15 answers
Harish
Student
Thanjavur, Tamil Nadu, India
1
Question
Updated
2702 views
What skills and experiences helped you transition from being a technical professional to a leader in AI driven organizations, and what should a student start building today?
I am a final year Computer Science and Business Systems student from India. My interests include AI, healthcare technology, data analytics, research, and entrepreneurship. I have completed internships at ICMR NIE and Menmozhi Technologies and am building AI based solutions for accessible education and healthcare. I hope to pursue graduate studies abroad and would appreciate advice on the technical and leadership skills I should prioritize.
Login to comment
14 answers
Samuel Mutua
11
Answers
Nairobi, Nairobi County, Kenya
Updated
Samuel’s Answer
Hello Harish, I trust you're doing well.
To answer your question, my advice to you would be to focus on building strong technical foundations in AI, data science, software engineering and cloud technologies, but don't stop there.
The professionals who become leaders are those who can solve real world problems, communicate clearly and work effectively across multidisciplinary teams. Since you're already exploring healthcare AI and accessibility, continue building impactful projects, contribute to research, seek mentorship and strengthen skills in leadership, critical thinking, and ethical AI.
If you're planning to pursue graduate studies abroad, a portfolio of meaningful projects, publications, and evidence of initiative will often distinguish you more than grades alone.
I hope you'll find this useful.
My best wishes and regards to you in this path.
To answer your question, my advice to you would be to focus on building strong technical foundations in AI, data science, software engineering and cloud technologies, but don't stop there.
The professionals who become leaders are those who can solve real world problems, communicate clearly and work effectively across multidisciplinary teams. Since you're already exploring healthcare AI and accessibility, continue building impactful projects, contribute to research, seek mentorship and strengthen skills in leadership, critical thinking, and ethical AI.
If you're planning to pursue graduate studies abroad, a portfolio of meaningful projects, publications, and evidence of initiative will often distinguish you more than grades alone.
I hope you'll find this useful.
My best wishes and regards to you in this path.
Thank you so much, Samuel. Your advice really resonates with me, especially the importance of combining strong technical skills with communication, leadership, and solving real-world problems. I'll continue focusing on impactful AI projects, research, and building a portfolio that creates meaningful value. I truly appreciate your encouragement and best wishes. I already had a portfolio if possible you can check it "www.ymira.in/HarishKV"
Harish
Hello, Harish.
You're welcome. Thank you for the feedback. My best wishes to you. I will go through your portfolio and get back to you.
Warm regards.
Samuel Mutua
Sahida Khatun
Software Consultant and Analyst
136
Answers
Kolkata, West Bengal, India
Updated
Sahida’s Answer
I've worked with many advanced technologies and have strong skills in problem-solving and data analysis. This helped me move from being a tech expert to a leader in AI-focused companies. Learning the basics, solving real-world problems, and always learning new things were key. What really made a difference was improving my communication, strategic thinking, and connecting AI solutions to business goals while leading diverse teams.
Thank you, Sahida. I appreciate you sharing your journey. Your point about connecting AI solutions to business goals while strengthening communication and strategic thinking is something I'll consciously work on. It's inspiring to hear how those skills helped you transition into leadership.
Harish
Sourabh Mishra
Presales
3
Answers
Bengaluru, Karnataka, India
Updated
Sourabh’s Answer
To move from a technical role to a leadership position in an AI-focused company, here are some steps you can take:
Start by gaining a solid grasp of AI technology and how it can help achieve business goals. Look at how AI can boost profits, grow revenue, improve efficiency, and reduce risks.
Remember, as a leader, human values matter most. Traits such as trust, compassion, selflessness, and strategic vision are essential, and they can be developed through practice and experience.
While knowing the technical side is important, being able to communicate well is just as crucial. Aim to be positive, clear, and effective in your communication with your team peers, and leaders.
Lastly, a leader’s role is to guide the team and help resolve challenges. As long as you continue to strengthen your problem-solving skills and leverage your network to support your team, you will be well positioned for success.
Start by gaining a solid grasp of AI technology and how it can help achieve business goals. Look at how AI can boost profits, grow revenue, improve efficiency, and reduce risks.
Remember, as a leader, human values matter most. Traits such as trust, compassion, selflessness, and strategic vision are essential, and they can be developed through practice and experience.
While knowing the technical side is important, being able to communicate well is just as crucial. Aim to be positive, clear, and effective in your communication with your team peers, and leaders.
Lastly, a leader’s role is to guide the team and help resolve challenges. As long as you continue to strengthen your problem-solving skills and leverage your network to support your team, you will be well positioned for success.
Ahmed M. Hamdy
Digital Growth & Transformation Director
6
Answers
Cairo, Cairo Governorate, Egypt
Updated
Ahmed’s Answer
Hi Harish!
First off, your background is already off to an impressive start. Combining Computer Science with Business Systems, plus having solid internship experience at places like ICMR NIE and building actual tools for healthcare and education, gives you a huge head start.
Transitioning from a purely technical role into leading AI-driven teams or organizations usually comes down to moving from "how do I build this model?" to "why are we building this, and how does it actually create value?"
When technical professionals step into AI leadership, a few specific skills and experiences make all the difference:
First, learning to bridge the gap between technical details and business outcomes. In an AI leadership role, you spend less time tweaking hyperparameter settings and more time explaining to non-technical stakeholders why a specific model matters, what its limitations are, and how it impacts the bottom line or the end user. Your dual background in CS and Business Systems is going to be a major asset here.
Second, managing risk, ethics, and deployment realities. AI leadership isn't just about cool algorithms. It is about knowing how to handle data privacy, model bias, hallucinations, and maintenance once a system goes live. In healthcare especially, understanding regulatory compliance, patient privacy, and safety is just as important as the code itself. Showing that you think about these real-world constraints sets you apart from engineers who only care about model accuracy.
Third, system architecture and product sense. Leaders need to see how all the pieces fit together. That means understanding how data flows from raw inputs, through pipelines, into the model, and out to a user-friendly interface. It also means knowing when not to use AI if a simpler software solution gets the job done faster and cheaper.
Since you are in your final year and looking at graduate studies abroad, here is what I would recommend focusing on right now:
Keep sharpening your core AI and software fundamentals, but focus heavily on end-to-end execution. Don't just train a model in a Jupyter Notebook; build full-stack prototypes where a real user can interact with your application. Having live, deployed projects in accessible education or healthcare that people can actually test will make your grad school applications and resume pop.
Start taking ownership of the product side, not just the code. When you work on projects, practice writing brief product specs or user stories. Ask yourself: Who is using this? What problem does it solve for them? How do we measure success? This shows admissions committees and future employers that you think like a project lead or founder, not just a programmer.
Lean into your research and writing. Since you are interested in grad school abroad, having clear technical documentation, a well-kept GitHub, or even a co-authored research paper/preprint goes a long way. It shows you know how to communicate complex ideas clearly, which is half the battle in both academia and leadership.
Finally, seek out opportunities to mentor or coordinate. You don't need a formal "manager" title to build leadership skills. Lead a team project for your final year, mentor junior students, organize a campus hackathon, or run workshops on data analytics. Showing a track record of bringing people together around a technical goal is exactly what top grad programs and AI companies look for.
You are already working on meaningful problems in healthcare and education, which gives your profile a strong purpose. Keep building real tools, focus on the big-picture impact of your code, and you will be in a great position for both graduate school and future leadership roles!
First off, your background is already off to an impressive start. Combining Computer Science with Business Systems, plus having solid internship experience at places like ICMR NIE and building actual tools for healthcare and education, gives you a huge head start.
Transitioning from a purely technical role into leading AI-driven teams or organizations usually comes down to moving from "how do I build this model?" to "why are we building this, and how does it actually create value?"
When technical professionals step into AI leadership, a few specific skills and experiences make all the difference:
First, learning to bridge the gap between technical details and business outcomes. In an AI leadership role, you spend less time tweaking hyperparameter settings and more time explaining to non-technical stakeholders why a specific model matters, what its limitations are, and how it impacts the bottom line or the end user. Your dual background in CS and Business Systems is going to be a major asset here.
