Artificial intelligence

The Main Roadblocks to Any Industry for Adopting AI Technologies

Artificial intelligence has been projected to boost the world’s economy by a whopping $15.7 trillion by 2030; at this time, about 70 percent of companies are expected to have adopted at least one kind of AI technology – be it through their products, applications, and techniques. 

In modern-day society, AI technology and systems have been applied across various industries, from robotics to automotive, energy, healthcare, education, retail, customer service, and more. It’s been proven to provide a wide range of benefits to businesses, including optimization for delivering high ROI. AI can also enhance various technological products and automate internal and external business processes and operations. 

Lastly, AI can enable companies to improve their services and enter new markets. However, as the year draws near, many organizations have yet to adopt AI technology because of some significant roadblocks. 

Below are listed the top five barriers to adoption that are worth knowing and considering.

  1. Organizational Structure

Many businesses have a mindset that AI isn’t beneficial yet, and also, because they dread lost control or lost jobs, they still refrain from adoption. Not having an organizational structure or one that is ineffective will make it very difficult to implement various automation and technology solutions, including AI. 

Artificial Intelligence, wherever it might be implemented, is built and based on AI models which require human assistance to gather data, at least for now. It needs to be fed with relevant data in order to formulate solutions. 

Without organizational structure, it can be difficult to work collaboratively and feed AI applications the relevant data they need systematically. Essentially, it can cause chaos within an organization and lead to failure to implement or adopt AI. 

  1. Poor Data Quality

AI will only work based on the quality of data used. Unfortunately, several organizations collect excess data, which may have too many inconsistencies or data decay. This is mainly because organizations are using archaic solutions to gather data. Many companies, especially those just starting out or lacking sufficient capital, often still resort to manual data-gathering methods. 

Analytics is done through excel spreadsheets and paper. This is tedious and time-consuming, which results in the delayed data processing. Insights are generated late. Hence it’s outdated, irrelevant, and can no longer add value to your decision-making process. 

Feeding irrelevant data to AI will only cause it to fail and become unreliable. It will not deliver the solution it is designed to do. Poor data quality will result in just as poor utilization of AI. It’s important that along with the implementation of AI, organizations must prepare their data, as well as their data-gathering processes.

This is to get the highest quality of data available in real-time, and to generate models that can provide accurate insights, predictions, and projections. With the reasons stated above in mind, investing in data gathering and analytics is important. 

There are also efficient ways to automate data gathering, like using RPA Cloud tools, which can take repetitive tasks off of employees’ hands and enable complete enterprise automation. 

  1. Shortage of AI Professionals

A lead bottleneck to AI adoption is the shortage of technology professionals with the required skills to implement the necessary infrastructure and organizational transformation. Implementing AI technology within a single organization may take a team of AI builders, including AI researchers, software developers, data scientists, and project managers, among other tech experts. 

Therefore, each organization will require hundreds of AI builders to implement the technology. A lot of the pioneering organizations already have the best AI experts. The rest of those companies that are yet to adopt AI often have to deal with a shortage of talent. 

There were only over 28,000 AI experts in the US in 2018. These professionals have data and AI skills, which take a lot of time to gain and develop. With that, it is anticipated that the growth in the number of AI professionals will be slow. 

But this is not the only cause of the shortage of people with knowledge in Machine Learning and AI. Businesses also have to address the internal AI skills gap to adopt AI faster and more successfully. Consequently, this also poses an additional challenge regarding finding AI experts to train their current tech team. 

  1. Fear of the Unknown

A study revealed that 42 percent of respondents in an organization do not fully understand AI benefits and essence in the workplace. Many people are still wondering what AI is—its technicalities, how it works, and can help. Its fast development made it even more difficult to chase after and understand. 

People could not yet grasp its fundamentals; suddenly, it can turn text into speech and even generate hyper-realistic images. It keeps getting better at a surprising speed, and experts dubbed the past ten years as the “golden age of AI.” 

Hearing all this news and advancements can often lead to questions that are often left unanswered for many. Understanding AI will take a lot of training and lessons, especially for non-tech professionals. And the internet can only go as far as online articles and explanatory videos on Youtube—not all of which are always right, accurate, or understandable. 

Humans are known to have a fight or flight instinct, to feel challenged and threatened by unfamiliar things. Hence, the lack of proper introduction and guidance to AI can lead people to acquiesce and underutilize AI or resist adopting it completely.  

  1. AI Implementation Costs

Many organizations still find AI implementation costly, including getting the required infrastructure, tools, talent, and time. The idea of implementing a new technology alone already means additional expenses for a company. What more to implement an innovative, in-demand, and continuously advancing solution such as AI? 

AI seems too complex, with various technicalities involved – from data science to software programming and model generation. It can appear miscellaneous and expensive without proper knowledge about it and its benefits. The key to finding the most cost-efficient and sustainable way to implement AI is within an organization’s needs and goals. 

By determining what it is that you want to do with AI and how you want it to help your business, you can find the best solution and implementation strategy that will work. This can also be easier to fully leverage and generate measurable ROI for an organization, which can pay off the implementation costs, if not more.

Overcome Roadblocks in AI and Get Ahead

AI technologies can provide organizations with truly advanced benefits. Adopting this can easily give a company a significant edge over its competitors. But with great benefits also come greater barriers. 

There will always be challenges in rolling out new technologies across organizations, from a skills gap to the lack of facilities, tools, and infrastructure, as well as the cost it can incur. But all this can be solved through preparation and a proper understanding of the technology you are looking to implement. 

Determine how and why you want to implement it. Get the organization involved and properly oriented and equipped. This way, everyone will be encouraged to welcome technologies like AI into their workflow and systems. This can lead to smooth implementation outcomes and better ROIs on your chosen AI technology, whichever industry you are in.

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