AI generated
What if employees could enter their credentials and skills, share their problem-solving interests, and get dynamically assigned to a team based relevant to the organization’s needs? No more silos. The organization could also crowdsource expert citizen engineers with similar interests and skills for inside-outside problem-solving, where appropriate.
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The concepts of taxonomy and folksonomy hold significant implications, especially in the context of emerging technologies like OpenAI. While traditional taxonomies offer structured hierarchies of knowledge, allowing for a systematic approach to information organization, folksonomies represent a more fluid and emergent way of categorizing information based on user-generated tags and metadata.
However, the challenge arises when technological advancements fail to incorporate divergent thinking and promote groupthink through convergent taxonomies. This phenomenon is particularly evident in language models, where developers' linguistic and cultural biases can influence the interpretation and representation of (the dominant) language.
Read MoreA little while ago, I chatted with Gartner Analyst David Pidsely about a trend I noticed in the job market. It seemed the last 2-3 years, data strategy and governance roles suddenly required coding experience.
It wasn’t my imagination, he confirmed. In 2023, skills and talent shortage were the number one inhibitor to CDAO success. Hiring managers and recruiters have been packing job descriptions with coding skills that don’t always require them.
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Image generated by Bing Image Generator
Is it ARTIFICIAL intelligence or AUGMENTED intelligence?
The truth? It depends on the design's purpose. An organization’s purpose is informed by its values and profit motivation. Artificial intelligence aims to create autonomous systems that can perform tasks without human intervention, while augmented intelligence seeks to enhance human capabilities by providing AI-powered tools and assistance.
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The transition from traditional spreadsheets to sophisticated data management and analysis algorithms represents a significant evolution that has revolutionized how businesses process and leverage information. Algorithms have reshaped the landscape of data-driven decision-making. Facebook's filter bubble is an early example of a machine learning system individualizing the user experience based on user patterns.
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Image Credit: Wanda Tuerlinckx , Humanoids
Employees have been encouraged to ‘automate their roles’ to demonstrate self-direction and continuous learning. In the past, an employee's skills, motivation, and business interests determined the pace of change. Soon, the pace may be beyond their control, risking job loss before they can adapt to consider the next set of problems. If they can’t find problems faster than the pace of automation, they are not adequately prepared for transition.
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Image Credit: (European Parliment) This illustration of artificial intelligence has in fact been generated by AI.
We often consider digital technologies like data platforms, AI, and copilot features as tools. But if we're rethinking the future of work and the future of careers and companies, it's helpful to think of these things as augmenting our efforts. For a copilot in particular, it becomes a junior coworker or maybe a more senior co-worker as the AI skills get better.
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