Artificial intelligence (AI) weaves through the fabric of everyday lives, even among those unaware of how often they use the technology. Virtual digital assistants are in 110 million American homes, and driver-assisted technology functions in 100 million cars worldwide. AI-driven predictive text systems also assist countless people in writing on smart devices and computers. AI is everywhere, even if we do not realize it. 

As the use of AI expands, many consumers have started to wonder whether this technology and the companies operating it are adequately and ethically serving them. This has resulted in a heightened focus on how companies use customer data. For companies using AI-powered technology, protecting customers is not only ethical, but it also makes business sense, as it ultimately helps protect the organization. As a result, businesses using AI must act responsibly to protect their customers and bottom lines.


To err is costly

AI adoption is growing steadily. An IBM-commissioned study shows that one-third of businesses globally are using it, and another 40% will be soon. Companies are mainly moving to AI and machine learning to improve reliability, boost customer experience and help build trust. These capabilities have also become valuable tools for real-time language translation.

Yet, in an average data breach, U.S. companies lose over $4 million, and these breaches are significantly more costly among organizations with no AI and automation security plan. If harm to their reputation doesn’t make them hyper-vigilant of data security, bottom line profit — or losses — must.


Demonstrate qualifications

To foster trust between companies and customers, organizations must do more than simply protect financial resources. Businesses must also actively seek and eliminate bias from automated tools that process customer data.

They can do this by reviewing technology before and during its implementation, regularly evaluating whether bias is present and correcting it via various techniques such as active learning, data cleaning and augmentation, and even rules. Even if all biases might not be eliminated entirely from AI systems that handle enormous amounts of intelligence, these measures will still prevent harmful consequences. 


Obtaining and maintaining security certifications also ensures that companies are committed to protecting the data they compile. Stringent regulations and certifications include GDPR, PCI, HIPAA, SOC, ISO 27001 and other security frameworks.

Compliance with these qualifications demonstrates a company’s ability to operate transparently and help outside companies assess the business’s IT capabilities.


The world is watching

The world’s largest and most recognizable brands that use AI and ML are laser-focused on responsible and ethical use of these tools. They demonstrate this by displaying their oversight structure for public review. Search engine giant Google has posted its policies for responsible AI use directly on its website.

In contrast, Microsoft has similarly published an extensive review of its advances in responsible AI research. When new services are released, an AI evaluation is carried out to ensure minimal risk, and services might be canceled if they are not used responsibly.


It behooves every company working with AI and ML in customer service to confront this topic directly and openly, if for no other reason than regulators are watching. The European Commission has prioritized AI policies and declares excellence and trust as the two overriding principles to maintain when implementing AI.

To further show the need for transparency in AI policy, domestic policy is being shaped at the federal level and state by state, with ethics, responsibility and privacy concerns at the forefront.


Where AI is headed

The public’s expectations for exemplary customer service grow daily, and businesses are turning to AI to achieve it. As a patchwork of companies adopt ethical and responsible AI frameworks, anticipation will grow among partnering businesses, aligned organizations and the public for more widespread implementation across industries. Interested parties will be looking to reduce biases in tech models and preserve data confidentiality and privacy. 


In the meantime, companies should involve senior leadership to review and adopt certifications, qualifications and training in their current operations that address these areas. They should also seek out and partner with businesses that already operate at these essential benchmarks.

When this is all done collectively, the AI policy will be responsible and ethical.