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What steps are you taking to build a culture of continuous learning and adaptability in response to AI advancements?
What steps are you taking to build a culture of continuous learning and adaptability in response to AI advancements?
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18 answers
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- I make it a point to ask my team how they have done things differently on the day. How much have you leveraged AI ? How much did you need to review ?
- I set aside time to learn about AI and then think about practical applications for my day-to-day, whether that's work-related or personal.
- I explore different tools and learn from others who keep up with the latest developments.
- I have built a personalized learning path to remain competitive and innovative.
- I set aside time to learn about AI and then think about practical applications for my day-to-day, whether that's work-related or personal.
- I explore different tools and learn from others who keep up with the latest developments.
- I have built a personalized learning path to remain competitive and innovative.
Zainab Jamal - PhD MBA MA - Marketing, Data Sc, Economics
Management - AI and Data Science
1
Answer
Saratoga, California
Updated
Zainab’s Answer
Thank you for asking a great question.
The way we have been able to build such a culture is to focus first on the business problem we are trying to solve, who are the end users and what are their pain points. By focusing on building a solution to solve the business problem, it helps to separate the different components of a business solution - people, process, technology and data.
Once you have a viable business solution with existing technology and tools and AI, it becomes easier to continuously improve on that foundation as new technological enhancements come thru and make them part of next iteration and release.
So the culture we emphasize among our team members is problem solving, continuous learning and agile and iterative solution development cycles.
The way we have been able to build such a culture is to focus first on the business problem we are trying to solve, who are the end users and what are their pain points. By focusing on building a solution to solve the business problem, it helps to separate the different components of a business solution - people, process, technology and data.
Once you have a viable business solution with existing technology and tools and AI, it becomes easier to continuously improve on that foundation as new technological enhancements come thru and make them part of next iteration and release.
So the culture we emphasize among our team members is problem solving, continuous learning and agile and iterative solution development cycles.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- Try new areas, challenge yourself to explore new ways of learning and doing and have fun
- Get 1% smarter every day . Read , consume all that is around you
- I block time in my schedule each day to learn about ai and any new developments.
- Get 1% smarter every day . Read , consume all that is around you
- I block time in my schedule each day to learn about ai and any new developments.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
Here’s a practical, scalable playbook you can adapt to build a culture of continuous learning and adaptability in response to AI advancements. It covers leadership, learning systems, teams, governance, and measurement.
What we’re aiming for
- A learning ecosystem where experimentation is routine, failures are treated as evidence, and people expand skills that matter for today and tomorrow.
- AI literacy across the organization (from executives to frontline staff) paired with deep, role-based capabilities for those who build and deploy AI.
- Clear guardrails for responsible AI, data governance, and risk management so learning happens safely.
- Momentum: regular practices, rituals, and incentives that align learning with business impact.
Five core pillars
- Leadership and vision
- Leaders model learning: share what they’re learning about AI, invite questions, and allocate time for learning.
- Translate AI trends into strategy and measurable bets (projects, roles, capability gaps).
- Learning architecture
- Broad AI literacy for all and specialized upskilling for key roles (engineers, data scientists, product managers, designers, operators).
- A mix of microlearning, hands-on projects, and real-world practice with feedback loops.
- People and teams
- Cross-functional “AI learning squads” to prototype, test, and scale AI-enabled solutions.
- AI champions or guilds to curate learning resources, share tacit knowledge, and mentor others.
- Tools and ecosystem
- Central access to learning platforms, curated courses, labs, datasets, and prompts repositories.
- Safe experimentation spaces (sandboxes) with governance controls and data access rules.
- Governance and risk
- Clear responsible-AI principles, ethics guidelines, and compliance checks embedded in learning and projects.
- Data governance, privacy, bias mitigation, and security baked into training and deployment.
- Measurement and incentives
- Leading indicators (participation, skill progression, number of experiments) and business outcomes (time-to-value, quality, cost savings).
- Rewards for learning activity, collaboration, and successful AI-enabled innovations.
Concrete steps and activities (phased)
Phase 0: Foundation (0–4 weeks)
- Establish a shared AI learning vision
- Define 2–3 strategic AI-enabled outcomes for the organization.
- Assess current state
- Quick skills inventory by role; identify top 3–5 skills to build first.
- Create a learning budget and calendar
- Reserve regular time for learning (e.g., 2 hours per week per person) and fund essential courses/tools.
- Set up learning infrastructure
- Choose a learning platform, curate core AI literacy resources, and set up a “prompt library” and an AI sandbox with governance rules.
Phase 1: Build and pilot (1–3 months)
- Launch AI literacy for all
- Roll out a baseline AI literacy program (terminology, ethics, data basics, prompt engineering at a high level).
- Create role-based pathways
- Develop 1–2 practical, hands-on tracks for key roles (e.g., product managers: AI product lifecycle; engineers: prompt engineering + model integration; analysts: data storytelling with AI).
- Start cross-functional AI squads
- 3–5 small teams work on real, bounded AI experiments aligned to business bets.
- Implement rituals and feedback loops
- Weekly learning huddles, biweekly demos, and after-action reviews for AI pilots.
- Pilot governance and ethics checks
- Simple checklists to review data sources, bias, privacy, and security before deploying pilots.
Phase 2: Scale and mature (3–12 months)
- Expand and sustain learning programs
- Make advanced tracks available (model evaluation, MLOps basics, responsible AI, domain-specific AI).
- Institutionalize learning in operations
- Tie learning progress to performance reviews, career paths, and promotions where appropriate.
- Grow AI capability across functions
What we’re aiming for
- A learning ecosystem where experimentation is routine, failures are treated as evidence, and people expand skills that matter for today and tomorrow.
- AI literacy across the organization (from executives to frontline staff) paired with deep, role-based capabilities for those who build and deploy AI.
- Clear guardrails for responsible AI, data governance, and risk management so learning happens safely.
- Momentum: regular practices, rituals, and incentives that align learning with business impact.
Five core pillars
- Leadership and vision
- Leaders model learning: share what they’re learning about AI, invite questions, and allocate time for learning.
- Translate AI trends into strategy and measurable bets (projects, roles, capability gaps).
- Learning architecture
- Broad AI literacy for all and specialized upskilling for key roles (engineers, data scientists, product managers, designers, operators).
- A mix of microlearning, hands-on projects, and real-world practice with feedback loops.
- People and teams
- Cross-functional “AI learning squads” to prototype, test, and scale AI-enabled solutions.
- AI champions or guilds to curate learning resources, share tacit knowledge, and mentor others.
- Tools and ecosystem
- Central access to learning platforms, curated courses, labs, datasets, and prompts repositories.
- Safe experimentation spaces (sandboxes) with governance controls and data access rules.
- Governance and risk
- Clear responsible-AI principles, ethics guidelines, and compliance checks embedded in learning and projects.
- Data governance, privacy, bias mitigation, and security baked into training and deployment.
- Measurement and incentives
- Leading indicators (participation, skill progression, number of experiments) and business outcomes (time-to-value, quality, cost savings).
- Rewards for learning activity, collaboration, and successful AI-enabled innovations.
Concrete steps and activities (phased)
Phase 0: Foundation (0–4 weeks)
- Establish a shared AI learning vision
- Define 2–3 strategic AI-enabled outcomes for the organization.
- Assess current state
- Quick skills inventory by role; identify top 3–5 skills to build first.
- Create a learning budget and calendar
- Reserve regular time for learning (e.g., 2 hours per week per person) and fund essential courses/tools.
- Set up learning infrastructure
- Choose a learning platform, curate core AI literacy resources, and set up a “prompt library” and an AI sandbox with governance rules.
Phase 1: Build and pilot (1–3 months)
- Launch AI literacy for all
- Roll out a baseline AI literacy program (terminology, ethics, data basics, prompt engineering at a high level).
- Create role-based pathways
- Develop 1–2 practical, hands-on tracks for key roles (e.g., product managers: AI product lifecycle; engineers: prompt engineering + model integration; analysts: data storytelling with AI).
- Start cross-functional AI squads
- 3–5 small teams work on real, bounded AI experiments aligned to business bets.
- Implement rituals and feedback loops
- Weekly learning huddles, biweekly demos, and after-action reviews for AI pilots.
- Pilot governance and ethics checks
- Simple checklists to review data sources, bias, privacy, and security before deploying pilots.
Phase 2: Scale and mature (3–12 months)
- Expand and sustain learning programs
- Make advanced tracks available (model evaluation, MLOps basics, responsible AI, domain-specific AI).
- Institutionalize learning in operations
- Tie learning progress to performance reviews, career paths, and promotions where appropriate.
- Grow AI capability across functions
Michael Rill
Program Management
5
Answers
Redmond, Washington
Updated
Michael’s Answer
Building a culture of continuous learning in the middle of rapid AI change starts with curiosity. The best way to stay adaptable is to pick a part of the AI space that you’re genuinely interested in, not just what everyone else is talking about. Curiosity makes learning feel less like homework and more like exploration.
Once you’ve found that interest, follow it down a few rabbit holes. Podcasts and YouTube are great starting points, and there are plenty of creators and influencers breaking down complex topics into clear, engaging content. But here’s the key: don’t just binge-watch or listen for entertainment. That’s a lean-back experience, where the information washes over you without sticking.
Instead, turn it into a lean-in experience. Take quick notes on what you hear, write down the “so what” (what this means for you), and list one or two next steps you want to try out or dig into further. Even a small action helps cement the learning, like testing a new tool, rephrasing an idea in your own words, or bringing it up in a team discussion.
Do this consistently, and over time you’ll notice patterns, connect new concepts more easily, and build a habit of staying adaptable. It’s about deliberately turning curiosity into progress, one step at a time.
Once you’ve found that interest, follow it down a few rabbit holes. Podcasts and YouTube are great starting points, and there are plenty of creators and influencers breaking down complex topics into clear, engaging content. But here’s the key: don’t just binge-watch or listen for entertainment. That’s a lean-back experience, where the information washes over you without sticking.
Instead, turn it into a lean-in experience. Take quick notes on what you hear, write down the “so what” (what this means for you), and list one or two next steps you want to try out or dig into further. Even a small action helps cement the learning, like testing a new tool, rephrasing an idea in your own words, or bringing it up in a team discussion.
Do this consistently, and over time you’ll notice patterns, connect new concepts more easily, and build a habit of staying adaptable. It’s about deliberately turning curiosity into progress, one step at a time.
Nailed it!
Mark Alcarez
Steve Iatropoulos
IT Consultant
5
Answers
Melbourne, Victoria, Australia
Updated
Steve’s Answer
I listen to podcasts, youtubers and read every day because its changing everyday! But also practice. Use AI in all aspects of your life whether its coding asking for advice - whatever!
Goodera Careervillage
63
Answers
Bengaluru, Karnataka, India
Updated
Goodera’s Answer
Always stay curious and eager to learn. Dive deep to understand how AI works and what each function does. Think of it as a way to find the truth. Read the manuals that come with your hardware and software. Make sure you understand why things work the way they do.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- Use the tools. Understand the tools, and associated risks. Continued upskilling on tools and how best to use them.
- Trying to learn and compare what I knew with what is change but remain the same.
- Sharing my experiences with team members and listening to my teams experience.
- Taking advantage of classes and demos offered by the company. Pursuing certifications and completing learning paths available to me.
- Trying to learn and compare what I knew with what is change but remain the same.
- Sharing my experiences with team members and listening to my teams experience.
- Taking advantage of classes and demos offered by the company. Pursuing certifications and completing learning paths available to me.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- I am continuing to adapt along with AI and encouraging those around me to do the same. I challenge myself to complete at least one task a day using AI to strengthen my skills and learn how I can better use AI tools. Staying update on AI advancements is imperative in today's work environment and can help create a more efficient work stream and stronger deliverables.
- I am leveraging the AI advancements to further ask questions about topics I am interested in.
- I am taking the time to use the tools. I also just ask the tool how other people are using it sometimes. It helps!
- Hands on experiential learning. I am a visual learner so I pick up fast when I do peer learning or try hands on projects.
- I am leveraging the AI advancements to further ask questions about topics I am interested in.
- I am taking the time to use the tools. I also just ask the tool how other people are using it sometimes. It helps!
- Hands on experiential learning. I am a visual learner so I pick up fast when I do peer learning or try hands on projects.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
In response to AI advancements, I am actively integrating AI tools into my daily workflow—not to replace my thinking, but to enhance it. I use AI for brainstorming, research support, and skill development while making sure I critically evaluate the outputs. This helps me become both technologically fluent and analytically strong.
I dedicate time to understanding how AI works at a foundational level, including its capabilities, limitations, and ethical implications. By doing this, I position myself not just as a user of AI, but as someone who understands how to collaborate with it effectively and responsibly.
I also set personal learning goals each semester or quarter, such as mastering a new software, improving my data literacy, or strengthening a communication skill. I reflect on my progress regularly and adjust my learning plan based on industry trends and personal interests.
