The Evolving Workplace: AI's Influence on Jobs and Skills

UPI Study › Courses › Introduction to Artificial Intelligence › Free lessons › Lesson 9.7 Free with a free account

The Evolving Workplace: AI's Influence on Jobs and Skills

Introduction to Artificial Intelligence · Chapter 9: AI Applications, Ethics, and the FutureSection 9.7 · lesson 68 of 6912 min · free with a free account

← How AI Reshapes Economies and MarketsNext lesson: AI's Broader Impact on Society, Policy, and Global Governance →

Earn 3 college credits for Introduction to Artificial IntelligenceRecommended for credit by NCCRS · Computer Science 373 · self-paced · one payment, lifetime access
Enroll in Introduction to Artificial Intelligence · $250 →Download the syllabus (PDF)

In this lesson

How AI changes tasks, skills, hiring and monitoring in the workplace.

  • Explain why jobs change task by task, using substitution, complementarity, demand growth and new tasks
  • Calculate the share of a job's time an AI tool could free, weighting each task by its time
  • Compare patterns of human-AI collaboration and the risks of automation bias and deskilling
  • Describe good practice for using AI in hiring and workplace monitoring

Free study notes

Get this lesson's notes by email

The key points and 4 practice questions with answers, in your inbox. One email, free.

One email. Unsubscribe in one click.

Transcript

Everything said in the video, section by section (1,551 words). Read along, or search it.

Opening Question

Will the Claims Tool Take Jordan's Job

Jordan processes insurance claims. This month, the company brought in an AI tool, and a rumor spread that it can do a third of Jordan's job. Will Jordan lose the job? Headlines ask a yes-or-no question: will AI take my job? A more useful question is which tasks in the job are changing, and what people and organizations can do about it.

Jobs as Bundles

Technology Reaches Tasks Before Jobs

Technology has changed work before. Looms changed weaving, typewriters changed offices, spreadsheets changed accounting. Each time, some tasks disappeared, some jobs changed shape, and new roles appeared. That's because a job is a bundle of tasks. A bank teller once counted cash, checked identity, answered questions, and helped customers choose products. Technology usually reaches single tasks first, and a whole job only later, if at all. That's why one tool can wipe out one role, reshape another, and leave a third untouched.

Key Terms

Automation, Augmentation, Displacement and Creation

Four terms. Automation: a machine performs a task a person used to do, like software filling in forms from scanned documents. Augmentation: a tool helps a person do a task better or faster, and the person stays in charge, like a writing assistant whose suggestions the author edits. Displacement: a worker loses a role because its tasks shrink or vanish, like a job that was mostly copying data. And job creation: new roles appear, like technicians who maintain machines, or people who check and improve AI output.

Four Forces

Substitution, Complementarity, Demand and New Tasks

When a new tool arrives, four forces act at once. Substitution: the machine takes over some tasks, so fewer human hours go to them. Complementarity: the remaining human tasks become more valuable. Demand growth: lower costs or better service raise demand, which can offset substitution. And new tasks: the technology opens work that didn't exist before. The result depends on which force wins.

Classic Case

ATMs and Bank Tellers

The bank teller is the classic case. Automated teller machines took over much of the cash handling. Yet for a long time, teller numbers didn't fall as many expected. Branches became cheaper to run, so banks opened more of them, and tellers shifted toward relationships and selling services. Later, as online and mobile banking spread, teller numbers did decline. So technology doesn't always create jobs, or always destroy them. It depends on costs, demand and redesign, and it changes over time.

Worked Example

How Much of Jordan's Time Is Freed

Back to Jordan. Jordan's time splits into four tasks. Data entry is 30 percent of it, and the tool can handle 80 percent of that: 24 percent of the job. Checking documents is 25 percent, and the tool handles half: 12.5 percent. Talking with customers, 25 percent, and handling unusual cases, 20 percent, it can't do at all. So 36.5 percent of the job's time is freed. What happens to that time isn't decided by the tool. The company could process more claims, keep fewer clerks, or move clerks toward customers and complex cases.

Task Grid

AI Ability Versus Judgment Needed

To think about any job this way, sort its tasks on two questions: how well can AI do it, and how much judgment or relationship does it need? Data entry: AI does it well, little judgment. Checking documents sits in between. Customer talks and unusual cases need judgment and relationships, so they stay with people. One caution. People once thought only routine tasks were exposed. But generative AI drafts text, writes code and summarizes documents, so that line has blurred. And exposure isn't loss. Whether it turns into change depends on cost, regulation, how fast organizations adopt, and whether demand grows.

Quick Check

A Paralegal's Freed Time

Quick check. A paralegal spends 40 percent of the time reviewing documents, 30 percent meeting clients, and 30 percent drafting. A tool can do 70 percent of the reviewing and half the drafting, but none of the meetings. What share of the job's time is freed? Pause, and work it out. Forty-three percent. Reviewing frees 40 times 0.7, which is 28 percent. Drafting frees 30 times a half, 15 percent. Together, 43. Sixty is the trap: it averages the tool's seventy and fifty percent, and forgets how much of the job each task takes.

Human-AI Collaboration

Human In, On or Out of the Loop

Often the real question is how people and AI share a task. AI tends to be strong at speed, consistency, patterns in huge data sets, and quick first drafts. People tend to be strong at judgment in unusual situations, empathy and trust, ethical reasoning and accountability, and understanding context. Sharing comes in patterns. Fully human. Human in the loop: AI suggests, a person decides. Human on the loop: AI decides, a person monitors and can step in. And out of the loop: fully automated.

Collaboration Risks

Rubber-Stamping, Deskilling and Blurred Blame

Collaboration has its own risks. Automation bias is trusting a machine's suggestion too readily, so reviewers approve without really checking. That's rubber-stamping. Deskilling: people stop practicing a skill the tool handles, then can't step in when it fails. And responsibility blurs: who answers for an AI-assisted mistake? Research on human-AI teams is mixed. Sometimes the pair beats either alone, and sometimes it does worse than the better of the two.

Good Design

Dividing Tasks Deliberately

So divide tasks deliberately, show reasons and confidence, make overriding easy, keep skills in practice, and measure whether the combination really is better.

Valued Skills

Literacy, Durable Skills and Domain Knowledge

As tasks shift, so do the skills employers value. First, AI and data literacy: knowing what AI can and can't do, writing clear instructions, and checking outputs. Second, durable human skills: critical thinking, communication, collaboration, empathy, creativity and adaptability. Third, deep domain knowledge, to judge whether an AI's answer is sound. Upskilling deepens skills in your current role. Reskilling prepares you for a different one. Both are lifelong learning, through courses, on-the-job learning, apprenticeships and communities of practice. The barriers are real: time, cost, access, confidence, and whether employers support it.

Personal Plan

Five Steps to Stay Ready

A personal plan takes five steps. Map your tasks: list what you actually do in a week, and sort them on that grid. Try the tools on your own work, and always check the result. Strengthen durable skills. Set a learning routine, even a few hours a month. And keep evidence: save examples of your work, and record new skills and credentials.

AI in Hiring

Fast Screening, Learned Unfair Patterns

AI also enters hiring: targeting job ads, screening resumes, scoring assessments and video interviews, and ranking candidates. The benefits are real. It handles huge numbers of applications quickly, applies the same criteria to everyone, and answers routine questions at any hour. But a tool trained on past hiring decisions can learn their unfair patterns, even without seeing a protected characteristic, because other details act as stand-ins. Tools can also be invalid, scoring things that don't predict job performance. They can be opaque, inaccessible to people with disabilities, and collect sensitive voice and video data.

Good Hiring Practice

Validation, Audits and Human Review

Good practice: validate the tool, showing its scores relate to success in the job, not just to past choices. Check outcomes by group regularly. Keep a human review for borderline cases, with the power to overrule. Tell candidates when AI is used, and how to ask for another route. And hold vendors accountable: the employer stays responsible, even when someone else built the tool. Some places now require bias audits and notice to candidates.

