Integrating Artificial Intelligence in Businesses
Wed, April 21, 2021

Integrating Artificial Intelligence in Businesses

Small and mid-size enterprises are struggling to implement AI solutions to their business processes compared to tech giants / Photo by Kittipong Jirasukhanont via 123RF

 

Artificial intelligence is not a new concept, but it took decades of work to make significant progress. Currently, the technology delivers new opportunities to several sectors, especially in business. Recent artificial market research showed that even as many companies lag in AI adoption, others have fully embraced the technology.

Finances Online, the fastest-growing independent software review platform, reported that 71 percent of executives surveyed believed that AI and machine learning are game-changers; 61 percent of executives with defined strategies said that the technology can be used to single out opportunities using data; 61 percent of business professionals said machine learning and AI are their organization’s most significant data initiative; 54 percent said AI tools have boosted productivity; and 47 percent of digitally mature companies revealed that they have a defined AI strategy.

AI can process and analyze the troves of data far more quickly than a human brain could, which is why businesses are incorporating it in their decision-making process and production. However, many companies still have no plan to adopt it. The 2017 State of Cognitive Survey reported that only 6 percent of business enterprises were having a smooth ride with AI. The staggering 94 percent still faced challenges in implementing an AI solution to its enterprise. This report also held true for other fields.

According to ZDNet, a business technology news website, survey for the O'Reilly “AI Adoption in the Enterprise” reported that only 36 percent in computers, electronics, and technology companies were using AI in their workplace; 32 percent in media and entertainment; 30 percent in telecommunications; 30 percent in financial services; 29 percent in healthcare and life sciences; 16 percent in public sector/government; and 9 percent in education. 

 

AI in Businesses

Many companies are being encouraged to use autonomous processes to improve their operations and change the face of customer service. Now, it is easier for AI to be accessible to companies as more and more businesses have developed AI applications while competition and adoption rates have increased. Systematized processes are becoming more important in a competitive market. Thus, humans handling tasks to review, research, and analyze every piece of information and understand all its implications in the processes will likely be left behind. 

Amir Husain, founder and CEO of machine learning company SparkCognition, said, "Artificial intelligence is kind of the second coming of software. It's a form of software that makes decisions on its own, that's able to act even in situations not foreseen by the programmers. Artificial intelligence has a wider latitude of decision-making ability as opposed to traditional software."

When done right, implementing AI will allow businesses to grow product lines, revenues, and offer customized user experiences. Also, the combination of AI and automation will help advance other technologies into the mainstream business environment. As a result, the workplace becomes less tangled up in complexity and repetition. According to Information Age, an online site that provides the latest news, analysis, guidance, and research, AI and automation will allow companies to cut costs because it significantly boosts productivity in the workplace. They free up employees from more mundane tasks, increase agility and flexibility, and spur innovation.

At the same time, AI and automation are important in managing massive data as well as supporting the increased strain on business networks. For instance, smart energy management systems collect data from sensors affixed to various assets through machine learning. The data the system gathered will help human decision-makers to better understand maintenance demands and energy usage. 

However, Barry Mathews, head of the UK, Ireland, and the Netherlands ISG, stated that businesses need to ensure that the technology solution is addressing a problem or gap. “Without a clear strategy, the return on investment is far less likely to be maximized,” he said. 

 

Challenges in Integrating AI

Despite the huge benefits that AI can provide to companies, a 2018 survey by Spiceworks showed that 50 percent of organizations have not implemented AI due to the lack of use cases in the workplace. According to Analytics Insight, a media, branding, and technology platform with a unique focus on insights, trends, and opinions from the world of data-driven technologies, it’s one reason why businesses are having a difficult time integrating AI data acquisition and storage. Huge amounts of data that AI systems collected may present noisy datasets that are difficult to store and analyze, thus causing an obstruction.

Aside from that, 29 percent of the organizations in the said survey expressed their security and privacy concerns toward the technology. However, Etienne Greef, CTO and founder of SecureData, stated that AI and machine learning can handle those concerns. Both technologies are excellent in dealing with lots of information and trying to understand what is normal and what’s anomalous. 

50 percent of organizations have not implemented AI due to the lack of use cases in the workplace / Photo by wrightstudio via 123RF

 

Also, small and mid-size enterprises are struggling to implement AI solutions to their business processes compared to tech giants that have separate budget allocations for the implementation of the technology. Apart from spending on the integration itself, data scientists, data engineers, and subject matter experts are rare and expensive in today’s market. Several organizations have no capacity to hire them. 

As Mark Skilton, professor of practice at Warwick Business School, said, "The technology and the promise is there—the problem is really the tagging of the data and having the knowledge and the skills within the company to understand, ‘How do I prepare my data so I can start learning from it?’”