Additionally, I encourage collaboration and knowledge-sharing within my peer groups. Discussing new tools, sharing resources, and working on group projects helps create an environment where learning is continuous and supported.
I dedicate time to understanding how AI works at a foundational level, including its capabilities, limitations, and ethical implications. By doing this, I position myself not just as a user of AI, but as someone who understands how to collaborate with it effectively and responsibly.
I also set personal learning goals each semester or quarter, such as mastering a new software, improving my data literacy, or strengthening a communication skill. I reflect on my progress regularly and adjust my learning plan based on industry trends and personal interests.
Additionally, I encourage collaboration and knowledge-sharing within my peer groups. Discussing new tools, sharing resources, and working on group projects helps create an environment where learning is continuous and supported.
Sumitra Biswal
Independent researcher in the intersection of AI and Cybersecurity
42
Answers
India
Updated
Sumitra’s Answer
Hi!
AI is here to stay, not just as a passing trend, but as a driver of opportunities and breakthrough innovations. To build a culture of continuous learning and adaptability, the first step is to focus on the bright side of AI and the benefits it brings as an assistant and enabler.
Just as we learn a new language to connect with people better, we also need to learn the “Language” of AI, its fundamentals, principles, and ethical boundaries. By encouraging ongoing learning, hands-on practice, and open discussions about AI’s role, we help people see AI not as a threat but as a collaborator.
In practice, this also means encouraging curiosity, sharing learnings openly in teams, and creating a safe space where people feel comfortable experimenting with AI tools. When learning is celebrated and mistakes are seen as part of growth, adaptability becomes second nature.
In this way, teams become more confident, adaptable, and ready to grow alongside AI rather than resist it.
Warm regards,
Sumitra
AI is here to stay, not just as a passing trend, but as a driver of opportunities and breakthrough innovations. To build a culture of continuous learning and adaptability, the first step is to focus on the bright side of AI and the benefits it brings as an assistant and enabler.
Just as we learn a new language to connect with people better, we also need to learn the “Language” of AI, its fundamentals, principles, and ethical boundaries. By encouraging ongoing learning, hands-on practice, and open discussions about AI’s role, we help people see AI not as a threat but as a collaborator.
In practice, this also means encouraging curiosity, sharing learnings openly in teams, and creating a safe space where people feel comfortable experimenting with AI tools. When learning is celebrated and mistakes are seen as part of growth, adaptability becomes second nature.
In this way, teams become more confident, adaptable, and ready to grow alongside AI rather than resist it.
Warm regards,
Sumitra
Gabriela Navarrete Mancera
Human Resources Talent Leader
5
Answers
Tampa, Florida
Updated
Gabriela’s Answer
Across our workplaces, we're taking real steps to make sure our people grow alongside AI rather than scramble to keep up. We're investing in AI upskilling across the board, not just for technical roles, with learning pathways that meet people where they are. Our multiple initiatives give teams a space to learn from each other, sharing what's actually working in practice rather than relying solely on top-down training. We're also growing talent from the ground up through apprenticeship programs that pair emerging professionals with experienced practitioners, giving them hands-on exposure to AI in real work settings. Responsible AI is part of the conversation from day one, and our leaders are leaning in too, learning alongside their teams and creating space where it's safe to experiment and adapt. It's not a single initiative, it's becoming part of how we work.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- For me personally, as part of the learning and development team, I help to promote ongoing training and skill development, emphasizing both technical knowledge and human-centered skills like creativity and critical thinking. Creating a safe environment for experimentation and feedback helps teams stay resilient and embrace change.
- Continuous learning through practical work experience or anywhere and adaptable to the changes. If technology changes then we should be updated and learn the usage in our job.
- Continuing to stay involved in the AI updates and listing to AI related podcasts to keep up to date
- Create a safe space to learn, try and fail and allocate time for learning
- Continuous learning through practical work experience or anywhere and adaptable to the changes. If technology changes then we should be updated and learn the usage in our job.
- Continuing to stay involved in the AI updates and listing to AI related podcasts to keep up to date
- Create a safe space to learn, try and fail and allocate time for learning
Cameron Krug, CPA, CISA
Internal Audit Manager
4
Answers
San Marcos, California
Updated
Cameron’s Answer
We aim to make AI a useful tool that boosts our team's performance. Here’s what I've done:
Incorporating AI into everyday work. We use AI to brainstorm audit risks, draft findings, and tidy up meeting notes. This speeds up our reporting and enhances our work right from the start.
