The commercial world is being overtaken by artificial intelligence (AI). According to a McKinsey survey from three years ago, 50% of businesses reportedly used AI for a minimum of one task.

And even that is simply the beginning of what lies ahead. According to projections, artificial intelligence revenues will increase by twofold between the time this report was made and 2024, indicating the scale of the business use of the AI technology.

Despite this, businesses still find it difficult to integrate AI; today, we will discuss the top 10 difficulties business owners encounter.

Let us start… 

10 Obstacles to Overcome in Adopting AI

It can be beneficial to be cognizant of potential challenges if you are thinking about implementing AI in your business. By doing this, you can make the adoption of AI easier.

These constitute the most typical difficulties that exist:

Your Business Lacks the Necessary Data 

Effective AI can only be created and trained with an adequate quantity of high-quality data. And the results will be better the better the data. However, an absence of stakeholder support might lead businesses to not make sufficient investments in the data handling infrastructure needed to allow AI, preventing them from accurately training an algorithm to solve their company's challenges.

In light of this, you might have a data set you can use if your business uses a CRM platform to gather purchase behavior, consumer demographics, or on-site interactions. When we say "on-site" we mean both on the spot and regarding notifications that appear on the user's screen while they navigate a company website whether created through website builders or by developers. Website builders can create incredibly gorgeous web pages both in terms of the sites they produce and the user interface. 

Pop-ups for live sales, content recommendations, and email signups are a few examples of on-site interactions via the company website. The user is motivated to act as a result of this.

Additionally, continuing to speak about the importance of necessary data – online data banks, even generated data, can frequently replace any gaps. But you will not know what kind of data you need or how to organize it if your organization is not enthusiastic about AI in principle. 

Your Business Does Not See the Value of AI

It is a strong force, this rigidity. After all, teams are frequently hesitant about taking a risk by implementing a substantial change while a firm is performing well. Moreover, implementing new technology, such as artificial intelligence, may seem like a significant change. The next difficulty is persuading partners to make an investment in a solution whose potential profits are not always evident.

Since you will not always know what you are constructing when it comes to AI until you have started, that presents another stimulating challenge to get beyond. 

Your Business Has Trouble Partnering with Trustworthy Vendors 

Despite its expansion, AI usage in most enterprises is still very low. 

This can be attributed partly to the fact that many companies have worked with AI service providers who do not fully grasp how to use the technology to make money. As a result, many businesses are hesitant to get involved in AI development after having unpleasant experiences when dabbling in it. 

The outcomes would have spoken for themselves if they had started out working with respected and experienced AI vendors.

Better yet, the interested parties would have been more willing to support more ambitious goals, later on, had the suppliers promised to solve a small business issue first, demonstrating the usefulness of AI. 

Lack of the Necessary Skill Sets 

Data alone cannot solve the problem. To make AI operate, you must possess the necessary skills. However, many businesses find it difficult to find both data and machine learning expertise, which prevents them from achieving their full potential.

Lack of experience in the appropriate sectors can impede development even in organizations with some degree of in-house expertise. It can even have an impact on hiring because departments are unlikely to understand what positions to fill or how to evaluate candidates. In some cases, department heads are simply unqualified to oversee the installation of AI, which leads to ineffective procedures, integration problems, or continued manual effort that reduces the value of the solution. 

AI Team's Inability to Articulate How a Solution Operates 

Our research shows that humans will only accept a computer framework if they are aware of its inner workings. And implementations may come to an end if an AI team is unable to provide an explanation. Stakeholders typically demand to understand why a decision is incorrect (and the reason they should think about altering their minds), therefore the challenge only gets more difficult in circumstances where a model's output conflicts with their assumptions.

To make this clear, let us place it in a medical setting. Let us say a doctor makes a diagnosis, but an algorithm disagrees. Before approving the alternative diagnosis, the doctor must first try to understand the reasoning behind the machine's thinking (for example, by observing that a model diagnosed flu based on indications such as headaches and sneezing rather than a patient's age or weight).

