As we move closer to 2020, it has become increasingly difficult to imagine daily life without AI. Like any other emerging technology, AI is surrounded by a fair number of myths. Let’s shed light on some of them:
AI as a technology has changed the way we lead our lives. From enabling the simplest of tasks with voice assistants such as Google Home and Alexa, to making breakthroughs in complex sciences such as Quantum physics, AI is bringing about a technological revolution!
Speaking of SMEs, 61% of them alone used AI in some form or another in 2017. In fact, AI has been instrumental in improving their workforce productivity and automating repetitive tasks of their teams – all of which has enabled them to compete with peers efficiently.
Furthermore, according to Gartner, more than 20% of CIOs aim to prioritize AI in 2020. A Statista survey reports that the global revenue generated from AI in 2018 was $482 billion. The figure is set to hit $3061 billion in five years.
But, like any other emerging technology, AI has had to deal with its fair share of myths and misconceptions. In this article, we aim to shed some light on seven common myths that surround the technology. They are:
Myth #1: More training data equals better AI solutions
It is a commonly misconstrued notion that AI systems can be made more efficient if the AI and ML-powered tools are fed with more training data. While that is significantly important for AI systems, the quantity of the data does not matter. What matters is the quality of the training data fed to the systems.
You see, qualitative training data enables the development of better, more efficient AI solutions. For example, feeding the machine learning model with vast amounts of data that doesn’t have enough variety will not improve the results.
Myth #2: Adopting AI in business will bring immediate results
More and more organizations are adopting AI in business. There is a common misconception among many that AI can bring about immediate results when deployed.
This is untrue because AI systems are dependent on data. The models need to be trained with real-time data so they can learn from that data. This is a gradual process that happens over time and not immediately.
Myth #3: AI will only replace the mundane, repetitive jobs in the market
AI has enabled organizations to automate many manual tasks, thereby increasing speed, productivity, and efficiency. But the usual notion that AI will only replace those tasks that are repetitive and mundane is incorrect.
While AI does a great job in automating document processing in banks, it can also help improve more complex tasks in various industries.
For instance, AI is used in banks for fraud detection by ensuring that all transactions are recorded, and discrepancies are immediately taken note of. In healthcare, AI has made a significant impact by being able to predict diseases based on the data.
Myth #4: Not all businesses need AI to grow
Not everyone was quick enough to jump on the AI bandwagon. Initially, most industries believed that artificial intelligence was only relevant to the technology domain. This is farther from the truth.
Therefore, before assuming that your business does not require AI, do thorough research and understand all areas of your business. Choosing to deploy AI or not should be a conscious decision backed by relevant research and options to choose from.
Myth #5: AI algorithms are capable of reading and understanding data on their own
Another common misconception most non-technical business owners have about AI is that it is capable of understanding and making sense of the data being fed to it. This doesn’t happen automatically.
As mentioned previously, AI algorithms are heavily dependent on data to learn and make predictions. Although AI systems work on machine learning and deep learning models, they need a certain amount of human intervention to make sense of the data it is being fed.
Besides, the data being fed to the training models must be qualitative.
Myth #6: AI automation requires a high level of expertise and finances
Yet another primarily assumed notion about AI in business is that it is expensive and hence, can be afforded only by large multinational organizations. AI implementation does not require you to spend beyond your means as a business enterprise.
While it is true that the technology is used for carrying out tasks that are both simple and complex, the design and development of the models depend on what the AI system needs to accomplish.
Most companies today employ AI for automation of basic manual tasks such as the digitization of paperwork in KYC, or automated document processing for bank documents, and so on. What this essentially means is that even small to medium business enterprises can employ AI for business.
Myth #7: AI implementation doesn’t need any planning
Because of the hype around AI, most organizations dive into the implementation of AI in their business without completely understanding where and how it can be used for the same.
The lack of preparation can lead to a lot of confusion for the developers and can cause you to spend organizational resources unnecessarily. Therefore, it is essential to have a plan of execution from initiation to actualization.
Wrapping it up
As intelligent as artificial intelligence may seem (and it is), the technology cannot learn on its own, duplicate the human brain, or operate free from human bias. In fact, it’s difficult for an AI system to achieve super intelligence because of the semantics and physics of information.
With AI being implemented across industries, it is necessary for business owners to fully understand the value AI can create for you and where its limitations lie.