Adopting artificial intelligence (AI) is more than just embracing technological competencies–it’s an extension of organisational change. AI is the all vital cog in the wheel of Big Data, and can transform the archaic, mostly physical-asset based model into new, information-laden world, where competitive advantage is won by those who draw out maximum data in the shortest time span possible.
Both powerful incumbents trying to shoo away disruption as well as SMEs looking for growth opportunities need to learn to drive AI, or else subscribe to its crushing strength currently calling the shots.
Integrating AI into business isn’t just about appreciating its technological application, but creating value for business and society. Three broad challenges are involved: deciding when and how to implement AI; getting across AI so that managers and employees find it approachable; and defining moral and ethical limits.
The tendency of the people is to resist technology they find challenging. This could presumably stand in the way of AI, as it has no takers among particularly old-timers. Managers who are mystified by AI try to work their way out with dealing with it directly, but that may be the wrong route. Going further, it is required that they try their hand at coding, start building basic Machine Learning and AI solutions, given the support and customization of the open source code on GitHub. Learning Python as a programming language can be an uphill task for upstarts. This said Python application framework helps create application with minimum amount of code. Long words short, AI is accessible…only when one has an open mind for learning.
The next stage primarily involves the application of AI-driven solutions in businesses. Algorithms can work their way around in helping firms find better ways to work. There are several tasks that only humans can execute, while there are others that can be executed by a combination of humans and algorithms. Enterprises can identify areas where use of algorithms would prove valuable. It could also mean that there are certain areas where a marginal increase in accuracy could result in disproportionately loads of benefits for which new, reliable data is ready for use. This said enterprises already have copious data to deal with in theory, but that which is not centralized and qualifying. All said and done, integrating it will no longer be nightmarish.
Defining the boundaries responsible:
It is important for enterprises to delineate the boundaries of AI. Experts propose three pillars of responsible: regulatory compliance, human-centred and ethical design. Presumed violation of ethics can also negatively impact consumer’s psychological well-being and ultimately rock companies’ bottom-line. For example, if use of AI bots seems to be too pushy such that customers are served against their own best interests, it may seem to flout an almost indiscernible line delimiting individual free will. Remember, customers are no pushover even when everything is algorithmically spoon-fed.
Again, customers here have to choose between predictability and consistency. Predictability is based on past patterns which can take customers away from their preferred choices and options. Only when references to consistency replaced predictability-related language, customers were able to sense a return of individuality and autonomy. Sometimes, companies also use all the data at hand to sugar-coat buying prospects. They need to walk the talk by ethical means and not by doing what they think is right.