13 Common Mistakes That Can Derail Your AI Initiatives

Artificial intelligence is shaping digital life and augmenting human capacities all while presenting previously unimagined opportunities. While some see it as a threat to human autonomy, businesses implement AI to operate smarter and faster. As emerging algorithm-driven artificial intelligence (AI) proliferates, are we better off than we are today?

Artificial intelligence has made its way into businesses in nearly every industry, and it’s only continuing to rise in popularity. When utilized correctly, AI can be an incredible productivity booster and provide valuable insights to business leaders. However, many leaders are unsure of the best way to leverage it, and a misdirected AI initiative may cause more harm than good.

If you’re looking to implement artificial intelligence in your business, make sure you go in with a well-considered plan. Below, 13 experts from Forbes Technology Council share common mistakes to watch out for when implementing AI.

Source and Photo Credit: Forbes.com – Members of Forbes Technology Council share common mistakes businesses make when implementing AI initiatives. PHOTOS COURTESY OF THE INDIVIDUAL MEMBERS.

1. Adopting Too Many Tools At Once

The biggest mistake I see tech business owners make when implementing AI is trying to adopt too many different tools at once. AI is a delicate tool that can provide tremendous value to your business, but you have to be attentive and improve it. Some people think AI is “set it and forget it,” so they implement many different AI programs at once and ultimately don’t see positive results. – Thomas Griffin, OptinMonster

2. Not Having A Clear Objective

You must first define the problem you are trying to solve and how you will measure the impact of a solution. I’ve seen too many companies start AI initiatives without clear objectives, hoping to find something. This is usually a recipe for failure. – David Vasko, Rockwell Automation Inc.

3. Not Having A Single Source Of Truth

As data-driven solutions proliferate, the thing that is most needed is a single source of truth that the data can be funnelled into. You can then use AI to drive models and build solutions. More often than not, this is forgotten amid the enthusiasm to build AI-driven models and solutions in silos. – Lydia Miller, TATA Consultancy Services

4. Not Analyzing Enough Data

It’s important to remember that AI is only as good as the data it ingests. If you aren’t analyzing a sufficient amount of data with your AI engine or if the data isn’t reliable, the outputs won’t add much value. Another challenge is continuously training the algorithm in production and providing feedback. Doing this manually on huge amounts of data isn’t possible. Automation is needed. – Hed Kovetz, Silverfort

5. Incorrectly Structuring Datasets

The foundation of any successful AI initiative is a well-structured dataset. If you’re an entrepreneur or small business, you likely won’t start off with sets of data large enough to make AI effective. Avoid making the mistake of not structuring your datasets correctly from the beginning, as it will be difficult to do so when you have a sufficient volume of data. – Maddison Long, CloudOps

6. Implementing Siloed Solutions

Some businesses implement siloed AI solutions or proofs of concept before aligning on a broader transformation strategy and cultural readiness. Such an approach significantly limits scalability and return on investment, even if it does not cause direct harm. – Didem Un Ates, Microsoft

7. Not Having The Right Size Team

Most businesses know that AI solutions pack a lot of power, but many still forget about the complexities it brings. Implementing AI requires a right-sized team to always keep your algorithms in their best shape. That’s why many companies prefer to outsource their AI development projects or scale their AI development teams via on-demand staff augmentation services. – Nacho De Marco, BairesDev

8. Not Doing The Necessary Groundwork

AI is often seen as a “silver bullet,” but any AI initiative requires a solid data foundation. Working with Fortune 500 enterprises, we see leadership pushing to implement emerging technology. However, any team needs to go back to basics first and make sure their data is clean and structured in a way that will be useful to the model and real-world application. – Michael Paladino, RevUnit

9. Assuming AI Is A Catch-All Solution

Too many businesses assume that AI is a panacea for all of their problems. Consequently, many of them erroneously jump on generalized tech trends without thinking it out. I’d recommend the opposite approach. Focus on a particular purpose for your AI. Strive to address a specific problem. Then decide if you can employ an off-the-shelf solution or if a custom one is really required. – Marc Fischer, Dogtown Media LLC

10. Misidentifying Both The Problem And The Best Solution

“Automation,” “machine learning” and “AI” are often used interchangeably. Leaders may misidentify the business problem; further, whether an issue can be solved via ML, AI or the best automation candidate within the business is still foggy for many business leaders. Identifying the right business problem, choosing the best tool or platform, inputting the required data sets, and finding the key partners to deliver are the four pillars of AI success. – Soumen Chatterjee, Wipro

11. Implementing AI For Its Own Sake

AI is a technology like any other. We’ve seen many projects fail because businesses wanted to implement “buzzy” technology and used that as a starting point. A more effective way to begin an AI initiative is with a really clear view of the fundamental business problem you’re looking to solve and work backward from that. Focus on a specific solution and use AI in service of that. – Konrad Feldman, Quantcast

12. Implementing Solutions Without Sufficient Data

Lots of data is required for most AI solutions to work well. I also see people assuming that a generic AI solution is appropriate when an industry-specific or use-case-specific solution is the right answer. Implementing AI can be hard, but it can provide some incredible value when it’s done right. – Michael Fulton, Expedient

13. Thinking AI Is ‘One-Size-Fits-All’

AI is not “one-size-fits-all.” It is a buzzword, and too many people think it will make them look advanced on the tech front. Utilizing AI for data-driven projects is where the success is. – Bhavna Juneja, Infinity, a Stamford Technology Company

Source: Forbes.com 13 Common Mistakes That Can Derail Your AI Initiatives