5 Common misconceptions about AI that will kill your business

Frontier Team

4/27/2023

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Recent advances in AI have been staggering. Yet, business leaders in all sectors, even with strong IT competencies, continue to misinterpret the uses and benefits of AI due to common misconceptions. They do so at their peril, especially in the current downturn. 

Common AI Misconceptions #1 – “AI is Exotic and Futuristic”

Folks see the crazy world of machines that make artwrite essays, or drive cars, and proceed to position AI in their mind as some kind of exotic technology done by brainiacs working at Google.

AI is simply a method to find predictive patterns in data, patterns so subtle that often humans cannot see them — patterns with economic value for your business.

AI can find patterns in manufacturing data, sales data, user data, marketing data, pricing data, logistics data, fitness data, health data, inventory data — your data. If you have data, you can use AI right now to boost your business, including your bottom line.

The true economic power of AI lies in the mundane, not the exotic.

Our prediction is that Generative AI will most likely reap the greatest rewards by empowering citizens to deliver more “vanilla AI” – i.e. less exotic, but more valuable.

Common AI Misconceptions #2 – “AI is for Experts”

Whilst it’s true that hardcore AI research requires brainiac talent, many enterprise AI applications are within direct reach of mere mortals. Will even named it: Direct AI.

Notably, the Fast AI course, specifically aimed at AI newbies, has empowered many students who, hitherto completely ignorant of AI, went on to achieve performance breakthroughs in their domains.

Many of those achievers had no technical background, except for some basic coding skills. Sure, if your plan is to innovate in the latest algorithmic development to fly rockets to Mars, then maybe you need a brainiac, or two.

But if your plan is to apply AI to your business to get results, then there’s really nothing stopping you. AI courses start for as little as $0 and online resources are in an embarrassing abundance. Oh, and most of the tools are also free. All you need is data and some enthusiasm. Of course, product-centric IT might help, but it’s not compulsory.

Common AI Misconceptions #3 — “During Economic Downturns, AI is a Luxury” 

In the current climate, this one is a real business killer!

I see it everywhere — potential AI programs seen as “expensive science projects” and so first on the list of programs to cut during a downturn. BIG MISTAKE!

The opposite is true.

The powerful pattern-finding capacity I referred to in #1 is exactly what you need during a downturn. The most critical ingredient for AI is data — and you already have it. The most critical thing you need to dig your way out of a downturn is insights! AI can deliver!

Leaders everywhere always want the paradoxical: to do more with less. If ever there was a technology for achieving this, then AI is potentially it.

Why?

Because it finds answers “for free”, as in answers already sitting dormant in the data. But it requires a firm commitment to the belief that there are answers in the data, which leads to the next misconception.

Misconception #4 — “AI needs a ‘Data-driven Org’”

Everybody knows that AI needs data, typically lots of it. After all, those impressive essay-writing machines like ChatGPT consumed the entire Wikipedia corpus, and then some.

This is where AI’s secret sorcery comes into play, called Fine-Tuning.

The information  AI learns about language from reading all of Wikipedia gets built into the models for use in your project.

Say you want to build an AI to read manufacturing reports, you don’t have to start from scratch. You can start from where the Wikipedia model left off and focus on adapting to your use case via fine-tuning. This method means more results with far less data — data you already have.

AI only needs enough data, and often a surprisingly small amount. Thanks to pre-trained models, many state-of-the-art image recognition problems now only requires thousands of domain-specific samples, not millions.

Perhaps the greatest misconception is the meaning of data-driven.

Leaders have taken it to mean the art of making decisions via data, such as analytics. For AI, data-driven means that you let the AI decide which pieces of data to pay attention to, dispensing with having analysts poring over tons of dashboards.

This is not to diminish the importance of good data practices, like DataOps, etc. But you don’t need all your data ducks in a row to get started.

Misconception #5 — “Build vs. Buy? AI is a Buy.”

This misconception is driven by the marketing of many vendors who claim that their tool is “powered via AI”. It’s somewhat reinforced by the second misconception that AI is all about expertise. So, the logic goes: we have not expertise, we better buy in.

Wrong!

Leaders are slowly trained to think that AI adoption is about buying tools with embedded AI. This is often the weakest option. AI is powered via data, as in the data you already have and understand.

The dirty secret is that many of these vendors are taking your data and, behind the curtain, using fine-tuning without any other magic sauce. In fact, there is no other sauce. Getting results from AI often requires lots of trial and error in setting parameters. It is often better to learn this skill in-house as a fungible skill applicable to a range of projects beyond what a single vendor’s tool has to offer. 

We call this Direct AI.

The Fast AI course has already demonstrated that the power of fine-tuning is available to ordinary folks prepared to learn the techniques. Right now, there are folks in your organization who could pick these skills up.

One of the greatest missteps in the digital transformation revolution has been handing too much power to data science. Many techniques are within the grasp of folks who can write Excel macros, yet many such analysts remain with Excel instead of migrating, as they should, to more powerful AI tools.

The levels of automation in these tools has made them accessible to a far wider audience than most business leaders imagine.

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