Second, managing risk, ethics, and deployment realities. AI leadership isn't just about cool algorithms. It is about knowing how to handle data privacy, model bias, hallucinations, and maintenance once a system goes live. In healthcare especially, understanding regulatory compliance, patient privacy, and safety is just as important as the code itself. Showing that you think about these real-world constraints sets you apart from engineers who only care about model accuracy.
Third, system architecture and product sense. Leaders need to see how all the pieces fit together. That means understanding how data flows from raw inputs, through pipelines, into the model, and out to a user-friendly interface. It also means knowing when not to use AI if a simpler software solution gets the job done faster and cheaper.
Since you are in your final year and looking at graduate studies abroad, here is what I would recommend focusing on right now:
Keep sharpening your core AI and software fundamentals, but focus heavily on end-to-end execution. Don't just train a model in a Jupyter Notebook; build full-stack prototypes where a real user can interact with your application. Having live, deployed projects in accessible education or healthcare that people can actually test will make your grad school applications and resume pop.
Start taking ownership of the product side, not just the code. When you work on projects, practice writing brief product specs or user stories. Ask yourself: Who is using this? What problem does it solve for them? How do we measure success? This shows admissions committees and future employers that you think like a project lead or founder, not just a programmer.
Lean into your research and writing. Since you are interested in grad school abroad, having clear technical documentation, a well-kept GitHub, or even a co-authored research paper/preprint goes a long way. It shows you know how to communicate complex ideas clearly, which is half the battle in both academia and leadership.
Finally, seek out opportunities to mentor or coordinate. You don't need a formal "manager" title to build leadership skills. Lead a team project for your final year, mentor junior students, organize a campus hackathon, or run workshops on data analytics. Showing a track record of bringing people together around a technical goal is exactly what top grad programs and AI companies look for.
You are already working on meaningful problems in healthcare and education, which gives your profile a strong purpose. Keep building real tools, focus on the big-picture impact of your code, and you will be in a great position for both graduate school and future leadership roles!
Richa Prajapati
UX Researcher
2
Answers
Oakland, California
Updated
Richa’s Answer
Hi Harish,
I love how clear you are in what you want to pursue and the steps you are taking to achieve them.
To answer your question, I would love to understand what you mean by being an "AI Leader" and how do you think it is different from being a "technical professional".
I think understanding your thoughts would help us better answer your questions because there are so many definitions of what each of these terms can mean and I want to make sure you get the right info and not be led down a stray path.
Re: pursuing graduate studies abroad, I agree with Samuel's advice above but I would also encourage you to think what exactly are you hoping to gain from the study abroad? Will the experience and cost of studying abroad worth it? What if you are unable to find a job abroad to pay off your education load?
I love how clear you are in what you want to pursue and the steps you are taking to achieve them.
To answer your question, I would love to understand what you mean by being an "AI Leader" and how do you think it is different from being a "technical professional".
I think understanding your thoughts would help us better answer your questions because there are so many definitions of what each of these terms can mean and I want to make sure you get the right info and not be led down a stray path.
Re: pursuing graduate studies abroad, I agree with Samuel's advice above but I would also encourage you to think what exactly are you hoping to gain from the study abroad? Will the experience and cost of studying abroad worth it? What if you are unable to find a job abroad to pay off your education load?
Thank you, Richa, for such a thoughtful response. When I say "AI leader," I mean someone who not only builds AI systems but also leads multidisciplinary teams to create ethical, impactful solutions that solve real-world problems. I also appreciate your questions about studying abroad—they've encouraged me to think more deeply about my long-term goals and the value I want to gain beyond the degree itself.
Harish
Bryan Chau
software engineer
2
Answers
Los Angeles, California
Updated
Bryan’s Answer
The biggest change for me wasn't just learning another programming language or AI tool. It was becoming more curious about why the system worked the way it did and how everything was connected.
At the start of my career, I focused on just completing tasks. But when I created my own AI language-learning app, I realized features weren't just isolated pieces of code anymore.
For instance, if a user sends a message in an AI-driven story, it affects much more than just the next reply. The system needs to know the story's current state, past events, the user's level, what they're practicing, and the character's goals. The response must be saved properly and used for story summaries, phrase extraction, review materials, and tracking progress.
When changing any part of this flow, I had to consider:
Where does this data come from?
