Challenges in successful implementation of Machine Learning AI in SMEs

There is a general debate going on how ethical or unethical the use of AI is, however not many people are talking about the challenges in adoption of AI by Small and Medium-sized enterprises. So, before we go one pondering about how people will lose their jobs due to AI , or before we actually start looking for new careers without actually knowing what AI is about, let me take you through a few challenges we are facing in the implementation of Machine learning and Deep learning programs and apps developed on AI platforms, in the real world especially by the majority of businesses around the globe. Widespread AI Phobia and insecurities caused by Sci-fi books and films AI phobia is not a new kind of fear, it is a fear which we have been living with all our lives due to the irrational works of fiction writers and movies. This fear has been around long before the technology was even developed if you have watched movies like Terminator, you know exactly what I am talking about. This phobia is so rampant that even great minds like Stephen Hawkings and Elon Musk have been very vocal about their irrational fear of AI. Musk has been quoted several times that AI is more dangerous than nukes. So the question here is…Should we give machines the power to learn? Actually yes, according to experts in Machine learning A.I, this is kind of fear is very natural and we as humans have always resisted change and machines can only learn what you have programmed them to learn. Email Security Expert, Talha Obaid who has worked extensively on developing Machine learning A.I and has patents of his own, believes that AI can never actually replace humans. “Think about it this way, although self-driving cars have already been invented, we prefer using the GPS instead,” Obaid said, before adding that its human nature, that human species will never be comfortable letting go of the reigns. Obaid also stated that AI cannot ever be smarter than the human mind and can only learn to do the things we program them to learn, they might have glitches but as long as the person creating them does not have an unethical reason to create them, the technology itself is not unethical. “An AI robot may be able to brew me a cup of tea or fix me lunch, but that does not mean it will replace my wife or mom in the kitchen. It will only be used to help them out with the chores and not wipe them out of their kitchens,” he said. The financially back-breaking and lengthy development process AI with Machine learning capabilities cannot be created in a day or two or even in a week or two, it requires a lot of time and effort. It also requires companies to hire experts in AI technology, to work with their team. This can turn out to be exceptionally expensive as many developers find it difficult to learn Machine learning programming on AI and in turn, there is a void in the number of experts who are proficient with machine learning and so hiring them can be quite expensive. Although we may argue that this is a one-time investment, many startups and Small and medium-sized enterprises do not have surplus cash for innovations, no matter how successful they are. Similarly, developing a Machine learning projects on AI platform, app or device is a lengthy process. According to Obaid, you need two teams involved one team will have the expertise on the subject that the AI is being developed for and the other will be proficient in AI. “ Most companies already have domain experts who can provide the AI team with the required data for them to build the AI technology that the company requires,” he said. He further explained that this can be a tedious and rather long process because the exchanges between the two teams are generally never-ending. “The domain experts know what the final product should look like and they need to provide the AI team with the information, one slip in providing the right information could be a recipe for disaster. So there is always a lot of exchange between the two teams, which is very time consuming and the result is not always as expected. In fact, according to a recent report by , only 5 percent of businesses worldwide have adopted implemented AI technology successfully. According to him, things could get much simpler if the domain experts were trained in house on Machine learning AI. “I am saying this from my own experience, if the domain team is trained in AI than it saves the company a lot of time and money because it would be just one team handling the whole development process,” he explained, before adding that the lines between domain and AI experts need to be blurred for successful implementation of the technology for business purpose Mc Kinsey Return of Investments uncertain Many small and medium-sized enterprises do not have enough cash flow to develop and use their own AI technology and this is why digitization of this nature at this point is mostly prevalent in MNCs and other corporate players with big money. Also apart from the cost-based scenario, many businesses still lack faith in the difference that AI can make in their business. “Business is all about making money and return on investments are extremely important, so many businesses are just not ready to take the plunge,” said Israeli Pre-Seed investor and Quality Assurance Expert Amos Raber. “Most businesses according to him fail to use AI technology appropriately because they tend to jump into the bandwagon and not align their businesses with the technology they require to earn them profits”, he added. So, although AI one day, maybe used in every sector, successful implementation of this technology in the real world has become a challenge and though we do have the technology to make our lives simpler, we just haven’t been able to provide it to the people who need it.