The rapid emergence of artificial intelligence (AI) tools like chatbots, robotic process automation, and generative writing systems is transforming how work gets done.
As these technologies grow more advanced, understanding their responsible and ethical integration will define the future of work.
AI has moved beyond narrow applications like chess programs into pervasive systems capable of mimicking human capabilities across industries. Pre-trained general purpose AI models like GPT-3 exhibit surprisingly wide competencies, albeit with limitations. AI is no longer science fiction, but a disruptive reality needing measured adoption.
Organizations have an opportunity to strategically implement AI to remove rote tasks from human workloads, while augmenting productivity, creativity, and impact. But along with the benefits, AI risks encoding biases, over-automating, eroding transparency, and displacing workers without a plan.
This article analyzes the three predominant types of AI — symbolic, connectionist, and generative — their implications for transforming work, associated risks, and recommendations for responsible adoption. Key focus areas include:
- Defining use cases suited to different AI approaches
- Maintaining human oversight and control
- Promoting transparency and accountability
- Testing for unwanted biases and behaviors
- Considering impacts on workforce composition and planning
- Developing guidelines and guardrails for usage
The goal is providing both organizational leaders and individual workers guidance on preparing for an AI-enabled workplace. Realizing the promise of AI while avoiding pitfalls requires diligence. But with ethical frameworks, deliberate policies, and proper use case tailoring, AI can positively augment human potential. The future need not be characterized by one-sided automation, but rather the emergence of collaborative intelligence combining the complementary strengths of human and artificial capabilities.
Symbolic AI systems rely on rules, logic, and knowledge representations encoded by people rather than learning purely from data. Two common forms of symbolic AI used in the workplace are: