The rise of artificial intelligence (AI) and machine learning in business applications has radically altered our workplace. More and more, AI is used to automate routine tasks, optimize resources and processes, detect anomalies in infrastructure, and engage with customers and internal staff. This new technology not only shifts the way we work but also requires us to take stock of how we lead in business.
AI is good at performing certain tasks that are repetitive and routine, as well as tasks that require an integration and processing of massive amounts of data in a systemic and logical way. For business leaders to remain relevant, the pertinent questions become: How can we leverage AI to shape our organizations for the better, and what roles do leaders play in guiding the impact AI has?
Detecting and Identifying Patterns
By analyzing employees’ learning and performance data from HR systems and learning platforms, AI can be used to detect patterns in how people learn, including the key point people access the systems for performance support (e.g., while they are having trouble interacting with customers?), how they navigate the content (e.g., linearly or by skipping around?), and which time periods have heavy traffic (Monday morning because they are less busy?). Together, these patterns tell a story of what happened and why we need to pay attention. To add this narrative to the data, leaders need to know how to read the data and how to use it. More importantly, we need to ask ourselves what questions we are trying to answer, or what issues we are trying to solve.
Since AI is used to capture patterns in the dataset, the next logical step is to apply this analysis and make predictions on a range of workplace scenarios. For example, AI-enabled candidate selection tools can find correlations between applicants’ prior work experience, personalities, interests and how potentially successful a particular candidate is for the job she applied for. AI shows great promise in reducing unconscious bias by ignoring information such as age, gender and race, but any existing data might already have bias built-in, whether conscious or unconscious, and using an AI system will inadvertently amplify the bias that already exists within the organizations such as a historical preference for hiring men for technical roles. We need to ask AI vendors detailed questions about safeguarding and bias-detection practices and how to audit the systems over time.
As more data is collected, AI can learn to improve its predictions and provide recommendations on the next course of action. For example, internal-facing chatbots are used to interact with employees for coaching and mentoring sessions. When encountering an interaction that needs escalating or more nuanced discussions, some chatbots can automatically facilitate an anonymous conversation between a human mentor and mentee.
While AI can potentially enhance decision-making processes, we would be wise in safeguarding employees’ and customers’ privacy as well as flagging other ethical concerns. AI-informed decisions made incorrectly and without human intervention could harm people and foster mistrust.
While AI is good at many tasks, its applications need to work in tandem with human intelligence and experiences. To lead in the age of digital transformation, we need to take ownership and familiarize ourselves with the technology, understand its opportunities and limitations, and always question the purpose technology serves and at what cost.