Still, like any new technology, using artificial intelligence at work can come with questions: How can AI improve my productivity?Will AI replace my role? Concerns are common—52% of respondents in a recent Pew Research study reported feeling worried about the future use of AI at work.
Yet, despite the uncertainty some may feel, AI is here to stay. 92% of companies plan to increase their AI investments within the next three years, and it’s estimated that long-term, AI could add up to $4.4 trillion annually to the global economy through productivity gains and new business applications. With this much potential, learning to use AI at work will be crucial for career growth in the years ahead.
Types of AI
Artificial intelligence refers to computer systems that can perform complex tasks and reasoning like human beings. AI is classified into different types based on the tasks the AI is asked to perform and its capabilities to learn, mimic human intelligence, and make decisions without human input. The only type of AI that exists today is Narrow AI, or Weak AI, which is designed to perform a specific task or set of tasks. Narrow AI cannot perform tasks it hasn’t been programmed to do, nor can it understand abstract concepts or learn new information without human intervention. The other AI types are General AI, or Strong AI, and Super AI, or Superintelligence. Both capabilities are currently theoretical. They involve hypothetical artificial intelligence models that can learn to accomplish new tasks, reason, make judgments, and possess cognitive abilities on par with or surpass human intellect.
Within Narrow AI, there are two sub-categories based on the AI’s functionality. Reactive Machine AI is the simplest form of AI. These systems don’t learn from past experience and perform only the tasks they were designed for. Many forms of modern technology use Reactive Machine AI, including social media recommendation algorithms, industrial robots, smart home devices, and the most famous example, IBM’s chess-playing supercomputer, Deep Blue.
Limited Memory AI is becoming more ubiquitous, especially within the workplace. It involves AI systems that temporarily store information to make decisions and improve over time to reach a desired result. Generative AI, like ChatGPT, Virtual Assistants like Siri or Alexa, and self-driving vehicles are all forms of Limited Memory AI.
Other subsets of Narrow AI systems include machine learning (ML), Natural Language Processing (NLP), and Language Learning Models (LLM). These subsets often apply to AI systems used by corporations and individuals in the workplace. Depending on their needs, organizations may use a combination of these AI capabilities simultaneously.
Sales Sales teams integrate AI with Customer Relationship Management (CRM) solutions and communication platforms to analyze customer data, generate email templates, and improve customer interactions.
Customer Service Automated chatbots use Agentic AI to problem-solve customer issues in real-time without the need for human intervention.
Human Resources AI tools screen resumes, schedule interviews, and identify qualified candidates more efficiently.
AI Use by Individuals at Work
Even when companies lag in adoption, employees increasingly use AI independently. Nearly 78% of AI users say they use their own AI tools at work, rather than relying on company-wide systems. With the use of Generative AI tools, employees are turning to individual AI solutions such as:
Brainstorming and Writing AI can help with idea generation, drafting, or editing of written work, including emails, proposals, or articles.
Background Research AI can assist with combing through articles, generating keywords, and summarizing long texts.
AI Assistants AI assistants can take notes or write transcripts of meetings or organize calendars to create a personalized daily agenda.
Ethical Considerations of AI in the Workplace
While AI enables efficiency and innovation, it also raises important ethical and operational questions. Corporations and employees should use AI responsibly. The following ethical issues that may arise include:
Data Security Risks Employees may unintentionally expose sensitive company information if no clear AI policy exists. To get ahead of the curve, companies should take the time to write AI use guidelines. They can also invest in enterprise accounts for common Generative AI tools like ChatGPT or Microsoft Copilot, for better security.
Bias and Discrimination AI trained on biased data can produce discriminatory outcomes, especially in HR. Regular audits and bias-mitigation practices are essential. Examine outcomes carefully to avoid potential bias or discrimination in hiring or performance reviews.
Accountability in Decision-Making If an autonomous AI makes a mistake, who is responsible for the repercussions? Organizations should be careful not to offload crucial decisions to AI. Having humans make key decisions ensures harm reduction.
AI is the Future
The AI industry is still in its infancy, but it’s clear that it will completely transform the workplace. As artificial intelligence continues to develop, it’s almost impossible to know how our roles and organizations will change in the next decade. Exploring today’s AI tools—and learning to use them responsibly—offers workers and organizations a head start in preparing for the workplace of tomorrow.
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