How AI is Giving Every Employee a Voice Without Having to Ask

The following is an excerpt from my FREE Workplace Intelligence Insider Newsletter. You can?access the full article in the?Newsletter Archives . And don't forget to?subscribe ?so you receive the new edition every Monday morning.?

For this week’s Workplace Intelligence Newsletter, I interviewed Kevin Bobowski, Chief Marketing Officer at Aware, the leading AI Data Platform for Employee Listening. In his role, Kevin is responsible for the company’s go-to-market strategy, including brand marketing, product marketing, demand generation, field marketing, and sales development for their full suite of solutions. Kevin has nearly 20 years of SaaS experience and was most recently the CMO of Siteimprove.

In our conversation, we discussed how executives have been impacted by AI, how to responsibly manage AI in the workplace, and using AI to give employees a voice. We also explored how AI builds trust between employees and executives, and how companies are harnessing AI to capture real-time employee feedback to improve employee engagement and the customer experience.

Below is a sneak peek of our conversation — subscribe to my?FREE Workplace Intelligence Insider ?newsletter and you’ll immediately receive the full Q&A.

Read on for Kevin’s insights about this important topic. And be sure to join us during our LinkedIn live event on July 17 at Noon PM EST , where we’ll continue our discussion.

Artificial Intelligence is already changing the business landscape. ?About two-thirds of current jobs are exposed to some degree of AI automation, impacting 300 million full-time jobs. Companies are scrambling to understand how to leverage it.?What advice can you offer to peers in the market seeking to navigate this change?

Face it. AI will transform every industry, business, and job function so everyone must learn to leverage this technology.??It’s no different to how people might have thought about other emerging technologies (e.g., mobile) of the past. The promise of AI is mesmerizing but don’t be too ambitious in your application of AI. Yet.?Instead, look for “low-hanging fruit” to improve what you are doing today.?These projects have a higher degree of success than large, ambitious projects.

Take employee surveys as an example.?Surveys have been used for years to capture the voice of the employee, but twice-a-year surveys just don’t cut it anymore. Our world is moving too fast.?

Instead, Aware customers use our AI Data Platform for continuous employee listening to get a real-time pulse on daily operational challenges facing employees, their concerns, reactions to company and global events, and even concerns about the impact of AI on their jobs.??It’s a continuous feedback loop giving employees a stronger voice – and managers real-time actionable insights.

Ironically, it’s AI that is giving employees a new, more powerful voice.?And insights from the collective voice of the employee are helping managers respond and react—in real-time— to the concerns of their workforce.?

What is “Responsible AI” and what is the responsibility of the business when it comes to the ethical implementation and use of AI in the workplace? ?

Responsible AI starts with good data because AI models are only as good as the data used to train them. It starts with the decisions on how you train the model and the data sources feeding it. You must be thinking about the data sources you use on day 1.

Large language models need massive volumes of data for training and building generalized models. However, you may not know what data went into any individual result coming out of it. You have no idea how it will respond to types of content or questions. For this reason, these models may introduce bias, lack of context, or deliver inaccurate outputs that lead to poor decisions.

Responsible AI may use smaller models trained on what you know, the data you understand, and the results you want. That delivers high-quality, responsibly built models. The data used to train these models is targeted and specific to solving real use cases. When a CEO is making a business decision about the business using the insights from AI, accuracy, and context truly matters.

We live in a dynamic, constantly changing world. This means that once you create a model, it is immediately out of date. The ability to refresh AI models is key to keeping them useful and valuable. At Aware, we're focused on business use cases and how our data is interpreted. The models that provide a single interpretation, something like a sentiment model, can be small. This allows them to be retrained rapidly to adjust to the market, to adjust to new customers, to adjust to the way language is being spoken in a much quicker way than a large language model.

The final component of Responsible AI is data governance or access to data. Role-based access control (RBAC) is a method of restricting access to data based on the roles of individual users within an enterprise. This limits user access to only the data and systems they need to do their jobs, which helps to protect sensitive data and systems from unauthorized access. RBAC can help to improve compliance with security regulations by providing a clear and auditable way to control access to data and systems.

Responsible AI is a complex topic, but it is essential for organizations that are using AI to make these decisions about the business they run. By following the principles of Responsible AI, organizations can ensure that their AI models are accurate, unbiased, and safe to use for making business decisions.

Be sure to join our upcoming LinkedIn live event on July 17th at Noon PM EST , where Mark and I discussed this topic.

Want to read the full article? You can access it in the?Workplace Intelligence Insider Archives . To receive the new edition every Monday morning,?subscribe ?for free.