The rapid advance of technology is forcing companies to reinvent themselves almost daily, it is no longer worth being left behind. In the midst of this race, a trend arises that is transforming entire sectors: what is generative. For the cios (Chief Information Officers), understanding this concept is no longer optional, it is key if they want to successfully lead highly competitive and digital environments.
What is the generative AI?
For those who wonder, or are not yet clear what generative AI is, this is, those advanced artificial intelligence techniques that have the ability to create new content, such as text, images, audio or video, based on existing data. Instead of simply recognizing patterns or classifying data, this AI generates original results based on deep learning models (Deep Learning), generative neuronal networks (such as gans), self -giving models or transformers, among others.
By understanding what generative AI is, it must be clear that it is not only about replicating or responding, but about innovating: creating answers, designs or works that did not exist previously, with variable degrees of autonomy.
Why has it become relevant now?
- Data availability and computational power: advances in hardware (GPUS, TPUS) and access to large volumes of data have allowed to train increasingly powerful models.
- Pre-and APIS models: Many organizations no longer need to start from scratch; They can take advantage of existing models, adjust or integrate with third -party solutions.
- Customization and Creativity Demand: Either for marketing, content generation, design or digital products, the ability to generate automatically adapted solutions marks a competitive advantage.
By understanding what generative AI is, it must be clear that it is not only about replicating or responding, but about innovating: creating answers, designs or works that did not exist previously, with variable degrees of autonomy.
How it affects the work of the cios
Understanding what generative is only the first step. The cios face important opportunities, as well as new risks, and their role is changing accordingly:
–Renewed technological strategy
Cios must include generative AI within their innovation roadmap. It is not enough to modernize infrastructure or move loads to the cloud; It implies defining how these models can be integrated into the key processes of the company.
–Talent management
New skills become essential: knowledge of Machine Learning, ability to evaluate generative models, ethics of AI, datasets management, etc. It will be the responsibility of the CIO promoting internal training or hiring specialized profiles that understand what generative AI is and how to exploit it responsibly.
–Governance, Ethics and Security
Automatic content creation can generate risks: biases, offensive content, copyright rapes, deepfake generation. CIOS must implement policies for use, human review, traceability and audit to mitigate these risks.
–Infrastructure and operational costs
Generative models generally demand high computational power, storage for training data and resources to maintain updated models. This requires that the cios manage budgets with a medium-long term vision.
–Innovation in products and services
The generative AI opens doors for new product lines: automatic marketing generation, smarter virtual assistants, generation of visual prototypes, personalization of experiences, etc. CIOS must identify where these advances can generate real value for their customers.
Main challenges of generative AI
Although understanding what generative AI is opens the door to multiple opportunities, it is not exempt from obstacles that the cios must address with strategic vision. One of the most marked is the lack of transparency. Generative models function as authentic “black boxes” and it is complex to explain why they have reached a certain output. This opacity forces to establish traceability mechanisms that guarantee confidence in business use.
To this is added the risk of biases and lack of equity. If the algorithms are trained with little representative data, the conclusions or content that they produce will replicate those same biases. Here the role of the CIO is to ensure that the data are audited, are diverse and submit to continuous tests.
Another relevant challenge is privacy and intellectual property. The use of sensitive information or the generation of content that may violate copyright may be a serious legal and reputational problem. Data governance and regulatory compliance must become priorities from the beginning.
There is also the issue of computer and maintenance costs. Training, deploying and updating generative models requires considerable investment in technological infrastructure. The Cios must evaluate whether to bet on the cloud, by hybrid solutions or by their own model based on their resources and objectives.
Finally, internal acceptance cannot be ignored. The emergence of the generative AI wakes up suspicion in equipment that fear being replaced. The work of the CIO is clearly communicating that these tools do not seek to replace, but to enhance human work, in addition to promoting the necessary training so that all employees can benefit from them.