Where does it go next?
What other features rely on it?
What if the AI gives an unexpected result?
How will this change impact the user experience?
This understanding is crucial when using AI to build software. AI can help generate code, but it doesn't automatically grasp the entire product. You need to understand the system well enough to guide it on which parts matter, what behaviors must remain intact, how data moves, and what the feature should achieve for users.
For me, this is also a key part of technical leadership. It's about seeing beyond individual tasks to understand the whole system, the customer, and the impact of decisions.
For students interested in AI, healthcare, education, or entrepreneurship, I suggest building a small but complete system to solve a real problem. Don't stop at just training a model or making a demo that works once. Share it with users, trace the data throughout the system, and notice what happens when things go wrong or are confusing.
Technical skills are important, but the habit I value most is this: never let the system become a mystery. Keep asking why it works, what it affects, and how every part connects to the whole.
At the start of my career, I focused on just completing tasks. But when I created my own AI language-learning app, I realized features weren't just isolated pieces of code anymore.
For instance, if a user sends a message in an AI-driven story, it affects much more than just the next reply. The system needs to know the story's current state, past events, the user's level, what they're practicing, and the character's goals. The response must be saved properly and used for story summaries, phrase extraction, review materials, and tracking progress.
When changing any part of this flow, I had to consider:
Where does this data come from?
Where does it go next?
What other features rely on it?
What if the AI gives an unexpected result?
How will this change impact the user experience?
This understanding is crucial when using AI to build software. AI can help generate code, but it doesn't automatically grasp the entire product. You need to understand the system well enough to guide it on which parts matter, what behaviors must remain intact, how data moves, and what the feature should achieve for users.
For me, this is also a key part of technical leadership. It's about seeing beyond individual tasks to understand the whole system, the customer, and the impact of decisions.
For students interested in AI, healthcare, education, or entrepreneurship, I suggest building a small but complete system to solve a real problem. Don't stop at just training a model or making a demo that works once. Share it with users, trace the data throughout the system, and notice what happens when things go wrong or are confusing.
Technical skills are important, but the habit I value most is this: never let the system become a mystery. Keep asking why it works, what it affects, and how every part connects to the whole.
Mario Ramirez
Sr. Leader, Customer Delivery
60
Answers
Mexico City, Mexico
Updated
Mario’s Answer
Hey Harish, thanks for sharing your concern! Just wanted to let you know that after over 15 years in leadership roles, starting from an engineering background, I feel really grateful for my journey.
Focus on building those personal relationships! Sure, AI is great, but at the end of the day, it’s humans who drive the edge and make the important decisions. Having a solid technical background is definitely key.
1. Make sure to connect person to person, not just Manager to collaborator. It’s a thin line, but always show respect.
2. Adapt your leadership style to different situations. Define your own brand and pay attention to each person's behavior so you can effectively influence ideas and objectives.
3. Never cross the line between collaborator and leader; that’s crucial for delivering value and maintaining respect across the company.
4. No one is irreplaceable, but you are unique, and that’s what makes you capable of delivering quality and respect.
Keep learning and aim for a good balance of emotional intelligence. There’s always room to improve as humans because we’re not perfect, and that’s what makes growth exciting!
Cheers!
Focus on building those personal relationships! Sure, AI is great, but at the end of the day, it’s humans who drive the edge and make the important decisions. Having a solid technical background is definitely key.
1. Make sure to connect person to person, not just Manager to collaborator. It’s a thin line, but always show respect.
2. Adapt your leadership style to different situations. Define your own brand and pay attention to each person's behavior so you can effectively influence ideas and objectives.
3. Never cross the line between collaborator and leader; that’s crucial for delivering value and maintaining respect across the company.
4. No one is irreplaceable, but you are unique, and that’s what makes you capable of delivering quality and respect.
Keep learning and aim for a good balance of emotional intelligence. There’s always room to improve as humans because we’re not perfect, and that’s what makes growth exciting!
Cheers!
Zakaria Laouabdia Sellami
4
Answers
Annaba, Annaba Province, Algeria
Updated
Zakaria’s Answer
To transition from a technical professional to an AI leader, you must shift your focus from writing code to solving system-level problems.
