The advent of Generative Artificial Intelligence (AI) has brought extraordinary promises to the world of business and technology. These emerging technologies have the potential to change the way companies work and make decisions. However, it is essential to understand that Generative AI is not the “solution to every problem” and there are technical and financial challenges to overcome in order to extract value from this technology.
It's no wonder that, according to consultancy FrontierView, AI could contribute to a 4.2% increase in Brazil’s GDP by 2030. This forecast is due to the fact that Artificial Intelligence is able to carry out not only repetitive, numerous and manual activities, but also those that require analysis and decision-making - with structured data, we are able to move forward profitably and bring better business results.
Structured data: the backbone of decision-making and continuous improvement
In an increasingly competitive world, companies are constantly looking for ways to optimize processes and the way decisions are made. However, companies still rely on structured data to support all that, and Generative AI is no exception.
The efficient integration of structured data is crucial for training models that are both accurate and useful, but many organizations still struggle to collect, clean and structure data effectively. Investing in data management systems and understanding how to align this infrastructure with business objectives is a fundamental step toward success.
Search engines: an interdependence that is unclear, but necessary
By Everton Gago, Chief Data Officer at act digital
**Generative AI is nothing without efficient search engines.**The ability to search and retrieve relevant information is essential to formulate suitable contexts for Large Language Models (LLM), such as GPT. The complexity of these engines requires a sophisticated approach to indexing and retrieving data, which demands a deep understanding of search algorithms and indexing techniques.
Many times, integrating search engines with Generative AI models requires close collaboration between data scientists, engineers and business leaders. Companies need to understand this interdependence and invest in technologies and teams that can build and maintain these complex systems.
Click here to find out more about the interdependence between Generative AI and search engines - I've prepared this extra article which is well worth a read.
Costs: a potential barrier
The promise of Generative AI is impressive, but the costs could make it unfeasible to use in some applications. By now, it should be clear that developing your own LLM is unfeasible - at least for most companies. So it's up to us to adopt a pre-trained model available on the market. OpenAI (with ChatGPT) and Google (with Bard) are the most robust solutions to date, although there are open source solutions that could be promising for specific contexts.
Organizations should carefully evaluate how data and artificial intelligence drive their monetization strategies before embarking on the Generative AI journey. Choosing experienced partners capable of providing adequate technological direction, security in the use of information, as well as a clear understanding of the long-term benefits, can help mitigate these challenges.
Generative AI is undoubtedly a promising area high potential to transform business and technology. However, companies should be adequately prepared to extract value from these technologies. A commitment to data management, integration with efficient search engines, and a realistic assessment of costs and monetization strategies are key steps in this process. The future is bright, but it requires a thoughtful and well-informed approach. With the right strategy, organizations can see beyond the hype and unlock the true potential of Generative AI to drive success and innovation in their operations.