Intelligent Artificiality: Why ‘AI’ Does Not Live Up To Its Hype – and How to Make It more Useful Than It Currently Is

By Mihnea Moldoveanu

Organisations need to urgently take action to address the communication and skills gap between their corporate leaders, key decision makers and AI developers, in order to reap the full benefits of AI innovation and transform into an AI-competent organisation. 


What lies at the root of the gap between the promise of AI and the practice of an AI–based strategy? As recent evidence-based inquiry suggests1, companies widely report that the adoption and use of AI techniques significantly lag the promise they were led to believe AI holds for making work more efficient and productive. The answer is not technical.  It is organisational and cultural: A massive skills and language gap has emerged between key organisational decision makers and their ‘AI teams’.  It is a barrier to innovation in the workplace that promises to stall, delay or sink algorithmic innovations for the next decade or more. And it is growing, not shrinking.

The Skills Gap. Here is the crux of it. The skill sets of those in the upper echelons of organisations are out of sync with those creating ‘AI solutions’: 

• Executives know how to talk to other people.   (See figure 1) They have complex and well-honed abilities for listening, empathising, deliberating, energising and de-energising 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 know how 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, specify and code interfaces for users that incentivise them to interact with the machines they build via combinations of words, images, colors, haptics, and action prompts.

• Developers want clear, precise instructions that are easily translatable into code or pseudo-code; but – 

• Business development executives provide them with stories and anecdotes. This lack of computational savvy gap near the top of hierarchical organisations has been a problem for every ‘IT’ wave in business dating back to the 1990’s – but the widespread use of ML algorithms working on large data sets exacerbates this problem and brings it to a boil.


The algorithmic skills gap arises because people belonging to these two groups cannot speak to one another in productive ways. They aim differently, see differently, think differently and feel differently:

• 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’.

• Big data, multi-user platform developers want clear allocations of decision rights among the end users of the platforms that specify who gets access to what information when and who gets access to information about the identities of users having access to information and the specific levels of user privacy that are achievable given the precision and reliability of the statistical analyses these data are used for; but – 

• Clients will only talk about broad principles of fairness, diversity and inclusivity that should be used to design the platform they are contemplating purchasing, but do not specify these concepts to levels of precision that makes them amenable to algorithmic implementations. 

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About the Author

Mihnea Moldoveanu is Desautels Professor of Integrative Thinking, Professor of Economic Analysis and Vice Dean of Learning, Innovation and Executive Programs at the Rotman School of Management, University of Toronto, where he is also the Founding Director of the Mind Brain Behavior Institute and the Desautels Centre for Integrative Thinking. He is the Founder of Rotman Digital, the Rotman Self Development Laboratory and the Joe Weider Foundation Leadership Development Laboratory. He is past Founder, CEO and CTO of Hefaistos, Inc. (designer manufacturer of ADSL modems) and of Redline Communications, Inc. (TSX: RDL) a leading manufacturer of cellular base stations and broadband wireless networks. He is a Senior Advisor to the Boston Consulting Group and a member of the global advisory board of McKinsey Academy. A Top 40 under 40 for his contributions to business and academia, Moldoveanu is the chief architect of 3 machine learning platforms for increasing the effectiveness of skill development in business and higher education.


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