How Companies and Workers Use AI

A2

How Companies and Workers Use AI

Introduction

Many companies and workers now use artificial intelligence (AI). This changes how people work and how companies find new workers.

Main Body

Big companies like Amazon and Disney use AI. They watch how often workers use AI. Some workers pretend to work hard with AI, but they do not. The companies want workers to use AI in smart ways. Company bosses and board members do not agree. Many board members want to use AI very fast. The bosses think this is too fast. This happens because some board members do not understand AI well. Many people use AI to get jobs. Some people use AI during their interviews. They do this because companies also use AI to pick workers. Some people lie about how well they know AI.

Conclusion

Companies must use AI now. However, bosses still disagree on the plan, and hiring is changing.

Learning

💡 The Power of 'Too'

In the text, we see: "The bosses think this is too fast."

When we use too + adjective, it means there is a problem. It is more than what we want.

Examples from the real world:

  • This coffee is too hot \rightarrow I cannot drink it.
  • This shirt is too big \rightarrow I need a smaller one.
  • The English lesson is too long \rightarrow I am tired.

🛠️ Action Words (Verbs) for Work

Let's look at the words that show 'doing' something in this story:

  • Use \rightarrow To employ a tool (AI).
  • Watch \rightarrow To look at something for a long time.
  • Agree \rightarrow To have the same opinion.
  • Pick \rightarrow To choose someone for a job.

Simple Pattern: Subject + Verb \rightarrow Companies + use AI.

Vocabulary Learning

companies (n.)
businesses that sell goods or services
Example:Many companies hire new workers every year.
workers (n.)
people who do jobs for a company
Example:Workers need to follow safety rules.
artificial (adj.)
made by humans, not natural
Example:Artificial intelligence helps computers think.
intelligence (n.)
the skill of learning and understanding
Example:Artificial intelligence is a type of intelligence.
changes (n.)
when something becomes different
Example:The changes in the office made everyone nervous.
watch (v.)
to observe something carefully
Example:The company watches how often workers use AI.
pretend (v.)
to act as if something is true when it is not
Example:Some workers pretend to work hard with AI.
smart (adj.)
clever or using technology
Example:The company wants workers to use AI in smart ways.
agree (v.)
to think the same way
Example:The bosses and board members do not agree.
hiring (n.)
the act of selecting people for work
Example:Hiring is changing because of AI.
B2

How Companies and Job Seekers are Adapting to Artificial Intelligence

Introduction

Companies and job seekers are increasingly using artificial intelligence (AI) in their professional work. This shift has led to new ways of monitoring employees and created ethical challenges during the hiring process.

Main Body

Many large corporations, including Amazon and Disney, have introduced digital dashboards to track how often employees use AI. For example, KPMG's US advisory division expects staff to use AI on 75% of their working days. However, some reports suggest that employees may enter simple prompts just to make their productivity numbers look better. To solve this, KPMG has introduced innovation awards and is working with the University of Texas at Austin to identify truly advanced ways of using AI. At the same time, there is a disagreement between CEOs and board members regarding the speed of AI adoption. According to a Boston Consulting Group survey, 61% of CEOs feel that their boards are rushing the transition. This tension may exist because some board members lack technical AI knowledge, which leads them to push for faster implementation based on industry hype rather than operational reality. Finally, the recruitment process is also changing. The 2026 Job Seeker Insights Report shows that 78% of candidates use AI during the application process, and 22% use it during live interviews. This is largely a response to employers using automation to screen candidates. Consequently, employers are now focusing more on 'AI fluency,' although 36% of candidates admit to exaggerating their AI skills to get hired.

Conclusion

AI is now a necessary part of the corporate world. However, its rollout is complicated by disagreements between executives and a shift toward AI-driven interactions in recruitment.

Learning

🚀 The 'Sophistication' Shift: Moving from Simple to Complex

To move from A2 to B2, you must stop using basic verbs like say, think, or do and start using precision verbs.

Look at how the text describes a disagreement between bosses. An A2 student would say: "They have a problem because they think differently."

The B2 approach uses these 'Power Words' from the text:

  • Adopting \rightarrow Adoption: Instead of saying "using a new tool," use adopting. It implies a formal process of starting to use something new.
  • Exaggerating: Instead of saying "lying a little bit," use exaggerating. This is a precise B2 word for making something sound better or bigger than it is.
  • Implementation: Instead of saying "putting the plan into action," use implementation. This is a classic corporate B2 term.

💡 Grammar Upgrade: The "Cause and Effect" Connector

At A2, we use 'so' for everything.

  • A2: "Companies use AI, so candidates use AI too."

B2 speakers use Consequently. It sounds more professional and signals a logical result.

Example from the text:

"Employers are using automation to screen candidates. Consequently, employers are now focusing more on 'AI fluency'."

Pro Tip: Start your sentence with Consequently, followed by a comma to instantly sound more advanced in a business meeting or essay.


🔍 Vocabulary Nuance: 'Hype' vs. 'Reality'

In the B2 level, you need to describe abstract concepts. The text mentions "industry hype."

  • Hype (Noun): Excitement that is often exaggerated.
  • Operational Reality (Noun Phrase): How things actually work in the real world.

When you can contrast a "dream" (hype) with a "fact" (reality) using these terms, you are speaking at a B2 level.

Vocabulary Learning

adapting (v.)
to change or adjust to new conditions or situations
Example:Companies are adapting to artificial intelligence by updating their hiring processes.
monitoring (v.)
to observe and check the progress or quality of something over a period of time
Example:HR departments are monitoring employee use of AI through digital dashboards.
ethical (adj.)
relating to moral principles of right and wrong
Example:The use of AI raises ethical challenges about privacy and fairness.
challenges (n.)
difficult tasks or problems that require effort to overcome
Example:AI implementation presents many challenges, including data security concerns.
dashboards (n.)
control panels that display information and metrics
Example:Digital dashboards help managers see how often employees use AI tools.
innovation (n.)
the introduction of new ideas or methods
Example:KPMG introduced innovation awards to reward creative AI applications.
implementation (n.)
the act of putting a plan or system into effect
Example:The company is working on the implementation of AI‑driven recruitment.
disagreement (n.)
a lack of agreement or conflict between parties
Example:There is a disagreement between CEOs and board members about the speed of AI adoption.
transition (n.)
the process of changing from one state or condition to another
Example:The transition to AI tools is faster than many expect.
tension (n.)
a state of mental or emotional strain, often caused by conflict
Example:The tension between executives can delay decision‑making.
technical (adj.)
relating to a particular subject or skill, especially in science or engineering
Example:Board members lack technical AI knowledge, which hampers progress.
hype (n.)
excessive excitement or publicity about something
Example:Industry hype can lead to unrealistic expectations about AI.
operational (adj.)
relating to the functioning or running of a system
Example:Operational reality often differs from theoretical models.
automation (n.)
the use of machines or software to perform tasks automatically
Example:Employers use automation to screen candidates efficiently.
fluency (n.)
the ability to speak or write smoothly and easily
Example:Job seekers must demonstrate AI fluency to impress recruiters.
C2

Institutional Integration and Behavioral Adaptation in the Deployment of Artificial Intelligence

Introduction

Corporate entities and job seekers are increasingly integrating artificial intelligence into professional workflows, leading to new monitoring mechanisms and ethical tensions in recruitment.

Main Body

The institutionalization of artificial intelligence (AI) has manifested in the implementation of quantitative surveillance tools within major corporations. KPMG, JPMorgan Chase, Amazon, and Disney have deployed internal dashboards to monitor AI utilization metrics, such as token generation and frequency of use. Within KPMG's US advisory division, a target usage rate of 75% of business days has been established. However, internal reports suggest a susceptibility to metric manipulation, wherein employees may execute superficial prompts to simulate productivity. The firm has attempted to mitigate this by introducing the 'AI Spark Innovation Awards' and collaborating with the University of Texas at Austin to identify 'sophisticated' usage patterns characterized by iterative partnership with AI rather than basic task execution. Parallel to internal corporate adoption, a divergence in strategic velocity has emerged between executive leadership and governance bodies. A Boston Consulting Group survey of 625 global leaders indicates a systemic friction: 61% of CEOs perceive their boards as rushing AI transformation, while board members frequently advocate for more aggressive implementation. This discrepancy may be attributed to a correlation between lower AI literacy among board members and a heightened sense of urgency, potentially driven by a failure to distinguish between speculative hype and operational reality. Furthermore, the recruitment landscape is undergoing a reciprocal transformation. Data from the 2026 Job Seeker Insights Report indicates that 22% of candidates utilize AI during live interviews, while 78% employ it throughout the broader application process. This trend is framed as a response to the automation of hiring processes by employers. The resulting environment has shifted the evaluative focus from raw recall to 'AI fluency,' although it has also increased the prevalence of candidate misrepresentation, with 36% of respondents admitting to exaggerating their AI proficiency.

Conclusion

AI adoption is now a systemic requirement across the corporate sector, though its implementation is marked by strategic misalignment at the executive level and a shift toward algorithmic interaction in hiring.

Learning

The Architecture of Nominalization and 'Abstract Density'

To move from B2 to C2, a student must transition from describing actions to conceptualizing systems. The provided text is a masterclass in Nominalization—the linguistic process of turning verbs (actions) and adjectives (qualities) into nouns. This shifts the focus from who is doing what to the phenomenon itself.

1. The 'Action-to-Concept' Pivot

Observe how the text avoids simple subject-verb-object constructions in favor of dense noun phrases. This is the hallmark of C2 academic and professional register.

  • B2 approach: "Companies are using AI more, and this is changing how they monitor workers." (Focus on agents and actions)
  • C2 approach: "The institutionalization of artificial intelligence (AI) has manifested in the implementation of quantitative surveillance tools..." (Focus on systemic processes)

Analysis: By using institutionalization and implementation, the writer removes the 'human' element, creating an objective, authoritative tone that characterizes high-level institutional discourse.

2. Strategic Lexical Collocations

C2 mastery involves pairing abstract nouns with precise, low-frequency modifiers to create a 'sophisticated' semantic field. Note the following clusters from the text:

extStrategicvelocitySystemic frictionReciprocal transformation ext{Strategic velocity} \rightarrow \text{Systemic friction} \rightarrow \text{Reciprocal transformation}

These are not mere synonyms for 'speed,' 'problem,' or 'change.' They imply a specific mechanical or structural relationship. Strategic velocity suggests a measurable rate of organizational change; Systemic friction suggests that the problem is built into the structure of the organization, not just a personal disagreement.

3. The 'Nuance Gap': Speculative vs. Operational

Crucial to C2 proficiency is the ability to distinguish between degrees of reality. The text contrasts:

  • Speculative hype: Theoretical, unproven excitement.
  • Operational reality: The practical, day-to-day functioning of a system.

This binary opposition allows the writer to critique board members without using emotive language (like 'stupid' or 'wrong'), instead using a conceptual framework to describe a cognitive failure in distinction.


C2 Synthesis Note: To emulate this, stop asking 'What happened?' and start asking 'What is the name of the phenomenon that occurred?' Convert your verbs into nouns, and pair them with adjectives that describe the nature of the system (e.g., reciprocal, iterative, quantitative).

Vocabulary Learning

institutionalization (n.)
The process of establishing or embedding something within an institution or system.
Example:The institutionalization of remote work has reshaped office dynamics worldwide.
quantitative (adj.)
Relating to, expressed in, or measured by quantity or number.
Example:The study employed a quantitative approach to analyze survey responses.
surveillance (n.)
Close observation, especially of a suspected person or group, for gathering information or monitoring.
Example:The city increased surveillance in high-crime neighborhoods to deter vandalism.
susceptibility (n.)
The quality or state of being easily affected or harmed by something.
Example:Her susceptibility to colds made her frequent doctor visits.
superficial (adj.)
Existing or occurring at or on the surface; not deep or thorough.
Example:He offered a superficial apology that did not address the underlying issue.
mitigate (v.)
To make something less severe, harmful, or painful.
Example:The company introduced new safety protocols to mitigate workplace accidents.
sophisticated (adj.)
Highly developed, complex, or refined in design or function.
Example:The museum showcased a sophisticated network of interactive exhibits.
iterative (adj.)
Involving repetition or cycles, often with incremental improvements.
Example:The software development process is iterative, refining features with each sprint.
discrepancy (n.)
A lack of compatibility or similarity between two or more facts.
Example:A discrepancy between the budget and actual expenses prompted an audit.
correlation (n.)
A mutual relationship or connection between two or more things.
Example:There is a strong correlation between exercise frequency and mental well‑being.
literacy (n.)
The ability to read and write; broader knowledge or proficiency in a subject.
Example:Digital literacy is essential for navigating modern workplace tools.
speculative (adj.)
Based on conjecture rather than solid evidence; involving risks or uncertainties.
Example:Investors engaged in speculative trading during the market boom.
fluency (n.)
The ability to speak or write smoothly, easily, and accurately.
Example:Her fluency in three languages made her a valuable asset to the multinational firm.
misrepresentation (n.)
The act of giving a false or misleading account of something.
Example:The advertisement was criticized for misrepresentation of the product’s features.
algorithmic (adj.)
Relating to or derived from an algorithm; systematic and rule-based.
Example:Algorithmic trading uses complex models to execute stock orders automatically.