Posted by Marbenz Antonio on March 17, 2022
Although artificial intelligence has pervaded the technological environment, businesses are still struggling with how to incorporate AI operations to accomplish business objectives. Only until corporate decision-makers and AI practitioners grasp how AI may improve business operations will AI’s transformational potential be realized.
To that end, CompTIA’s AI Advisory Council published AI in Business: Top Considerations for Before Implementing AI, a handbook that aims to assist both business executives and AI practitioners in determining how to effectively use AI to improve operational efficiency and profitability. It provides an overview of main AI technologies and implementation challenges, followed by a list of 15 critical questions firms should consider before embarking on an AI journey, based on the experience and knowledge of council members.
The success examples from many industries are inspiring more firms to explore AI, according to Rama Akkiraju, an IBM colleague and one of the contributing council members.
According to Lloyd Danzig, chairman, and creator of the International Consortium for the Ethical Development of Artificial Intelligence, there are compelling reasons to do so. He claims that more efficient methods, together with increases in processing power, have made it possible to tackle previously impossible problems. “Now, businesses can use data to make better forecasts, run more effectively, and provide a better customer experience,” he added.
That has been most apparent in customer service operations, particularly with chatbots, thus far. According to Akkiraju, IT operations systems management is another sector that is gaining interest. On the other hand, the healthcare sector has generated some applications that haven’t scaled since fields such as cancer and radiology may be too wide and complicated for AI to handle at this time. Small businesses, on the other hand, have had more consistent success using AI in tackling more specific challenges.
Any attempt at AI implementation necessitates the appropriate questions being asked. After reading AI in Organization, Danzig believes that the optimal next steps would differ depending on the sort of business and its demands. However, after reading the instructions and completing the questions, those processes should be much more straightforward.
Some readers will be able to turn to an internal data team to start answering the questions, according to Akkiraju. Others, presumably smaller businesses, may hire consultants to do the assessment. Everyone, on the other hand, must deal with one critical success component that is only mentioned briefly in the guide: expectations.
To achieve the required levels of accuracy and value, AI systems often require time and iterations of user input and learning in each company’s unique environment. That includes AI practitioners and vendors, who, according to Akkiraju, frequently panic and change direction too fast when early results don’t meet expectations.
A tiny start, according to Akkiraju, is the most important requirement for an early triumph. A timed proof-of-concept can provide valuable information. A minor notion that makes large strides can be managed by either an in-house data science team or an external provider.
“Experiment and investigate in tiny use cases to better identify the strengths and areas that require further refinement, and establish a plan for how those areas will be addressed by the vendor or firm itself, and build on first successes,” she suggests. “I’ve seen incredible success everywhere that road has been taken.”