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What’s an example of a time when a big technology shift forced you to rethink your approach, and how did you handle it?
What’s an example of a time when a big technology shift forced you to rethink your approach, and how did you handle it?
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11 answers
Priyanka K
Software Engineer
29
Answers
Little Elm, Texas
Updated
Priyanka’s Answer
A major technology shift often serves as a catalyst for innovation and adaptability. Here's a detailed example of how a team might confront such a change and successfully pivot their approach:
Scenario: The Shift to Cloud Computing
Challenge:
A company historically relied on on-premises IT infrastructure to manage its operations, including data storage, analytics, and enterprise applications. With the growing shift to cloud computing, competitors were moving faster, benefiting from scalability, cost savings, and flexibility. The company realized it needed to migrate to the cloud to remain competitive, but this required rethinking its entire IT strategy, retraining employees, and ensuring data security during the transition.
The Approach: Handling the Shift
The team tackled the challenge through a mix of strategic planning, collaboration, and creative problem-solving.
1. Assessing the Impact
Action: The team conducted a technology audit to understand the specific impact of moving to the cloud. They identified critical applications, data repositories, and workflows that needed to be migrated.
Outcome: They determined that moving to the cloud would improve scalability, enable real-time collaboration, and reduce maintenance costs—but also identified risks like potential downtime and security concerns during migration.
2. Building Knowledge and Expertise
Action: Recognizing a skills gap, the team invested in retraining employees. They partnered with cloud service providers (like AWS, Microsoft Azure, or Google Cloud) to provide hands-on workshops and certifications for the IT department.
Outcome: Employees became more confident in managing cloud-based systems and learned how to leverage new technologies like serverless computing and AI-driven analytics.
3. Creating a Migration Strategy
Action: The team devised a phased migration strategy to reduce risks:
Phase 1: Start with non-critical systems to test the cloud environment.
Phase 2: Migrate core applications and sensitive data once initial workflows were stable.
Phase 3: Optimize cloud performance and fully integrate systems.
Outcome: This systematic approach ensured minimal disruption to daily operations and reduced downtime.
4. Leveraging Human Ingenuity
Action: The team brainstormed creative solutions to tackle unforeseen challenges:
For Legacy Systems: They developed custom APIs to integrate older, on-premises systems with the cloud rather than replacing them outright.
For Security Concerns: They implemented a zero-trust security model, encrypting sensitive data and using multi-factor authentication.
Outcome: These human-led innovations bridged compatibility gaps and enhanced security.
5. Embracing Collaboration
Action: The team involved all departments in the transition:
They hosted "cloud adoption" town halls to gather feedback and address concerns.
They created cross-departmental task forces to ensure workflows were optimized for the cloud.
Outcome: Collaboration ensured that the migration wasn’t an IT-only initiative but a company-wide transformation, gaining buy-in from stakeholders.
6. Innovating with New Tools
Action: Once the migration was complete, the team explored advanced cloud capabilities:
AI/ML tools for predictive analytics.
Scalable data lakes for improved insights.
Cloud-based collaboration platforms for seamless teamwork.
Outcome: The adoption of new tools unlocked opportunities that weren’t possible with on-premises infrastructure, boosting productivity and innovation.
The Outcome:
After completing the migration:
The company saw a 30% reduction in IT costs due to less hardware maintenance.
Teams collaborated more effectively using cloud-based platforms, reducing project timelines by 20%.
The improved scalability allowed the company to handle spikes in demand effortlessly, opening doors to new markets.
Key Takeaway:
Big technology shifts—like the move to cloud computing—can be daunting, but they also present opportunities to rethink strategies and innovate. By breaking the problem into manageable steps, investing in human capital, and embracing collaboration, teams can turn technological disruption into a competitive advantage.
Scenario: The Shift to Cloud Computing
Challenge:
A company historically relied on on-premises IT infrastructure to manage its operations, including data storage, analytics, and enterprise applications. With the growing shift to cloud computing, competitors were moving faster, benefiting from scalability, cost savings, and flexibility. The company realized it needed to migrate to the cloud to remain competitive, but this required rethinking its entire IT strategy, retraining employees, and ensuring data security during the transition.