As a final-year CS & Business student with an impressive background at ICMR-NIE, focus on building these specific skills and experiences today:
Technical Skills to Build:
* AI Auditing & Deployment: Move beyond training models. Learn MLOps, model optimization for low-resource environments (crucial for accessible healthcare/education), and algorithmic bias auditing.
* Domain-Specific Data Analytics: Master biostatistics and health informatics datasets. Your ability to bridge clinical needs with data structures is rare and highly valuable.
Leadership Skills to Build:
* Translation: Learn to explain complex AI architecture to non-technical stakeholders (doctors, educators, investors) in terms of risk, ROI, and human outcomes.
* Ethical Framework Design: Develop expertise in AI governance, data privacy laws (like HIPAA or India's DPDPA), and compliance. Leaders must know when not to deploy a model.
What to Start Building Today:
* End-to-End Civic Products: Don't just build models; ship complete, open-source AI tools that local clinics or schools can actually use. Document the deployment challenges.
* A Research Narrative: Publish peer-reviewed papers on your healthcare/education solutions. For graduate studies abroad, a published paper exploring tech-ethics or accessibility carries immense weight.
As a final-year CS & Business student with an impressive background at ICMR-NIE, focus on building these specific skills and experiences today:
Technical Skills to Build:
* AI Auditing & Deployment: Move beyond training models. Learn MLOps, model optimization for low-resource environments (crucial for accessible healthcare/education), and algorithmic bias auditing.
* Domain-Specific Data Analytics: Master biostatistics and health informatics datasets. Your ability to bridge clinical needs with data structures is rare and highly valuable.
Leadership Skills to Build:
* Translation: Learn to explain complex AI architecture to non-technical stakeholders (doctors, educators, investors) in terms of risk, ROI, and human outcomes.
* Ethical Framework Design: Develop expertise in AI governance, data privacy laws (like HIPAA or India's DPDPA), and compliance. Leaders must know when not to deploy a model.
What to Start Building Today:
* End-to-End Civic Products: Don't just build models; ship complete, open-source AI tools that local clinics or schools can actually use. Document the deployment challenges.
* A Research Narrative: Publish peer-reviewed papers on your healthcare/education solutions. For graduate studies abroad, a published paper exploring tech-ethics or accessibility carries immense weight.
Teklemuz Tesfay
Software Engineer and Career Mentor
711
Answers
Ethiopia
Updated
Teklemuz’s Answer
Dive deep into AI and a field you're passionate about, while also learning about business, ethics, and human-centered design. Use this knowledge to create AI solutions that tackle real-world challenges in healthcare, accessibility, multimodal AI, medical imaging or educational platform. Boost your research skills by exploring important papers like NeurIPS, ICML, AAAI, and working with datasets like MIMIC and PhysioNet. Get involved in open-source projects and learn how AI systems are built and improved.
Gain valuable experience through internships, research, student clubs, volunteering, and workshops. Seek mentorship from your professors and build your network on LinkedIn, through alumni, and in AI communities. Join ACM or IEEE chapters, enter Kaggle competitions, and participate in AI hackathons or programs like Google Summer of Code. Learn to deploy AI using tools like Hugging Face, cloud services, and MLOps workflows, focusing on making your solutions scalable, reliable, fair, and user-friendly. Stay updated, embrace lifelong learning, and develop traits like curiosity, adaptability, communication, resilience, judgment, and empathy—these are the qualities that will shape future leaders in AI.
Gain valuable experience through internships, research, student clubs, volunteering, and workshops. Seek mentorship from your professors and build your network on LinkedIn, through alumni, and in AI communities. Join ACM or IEEE chapters, enter Kaggle competitions, and participate in AI hackathons or programs like Google Summer of Code. Learn to deploy AI using tools like Hugging Face, cloud services, and MLOps workflows, focusing on making your solutions scalable, reliable, fair, and user-friendly. Stay updated, embrace lifelong learning, and develop traits like curiosity, adaptability, communication, resilience, judgment, and empathy—these are the qualities that will shape future leaders in AI.