Creating helpful tools. I developed an AI tool to automate certain reports, allowing our team to focus on strategic tasks instead of manual writing. We also have an AI "mentor" that offers instant guidance to our junior auditors, helping them learn more quickly.
Making AI a regular part of our process. We’ve integrated these AI tools into our official audit methods, so everyone uses them consistently and gets great results.
Offering training. I provide training on our internal controls and new processes, fostering a shared understanding and promoting a culture of compliance and learning.
My goal is to show the team how these tools make their work easier and help them grow their skills.
Incorporating AI into everyday work. We use AI to brainstorm audit risks, draft findings, and tidy up meeting notes. This speeds up our reporting and enhances our work right from the start.
Creating helpful tools. I developed an AI tool to automate certain reports, allowing our team to focus on strategic tasks instead of manual writing. We also have an AI "mentor" that offers instant guidance to our junior auditors, helping them learn more quickly.
Making AI a regular part of our process. We’ve integrated these AI tools into our official audit methods, so everyone uses them consistently and gets great results.
Offering training. I provide training on our internal controls and new processes, fostering a shared understanding and promoting a culture of compliance and learning.
My goal is to show the team how these tools make their work easier and help them grow their skills.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- I have started using AI tools as and when I can to help facilitate redundant work, help me create decks, giving me starting thoughts and ideas, speeding up my productivity. I am part of the AI champion network , joined as a beginner and learning everyday from the Pros and the SMEs. Slowly and steadily I will develop my skills in leveraging AI to the best of my abilities. I try to attend office hours for AI tools where they share demos of use cases, enhancements to the tools, so that I can stay abreast of what is being offered in the firm.
- I create daily, weekly and monthly self-challenges. I'm not always on the most innovated projects but I still push myself daily. The more you do everyday, the more you learn and push yourself later. Take a snapshot of where you are today with AI. Lay out a plan of daily/weekly learning and then look to see where you are in 1 month, 3 months, 1 year! You'll be amazed at the progress you make.
- I create daily, weekly and monthly self-challenges. I'm not always on the most innovated projects but I still push myself daily. The more you do everyday, the more you learn and push yourself later. Take a snapshot of where you are today with AI. Lay out a plan of daily/weekly learning and then look to see where you are in 1 month, 3 months, 1 year! You'll be amazed at the progress you make.
Sankarraj Subramani
GenAI Opensource Contributor and GenAI Sr. SDET
13
Answers
Sugar Land, Texas
Updated
Sankarraj’s Answer
In my journey as an AI QA Automation Lead and open-source contributor, I’ve learned that the most important way to thrive in an AI-enabled environment is to embrace continuous learning and adaptability as a mindset, not just a skill. Technology is evolving rapidly, but what helps me and my teams stay ahead is creating a culture where learning is part of everyday work.
Some of the key steps I take include:
Hands-on Experimentation – I actively design and publish open-source AI projects such as AutoTestX, GenQAChat-RAG-AI, AutoBugPredictX, and RAG4HealthQA. These projects are not just technical proofs of concept—they are living labs where I and others can practice new techniques, test tools like LangChain or ChromaDB, and learn from real-world use cases
Knowledge Sharing – I publish articles on Medium and IEEE Collabratec to break down complex AI concepts into clear, actionable insights for the global QA and AI community. By teaching, I reinforce my own learning and help others stay adaptable.
Mentorship & Collaboration – Within client teams at United Airlines, Freddie Mac, and USAA, I’ve mentored engineers on AI testing, prompt engineering, and ethical AI practices. I also volunteer as a career coach at CareerVillage.org, helping students and professionals understand AI’s opportunities and challenges.
Staying Engaged with Communities – I’m an IEEE Senior Member and actively participate in technical working groups, hackathons (like MIT RAISE and Code for Change), and online AI forums. These communities expose me to fresh perspectives and innovations.
Ethical & Responsible AI – Continuous learning isn’t only about tools—it’s also about mindset. I’ve taken specialized courses in AI governance and compliance, and I encourage teams to think about fairness, transparency, and accountability whenever they adopt AI.
By combining technical upskilling, open-source experimentation, mentorship, and ethical awareness, I’ve been able to foster a culture of lifelong learning and adaptability. My guiding belief is simple: AI won’t replace people, but people who learn to adapt and collaborate with AI will have limitless opportunities.
