Organizations with diverse services and operations are embracing the adoption of technological innovations and artificial intelligence to maximize the usefulness of the same. The swift and unavoidable transformation to embrace automation is springing in the middle of the pandemic. Yet views are sculpted differently, presenting the problems and difficulties of the giant concept and approach to AI.
Many still fear the transition with adoption gaining momentum during the lockdown. The warning is unnerving because a few companies made negative headlines due to the flaws in their AI scheme. The pandemic impacts the global economic aftermath. Cost-cutting steps pave ways for mitigating losses that are of paramount concern to all organizations. Therefore, the adoption rates of AI tend to be undergoing reconstruction. Though AI is proving its importance according to the McKinsey Global AI Survey, but few impacts on the scale, most businesses are still seeing benefits.
The current outcry is in enhancing the business performance and visualizing long-term goals in case the virus prevails for longer than expected. Unfailingly, businesses are evaluating the possible areas of the outbreak, reducing workers to social distancing standards, leaving sickness-related penalties, sanitization costs, and much more, these variables are directly related to the manufacturing and service processes. Manufacturing line instability applies to the majority of boardrooms.
While AI systems recognize data for decision making and delivery of undeniable advantages, AI has a broad list of risks and problems. In a business, technical structures are the driving force behind decisions, improvements, and strategies. If the data entered is unclean and unfiltered, it could pose significant risks to the organization’s internal and external credibility.
Systems certainly learn and adapt as per the data entered. Systems architecture is focused on data learning and analysis; each outcome is focused upon data patterns. AI will also learn the data patterns and designs, and act appropriately. If the company has a history of prejudice, cultural disparities, and a certain mindset’s impact, the data would demonstrate it. For potential outcomes, the AI system will follow the same trends and this provides an unseen backdoor for the deciphering and tackling organization.
Even though AI could be a powerful tool for supporting decision-making, the challenge is “in cases where AI systems are based on assumptions on patterns of historical data, there is a risk of bias,” says Brian Kropp, Gartner’s Research VP.
Popularity and long-term business continuity are based on the brand/organization’s value and goodwill. Loyal customers prefer to select a wider variety of products and services from a firm with a powerful market reputation. Businesses with a significant and meaningful reputation draw more customers, as customers connect reputation with confidence and service or product quality.
Hence human interaction is essential to an AI system’s success. Developing systems that allow human oversight from time to time is an essential requirement to prevent hazards of reputation. AI and ML are successful when trained but regular human guidance is required to understand the learning pattern of the system.
