Romantic and Rational Approaches to Artificial Intelligence

A gap already exists between companies’ ability to collect data and managers’ skills at putting it to use. Will AI increase the divide?

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Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

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BCG
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The use of artificial intelligence in the criminal justice system offers a stark example of the contrast between knowing how to produce results and knowing how to consume them intelligently. Systems recommend bail and sentencing but offer little transparency about the basis for the recommendation, leaving the humans who digest the recommendations potentially under informed.

What if we knew so little about the production processes of the food we eat? We know more about what we put into our mouths than what we put into our minds.

Are Organizations Biting Off More Analytics Than They Can Chew?

In 2015, we observed a growing gap between the production and consumption abilities of analytics in organizations. The article “Minding the Analytics Gap” describes how organizations struggle to consume the analytics results they produce. If that wasn’t bad enough, not only did we observe a gap, but it was a gap that grew, not shrank, as organizations got better at analytics.

Yes, organizations were rapidly improving their ability to produce analytical results. They were gathering more and more data. They were building digital infrastructures to process these vast quantities of data. They were developing (or acquiring) the talent required to develop complex models of market behavior. When these pieces all came together, organizations could create sophisticated analytical results.

Unfortunately, managers and executives in those organizations often did not have the expertise to consume the analytics results that the organization was able to produce. Just having the analytics results available wasn’t enough. The organizational ability to develop business insight and strategy based on those analytical results was more limited.

The difficulty lies in the individual rates of improvement in production abilities and consumption abilities. As organizations matured analytically, they were able to improve their analytics production capabilities more quickly than they were able to improve their consumption abilities. As a result, maturing organizations found that, despite the fact that their consumption abilities were improving, they were able to consume less and less of what they produced. The analytics gap gets worse as organizations improve — the opposite of what leaders would hope and expect.

And yet this may have just been the tip of the iceberg. When it comes to artificial intelligence in business, the divergence and resulting gap between production and consumption of data analytics may be an even bigger concern.

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Topics

Artificial Intelligence and Business Strategy

The Artificial Intelligence and Business Strategy initiative explores the growing use of artificial intelligence in the business landscape. The exploration looks specifically at how AI is affecting the development and execution of strategy in organizations.

In collaboration with

BCG
See All Articles in This Section

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Comments (2)
Nik Zafri Abdul Majid
I do feel that we should know "what to apply" and "when to apply". It is not possible to apply "everything". It's like having so many systems but not everything can be implemented.

Thus, the managers and executives will soon feel that this is another way of "control" (both negative and regulative) and they will feel AI as a kind of constraint for them to perform - just like the case with organization attempting to apply too many systems.

We should be asking ourselves : "Do we need everything? or simply following "the trend"?

In my humble experience consulting and implementing AI in any organization in South East Asia, what the organization need to understand is "cognitive technologies" and it is already around them but how to make use or upgrade them are another issue.

When I say it is already around them, I am referring to e.g. Customer Relationship Management (CRM) and how to upgrade the current system combining and integration of cognitive technologies. All my customers want is to "make things more easier than before" example on marketing, service and sales (the ability to predict client's need and multi-demographic classification). Cognitive technologies are among others to incorporate e.g. cloud, automated e-mail reader and standard reply having scanning the keywords and counter-reference to a specific database, images classification, sales leads auto-scoring, auto-voice interactions etc - all these "value added" elements will depend on the organizational capacities in analytics (eg. models based on automated generation) etc.

Start small - take it one at a time - learn from past lessons and hopefully end big.

The other part such as machine learning or robotic technology (especially for manufacturing), partial responsibility to understand the client's needs/specs are the vendors. If the vendor is not effective, the system will end ineffective as well. To me as a consultant, I will likely follow the "IBM style" - start customers out with a "Cognitive Value Assessment" which requires some help and inputs from other subject matter experts.

Eventually, once the transfer of technology from the vendors have reached the maturity stage then the organization can start building on their own. There are so many open source cognitive-based applications/softwares - and it's cheap.
Peter Evans
Sounds a lot like my elementary school teacher explaining why calculators have not made memorizing multiplication tables obsolete. --PE