AI-driven solutions are businesses’ key preferences when it comes to optimization. Or is it?
Despite today’s businesses’ increasing expenditures on artificial intelligence (AI), the C-suite’s belief in AI may be hit or miss. Is it just that CEOs are wary of a new, untested, and unproven technology, or is there anything more to it? Executives have traditionally opposed the use of data analytics for higher-level decision-making, preferring instead to depend on gut-level choices based on field experience rather than AI-assisted suggestions.
In several sectors, AI has been widely embraced for tactical, lower-level decision-making – credit scoring, upselling suggestions, chatbots, and machine performance management are just a few examples. However, it has yet to demonstrate its worth when it comes to higher-level strategic decisions like recasting product lines, altering corporate strategy, reallocating human resources across departments, or forming new partnerships.
The first step in increasing executive confidence in AI-assisted decisions is by creating reliable models that provide businesses with consistent insights and recommendations.
Executive apprehension can originate from bad experiences, such as when an AI system produces false sales results. A lack of data quality is a common factor in almost every unsuccessful AI effort. Structured data was prevalent in the traditional business model, which categorized data as it entered from the source and made it very straightforward to put it to immediate use. However, AI also consumes large volumes of unstructured data in creating ML and DL models.
Therefore, it is no surprise that many data scientists devote half of their time to data preparation, which is still the most important task in developing dependable AI models that produce accurate results. Context and dependability are essential for gaining executive confidence. There are various AI tools available to aid with data prep – from synthetic data to data debiasing and data purification.
Secondly, after the whole data preparation, there should also be an avoidance of data biases to boost confidence in executives.
Executive apprehension may stem from a well-founded fear that AI outcomes would lead to discrimination inside their businesses or harm customers. In the same way, AI bias might be swaying company choices in the wrong direction. When an AI model is trained on skewed data, the model will be skewed and provide skewed recommendations.
Hence, data utilized in higher-level decision-making should be extensively examined to ensure that executives have confidence that information is proven, authoritative, validated, and derived from trustworthy sources. It must be free of known discriminatory practices that can cause algorithms to be skewed. For example, discrimination may be considerably decreased at a low incremental cost by regulating categorization accuracy. Controlling discriminating, reducing dataset distortion, and retaining usefulness should be the focus of this data pre-processing optimization.
And here we reach the question of AI ethics in decision-making processes.
Executive reluctance may reflect the reality that businesses are under greater pressure than ever before to guarantee that their operations are moral and ethical, and AI-assisted judgments must reflect these values as well. Currently, research and educational institutions are trying to apply human values to AI systems, and these values are being converted into engineering terminology that robots can grasp. For instance, Stuart Russell, a computer science professor at the University of California, Berkeley, pioneered the Value Alignment Principle, which basically “rewards” AI systems for more acceptable conduct. AI systems or robots may be taught to read tales, understand appropriate sequences of events, and better reflect successful behavior.
Lastly, we should consider transparency when implementing and relying on AI solutions.
If there is a lack of transparency, executives may be hesitant to absorb AI judgments. Thus, the data used to train algorithms must be stored securely, validated, audited, and encrypted to be held accountable. Immutable and auditable storage is also possible with emerging approaches like blockchain and other distributed ledger technologies. Furthermore, a third-party governance system must be established to verify that AI choices are not just understandable but also founded on facts and data. At the end of the day, it should be feasible to demonstrate that a human expert, given the identical data set, would have come to the same conclusions — and that AI didn’t tamper with the results.
For strategic reasons, more AI-assisted decision-making will inevitably be observed in the executive suite. For the time being, AI will be used to aid people in decision-making to conduct enhanced intelligence, rather than delivering perfect insights at the touch of a button like a unicorn. Assuring that the output of these AI-assisted judgments is based on trustworthy, impartial, explainable, ethical, moral, and transparent insights will help business leaders have confidence in AI-assisted decisions not only today but also in the future.
