Five Rules for Fixing AI in Business

Viewed in BCG

The promise and potential of artificial intelligence (AI) has been all the rage the past few years. One can be forgiven for concluding from this coverage that it has proven broadly helpful to those organizations utilizing it.

One out of 10 companies enjoys significant benefits from AI

But actually, as management consulting giant BCG relays in this study, that has not proven the case. “A 2020 study conducted by BCG GAMMA, the BCG Henderson Institute, and MIT Sloan Management Review found that only one out of ten companies has enjoyed significant benefits from AI.” Translated: Nine out of ten companies have not enjoyed significant benefit from AI. For the vast majority of companies, in other words, it has not proven at all meaningful. All the AI hype is really, at this point in 2022, more in its ultimate than its present tense value.

Technology complexity

That certainly bucks the marketing hype that has surrounded AI, and it also raises the obvious question: Why is AI failing to prove broadly helpful to organizations? The answer lies in a bridge that still is not yet completed between the well-intentioned business and technology experts seeking to utilize AI and the technology of AI itself, which (despite the unimpressive statistics on its present value) continues to hold considerable promise.

Five steps

All of this raises the obvious question of where the deficiencies lie and what companies seeking to leverage AI’s potential must do to extract those benefits.  It starts, this BCG study argues, with organizations recognizing that AI’s technical values can only be leveraged when broadly understood and leveraged throughout an organization.  In this study, BCG offers five steps companies can take to maximize their AI return.

Among their conclusions, for starters, they urge organizations to cease viewing AI as simply a data or technical function. It is wrong to think of AI as simply a management function, either.

Organizational challenge

In reality, to leverage the promise of AI, all stakeholders in an organization need to be engaged in understanding and contributing to AI’s ongoing success, especially including making sensible management decisions based on the actionable data it produces.

Take time to understand the input data

But this is where most organizations are falling short. They are failing to devote “…the time needed to fully understand the input data required by the specific business problem,” BCG argues.

Enrich your data with external data

A secondary challenge is that AI’s ultimate value can never be fully realized if the input data it is assessing is insufficient, and many times it is. Organizations often make the error of relying exclusively on internal data in their AI programs. Instead, external data that augments this internal organizational data is almost always essential for AI to operate in a holistic and ultimately successful way.

Slice the problem in small pieces that you can solve

It also is important for organizations to recognize that AI’s vastness means that enhancing its optimization often means breaking down the totality of it into smaller and more easily managed pieces. Instead of tackling all of the system, look instead to individual algorithms that can be adjusted to enhance that particular algorithm’s contribution to all of its success.

BCG identifies one such example in a construction company seeking to utilize AI in enhancing its most promising sales leads. The company assessed its AI capabilities in three slices: the scope of the project, product categories, and the relationship between stock keeping units (SKUs) and the project’s specific requirements. In so doing, the company experienced a two percent increase in sales.

Multidisciplinary

Perhaps the most important lesson in maximizing the value of AI is ensuring that organizations do not permit it to be monopolized as a data function or a sales or operations function exclusively, but utilize it through multidisciplinary teams that can assess the totality of opportunity and monitor and address opportunities for algorithmic adjustments or other yield that AI potentially offers.

As this is done, the rather unimpressive ten percent of companies currently extracting value from AI should begin to grow.

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