Some of the key steps I take include:
Hands-on Experimentation – I actively design and publish open-source AI projects such as AutoTestX, GenQAChat-RAG-AI, AutoBugPredictX, and RAG4HealthQA. These projects are not just technical proofs of concept—they are living labs where I and others can practice new techniques, test tools like LangChain or ChromaDB, and learn from real-world use cases
Knowledge Sharing – I publish articles on Medium and IEEE Collabratec to break down complex AI concepts into clear, actionable insights for the global QA and AI community. By teaching, I reinforce my own learning and help others stay adaptable.
Mentorship & Collaboration – Within client teams at United Airlines, Freddie Mac, and USAA, I’ve mentored engineers on AI testing, prompt engineering, and ethical AI practices. I also volunteer as a career coach at CareerVillage.org, helping students and professionals understand AI’s opportunities and challenges.
Staying Engaged with Communities – I’m an IEEE Senior Member and actively participate in technical working groups, hackathons (like MIT RAISE and Code for Change), and online AI forums. These communities expose me to fresh perspectives and innovations.
Ethical & Responsible AI – Continuous learning isn’t only about tools—it’s also about mindset. I’ve taken specialized courses in AI governance and compliance, and I encourage teams to think about fairness, transparency, and accountability whenever they adopt AI.
By combining technical upskilling, open-source experimentation, mentorship, and ethical awareness, I’ve been able to foster a culture of lifelong learning and adaptability. My guiding belief is simple: AI won’t replace people, but people who learn to adapt and collaborate with AI will have limitless opportunities.
Ranjana Krishnamoorthy
Technical program Manager
3
Answers
Seattle, Washington
Updated
Ranjana’s Answer
Hi,
Upskilling is always a good idea! When it comes to AI,
1. I would suggest starting with courses that will teach you the basics of AI first, and then dive deeper into specific parts that interest you. There are different types of courses that you can take, different tools you can learn based on your choice of career. For example, if you want to go into Business analytics, or product take deeper courses that will help you follow the AI PM path- like prompt engineering, RAG, AI prototyping, what are AI agents etc.
2. Another way to continuously learn is to gain some practical AI experience by downloading some tools and models and building some small apps, or using AI to help you in your daily life for small tasks!
3. You can also keep up with the latest AI news and releases through tech news magazines like Tech Crunch and Hacker news.
4. Engage in AI communities and follow people who post on LinkedIn who will help you learn more.
Upskilling is always a good idea! When it comes to AI,
1. I would suggest starting with courses that will teach you the basics of AI first, and then dive deeper into specific parts that interest you. There are different types of courses that you can take, different tools you can learn based on your choice of career. For example, if you want to go into Business analytics, or product take deeper courses that will help you follow the AI PM path- like prompt engineering, RAG, AI prototyping, what are AI agents etc.
2. Another way to continuously learn is to gain some practical AI experience by downloading some tools and models and building some small apps, or using AI to help you in your daily life for small tasks!
3. You can also keep up with the latest AI news and releases through tech news magazines like Tech Crunch and Hacker news.
4. Engage in AI communities and follow people who post on LinkedIn who will help you learn more.
PwC Impact
823
Answers
Orlando, Florida
Updated
PwC’s Answer
- To stay adaptable in a rapidly evolving AI landscape, I continuously learn coding to stay close to technology, attend industry seminars and workshops for exposure to emerging practices, and design and refine accelerators that transform ideas into solutions. I also lead efforts in AI-focused GTM content creation, bridging learning with real-world application and strategy
- In our current culture where things are changing so quickly, it's important to keep an open mind about change and be willing to jump in a learn new things each day. Practicing with AI is the key, even if the outcome isn't exactly what you expected, there is much to be learned from every experience. You just need to try again and be willing to change your approach. Then to truly be continuously learning, share what you tried and learned with others. Ask questions of others on how they would have approached the same situation. Collaboration is key and PwC's culture promotes us working together each day.
- In our current culture where things are changing so quickly, it's important to keep an open mind about change and be willing to jump in a learn new things each day. Practicing with AI is the key, even if the outcome isn't exactly what you expected, there is much to be learned from every experience. You just need to try again and be willing to change your approach. Then to truly be continuously learning, share what you tried and learned with others. Ask questions of others on how they would have approached the same situation. Collaboration is key and PwC's culture promotes us working together each day.