Successful CIOs, CTOs & executives from Forbes Technology Council offer firsthand insights on tech & business.
In recent months, artificial intelligence has taken technology’s center stage. Professionals ranging from marketers to teachers to artists have experimented with creating content and images with generative AI, and businesses across industries are scrambling to incorporate AI to enhance productivity, cut costs and even improve worker safety.
AI enabled hyper-automation helps enhance the speed, efficiency and precision of work operations. However, problems start when systems do not behave as expected. In an AI environment driven by bots and agents, tracing the human who triggered a task that leads to an anomalous action is essential. However, attribution and traceability are arduous, which makes fixing issues impossible. -
Over-automating customer service and making it difficult to access human help is a pitfall that needs to be avoided. Ensuring chatbots are fed with user context and enabling AI chatbots to carry out actions specific to the user’s situation, as well as adding the ability for users to chat with a human and/or request a human call, are great remedies. -
Every industry is looking at how to use AI to gain a competitive edge, but organizations need clarity about the purpose and potential benefits of implementing AI before they begin. Without a firm data foundation, AI quickly becomes a liability, not an asset. To address specific business challenges and prevent mishaps, companies need to make sure the data they use will reach the desired outcome. -
As AI-generated content becomes more realistic and deepfakes become more convincing, there’s a risk that we will see an erosion of trust in legitimate images, audio recordings and written documentation. This crisis of credibility could be detrimental to productive dialogue when it comes to matters of accountability, decision making and reconciliation that rely on shared perceptions of reality. -
AI’s collection of personal data raises privacy and security worries. Tighter rules, transparent practices and educating consumers can reduce risks. Bias in decision making stemming from skewed data is another concern. Diverse data, ongoing monitoring and human oversight are vital. Addressing these challenges up front promotes responsible AI development, fostering trust. -
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