Keys to the future of businesses: Responsible and strategic AI and data science
Artificial Intelligence (AI) and data science are redefining the business landscape, offering tools that enable companies to make more informed strategic decisions and increase the efficiency of internal tasks and processes. As scientists and entrepreneurs specialized in this field, at Datharsis, we closely observe this evolution, especially the impact on business decision-making processes and data valuation.
The data and AI revolution
The massive proliferation of data, driven by the widespread adoption of the Internet and mobile devices, has laid the groundwork for the current revolution of Large Language Models (LLMs) and AI agents. Although AI is attracting all the attention, in our opinion, the biggest revolution has been the valuation of data as an engine of innovation. We can establish three fundamental types of data-driven innovation strategies for companies, very different from each other:
- Support for information generation and search tasks: This includes examples such as content creation (posts, emails), search automation, and customer support using LLMs. AI can be tremendously effective in these types of tasks, allowing for very high productivity improvements due to reduced time and costs. If we want to integrate the power of LLMs with the company’s knowledge base, it is vital to jointly consider data privacy and current regulations with specialists.
- Using data to improve internal company processes: This category includes Business Intelligence tasks, such as optimizing business processes (sales margin, marketing, customer tracking), and other tasks more dependent on the company’s business model, such as predictive maintenance or anomaly detection with the aim of minimizing costs in production processes. In many of these cases, visualization analytics and dashboards, with high interpretability and allowing for variable data volumes, are more suitable than advanced AI techniques.
- Data monetization for third parties: This covers data services that come from state-of-the-art sensors in sectors such as precision agriculture and livestock farming, smart cities, energy, telecommunications, etc. The valorization of this data requires transparent (interpretable) techniques that, again, produce benefits across the entire range of data volumes: from the first samples to Big Data management.
The importance of data governance
A fundamental pillar in any AI application or, in general, data science, is understanding the potential of a company’s own data. This implies knowing how to get the most out of them and following good governance practices: design, collection, cleaning, and labeling. It is crucial to remember that the most impressive advances in AI, such as ChatGPT, primarily impact automation and efficiency gains, but key applications for decision-making and data monetization are still based on more transparent and interpretable techniques, which better fit the specific characteristics of the data and the case study. When data volume or quality is limited, statistical experimental design theory is fundamental for drawing reliable conclusions.
Advice for companies in the data era
For a company seeking to innovate with data-driven strategies, it is advisable to integrate experienced experts into the team to determine the potential of available data and/or consider new ways to collect and monetize data. Experience is relevant in a context where tools proliferate and there is a lack of good understanding of their proper use. A central decision is the type or types of strategy, among those mentioned above, that are to be implemented. Let’s remember that advanced AI, represented by LLMs, agents, agentic AI, and variants, is very effective for supporting content generation and search tasks, but it is not as central, at least today, in the other categories. Even in the former tasks, their internal use must be regulated following good ethical practices and respecting current legislation. At Datharsis, we emphasize transparency and interpretability in our techniques, fundamental pillars of the EU’s “AI Act”.
Regarding the effect on society of such disruptive changes as the introduction and improvement of LLMs, rather than job destruction, we expect that the tasks in which professionals must specialize will change. Human control over decision-making processes is still necessary both for regulation and for caution, and AI should impact our work by making us more productive, for which continuous learning will be necessary for the professional of the future.
Conclusion
AI in particular and data science in general are powerful tools for business transformation. At Datharsis, we are committed to developing solutions that are not only innovative but also secure, transparent, and adapted to the real needs of businesses, always with a focus on the value that can be extracted from data.
If your company is looking to strategically and securely implement Data Science, Dahtaris is here to help.
