Since late 2022, with the global launch of ChatGPT, the topic of generative artificial intelligence (Generative AI) has seen exponential growth. Unsurprisingly, the Italian AI market experienced a 52% increase in 2023, reaching a total value of €760 million, following a remarkable +32% growth in 2022 (Osservatorio PoliMI). Despite its recent emergence, generative artificial intelligence already accounted for 5% of AI projects by the end of 2023—an astonishing achievement, reflecting its immediate impact and the trust businesses place in it.
This technology, capable of creating data and content with unprecedented levels of personalization and automation, has established itself as one of the pillars of innovation. It has the potential to impact productivity, efficiency, and business competitiveness decisively. However, while its potential is immense, successfully implementing it within processes and deriving tangible value is a different challenge altogether. This article will outline how to achieve that.
How to Approach a Generative AI Project
Recognizing the extraordinary potential of generative artificial intelligence, many companies have started exploring how to integrate it into their processes. The ubiquity of this topic, which transcends industries, corporate functions, and operational activities, has positioned its practical implementation at the heart of technological debates and strategic discussions.
Generating "Use Case Ideas"
The first step in launching a Generative AI project, as highlighted by Gartner, is generating use case ideas. While this might seem conceptually straightforward, it is a complex process due to this technology's vast range of opportunities. Having a clear understanding of strategic business goals is essential to identifying the most relevant use cases, prioritizing them effectively, and linking them to expected outcomes defined by specific productivity and business indicators. This ensures that every initiative aligns with the organization’s needs and ambitions.
Defining the right Technological Approach
When implementing generative AI solutions, the IT department must carefully evaluate various options, ranging from adopting existing applications to fine-tuning pre-trained models, or even developing proprietary models for specific use cases and circumstances.
The choice will depend on the company’s specific needs, the strategic objectives identified in the earlier phase, the available budget, and the existing data infrastructure. Since every Generative AI project relies on data for model training and fine-tuning, it is critical to first assess the quality, quantity, and accessibility of available data.
The same applies to deployment models, whether on-premise or hybrid cloud, as these depend on business requirements, the capabilities of the existing infrastructure, and compliance constraints. Regardless of the approach chosen, it will be essential to address challenges such as integrating with existing infrastructures, scalability, data security, and managing biases or hallucinations to ensure the final output is reliable and robust.
Starting with a Pilot Project
In a fast-evolving environment, the most effective approach is gradual and incremental. The ideal strategy involves launching a focused pilot project designed to test specific functionalities and quickly validate value hypotheses before moving to large-scale development. The pilot does not need to be perfect but should be functional enough to measure key indicators, such as process time reduction, error minimization, and improved time-to-market.
From a technical perspective, this phase typically involves selecting the most suitable model available on the market, fine-tuning pre-trained models, and testing model performance while developing any necessary integrations with existing IT systems. At this stage, company teams can be engaged to test functionalities and results in a controlled environment.
Scaling up: Development and Implementation
During the pilot phase, it is crucial to assess the project’s cost-benefit ratio to determine whether scaling up is justified. Once a favorable decision is made, the company must plan for gradual scaling to maximize results without compromising operational stability.
A key element is the continuous monitoring of system performance. Defining precise and updated metrics allows for evaluating whether the technology is achieving its intended goals. This is critical for ongoing improvement, where models and operational strategies are adapted based on collected data and emerging needs.
Another crucial step is conducting tests designed to identify vulnerabilities in the models, such as biases, errors, or potential security threats. Advanced simulations stress the system under critical conditions, ensuring the AI operates reliably, ethically, and securely, even in complex scenarios.
The importance of change management
Alongside integrating the solution into business processes, an essential aspect must be addressed: change management. The introduction of Generative AI solutions can create significant disruptions by redefining roles, processes, and established routines.
To ensure an effective transition, a structured strategy should include:
- Transparent communication, focusing on the project’s value and objectives. This is essential to overcome resistance to change.
- Targeted training to equip employees with the necessary skills to work with the new technology.
- Constant monitoring not only of adoption levels but also of how employees use the technology in practice, enabling the resolution of any operational challenges.
By carefully managing these aspects, companies not only adopt Generative AI and integrate it into their processes but also foster a sustainable innovation environment. In this context, technology becomes an enabler of change, translating into increased productivity and competitiveness.
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