Workplace Monitoring

Algorithmic Management and Its Trade-Offs

AI also appears after hiring. Systems log keystrokes and screens, analyze email, track location, and score productivity. Some even assign tasks, set schedules and issue warnings automatically. That's algorithmic management: software making decisions a manager once made. Each use has a benefit and a risk. Activity tracking can spot security problems, but measures typing, not value. Location tracking can warn about hazards, but can follow people after hours. Productivity scores can show who needs help, but invite gaming and stress. Automated scheduling matches staff to demand, but makes hours unpredictable.

Measures as Targets

Gaming the Metric

Monitoring has a recurring problem: when a measure becomes a target, people optimize the measure, not the goal. Score keystrokes per hour, and some people will type meaninglessly. Heavy monitoring can also cut trust and autonomy, and raise stress. Responsible use rests on purpose, a stated reason. Proportionality, no more intrusive than needed. Transparency. Human review and appeal. And worker voice in what's introduced.

Quick Check

Radiologists Approving Every Flag

Quick check. A hospital adds an AI tool that flags suspicious scans. A year later, an audit finds radiologists approve almost every flag without opening the image. Which risk is this? Pause, and decide. Automation bias. The reviewers trust the machine too readily, so review has become rubber-stamping. Deskilling is the tempting answer, and it may follow if this goes on. But right now, the radiologists can still read scans. They're simply not checking.

Back to Jordan

Tasks Change, Choices Remain

Back to Jordan. The tool frees about 36 percent of Jordan's time, not a whole job, and the rest needs judgment and relationships. What happens next depends on demand and on management's choices. Jordan's best move is to map the tasks, learn the tool, and grow the customer and complex-case work.

Key Takeaways

AI and Work in Summary

To sum up. Jobs are bundles of tasks. Substitution, complementarity, demand growth and new tasks act together, and the winner varies. Good collaboration divides tasks deliberately, keeps overriding easy, and guards against automation bias and deskilling. AI literacy, durable human skills and domain knowledge gain value, and lifelong learning keeps them current. And AI in hiring and monitoring needs validation, bias checks, transparency, human review, and care about what's being measured.

Think About It

Mapping the Tasks of a Job You Know

Here's something to think about. Pick a job you know well, and list its tasks for a typical week. Which tasks could AI handle well, which need judgment or relationships, and what would you do with the freed time?

About Introduction to Artificial Intelligence at UPI Study

Introduction to Artificial Intelligence (Computer Science 373) is a self-paced online course of 69 video lessons in 9 chapters, from intelligent agents and search to game strategy, neural networks, logic and reasoning, machine learning, language, robotics and AI ethics. It carries a NCCRS credit recommendation for 3 semester credits.

Every lesson transcript is free on pages like this one, and the videos are free with a free UPI Study account. Enrolling adds a practice quiz after each lesson, the graded work and final exam that earn the credit, lifetime access, and an official transcript on completion. UPI Study has helped over 130,000 students with college credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.

Enroll now → Course details Download the syllabus All Computer Science courses Every course with PRO →

Compare: AI vs Current Trends in CS and IT

Why take it here

Why students take Introduction to Artificial Intelligence with UPI Study

How machines search, reason and learnIntroduction to Artificial Intelligence covers how AI systems are built: intelligent agents, search and heuristics, game strategy, logic, neural networks, machine learning, language and perception, and the ethics of AI. It is a common upper-level course in computer science degrees.
Credit at a fraction of the priceThe same 3 credits can cost $750 or more at a US college. Here it is one payment, self-paced, with lifetime access.
Finish your degree soonerTransfer credits reduce the courses you still owe, so you can graduate earlier or lighten a heavy semester.
Strengthen an applicationA completed college-level course on an official transcript shows admissions and scholarship committees you can do the work.
Study on your own scheduleNo start dates, no deadlines. Watch the lessons, practice, and take the final when you are ready.
130,000+students helped with college creditsACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.

Where the credit goes

Universities that work with NCCRS

Introduction to Artificial Intelligence carries a NCCRS credit recommendation. These universities work with NCCRS; some are UPI Study's signed partners. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.

