Autor: Mihnea Moldoveanu
Why is the gap between companies’ AI ambition and their actual adoption so large? The answer is not primarily technical. It is organizational and cultural. A massive skills and language gap has emerged between key organizational decision makers and their “AI teams.” It is a barrier that promises to stall, delay, or sink algorithmic innovations. And it is growing, not shrinking.
The problem is that most executives are selected for their ability to talk to other people. They have complex and well-honed abilities for listening, empathizing, deliberating, energizing, and de-energizing meetings, emoting and reading others’ emotional landscapes and adapting their ways of being to seemingly intractable social situations.
Those who develop machine learning solutions to business problems are selected for their ability to talk to machines. They write pseudo-code and code, develop large-scale platforms that scale to millions of users, aggregate data in multiple formats from multiple sources. They write interfaces for users that incentivize them to interact with the machines they build via combinations of words, images, colors, haptics, and action prompts.
These two groups cannot, do not, and will not speak to one another in productive ways. They aim differently, see differently, think differently, and feel differently.
Developers want clear, precise instructions that are easily translatable into code or pseudo-code. Business development executives provide them with stories and anecdotes.
Machine learning programmers want clearly specified cost functions they can use to train their algorithms. But chief strategy officers and business development executives supply them with aspirational goals phrased in the fuzzy language that coders routinely call “corporatese.”
We need to bridge this gap. Organizations need people who can talk to both people and machines and they need people in their upper echelons who specialize in talking to machines.
The current lingua franca of business is part of the problem. The proliferation of economists in business school faculties since the 1960’s has contributed to the production of a common language system which executives use to plan their actions and justify their decisions. For example: cost-benefit analysis, competitive mapping and simulation of competitors’ responses, marginal cost and rates of substitution, portfolio planning. These are sometimes useful ideas, but in an age where competition depends on algorithms and massive, distributed data sets, this language is inadequate.
As Stephen Wolfram and Jeanette Wing have argued, computational thinking needs to be proactively expanded beyond the current reaches of computer science departments and technical teams. Wolfram points out that, for any field of human endeavor X (from linguistics to architecture, from logic to music, and from plasma physics to dance ethnography) there is now a specialized field of computational X (computational analysis of discourse, computational historical research, etc.). Businesses have been too slow to get with the computational wave and are paying the price.
To catch up, companies need to change how they communicate and how they frame problems. They need to offer their non-technical executives training in computational and algorithmic thinking. That means helping them learn to turn “business problems” into “computational business problems” intelligible to coders and scientists.
Equally important, organizations must develop the relational and communicative skill base of their technical team members. Functioning competently in a top management team or board meeting is about much more than accurate reporting, valid reasoning, critical thinking, or decision making. It is about finding successful modes and means of expression, choosing language to match context, and producing patterns of facial, vocal, and gestural expression that evince the right level of conviction, responsiveness, and trustworthiness.
These so-called soft skills are among the hardest to develop and wield. But they are as important for technical employees as for anyone else. Employees need to abandon the stereotype of coders and other technical experts as inevitably lacking in social graces.
AI strategies’ fail because AI is a means, not an end. “Do you have an AI strategy?” makes as much sense as asking, “Do we have an Excel strategy?” But for companies to get past the hype and focus on the real potential that AI offers, they’ll have to start with how they communicate.
Mihnea Moldoveanu is the Marcel Desautels Professor of Integrative Thinking; the vice dean of learning, innovation, and executive programs; and the director of the Desautels Centre for Integrative Thinking at the University of Toronto’s Rotman School of Management.
Fuente extraída de: https://bit.ly/2HqlWBQ