Intelligent Choices Reshape Decision-Making and Productivity

Schrage, M., & Kiron, D. 

(2024, October 29).

MIT Sloan Management Review

The article is here.

Better choices enable better decisions.

Profitably thriving through market disruptions demands that executives recognize that better decisions aren’t enough — they need better choices. Choices are the raw material of decision-making; without diverse, detailed, and high-quality options, even the best decision-making processes underperform. Traditional dashboards and scorecards defined by legacy accounting and compliance imperatives reliably measure progress but can’t generate the insights or foresight needed to create superior choices. They weren’t designed for that.

Generative AI and predictive systems are. They can surface hidden options, highlight overlooked interdependencies, and suggest novel pathways to success. These intelligent systems and agents don’t just support better decisions — they inspire them. As greater speed to market and adaptability rule, AI-enhanced measurement systems increasingly enable executives to better anticipate, adapt to, and outmaneuver the competition. Our research offers compelling evidence that predictive and generative AI systems can be trained to provide better choices, not just better decisions.

Machine-designed choices can — and should — empower their human counterparts. As Anjali Bhagra, physician lead and chair of the Automation Hub at Mayo Clinic, explains, “Fundamentally, what we are doing at the core, whether it’s AI, automation, or other innovative technologies, is enabling our teams to solve problems and minimize the friction within health care delivery. Our initiatives are designed by people, for the people.”

Leaders, managers, and associates at all levels can use intelligent systems — rooted in sophisticated data analysis, synthesis, and pattern recognition — to cocreate intelligent choice architectures that prompt better options that in turn lead to better decisions that deliver better outcomes. Coined by Nobel Prize-winning economist Richard Thaler and legal scholar Cass Sunstein in their book, Nudge: Improving Decisions About Health, Wealth, and Happiness, the term choice architectures refers to the practice of influencing a choice by intentionally “organizing the context in which people make decisions.”

The article is linked above.

Here are some thoughts summarizing the article:

Artificial intelligence is fundamentally reshaping organizational decision-making and productivity by moving beyond simple automation to create "intelligent choice architectures." These AI-driven systems are capable of revealing previously unseen options, highlighting complex interdependencies, and suggesting novel pathways to achieve organizational goals. This results in improved decision-making through personalized environments, accurate outcome predictions, and effective complexity management, impacting both strategic and operational decisions. However, the ethical implications of AI are paramount, necessitating systems that are explainable, interpretable, and transparent. Ultimately, AI is redefining productivity by shifting the focus from mere outputs to meaningful outcomes, leading to significant changes in organizational design and the distribution of decision-making authority.

Why your brain is not a computer

Matthew Cobb
theguardian.com
Originally posted 27 Feb 20

Here is an excerpt:

The processing of neural codes is generally seen as a series of linear steps – like a line of dominoes falling one after another. The brain, however, consists of highly complex neural networks that are interconnected, and which are linked to the outside world to effect action. Focusing on sets of sensory and processing neurons without linking these networks to the behaviour of the animal misses the point of all that processing.

By viewing the brain as a computer that passively responds to inputs and processes data, we forget that it is an active organ, part of a body that is intervening in the world, and which has an evolutionary past that has shaped its structure and function. This view of the brain has been outlined by the Hungarian neuroscientist György Buzsáki in his recent book The Brain from Inside Out. According to Buzsáki, the brain is not simply passively absorbing stimuli and representing them through a neural code, but rather is actively searching through alternative possibilities to test various options. His conclusion – following scientists going back to the 19th century – is that the brain does not represent information: it constructs it.

The metaphors of neuroscience – computers, coding, wiring diagrams and so on – are inevitably partial. That is the nature of metaphors, which have been intensely studied by philosophers of science and by scientists, as they seem to be so central to the way scientists think. But metaphors are also rich and allow insight and discovery. There will come a point when the understanding they allow will be outweighed by the limits they impose, but in the case of computational and representational metaphors of the brain, there is no agreement that such a moment has arrived. From a historical point of view, the very fact that this debate is taking place suggests that we may indeed be approaching the end of the computational metaphor. What is not clear, however, is what would replace it.

Scientists often get excited when they realise how their views have been shaped by the use of metaphor, and grasp that new analogies could alter how they understand their work, or even enable them to devise new experiments. Coming up with those new metaphors is challenging – most of those used in the past with regard to the brain have been related to new kinds of technology. This could imply that the appearance of new and insightful metaphors for the brain and how it functions hinges on future technological breakthroughs, on a par with hydraulic power, the telephone exchange or the computer. There is no sign of such a development; despite the latest buzzwords that zip about – blockchain, quantum supremacy (or quantum anything), nanotech and so on – it is unlikely that these fields will transform either technology or our view of what brains do.

The info is here.