Nearly a year since its public debut, ChatGPT has been the major player of the year in the tech world. With millions of users worldwide, it has positioned itself as one of the most disruptive tools remembered in recent years.
In this interview, Martín Calderón, Head of Engineering at Multiplica, details how Artificial Intelligence is shifting the way of working and creating digital products and experiences.
ChatGPT: Impact and Adoption
The arrival of ChatGPT has had a significant impact on the generative Artificial Intelligence industry and its users. How do you explain this technology’s quick and broad adoption in such a short time?
This is due to its ability to simulate human interactions and enhance the search experience, permanently revolutionizing the industry. It’s as if we had a team of experts reading, searching, and synthesizing information to provide precise results in record time. You no longer need to be a specialist in a topic to get comprehensive and detailed answers when exploring something new. Its versatility in offering this experience in various fields is what has driven its adoption. In comparison, Google search engines and assistants like Siri or Alexa now seem obsolete.
From a technological perspective, how can companies integrate these technologies into their daily operations?
In these initial stages, companies are starting to use AI tools like GitHub Copilot, Chat GPT, Blackbox, Mid Journey, and several Open AI services and others.
Users still face the challenge of understanding what they need in their workflow and how to incorporate these new technologies. They also must grapple with the differences between proprietary and open-source technologies within their specific toolkit and established teams, as well as effectively measuring the ROI of these technologies.
At this point, I see similarities with the adoption of DevOps practices in the past, where initially, there was a flood of tools and businesses adopting them, only to eventually consolidate a workflow around the best tools and discard those that weren’t suitable for their context. In summary, companies have grasped the developer experience, and productivity enhancement is only achieved if these technologies can positively impact it.
Recommendations and Use Cases for Generative Artificial Intelligence
What are your recommendations for the adoption of new technologies?
A strategy for adopting AI technologies might include the following steps:
Can you mention three use cases in which you already work with Artificial Intelligence technologies to assist your clients?
Case 1: Development Optimization with GitHub Copilot X
We’re currently implementing GitHub Copilot X in our projects. This allows us to document and better understand legacy code, enhance the quality of code reviews, and assist in pair programming.
We provide a usage framework by integrating GitHub Copilot X into our workflow and training our engineers to maximize its utility. Regular practice helps them develop intuition about when and how to use this technology effectively.
Case 2: Diving Deeper into Customer Service with Azure Cognitive and OpenA
We are undertaking an internal initiative using Azure services (Cognitive and Open AI) to process and enrich private data in PDF documents and relational databases.
This technology allows us to synthesize information and query GPT models for company-specific information and its context. Imagine gathering and understanding a client’s history in relation to the projects and consultancies we’ve provided them, enabling more informed decisions.
Case 3: Product Search Enhancement
We are in the research and development phase of incorporating specific AI algorithms into a digital product, especially those related to improving search results in a product catalog.
This will allow our clients to find products more efficiently and accurately, enhancing their online shopping experience.
The Future of Artificial Intelligence in the Workplace
How do you think artificial intelligence will impact the work of tech professionals?
Artificial intelligence is transforming the way skills are developed in the tech field. Traditional roles, like developers, must adapt to a new development mindset where AI simultaneously acts as a collaborator and tool.
The key is understanding when to use each approach and developing the ability to manage both effectively. This implies training in areas previously more associated with data scientists or machine learning engineers.
For instance, an e-commerce developer can now enhance the search experience using known AI services called «building blocks» without creating them from scratch, becoming a consumer of these services.
What three trends do you anticipate in applying artificial intelligence in digital product development in the coming months?