Thank you so much, Teklemuz. Your advice aligns closely with the direction I hope to pursue. I'll continue deepening my AI knowledge while strengthening my research, MLOps, open-source contributions, and healthcare applications. I also appreciate your emphasis on curiosity, empathy, and lifelong learning—qualities I believe are essential for becoming an effective AI leader. Again Thank you!
Harish
It's my pleasure!
Teklemuz Tesfay
Mohan Ganapathy
Managing Director
3
Answers
Los Angeles, California
Updated
Mohan’s Answer
Begin by learning the basics of AI and practice with tools like ChatGPT and Google Gemini to understand what they can do. Once you are comfortable, try using AI for more complex tasks like creating documents, summarizing information, making visual presentations, and coding. AI is a helpful tool that will support your work as a technical professional. Developing strong AI skills will be crucial for your career in technology.
Arvind Pandey
Engineering and Sports
8
Answers
Bengaluru, Karnataka, India
Updated
Arvind’s Answer
1. Domain knowledge
2. Active LISTENING, Lower and upper Management, know Who is of what
3. Communication and Presentation (practice), Negotiation, Anchoring,
4. Human connect and networking, diplomacy, conflict resolution, have confidence and get out of FOMO mindset. You have already taken first step to reach out to broader audience, so just keep it up.
5. Always have a mentor
HTH
2. Active LISTENING, Lower and upper Management, know Who is of what
3. Communication and Presentation (practice), Negotiation, Anchoring,
4. Human connect and networking, diplomacy, conflict resolution, have confidence and get out of FOMO mindset. You have already taken first step to reach out to broader audience, so just keep it up.
5. Always have a mentor
HTH
Thank you, Arvind. Your practical advice on communication, networking, mentorship, and developing a human connection is invaluable. I especially appreciate your encouragement about reaching out to a broader audience. I'll continue building these skills alongside my technical expertise.
Harish
Liz Gardner Howe
IT QA
6
Answers
New Hartford, Connecticut
Updated
Liz’s Answer
When I started my career, success was mostly about my own technical skills. I focused on testing software, finding defects, learning systems, and becoming a subject matter expert. As I grew into a lead role, I realized that technical knowledge alone wasn't enough.
If I were a high school student today, I would start building three things immediately:
o Be curious and develop the habit of teaching yourself new skills (which it sounds like you are doing).
o Practice writing, presenting, and explaining ideas - AI can generate information, but people who can communicate clearly will always be valuable.
o Use AI to research, brainstorm, analyze information, and improve your work. Don't just learn how to use AI. Learn how to evaluate its answers and think critically about them (you're already in flight with this).
My advice: Focus on building skills that transfer across many careers: communication, problem-solving, teamwork, adaptability, and learning agility. Those skills will create opportunities no matter how technology evolves.
Some of the projects I'm most proud of weren't successful because I had all the answers. They were successful because I stayed curious, kept learning, and helped people work together to solve problems. Those are skills that will never go out of style, even in an AI-driven world.
If I were a high school student today, I would start building three things immediately:
o Be curious and develop the habit of teaching yourself new skills (which it sounds like you are doing).
o Practice writing, presenting, and explaining ideas - AI can generate information, but people who can communicate clearly will always be valuable.
o Use AI to research, brainstorm, analyze information, and improve your work. Don't just learn how to use AI. Learn how to evaluate its answers and think critically about them (you're already in flight with this).
My advice: Focus on building skills that transfer across many careers: communication, problem-solving, teamwork, adaptability, and learning agility. Those skills will create opportunities no matter how technology evolves.
Some of the projects I'm most proud of weren't successful because I had all the answers. They were successful because I stayed curious, kept learning, and helped people work together to solve problems. Those are skills that will never go out of style, even in an AI-driven world.
Thank you, Liz. I really appreciate your perspective. Your advice on staying curious, improving communication, and thinking critically about AI-generated outputs is something I'll carry forward. I also like your reminder that lasting success comes from helping people solve problems together, not just from technical expertise.
Harish
William Vvuko
Retired engineer
255
Answers
Moyo, Northern Region
Updated
William’s Answer
Hi Harish,
Firstly, I'm a retired mechanical engineer by profession. As such, my advice will mainly focus on leadership skills & their development.