But "black box" models frequently spew out an estimate without a justification, so providing transparency is not always straightforward, leaving stakeholders unable to confirm the results. 

No Insight to Identify a Suitable Use CaseEnter heading here...

Some businesses believe that by introducing AI purely for show, they will promote adoption across the board. Sadly, this approach frequently has the opposite result.

A corporation will find it difficult to develop a solution that adds value to the business if it does not have a strong use case. There will therefore be no way to demonstrate the effectiveness of artificial intelligence in resolving common problems. It is better to hold off on implementing AI until you totally comprehend how you will use it because, in most cases, the plan will just serve to further the attitude of indifference toward the technology. 

The Leadership Team Dreads Having to Upgrade Legacy Systems 

It is amazing how many businesses still run their IT operations on old hardware, software, and infrastructure. And management frequently decides against using AI because of concern about the costs associated with upgrading these systems.

However, the reality is that by leveraging "Data Lake" functions in hybrid environments, cloud computing enables the deployment of AI without completely reworking an outdated IT network. Your business might need to establish an operational framework on-site in order to accomplish this. What is Data Lake? It poses a central location for processing, storing, and protecting huge amounts of organized, semi-structured, and unstructured data. No matter how diverse or large the data is, it can be processed and saved in the way it was originally created.

Still, you will continue to gain from operations that use AI to be more effective. 

Different AI Teams Cannot Cooperate as a Single Entity

 Large corporations frequently see AI teams working in silos, where they share technologies but operate independently. They consequently construct various infrastructures and employ various workflows, which only makes the deployment of AI more broadly challenging. A "hub-and-spoke" organization, in which one central unit unites various teams behind a common strategy, can help you prevent this.

As you roll out solutions, any investment in AI will benefit the entire organization and bring about economies of scale.

Some Solutions Are Not Simple Enough to Integrate

There is no assurance that your business will implement your ground-breaking AI solution, even if you are successful in doing so.

In 2009, Netflix made the costly discovery of that. A million US dollars was up for grabs for any developer who could improve the accuracy of the streaming giant's recommendation system. And although one team was able to improve it by about 10%, Netflix never incorporated the improvement. Why so? They claimed that because it required so much engineering work, the idea was never implemented.

In Many Cases, Regulation Proves to be the Toughest Challenge 

 If you were to create a cloud-based financial platform in, say, Poland, you would need Polish data centers in order to do so due to legal requirements. Such requirements are common in AI projects. Additionally, they frequently stop solutions in their tracks in sectors like finance. Likewise can be said about the issue of accountability, or more specifically, who is responsible for errors made by AI.

Let us use the medical setting as an illustration once more. Imagine if a doctor modifies their diagnosis in response to a machine's advice, only to discover the machine to be inaccurate. Is the physician at fault? Or is the algorithm's creator to blame? Regulators still have not provided a response to these moral issues. The issue of data management is another.

When developing a solution based on artificial intelligence, you must gather enormous amounts of data, whether or not it is sensitive, and maintain proper security for it. Your company runs the danger of paying a large fee if you do not follow the rules. Therein lies a danger that a lot of businesses would prefer to avoid.

No Obstacle Is Too High

Despite the numerous obstacles to AI adoption, you should have complete faith in your ability to introduce it to your business. In actuality, recognizing the risks is an essential first step. You will not be able to implement AI if:

• your organization is not enthusiastic about AI in principle;
• you fail in acquiring a trustworthy vendor and persuade partners that AI is a necessity;
• have unqualified department heads;
• believe that AI will solve all your operational and financial problems.

After all, once you are aware of the potential difficulties, you will be better equipped to develop a plan that increases your likelihood of success. It cannot be denied that strong workflows, stakeholder support, and investment are necessary for a successful AI deployment. However, there are enormous potential advantages, and no obstacle is insurmountable.