When to Let AI Drive Your Enterprise – And When to Keep Your Hands on the Wheel

Last month, Elon Musk said that Tesla would achieve fully self-driving cars by the same time next year (May 2023). Although he has made similar claims since 2020 at the earliest, it seems acceptable to require more time to create a self-driving car as it immediately pertains to the safety of every driver on the road. In Musk’s industry, there is frankly no room for error.

Similar conditions apply to the enterprise. I’ve found that one of the primary reasons why enterprise and B2B leaders hesitate to adopt AI is that they, too, have no room for error.

Musk is figuring out AI for self-driving cars.

CIOs and CTOs are figuring out AI for “self-driving” enterprises. The key to avoiding damaging errors is to consider the same questions as Musk: When should Enterprise AItake the wheel, when should it remain in driver assist mode, and when should it virtually step aside?

Here is a guide to help manage that decision process and make the most of AI technology in the enterprise:

When driving on the highway, AI just makes sense.

The highway scenario is one where AI thrives because of three main components:

1. Tasks are repetitive

2. AI has less actions to choose from

3. The outcomes are more predictable

In the enterprise world, there are scenarios that have the same highway-like qualifiers for which AI will make a big impact with low risk. These situations are usually those with excess amounts of data, benign or repetitive tasks and limited decision options. This is why AI has already taken off for chatbots, personalizing marketing messages, and much more. Many enterprise companies are already increasing their investments in these areas. If you haven’t incorporated AI in these scenarios, your business is already behind.

The high-risk, high-reward raceway is where Enterprise AI will have the greatest potential impact.

Consider what would happen if AI could recommend which customers your salesperson should call next, which offer they are most likely to accept and predict when a customer is about to churn and tell you what to do about it. Or, picture an AI-powered database that can differentiate between true and untrue data points, while consistently remaining up-to-date. These are all examples of more complex problems than the previous scenario, but because the decisions AI needs to make are still fairly limited, Enterprise AI can master the situation. And when it does, it’s a complete game changer for your business.

I’ve seen plenty of companies who successfully deploy AI and revolutionize their business for the better. I’ve also seen the alternative. Failed attempts to integrate AI can have devastating repercussions.

In order to come out on top, here are a few things to consider before going all in on a high-stakes, high-reward AI integration:

1. Do you have sufficient data? Most companies assume they don’t have enough data for AI, but I’ve found that even with limited data, AI can be extremely successful if the right modeling techniques for small data are applied.

2. Do you have enough time? Depending on the complexity of the problem, you’ll need months, if not years, to develop, test, and analyze to get the right models in place. If you are looking for a quick fix, it is not the time to adopt AI.

3. Do you have the right culture and mindset at your company? In order to create an environment suited for success with AI, managers and stakeholders should lead the charge in allowing science to drive some of the business processes that were traditionally left to humans alone.

Certain applications of AI are still evolving and considered treacherous terrain for enterprises.

There are certain instances, however, where AI is simply not ready to be deployed across the enterprise. While the mechanics of human learning are to explore and learn from trial and error — just like AI — humans also integrate knowledge across disciplines that AI hasn’t been exposed to yet. As humans, we also infer conclusions when we don’t have all the answers. We see, we hear, we learn over time, usually with decades of data to refer to, and then we connect the dots consciously and subconsciously. A machine’s learning mechanics don’t come close — yet.

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As Yann LeCun, one of the godfathers of applicable AI predicts, “I think significant progress in AI will come once we figure out how to get machines to learn how the world works like humans and animals do: mostly by watching it, and a bit by acting in it.”

Still, few vendors and consultants preach that AI is the silver bullet to our greatest challenges; that AI will tell you what your product should look like, what your business strategy should entail, and how to close complex deals. Some even go so far as to say AI can replace the physical workplace entirely.

While AI is getting more creative by the minute, enterprises will do well to create their own pitch decks, decide who to fire and hire, and give their own presentations, until, per LeCun’s assessment, AI can think more like humans.

In the meantime, AI continues to drive growth for enterprises. MIT Sloan found that 92% of businesses this year are seeing a return on their AI investments, which is more than double the reported amount in 2017. All of this revenue, however, is a byproduct of knowing when and where to let AI take the wheel. As discussed, the difference between businesses that will and will not reap the rewards of AI is based on the human understanding of AI’s power and limitations and where it will make the most impact. As the C-suite intelligently assesses AI integrations in this way, AI will increasingly be an asset to the enterprise.