It's useful for us to first focus on the qualities of a good leader. Such a leader is visionary, inspirational, courageous, exemplary, honest and trustworthy.
Vision clarifies the kind of future a given group of people aspires to. A good leader ensures such a vision is compelling enough to evoke followership through consent. Through courage, a leader ensures major risks and significant obstacles are successful negotiated often by leading by example (role model).
Trust is central to good leadership. It is built through consistency over a period of time. Common ground established through shared goals, experiences & values tend to promote trust. Good leaders are authentic and therefore exhibit both sincerity and integrity. Authenticity requires acceptance of both one's strengths and weaknesses in addition to admitting one's mistakes. Apologies for mistakes committed are a sure way of strengthening trust. Reputation is a critical pathway for nurturing trust too.
A good leader also needs to be consistently visible, available and responsive to the needs of the led.
As such good leaders empower their subordinates and the workforce under their care. Such an empowerment comes through mentorships, coaching, training and provision of resources.
Organizations invoke different solutions in equipping their leaders with leadership skills. At the basic level, competency acquisition process ensures occupants of roles into which there are direct reports receive foundational leadership training. Middle managers often acquire leadership skills through assignments - projects, disciplinary processes, collective bargain agreement negotiations. At the executive level, a formal extended training is often conducted, in addition to assignments in order to bolster experiential learning. Such a process is driven by talent management (people balance sheet) & performance management (appraisals and career discussions).
Emotional Intelligence (EQ) empowers leaders in many areas: gut feel, empathy, self-control, self-drive, assertiveness, resilience, commitment, diligence, attention to detail, passion, curiosity, creativity etc.
Some people are naturally better leaders than others. Leadership skills can, however, be acquired through training and experience.
I hope this provides a foundation you can build on.
Best of luck in your studies and career.
Firstly, I'm a retired mechanical engineer by profession. As such, my advice will mainly focus on leadership skills & their development.
It's useful for us to first focus on the qualities of a good leader. Such a leader is visionary, inspirational, courageous, exemplary, honest and trustworthy.
Vision clarifies the kind of future a given group of people aspires to. A good leader ensures such a vision is compelling enough to evoke followership through consent. Through courage, a leader ensures major risks and significant obstacles are successful negotiated often by leading by example (role model).
Trust is central to good leadership. It is built through consistency over a period of time. Common ground established through shared goals, experiences & values tend to promote trust. Good leaders are authentic and therefore exhibit both sincerity and integrity. Authenticity requires acceptance of both one's strengths and weaknesses in addition to admitting one's mistakes. Apologies for mistakes committed are a sure way of strengthening trust. Reputation is a critical pathway for nurturing trust too.
A good leader also needs to be consistently visible, available and responsive to the needs of the led.
As such good leaders empower their subordinates and the workforce under their care. Such an empowerment comes through mentorships, coaching, training and provision of resources.
Organizations invoke different solutions in equipping their leaders with leadership skills. At the basic level, competency acquisition process ensures occupants of roles into which there are direct reports receive foundational leadership training. Middle managers often acquire leadership skills through assignments - projects, disciplinary processes, collective bargain agreement negotiations. At the executive level, a formal extended training is often conducted, in addition to assignments in order to bolster experiential learning. Such a process is driven by talent management (people balance sheet) & performance management (appraisals and career discussions).
Emotional Intelligence (EQ) empowers leaders in many areas: gut feel, empathy, self-control, self-drive, assertiveness, resilience, commitment, diligence, attention to detail, passion, curiosity, creativity etc.
Some people are naturally better leaders than others. Leadership skills can, however, be acquired through training and experience.
I hope this provides a foundation you can build on.
Best of luck in your studies and career.
Thank you very much, William. I truly appreciate the time you took to write such a detailed and thoughtful response. Your insights on trust, authenticity, emotional intelligence, and empowering others have given me a broader perspective on leadership. I'll strive to develop these qualities as I grow both technically and professionally.
Harish
Gwen Richardson
Principal Solutions Consultant
19
Answers
Atlanta, Georgia
Updated
Gwen’s Answer
Begin with the basics of AI and keep progressing. Tackling real-world challenges is what sets a leader apart. Keep expanding your AI knowledge to solve real problems effectively.