AI Strategy and Governance

The rapid evolution of artificial intelligence (AI) has led to the proliferation of tools designed to enhance productivity and streamline operations across various industries. According to the toolify.ai website, there are currently 19,454 AI tools available, and this number is continually increasing. These tools can be broadly categorized into three main groups:

In the rapidly evolving landscape of generative AI, users often find themselves at a crossroads between marveling at the technology’s capabilities and grappling with its underlying mechanics. Anthropomorphism, the attribution of human traits to non-human entities, is a phenomenon that almost every user experiences, particularly during their initial interactions with generative AI models. This emotional attachment can either be seen as a bridge or a barrier in the effective utilization of AI technologies.

Would you hire ChatGPT? Before you use ChatGPT as a copywriter, personal assistant, translator, strategy consultant, or marketing analyst, shouldn’t ChatGPT go through an interview? If you rely solely on the massive buzz to try and play with ChatGPT you won’t get a fair assessment of its capabilities.

Good data is the basis of good decisions in companies. Good data is also necessary to enjoy the benefits of business intelligence and machine learning. It is then surprising how much corporates spend on shiny technology versus producing good data. High-quality data can be an asset to companies that know how to use them.

Remote work is not just about your information systems or any particular aspect of your business. It covers a much large scope so much so that it becomes a company culture for everyone to embrace it through everything they do.

Companies collect, store, process, and analyze data to make decisions. But the data is lying, the decision may not be the best, despite great care and effort. Why would data lie to you? There are many reasons why the data is lying. That could be the result of one of several mistakes at any stage of the process. That could be conscious or unconscious bias from one of the actors of the processing chain. There could be an agenda behind the data manipulation, or it could be an unfortunate coincidence. As companies are more and more data-driven, there is a tension between automating as much as possible the data management and processing, and the need for a bit of caution. There is a strong incentive to trust the data, but there should be a healthy suspicion about the data. Data must be treated as questionable until proven trustworthy. The issues with implementing this strategy are: