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Neelava Career School, Dhaka (2026)

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18/09/2026

Last year, a manager I know gave two employees the same assignment: research a new business opportunity and prepare a recommendation for the leadership team.
Both employees were capable. Both had access to the same information. Both had roughly the same amount of time.
One employee immediately started opening dozens of browser tabs, copying information into documents, reading reports and manually organizing notes. After several hours, he had collected a lot of information but was still struggling to understand the bigger picture.
The second employee approached the assignment differently.
She used AI to help organize the research, identify themes, summarize long documents and challenge her initial assumptions. But she didn't simply copy what the AI produced. She checked important information, applied her industry knowledge, added her own analysis and turned the research into a clear recommendation.
The difference wasn't that one employee worked hard and the other didn't.
The difference was how they used technology to amplify their capability.
This is the opportunity AI creates for professionals today.
You don't need to wait for some distant "future of work" to benefit from AI.
You can start using it right now to improve how you research, write, analyze, learn and make decisions.
But there is an important principle to remember:
Don't use AI simply to do more work. Use AI to do better work.
Here are five practical ways to start.
1. Use AI as a Research Assistant
Research is one of the most time-consuming parts of professional work.
You may need to understand a market, competitor, customer, industry trend, new technology, business problem or unfamiliar subject. Traditionally, that can mean searching through dozens of pages and documents before you can even begin forming an opinion.
AI can help you get to the starting point faster.
You can ask an AI tool to help you identify the major themes in a topic, explain unfamiliar concepts, organize questions you should investigate, compare different perspectives or create a research framework.
But don't make the mistake of treating AI's first answer as the research itself.
Use AI to accelerate the process, not to eliminate verification.
For example, instead of asking:
"Tell me everything about the global retail industry."
Give it a meaningful research objective:
"I'm researching how changing consumer behavior is affecting physical retail stores. Help me identify the five most important areas I should investigate, the questions I should ask, and the types of reliable sources I should look for."
Now AI is helping you structure your thinking.
You can then investigate those areas using credible sources and use AI again to organize the information.
The workflow becomes:
Question → Research → AI-assisted organization → Verification → Insight.
That's much more valuable than simply asking AI for an answer.
And this can save enormous amounts of time.
2. Use AI as a Writing Partner
Writing is another area where AI can add immediate value.
Think about how much professional writing happens every day.
Emails.
Reports.
Presentations.
Proposals.
Meeting summaries.
Job applications.
LinkedIn content.
Business plans.
Customer communication.
Internal documentation.
AI can help you overcome the blank page, organize your thoughts, improve clarity, adjust tone and create a first draft.
But there's a major difference between AI-generated writing and AI-assisted writing.
AI-generated writing often sounds generic because the machine doesn't automatically understand your experience, personality, audience or professional judgment.
AI-assisted writing starts with your thinking.
You provide the facts, context, objective and point of view. AI helps you structure and improve them.
For example, instead of:
"Write an email to my manager."
Try:
"I need to update my manager about a project that is slightly delayed because a supplier missed the agreed timeline. I want to be transparent, take responsibility for my part, explain the corrective action already taken, and avoid sounding defensive. Draft a concise professional email."
The result will be much more useful because you've provided the context.
Then review every important detail.
Your name is going on the message—not the AI's.
Use AI for speed and clarity.
Keep ownership of the message.
3. Use AI to Improve Your Analysis
This is where AI can become particularly powerful for professionals.
Most people have access to more information than they can realistically process.
Sales data.
Customer feedback.
Survey responses.
Product information.
Reports.
Operational records.
Market information.
The challenge isn't always finding information.
It's making sense of it.
AI can help you organize large amounts of information, identify patterns, categorize feedback, generate comparisons, highlight inconsistencies and suggest questions worth investigating.
Imagine you're reviewing hundreds of customer comments.
Instead of manually reading every comment and trying to remember recurring themes, you can use an appropriate AI workflow to categorize the feedback into themes such as product quality, price, delivery, customer service and usability.
But again, don't stop with the AI output.
Ask:
"Do these categories make sense?"
"What might the AI have missed?"
"Are some comments being interpreted incorrectly?"
"What additional data do I need?"
This is where human expertise becomes important.
AI can help you see patterns.
You still need to determine what those patterns mean.
That distinction is critical.
A professional who can combine AI-assisted analysis with domain expertise can potentially move from spending hours processing information to spending more time interpreting it and deciding what to do about it.
That's a career advantage.
4. Use AI as a Personal Learning Partner
Perhaps the most underrated use of AI is learning.
You no longer have to wait for a formal course to explain every concept you encounter.
If you're preparing for an interview, learning a new business skill, trying to understand finance or exploring an unfamiliar industry, AI can act as a conversational learning partner.
You can ask it to explain a difficult concept at your level.
Then ask for an example.
Then ask it to test you.
Then ask it to identify your mistakes.
Then ask it to give you a more difficult problem.
Imagine you're learning negotiation.
Instead of simply reading about negotiation techniques, you could ask AI to act as a difficult client. You negotiate with it. It responds. You try again. Then you ask it to evaluate your approach and identify where you could improve.
That's active learning.
The same approach works for communication, management, business analysis, interview preparation, writing, presentation skills and many other areas.
A useful learning loop is:
Learn → Practice → Get feedback → Improve → Practice again.
AI can support every stage of that cycle.
But remember one important rule:
AI should make you a better thinker, not a dependent thinker.
Don't always ask AI to give you the answer.
Sometimes ask it to give you questions.
Sometimes solve the problem yourself first.
Then use AI to challenge your reasoning.
That approach builds capability instead of dependence.
5. Use AI for Decision Support
This may be the most important application of all.
Professionals make decisions constantly.
Which option should we choose?
What risks are we missing?
What questions should we ask?
What are the advantages and disadvantages?
What could go wrong?
What assumptions are we making?
AI can help you explore these questions from different perspectives.
For example, suppose you're considering two career opportunities.
Instead of asking:
"Which job should I take?"
Give AI the relevant factors.
"Help me compare these two opportunities based on compensation, learning potential, career progression, manager quality, stability, location, work-life balance and long-term skill development. Identify the assumptions I should verify before making the decision."
Now AI isn't making the decision for you.
It's helping you structure the decision.
You can also ask it to argue the opposite side.
"Give me the strongest reasons why my preferred option might be the wrong choice."
That can expose blind spots.
This is what makes AI useful as decision support.
But there is a boundary we should never forget.
AI can support your judgment. It should not automatically replace your judgment.
Important decisions involving money, employment, legal matters, health, safety, privacy or significant business consequences require appropriate human expertise and verification.
AI can help you think.
You remain accountable for what you decide.
And that distinction is becoming increasingly important as AI becomes more capable.
________________________________________
The Bigger Career Lesson
These five applications—research, writing, analysis, learning and decision support—have something in common.
None of them require you to become an AI engineer.
They require you to become better at working with AI.
That is an important distinction.
You don't need to know every AI tool available.
You don't need to understand every technical detail.
You don't need to spend your entire day experimenting with prompts.
You need to understand your own work well enough to recognize where AI can create leverage.
Think about your working day.
Where do you spend time collecting information?
Where do you repeatedly write similar things?
Where do you process large amounts of information?
Where are you constantly learning something new?
Where do you face decisions that would benefit from seeing multiple perspectives?
Those are potential opportunities.
And here's the challenge I would give every professional reading this:
Don't try five AI applications tomorrow. Start with one.
Choose one recurring task.
Use AI to improve it.
Measure the difference.
Did it save time?
Did it improve quality?
Did it help you discover something you might have missed?
Did it allow you to spend more time on higher-value work?
If the answer is yes, you've found a useful AI workflow.
Then improve it.
Over time, these small improvements can compound.
Saving fifteen minutes on one task may not seem important.
But saving fifteen minutes every working day adds up.
More importantly, if AI gives you back time, don't immediately fill that time with more low-value tasks.
Use it to learn.
Think.
Build relationships.
Solve harder problems.
Improve your professional skills.
Create something new.
That's where the real career value lies.
The goal isn't to become the employee who uses the most AI.
The goal is to become the professional who creates better outcomes because they know how to use AI intelligently.
AI is a tool.
Your expertise gives it context.
Your judgment gives it direction.
Your human skills turn the result into action.
And your ability to create meaningful outcomes is what ultimately creates career value.
A practical challenge for this week
Choose one task you perform regularly.
Write down how you currently do it.
Then ask yourself:
Can AI help me research it faster?
Can AI help me write it better?
Can AI help me analyze it more effectively?
Can AI help me learn what I need to know?
Can AI help me think through the decision?
You may discover that AI can help with one—or several—of these areas.
Start small.
Experiment responsibly.
Verify the output.
Measure the result.
Then improve the workflow.
A reflection for today
If AI could give you back five hours every week, what would you do with those five hours?
Would you simply take on more work?
Or would you invest that time in becoming more skilled, more strategic and more valuable?
That answer matters.
Because the greatest benefit of AI may not be that it helps us work faster.
It may be that it gives us the opportunity to spend more of our limited time doing work that actually matters.
Don't learn AI just to keep up with technology. Learn to use AI to increase the value of your own capabilities.

17/09/2026

When Daniel first heard people talking about becoming "AI-ready," he assumed it meant learning ChatGPT.
So he opened an AI tool, created an account, tried a few prompts and watched some tutorials. Within a few weeks, he could generate emails, summarize documents, create ideas and ask AI to explain almost anything.
He felt prepared.
Then, during a team discussion, his manager asked a simple question:
"How can we actually use AI to improve the way our team works?"
Daniel suddenly realized that knowing how to use an AI tool was not the same as knowing how to use AI at work.
He knew the technology.
But he hadn't yet learned how to connect it to a real business problem.
That distinction is becoming increasingly important.
Because the new career advantage isn't simply being able to say:
"I know ChatGPT."
It is being able to say:
"I know where AI can improve my work, how to use it responsibly, how to evaluate the result, and how to turn it into better outcomes."
That is what being AI-ready really means.
AI readiness is not about becoming a programmer.
It doesn't require you to understand every technical detail behind a large language model.
And it certainly doesn't mean collecting dozens of AI tools just so you can say that you're "good at AI."
AI readiness is a professional capability.
It means understanding enough about AI to recognize where it can create value in your work—and having enough judgment to know where it should not be trusted blindly.
That distinction matters because AI is moving from being something people experiment with to something increasingly integrated into everyday work.
LinkedIn's recent labour-market research has reported strong growth in jobs requiring AI-literacy skills, while its analysis also shows AI literacy appearing across a wide range of non-technical roles.
The implication is important.
You don't necessarily need an "AI job" to need AI skills.
You may need AI skills because of the job you already have.
Consider a recruiter.
An AI tool can help summarize resumes, draft outreach messages and organize candidate information.
But an AI-ready recruiter doesn't simply press a button and accept the output.
They understand the recruitment objective, provide appropriate context, review the results, identify potential bias or inaccuracies, protect confidential information and use human judgment before making decisions.
That's AI readiness.
Consider a sales professional.
An AI tool can help research prospects and draft a proposal.
But an AI-ready salesperson knows which customer information matters, checks whether the research is reliable, adjusts the communication to the specific customer and uses the extra time to build relationships and improve the sales strategy.
Again, the technology is only part of the equation.
The real advantage comes from how the professional uses it.
This is why I would define AI readiness through five capabilities.
First is AI awareness.
You need to understand what modern AI can reasonably do.
It can generate and transform text, summarize information, analyze certain types of data, assist with brainstorming, support research, create images and help automate parts of workflows.
But it can also produce incorrect information, misunderstand context, reflect biases in its inputs, and confidently present an answer that sounds convincing but is wrong.
AI literacy begins with knowing both the capabilities and limitations.
Second is problem identification.
This is where many people miss the point.
They start with the tool:
"What can ChatGPT do?"
A more valuable question is:
"What problem in my work is worth solving?"
Maybe your team spends hours every week preparing repetitive reports.
Maybe customer feedback is scattered across multiple documents.
Maybe you repeatedly draft similar communications.
Maybe research takes too long.
Maybe employees struggle to find information buried in large documents.
Maybe you're spending too much time on administrative work and not enough time on strategic thinking.
Start with the problem.
Then explore whether AI can help.
Third is AI collaboration.
This means learning how to give AI useful context, clear instructions, examples, constraints and desired outcomes.
But don't reduce this to the fashionable idea of "prompt engineering."
A good prompt is useful.
A good professional workflow is more valuable.
The real skill is knowing what information AI needs, what you want it to produce, how you will evaluate the output and what you will do with it afterward.
The process becomes:
Problem → Context → AI assistance → Human review → Improvement → Outcome.
That is much more powerful than simply asking an AI chatbot a question.
Fourth is critical evaluation.
This may become one of the most important professional skills in the AI era.
AI can produce an answer quickly.
That doesn't mean the answer is correct.
An AI-ready professional doesn't ask only:
"Can AI do this?"
They also ask:
"Is the output accurate?"
"What information might be missing?"
"Does this make sense in my industry?"
"Could this create a legal, ethical or privacy problem?"
"What assumptions is the AI making?"
"Would I be comfortable being accountable for this decision?"
The faster AI becomes, the more important human judgment becomes.
Fifth is value creation.
This is the most important one.
Don't measure your AI capability by the number of tools you know.
Measure it by what those tools help you accomplish.
Did you reduce the time required for a task?
Did you improve quality?
Did you reduce errors?
Did you make better decisions?
Did you improve customer experience?
Did you learn something faster?
Did you free yourself to focus on higher-value work?
Did you help your team become more productive?
That's the difference between using AI and being AI-enabled.
And this distinction can have a significant impact on your career.
Imagine two employees.
Employee A tells everyone, "I use AI."
Employee B quietly redesigns a recurring workflow using AI, reduces preparation time, improves consistency, verifies the results and documents the process so the rest of the team can use it.
Who is demonstrating greater career value?
The answer isn't difficult.
The second employee has connected technology to an outcome.
That's what organizations increasingly need.
And this is where AI readiness connects with money and career growth.
If you can use technology to create more value, you potentially increase the range of problems you can solve.
If you can solve more valuable problems, you can potentially take on greater responsibility.
Greater responsibility can create opportunities for career progression and stronger compensation.
Of course, AI skills alone don't guarantee a promotion or higher salary.
But the ability to combine domain expertise + AI capability + human judgment can become a powerful professional combination.
Think of it this way.
A person who knows AI but doesn't understand the business may produce impressive outputs that don't solve the right problem.
A person who understands the business but refuses to adapt to new technology may eventually become less efficient.
But someone who understands the business, knows how AI can assist, and can apply sound judgment has the potential to create something more valuable.
Domain expertise + AI fluency + human judgment = career advantage.
So how do you start becoming AI-ready?
Don't try to learn everything.
Start with your own work.
Take one normal working week and observe where you spend your time.
Which tasks are repetitive?
Which tasks involve searching for information?
Which tasks involve summarizing or organizing information?
Which tasks involve drafting?
Which tasks consume time but don't require much creativity?
Which tasks require analysis?
Which tasks require human relationships and judgment?
Then choose one low-risk task and experiment.
Don't immediately automate something critical.
Use AI to create a first draft.
Use it to summarize information.
Use it to brainstorm possibilities.
Use it to structure a document.
Use it to compare ideas.
Then check the result yourself.
Notice where it helps.
Notice where it fails.
Improve the process.
That's how practical AI literacy develops.
Next, learn one AI workflow deeply rather than ten tools superficially.
You don't need to chase every new AI application that appears on social media.
Tools will change.
Names will change.
Interfaces will change.
Capabilities will change.
The underlying skill of identifying problems and designing effective human-AI workflows will remain much more valuable.
Also, protect your professional judgment.
Never assume that because an AI system sounds confident, it is correct.
For sensitive business information, confidential data, customer information or important decisions, understand your organization's policies and the relevant privacy and security requirements.
AI readiness includes knowing when not to use AI.
That is an underrated skill.
And don't forget the human side.
The more technology can handle routine work, the more valuable uniquely human capabilities can become: communication, empathy, leadership, creativity, negotiation, critical thinking, relationship-building and judgment.
The future isn't necessarily about becoming less human because machines become more capable.
It may be about using machines for what they do well so that humans can spend more time doing what humans do well.
That is why I don't think the most important career question is:
"Which AI tool should I learn?"
A better question is:
"How can I become the kind of professional who uses AI to create better outcomes?"
That question will lead you toward better skills.
It will also help you avoid one of the biggest traps of the AI era: confusing tool knowledge with professional capability.
Knowing where the buttons are is easy.
Knowing what problem to solve is harder.
Knowing whether the answer is good is harder.
Knowing how to integrate AI into a real workflow is harder.
Knowing how to turn that workflow into measurable value is harder still.
And that is where the real career advantage lies.
You don't need to become the person who knows everything about AI.
You need to become the person who understands how AI can make your expertise more powerful.
A reflection for today
Look at your current work or studies.
Think of one task that takes you a significant amount of time every week.
Now ask yourself three questions:
Can AI help me do this faster?
Can I use AI to improve the quality of the result?
If AI saves me time, how can I use that time to create more valuable work?
If you can answer those questions honestly, you have already taken your first step toward becoming AI-ready.
Because the future of work won't be defined only by the people who know how to use AI.
It will increasingly reward people who know how to use AI with purpose, judgment and measurable impact.

16/09/2026

AI Is Changing Careers — But Not in the Way You Think
A few months ago, a marketing professional named Sarah was worried about her career.
She had spent years developing skills in research, writing, presentations and customer communication. Then AI tools started becoming part of her team's daily workflow.
Suddenly, tasks that once took her hours could be completed in minutes.
She could generate a first draft of a report almost instantly. She could summarize a long document, brainstorm campaign ideas, analyze customer feedback and turn a rough outline into a presentation much faster than before.
Her first thought was not excitement.
It was fear.
"If AI can do all of this, what happens to my job?"
It is a reasonable question. Millions of professionals around the world are asking some version of it.
But there is another question that may be even more important:
What happens to your career when the tasks inside your job change?
That is a different conversation.
We often talk about AI as if the future will be a simple competition between humans and machines.
Humans do the work.
AI takes the work.
Humans lose the jobs.
Reality is more complicated.
AI is already changing many jobs by changing the tasks people perform, the way they perform them, and the skills employers expect them to have.
The World Economic Forum's Future of Jobs Report 2025 estimates that technological change will contribute to both job creation and job displacement by 2030. It projects 170 million new jobs and 92 million displaced jobs across broader labour-market transformations, resulting in a net increase of 78 million jobs.
That doesn't mean every individual whose task is automated will automatically move into a new role.
It means the nature of work is changing.
And this is where we need to understand an important distinction:
AI replacing a task is not the same as AI replacing a person.
Imagine an accountant who spends several hours every week preparing a basic report.
If AI can automate much of that reporting, one part of the accountant's job may become less time-consuming.
But the accountant can still interpret the numbers, identify risks, explain the situation to leadership, challenge assumptions and help make business decisions.
The task changed.
The professional's potential value did not disappear.
In fact, the professional who learns to use AI effectively may become more valuable because they can spend less time producing the report and more time thinking about what the report actually means.
That is AI augmentation.
The machine handles or accelerates parts of the work.
The human brings judgment, context, responsibility, communication and decision-making.
The World Economic Forum describes this emerging human-machine frontier as a combination of automation and augmentation. Its research indicates that employers expect human-machine collaboration to become an increasingly important way work gets done, alongside tasks performed mainly by humans or technology.
This is why I believe the most useful question for professionals isn't:
"Will AI replace my job?"
It is:
"Which parts of my job can AI change, and which human capabilities will become more valuable because of that change?"
That question creates possibilities instead of simply creating fear.
Consider a recruiter.
AI can help search candidate information, summarize resumes, draft communication and organize data.
But should AI decide who deserves an opportunity?
That requires judgment.
A sales professional can use AI to research prospects and prepare proposals.
But building trust with a major client still requires human communication.
A manager can use AI to summarize information.
But leading people through uncertainty still requires empathy, judgment and accountability.
A designer can use AI to generate concepts faster.
But understanding the customer's needs and deciding which idea actually solves the problem remains a human responsibility.
A teacher can use AI to create learning materials.
But understanding whether a student is confused, discouraged or ready for a greater challenge requires something very different from generating text.
The pattern is becoming clear.
AI can make certain tasks faster. It doesn't automatically make human judgment unnecessary.
And this is why the rise of AI should not only be viewed as a technology story.
It is a career story.
LinkedIn's 2026 labour-market research reports that jobs requiring AI-literacy skills in the U.S. grew 70% year over year. It also reports that 1.3 million new AI-enabled jobs have emerged globally over the past two years.
Perhaps even more interesting is that AI literacy is not limited to technical job titles.
AI-related skills are increasingly appearing across technical and non-technical work. LinkedIn's AI labour-market analysis has identified AI-literacy requirements across roles including business strategy, product management, writing, marketing, administration and program management.
That changes the conversation for professionals who think:
"I'm not in IT, so AI isn't relevant to me."
It probably is.
You don't necessarily need to become an AI engineer.
You may need to become an AI-enabled professional.
There is an important difference.
An AI engineer may build AI systems.
An AI-enabled professional understands how AI can improve the work they already do.
A merchandiser might use AI to analyze product information, summarize supplier communication, identify patterns in sales data or prepare research.
A human-resources professional might use it to organize information, draft communication or support learning initiatives.
A salesperson might use it for prospect research, proposal preparation and customer analysis.
A manager might use it to synthesize information, prepare decision options and improve team workflows.
A student might use AI as a learning assistant—but still develop the judgment to verify information and think independently.
The tool changes according to the profession.
The principle remains the same:
Use technology to increase your capability, not to outsource your thinking.
That last part is critical.
One of the biggest risks of becoming dependent on AI is not that AI becomes too powerful.
It is that we become less capable without it.
If you use AI to write everything, you may gradually weaken your own writing ability.
If you use AI to make every decision, you may weaken your judgment.
If you accept every AI-generated answer without verification, you may become faster at being wrong.
AI should increase your capability—not replace your responsibility.
So what should professionals actually do?
Start by understanding your own work.
Take your regular responsibilities and divide them into three categories.
Which tasks are repetitive?
Which tasks require analysis and judgment?
Which tasks depend heavily on human relationships?
The first category may contain opportunities for automation.
The second may benefit from AI-assisted analysis.
The third will often require strong human skills alongside technology.
Then experiment.
Don't try to become an AI expert overnight.
Choose one recurring task from your work and ask:
"Can AI help me do this faster, better or differently?"
Test it.
Compare the result with your normal process.
Check the accuracy.
Improve your workflow.
Learn what AI can and cannot do.
This is much more valuable than simply collecting AI tools.
Next, strengthen the human skills that technology doesn't make less important.
Learn to communicate.
Think critically.
Ask better questions.
Understand customers.
Solve problems.
Manage relationships.
Make decisions.
Lead people.
Adapt when circumstances change.
The World Economic Forum identifies AI and big data, networks and cybersecurity, and technological literacy among the fastest-growing skill areas, while also emphasizing creative thinking, resilience, flexibility, agility and lifelong learning.
The future is therefore unlikely to belong only to people who understand technology.
It will increasingly belong to people who can combine technology with judgment.
And there is a financial dimension to this as well.
If AI allows you to produce more value in less time, the opportunity isn't simply to finish your work earlier and take on even more tasks.
The bigger opportunity is to move toward higher-value work.
Spend less time collecting information and more time interpreting it.
Less time formatting reports and more time understanding what the numbers mean.
Less time performing repetitive tasks and more time solving meaningful problems.
Less time producing routine content and more time developing strategy.
That's how technology can become a career multiplier.
But there is a warning here.
Don't learn AI simply because everyone is talking about it.
Learn it because you can connect it to a real problem.
The professional who knows 50 AI tools but cannot explain how they improve business outcomes may have less career value than someone who knows three tools deeply and uses them to save time, improve quality or make better decisions.
Tools change. The ability to create value remains.
That brings us back to the central idea of this newsletter.
Your career is not protected by avoiding AI.
It is not protected by blindly adopting AI either.
Your career becomes more resilient when you understand how your work is changing and deliberately develop the capabilities needed for the next version of your profession.
You don't have to predict the future perfectly.
You need to prepare for it intelligently.
So, if you're a student, start learning how to use AI responsibly while strengthening your foundational skills.
If you're looking for a job, understand how AI is changing the roles you're targeting and develop evidence that you can work effectively with modern tools.
If you're already employed, identify tasks where AI can improve your productivity and use the extra capacity to move toward higher-value work.
If you're a manager, don't only ask how AI can reduce costs. Ask how it can help your people become more capable.
And if you're a leader, remember that successful AI adoption is ultimately a people-and-workflow challenge, not simply a technology purchase.
The future won't necessarily belong to people who compete against AI.
It may belong to people who learn how to work with AI while remaining distinctly human.
A reflection for today
Look at your current job or studies.
What are the three tasks you spend the most time doing?
Which of them could AI potentially make faster?
Which still require your judgment, creativity or human connection?
And most importantly:
If AI gave you back five hours every week, how would you use those five hours to become more valuable?
That may be the more important career question.
Because the goal isn't simply to protect your current job from AI.
The goal is to build a career that becomes more valuable as the world of work changes.

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