable governance framework. The management of operational costs and the mitigation of compliance risks can no longer be left to the discretionary use of users. A centralized control layer is needed that acts as a cost optimizer and as a guarantor of content policies. This governance ensures that Generative AI is used in an economically efficient and managerially secure way, transforming a potential financial risk into consolidated operational peace of mind.
Engagement with a specialized system integrator is crucial for navigating this complexity. Bitrock offers the necessary expertise to design and implement a mature adoption roadmap, transforming AI from an innovation exercise into a measurable and governed corporate asset. Contact our experts to explore the Reasonable AI solutions that can ensure the sustainability and optimization of your technological investment.
