Alejandro Martinez is CEO U.S. & Canada of SDG Group. Sharing insights on How Data & Analytics Generate Business Impact and Value.
OpenAI unleashed an explosion of interest in generative artificial intelligence when it launched ChatGPT at the end of last year (it reached 100 million monthly users in its first two months), stimulating large technology companies to compete to offer differential tools. Since that historical moment, computer programmers, as well as workers from all sectors and fields, have used generative AI tools to speed up their work. According to Gartner, 70% of global organizations are currently exploring the so-called GenAI.
However, beyond the hype of publicly available GenAI models, we face a great challenge when it comes to creating personalized artificial intelligence. We need AI that meets the needs of companies, produces correct and scalable results and guarantees security and privacy of data.
Taking Control Of GenAI At The Business Level
Understanding that generative AI can lead companies to make critical mistakes, organizations must ensure they control processes from start to finish. In other words, we have to focus on the business dimension of artificial intelligence so that it is intertwined with our business strategy.
In this phase, it is crucial to examine the examples of early adopters to determine how companies in each sector and industry can approach generative AI. These use cases from early adopters shed light on the range of options in terms of technological requirements, costs and operating models.
Since its adoption, generative AI has created a multitude of opportunities, but it also represents a series of risks and critical elements that we cannot ignore. Referring to what we call “responsible AI,” beyond business purposes and computational resources, we must ensure we comply with the principles of fairness, security and ethics in its use.
The basis of any generative AI business development is a well-established data ecosystem. That is, the stage of maturity of a company’s data at the level of governance, technology, culture and processes undoubtedly defines the possibilities of success of any action undertaken around AI. This entire base is fed and sustained by multidisciplinary talent provided by the company, AI providers and strategic partners. Furthermore, I understand that each company has to assume technological effervescence as a paradigm. Only by assuming the constant change brought about by such disruptive technologies as generative AI will we be able to digest it and keep pace at the business level.
In short, despite the initial astonishment caused by generative AI, companies should start with small projects. Moreover, before embarking on an AI project, ensure you have an appropriate data strategy. There is no time for large investments, so it is advisable to continue the progressive knowledge of this technology and concentrate at a business level on, for example, improving a specific process. This is where the role of the insights and analytics lead comes into play—the person in charge of identifying and exploring specific use cases that allow short-term benefits while also defining the strategy and relative objectives of this long-term technological adoption.
With the right strategic approach, gen AI can help drive innovation and boost productivity across an organization. However, it requires thoughtful planning and execution. Companies that can effectively leverage generative AI stand to gain a competitive advantage in the AI economy.
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