The Approach: Handling the Shift
The team tackled the challenge through a mix of strategic planning, collaboration, and creative problem-solving.
1. Assessing the Impact
Action: The team conducted a technology audit to understand the specific impact of moving to the cloud. They identified critical applications, data repositories, and workflows that needed to be migrated.
Outcome: They determined that moving to the cloud would improve scalability, enable real-time collaboration, and reduce maintenance costs—but also identified risks like potential downtime and security concerns during migration.
2. Building Knowledge and Expertise
Action: Recognizing a skills gap, the team invested in retraining employees. They partnered with cloud service providers (like AWS, Microsoft Azure, or Google Cloud) to provide hands-on workshops and certifications for the IT department.
Outcome: Employees became more confident in managing cloud-based systems and learned how to leverage new technologies like serverless computing and AI-driven analytics.
3. Creating a Migration Strategy
Action: The team devised a phased migration strategy to reduce risks:
Phase 1: Start with non-critical systems to test the cloud environment.
Phase 2: Migrate core applications and sensitive data once initial workflows were stable.
Phase 3: Optimize cloud performance and fully integrate systems.
Outcome: This systematic approach ensured minimal disruption to daily operations and reduced downtime.
4. Leveraging Human Ingenuity
Action: The team brainstormed creative solutions to tackle unforeseen challenges:
For Legacy Systems: They developed custom APIs to integrate older, on-premises systems with the cloud rather than replacing them outright.
For Security Concerns: They implemented a zero-trust security model, encrypting sensitive data and using multi-factor authentication.
Outcome: These human-led innovations bridged compatibility gaps and enhanced security.
5. Embracing Collaboration
Action: The team involved all departments in the transition:
They hosted "cloud adoption" town halls to gather feedback and address concerns.
They created cross-departmental task forces to ensure workflows were optimized for the cloud.
Outcome: Collaboration ensured that the migration wasn’t an IT-only initiative but a company-wide transformation, gaining buy-in from stakeholders.
6. Innovating with New Tools
Action: Once the migration was complete, the team explored advanced cloud capabilities:
AI/ML tools for predictive analytics.
Scalable data lakes for improved insights.
Cloud-based collaboration platforms for seamless teamwork.
Outcome: The adoption of new tools unlocked opportunities that weren’t possible with on-premises infrastructure, boosting productivity and innovation.
The Outcome:
After completing the migration:
The company saw a 30% reduction in IT costs due to less hardware maintenance.
Teams collaborated more effectively using cloud-based platforms, reducing project timelines by 20%.
The improved scalability allowed the company to handle spikes in demand effortlessly, opening doors to new markets.
Key Takeaway:
Big technology shifts—like the move to cloud computing—can be daunting, but they also present opportunities to rethink strategies and innovate. By breaking the problem into manageable steps, investing in human capital, and embracing collaboration, teams can turn technological disruption into a competitive advantage.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
I’m part of a generation that was introduced to AI while still in school. By the time I entered the workforce, being familiar with AI—and knowing how to use it to be more productive—was no longer a differentiator; it was the expectation. To stay competitive among my peers and within my organization, I’ve had to bring AI forward as an area of strength for my teams. I also feel a responsibility to share what I’ve learned over the past few years with colleagues who are only now beginning their AI journey.
Camille Williams
Program Manager
3
Answers
Kent, Washington
Updated
Camille’s Answer
A good example is how fast AI is growing today. I'm dealing with this change by being open to new technology, learning all I can about AI, and planning to use it in my work. By adapting quickly and finding practical uses, I can rethink my plans and stay ahead.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
Between the time we start and end an engagement, technology changes materially forcing us to reevaluate throughout delivery.
Today, Generative AI is enabling our teams to modernize code in minutes, as opposed to days or weeks.
Except it’s working only in specific cases, requiring us to plan our work differently.
And capabilities are evolving every three months, requiring us to stay on top of the art of the possible, to stay relevant, and offer the best quality and pricing.
