From an employee’s perspective, management is reluctant to adopt emerging technologies despite the potential value to the organization. In a recent report, more than half of employees interviewed believe emerging technologies are out of date once they reach the point of full adoption. This interesting topic came to us from CIO Dive in their article, “Businesses are too slow to embrace emerging tech, employees say.”
Businesses may be reluctant to embrace emerging technologies for several reasons, but cost is always at the top of that list. Adopting new technologies often requires significant financial investments. Smaller businesses or those with tight budgets might be hesitant to allocate resources to unproven technologies, especially if they are unsure about the return on investment.
Uncertainty and risk is a close second. Emerging technologies can be unpredictable and their success is not always guaranteed. Businesses may fear that adopting these technologies could lead to unexpected problems, potential data breaches, or operational disruptions.
The biggest challenge with any emerging technology is that most organizations have little knowledge regarding how artificial intelligence (AI) systems make decisions and how to interpret AI and machine learning results. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and it potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
