A useful objective through which to understand the planned and tacit behavior of Organizations is that of tensions. The redesign and cartography of these can help us to recognize the forces that amplify or inhibit change, as well as the visibility of our dead angles. But the tensions inherent in AI Generative Create new fault lines, which can be hidden either because of their innate subtlety, or by human exceptionalism (a belief that “AI can never do it …) which blinds us, or even through the dogma systems (the concept of ‘signs‘, this form boundaries in our ways of knowing) that codify ideas in the truth.
Some of them are obvious: we have a tension between the need to do what our systems know how to do (learning, providing, integrating, lever effect, leading for efficiency, codifying in systems, etc.) with respect to the need to do what we are less good to (eliminate power, to give up a control and evolution of our truths of truth and validation, to go to dynamic and short -term models and variety, etc.). Some are less immediately clear, such as the tension between the “integration” of the AI ​​from existing socio-cultural perspectives (where technology is part of an established social model, such as a tool), or a conceptual cropping of society in a anthropo-technical Model (human machine more, but not necessarily with humans in charge), which is more synergistic than utilitarian.
Tensions are in the personal space (what should I do with Genai vs what should I reveal that I do), as well as the organization (what efficiency gains can I get in relation to what I transmit), and to complicate things, the landscape In which we hold is always in motion – and will be – perhaps forever.
There are systemic characteristics that seek stability or predictability (market forces, market taxation, reports, legislation, etc.), which can continually reframe, but are mediocre to handle the fracture, and the fracture can be a central characteristic of the change that we undergo, or rather.
For example, we have noticed large efficiency gains in, for example, learning data design or processing, in parallel with pressure to reduce customer levels (which are not stupid, and soap that these efficiency is at stake). Thus, the opportunity window to exploit the benefit of innovation is practically zero in these cases – it is more table stations to be effective – but we have also seen the innovate markets by invoicing the “ bonus ” for humans (although it really bets on the “ familiarity ”, as opposed to something intreent “ better ”). We can count on human exceptionalism to support or distort the market for a certain time, but it is not a stable and inherent characteristic.
We already know that what people say and what they do are two different things: for example, people believe that they will prefer a human doctor, but once they have had a deeply pleasant and fast experience with an AI doctor (as opposed to a frustrating expectation with human expectations), they can change their mind. And the more we feel, for example, for example, rapid and effective chatbot experiences compared to human experiences, perhaps the less we will be passionate about returning to degraded and profitability-oriented human engagement models.
There are also deeper tensions that run under this subject – in part of the characteristics of Social agePart of the characteristics of our post -pandemic recovery (or inheritance – or the failure of learning), such as challenges of engagement, jobs, career, the concept of “local” and even ideas of citizenship and belonging, as I explore in the Planetary philosophy work.
What we need are not easy answers, but rather an ability to manufacture radical sense, at individual and structural levels, the ability to contain multiple stories and therefore multiple views of power, predictability and clarity, which testify to an ability to maintain ambiguity.
The tension and the fracture are linked, but not inevitable: a certain tension is a strong characteristic, something that we should seek, not fear, just as certain risks should be attenuated, but some must be burned like the fuel of change.
Today, I am #Workingoutloud on new works around the generative AI.
At Learnopoly, Finn has championed a mission to deliver unbiased, in-depth reviews of online courses that empower learners to make well-informed decisions. With over a decade of experience in financial services, he has honed his expertise in strategic partnerships and business development, cultivating both a sharp analytical perspective and a collaborative spirit. A lifelong learner, Finn’s commitment to creating a trusted guide for online education was ignited by a frustrating encounter with biased course reviews.