As much as AI and its abilities are being spoken about and speculated today, there are quite a few myths that are doing the rounds as well. While some believe that it to be a deus ex machina, a tool that can resolve seemingly unsolvable problems, there are others who see it as just hype that will eventually die down. The reality about AI however, lies somewhere in between.
With the growing need for technology to make processes more efficient and provide companies with a competitive advantage, it is crucial to understand how AI can create value in your business and what its limitations are.
Alexander Linden, a Vice President Analyst at Gartner says “Business leaders are often confused about what AI can do for their enterprise. This is understandable, as there are many definitions and variants of AI that are present in the general discourse”. Misconceptions and a lack of understanding about AI is an impediment for IT leaders who are trying to incorporate AI in their organizations.
In this article, we bust the 5 myths that are often associated with AI and make a case for why you should stop believing them NOW.
As the abilities of AI are evolving, organizations should consider tapping into its capabilities and understand the potential impact this technology may have in addressing the organization’s business needs.
Deliberately refraining from using AI is akin to forgoing the next phase of automation and could put your organization at a competitive disadvantage.
Even if you choose to abstain from adopting an AI strategy for your company, this decision should be made on pragmatic grounds supported by research and robust data. The need for AI in your organization should also be regularly evaluated and modified to fit the organization’s evolving needs.
While it is true that AI can automate several manual tasks, its range of capabilities are not always synonymous with automation itself. Automation is a subset of AI.
According to an extensive survey by Deloitte Research of AI applications in various industries, AI applications fall into three categories: product, process, and insight.
Product applications use AI to provide a better experience for the end user, either by enabling “intelligent” behavior or by automating tasks that a human user often performs.
AI is used in Process applications to enhance, scale up, or automate business processes.
Insight applications on the other hand use AI, machine learning and computer vision to analyze data and glean insights in order to drive better business decisions.
In only some of the cases is AI really used to automate human work. More often, AI is used to perform tasks that are unattainable by human cognitive abilities. While automation refers to the completion of a task without human interference, AI is used to power machines, making them capable of thinking or at least making intelligent decisions based on a series of predefined models and algorithms.
AI, Machine Learning and Deep learning are often interchangeably used or misunderstood to be the same, despite being fundamentally different.
Artificial Intelligence is the human-like intelligence exhibited by machines that encompasses various human cognitive abilities.
Machine Learning is a set of algorithms that allows a program to generate results accurately without the results being fed into the program explicitly. ML is in fact one of the tasks that AI performs by continually learning from the data fed into it and can be referred to as a sub-discipline of Artificial Intelligence.
Deep Learning, however, refers to the algorithms used to solve problems based on neural networks designed to mimic the neurons in the human brain. DL is one of the specialisations of ML and in turn, one of the aspects of AI.
A common AI myth is that it can outsmart and replace humans at some point. This misconception is also one of the major barriers to AI adoption.
While it is true that AI makes machines extremely intelligent, we need to understand that machines cannot acquire such a potential all by themselves. The capabilities of a machine is limited to the data that is fed to it by a human being and the actions that they have programmed the machine to carry out.
It is important to identify the obvious benefits AI and ML add when it comes to automatically identifying patterns from an expansive amount of data with little to no human intervention. However, the algorithms and models that make this feat possible, have to be built by humans. So essentially, AI only can get as smart as a human mind can make it.
Another crucial feature that sets human intelligence well above AI is that humans are capable of recognizing when there is a problem or redundancy with a certain approach they are taking. AI models on the other hand, tend to pursue the best possible answer out of nearly infinite possibilities, even if it leads to them never exiting the process.
AI is looked at with apprehensive often, based on the assumption that it is going to be a costly investment for the company. However, high cost is not the case for AI in general. There are many AI tools that are available for businesses that do not demand an exorbitant investment to implement AI solutions. Some important AI solutions which are cost effective and also yield a huge ROI and several business benefits include chatbots, RPA, modern intranets with AI capabilities and advanced analytics.
On the other hand however, AI development does require expertise in programming languages and development practices. While hiring data scientists for this job may rack up costs, organisations can consider training the existing resources to effectively implement the algorithms themselves.
It is necessary that companies take concerted initiatives to develop comprehensive strategies that accommodate AI and prepare themselves for futuristic environments that are compatible with AI powered technologies. It is imperative for companies to act on these initiatives before any new market disruptors jeopardise the competitive edge the organization may hold in the industry.
This demands the leadership of organisations to have a pragmatic and precise understanding about AI’s capabilities and its trajectory into the future. Having a strong academic foundation and practical experience in AI among the leadership allows organisations to avoid misinterpretations and misleading myths about AI.
Adopting AI just for just a few functional areas will be ineffective in making a great impact on the organization as a whole. Hence, companies must try to incorporate an effective blend of embedded, edge and centralized intelligence systems across all functions and teams.
Adopting AI and related technologies in building an intelligent business environment will strengthen the alliance of humans and intelligent machines and produce a powerful workforce for the future. Companies must acknowledge that humans and machines will continue to be the indispensable building blocks of the new workforce and plan to utilise their combined strengths effectively.
Acuvate believes that organizations need AI to accelerate their digital transformation journey. Our range of solutions and services around conversational AI, digital workplace, business intelligence and RPA are built with a strong AI foundation. If you’d like to learn more about AI and its capabilities, please feel free to get in touch with one of our AI experts for a personalized consultation.