Every technology-driven firm is buzzing about Artificial Intelligence (AI). AI integration provides a firm with a plethora of transformation options across the value chain. Adopting and integrating AI technology, no matter how business-friendly it may appear, is a roller-coaster journey. According to Deloitte research, about 94 percent of businesses face potential Artificial Intelligence implementation issues.
As users and developers of AI technology, we need to understand both the benefits and the drawbacks of AI adoption. Knowing the nitty-gritty of any technology allows the user/developer to minimize the dangers associated with the technology while also maximizing its benefits.
It’s critical to understand how a developer should approach AI challenges in the actual world. Artificial intelligence (AI) must be viewed as a friend rather than a foe.
Read on to learn about the top ten possible Artificial Intelligence issues that must be solved.
- Lack of technological expertise:
To integrate, install, and execute AI applications in the workplace, the company must have a thorough understanding of current AI advancements and technologies and their drawbacks. A lack of technical know-how hampers the adoption of this specialty sector in most of the organization. Only 6% of businesses are presently enjoying a smooth transition to AI technologies. To identify the bottlenecks in the deployment process, an enterprise needs an expert. Skilled human resources would also assist the team in measuring the ROI of using AI/ML technologies.
- The pricing factor:
Small and mid-sized businesses have significant challenges when it comes to implementing AI technology since it is a costly endeavor. Even large corporations such as Facebook, Apple, Microsoft, Google, and Amazon (FAMGA) set aside money to adopt and execute AI technology.
- Data gathering and storage:
Data gathering and storage is one of the most difficult Artificial Intelligence issues. Sensor data is used as input by business AI systems. A mountain of sensor data is gathered to validate AI. Irrelevant and noisy datasets might be a stumbling block since they are difficult to store and evaluate.
When AI has a large amount of high-quality data to work with, it performs best. As the amount of relevant data rises, the algorithm becomes more powerful and performs well. When not enough high-quality data is put into the AI system, it fails miserably.
With tiny differences in data quality having such a big impact on outcomes and predictions, there’s a genuine need for Artificial Intelligence to be more stable and accurate. Furthermore, adequate data may not be accessible in some areas, such as industrial applications, restricting AI adoption.
- Scarce and costly workforce:
As previously said, the adoption and deployment of AI technologies need the involvement of experts such as data scientists, data engineers, and other small businesses (Subject Matter Experts). In today’s market, these specialists are both pricey and scarce. Small and medium-sized businesses can’t afford to hire enough people to meet the project’s needs because of their limited budget.
- The issue of accountability:
The implementation of an AI application carries a lot of weight. Any individual must face the brunt of any hardware failures. Previously, determining whether an event was caused by the activities of a user, developer, or manufacturer was very simple.
- Ethical dilemmas:
Ethics and morality are some of the key AI issues that have yet to be resolved. The developers’ technological grooming of AI bots to the point where they can flawlessly replicate human interactions is making it more difficult to distinguish between a computer and a genuine customer care representative.
Based on the training supplied to it, an artificial intelligence program makes predictions. The program will label objects according to the data assumptions it was trained on. As a result, it will simply disregard the correctness of data; for example, if the algorithm is trained on data that displays racism or sexism, the forecast output will reflect this rather than automatically correcting it. Some contemporary algorithms have mistakenly classified black persons as “gorillas.” As a result, we must ensure that the algorithms are fair, particularly when private and corporate entities utilize them.
- Inadequate computation speed:
AI, machine learning, and deep learning solutions need high-speed computations, which are only available on high-end CPUs. Larger infrastructure needs and costs associated with these processors have become a barrier to AI technology’s widespread adoption. In this case, a cloud computing environment with numerous processors working in parallel is a viable option for meeting these computational needs. As the amount of data accessible for processing increases rapidly, so will the computing speed requirements. The development of next-generation computational infrastructure solutions is critical.
- Legal Obstacles:
A firm may face legal issues as a result of an AI application with an incorrect algorithm and data governance. This is another of the most difficult Artificial Intelligence issues that a developer must deal with in the actual world. A faulty algorithm created with the wrong collection of data may wreak havoc on a company’s bottom line. An improper algorithm will always provide inaccurate and undesirable results. Data breaches, for example, can be the result of inadequate data governance–how? A user’s PII (Personal Identifiable Information) works as a feed supply for an algorithm, which might fall into the hands of hackers. As a result, the organization will be caught in a legal quagmire.
- Myths & Expectations Regarding Artificial Intelligence:
There is a significant gap between the AI system’s real capabilities and the aspirations of this generation. Artificial Intelligence, according to the media, will eventually replace human occupations due to its cognitive powers.
On the other hand, the IT sector faces a difficult task in addressing these lofty expectations by correctly expressing that AI is only a tool that can only function with the cooperation of human minds. AI can undoubtedly improve the result of anything that will eventually replace human functions, such as regular or common task automation, industrial work optimization, data-driven forecasts, and so on.
However, AI cannot always replace the quality of the human brain and what it brings to the table (especially in highly specialized positions).
Not everything you hear about artificial intelligence is accurate. Artificial intelligence is frequently over-hyped. To clear up any misconceptions you may have regarding AI technology, read this Forbes article.
- Difficulties in evaluating vendors:
A tech procurement in any new sector is difficult, but AI is particularly vulnerable. Businesses have several challenges in determining how to utilize AI successfully since many non-AI firms participate in AI washing, and some organizations exaggerate.
True, AI technology is a wonderful getaway since you can’t control the drastic changes it brings to your company. However, in order to execute it, a company will require specialists who are difficult to come by. It requires high-degree computing processing for successful adoption. Rather than ignoring this ground-breaking technology, businesses should focus on properly managing these Artificial Intelligence issues.
The goal is to create a comprehensive technology adoption roadmap that recognizes the basic capabilities of artificial intelligence in order to minimize the difficulties and maximize the advantages of artificial intelligence.