Thomas Edison State UniversityThomas Edison State UniversitySigned partnerUniversity of the PeopleUniversity of the PeopleSigned partnerCharter Oak State CollegeCharter Oak State CollegeSigned partnerStrayer UniversityStrayer UniversitySigned partnerColumbia Southern UniversityColumbia Southern UniversitySigned partnerIndiana TechIndiana TechSigned partnerPenn StatePenn StateWorks with NCCRSSouthern New Hampshire UniversitySouthern New Hampshire UniversityWorks with NCCRSSUNYSUNYWorks with NCCRS +many more cooperating universitiesFind my college →

Find my college → Not listed? Ask Athena in the corner of this page, or book a 15-minute call.

FAQ

Questions students ask us

About The Evolving Workplace: AI's Influence on Jobs and Skills

What is the difference between automation and augmentation?
Automation means a machine performs a task a person used to do, while augmentation means a tool helps a person do a task better or faster and the person stays in charge. Software that fills in forms from scanned documents is automation. A writing assistant whose suggestions the author edits is augmentation. One job can contain both, because a job is a bundle of tasks that technology tends to reach one at a time.
How do you estimate how much of a job AI could take over?
Multiply each task's share of working time by the share of that task the tool can handle, then add the results. If reviewing documents takes 40 percent of the time and a tool handles 70 percent of it, that frees 28 percent; handling half of a 30 percent drafting task frees 15 percent more, for 43 percent in total. Averaging the tool's percentages without weighting by time gives the wrong answer.
Did ATMs eliminate bank teller jobs?
Not at first. ATMs took over much of the cash handling, yet for a long time teller numbers did not fall as many expected. Branches became cheaper to run, so banks opened more of them, and tellers shifted toward relationships and selling services. Later, as online and mobile banking spread, teller numbers did decline. The case shows that costs, demand and redesign shape what a technology does to jobs over time.
What is automation bias in the workplace?
Automation bias is trusting a machine's suggestion too readily, so reviewers approve its outputs without really checking. Radiologists who approve nearly every AI flag without opening the image are an example, and review turns into rubber-stamping. It differs from deskilling, where people lose a skill through lack of practice. Showing reasons and confidence, making overrides easy and measuring whether the human-AI combination performs better all help.

About the Introduction to Artificial Intelligence course

How many college credits is Introduction to Artificial Intelligence?
Introduction to Artificial Intelligence (Computer Science 373) carries a NCCRS credit recommendation for 3 semester credits. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities.
How much does Introduction to Artificial Intelligence cost and what is included?
$250, paid once, with lifetime access: all 69 video lessons, a practice quiz after each, the graded work and final exam, and an official transcript when you finish. Students who want several courses choose UPI Study PRO instead.
Which universities accept UPI Study credits?
UPI Study has signed partner agreements with universities such as Thomas Edison State University, University of the People and Charter Oak State College, and works with universities such as Penn State, SNHU and SUNY. ACE and NCCRS have over 2,100 cooperating colleges that will consider accepting credits; the final decision is with the universities. Use "Find my college" on this page to check yours.
How long does Introduction to Artificial Intelligence take?
It is self-paced with no deadlines. The 69 lessons total about 16 hours of video; most students add reading and practice time and finish in a few weeks to a few months, on their own schedule.
How are the exams done?
Quizzes and assignments are online in the course. The final exam is taken online with proctoring; a person on the UPI Study team checks assignments, usually within two days.
How do my credits reach my college?
When the course shows 100% complete, request the official transcript from your dashboard. It is sent to the college you name, and you can send it to more than one. Check your college first with "Find my college" on this page, or ask Athena.

Ready for the full course? Earn 3 credits in Introduction to Artificial Intelligence.

69 video lessons, a quiz after each one, and the graded work that earns the credit. Recommended for credit by NCCRS.

Enroll in Introduction to Artificial Intelligence →Download the syllabusEvery course with PRO →
Introduction to Artificial Intelligence
3 credits · NCCRSEnroll →