My team and I are trying, and retrying, every couple of months, the tools available to us. We’ve cut the time to review code and plan our work, have identified we prefer Chat GPT 5 over Copilot for now. But this could change in 3 months..
This is the world we now live in: we can no longer wait until the next engagement, we need to adapt as we deliver.
Today, Generative AI is enabling our teams to modernize code in minutes, as opposed to days or weeks.
Except it’s working only in specific cases, requiring us to plan our work differently.
And capabilities are evolving every three months, requiring us to stay on top of the art of the possible, to stay relevant, and offer the best quality and pricing.
My team and I are trying, and retrying, every couple of months, the tools available to us. We’ve cut the time to review code and plan our work, have identified we prefer Chat GPT 5 over Copilot for now. But this could change in 3 months..
This is the world we now live in: we can no longer wait until the next engagement, we need to adapt as we deliver.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
The firm transitioned its technology platform to Google. The change meant collaboration was at the forefront and changing the way we work was tangible. To live my mindset, "change is good", I applied and won a tour role for the Google implementation project and was responsible for business process. This might be an extreme way to feed my curiosity, yet it was a productive for the firm and me.
Presentation tactics - need to be more agile, learn to be adaptable to the new tech.
When AI and ChatGPT became part of our workflows, I realized my role wasn’t just to learn the tools but to embed them into processes the whole team could benefit from. A clear example is the Value Store, which I oversee.
I modeled structured documentation and proactive planning, and the AC team member managing day-to-day built on that by creating a detailed process guide and weaving in custom AI prompts. These prompts frame content with a buyer-lens, speed up drafting, and strengthen client conversations. They also save her hours of work each day, freeing capacity to handle the rising demands of Value Store content.
The result: seamless coverage when she took time off and a sustainable, AI-powered process that keeps our content sharper and more client-relevant. For me, it reinforced that adaptability is about enabling others to lead and amplifying impact through smart use of new tools.
Presentation tactics - need to be more agile, learn to be adaptable to the new tech.
When AI and ChatGPT became part of our workflows, I realized my role wasn’t just to learn the tools but to embed them into processes the whole team could benefit from. A clear example is the Value Store, which I oversee.
I modeled structured documentation and proactive planning, and the AC team member managing day-to-day built on that by creating a detailed process guide and weaving in custom AI prompts. These prompts frame content with a buyer-lens, speed up drafting, and strengthen client conversations. They also save her hours of work each day, freeing capacity to handle the rising demands of Value Store content.
The result: seamless coverage when she took time off and a sustainable, AI-powered process that keeps our content sharper and more client-relevant. For me, it reinforced that adaptability is about enabling others to lead and amplifying impact through smart use of new tools.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
***I’ve had to rethink my approach multiple times as technology, and the way organizations use it, has shifted.**
The first and hardest transition was moving from being a deeply technical individual contributor to becoming a manager. Success no longer came from knowing every detail myself or completing the work myself, it came from trusting others to deliver high‑quality work without telling them exactly how to do it. That shift forced me to let go of control, focus on outcomes instead of methods, and learn how to lead through influence rather than expertise alone.
**Another pivotal moment came after spending many years at the same company.**
I still had strong subject‑matter knowledge, but newly hired architects didn’t know my background and questioned my role. Instead of trying to re‑prove myself at the technical level, I reframed my value. As the release train engineer (RTE), I focused on building and empowering a strong leadership team, letting them lead their respective disciplines, while I concentrated on the broader portfolio — aligning work across teams, managing financials, and putting the right building blocks in place for long‑term success.
**The most overwhelming shift was moving to a new company in a similar role, but in a completely different industry and organizational structure.**
What had worked before didn’t automatically translate. I handled it by getting very clear on what my function was expected to deliver, identifying where I needed to learn, and building enough understanding across disciplines to add value without becoming a burden to the team. Over time, that learning mindset helped me become a trusted partner rather than an outsider.
**Across all of these shifts, the common thread was redefining how I created value.**
Each technology or organizational change required me to step back, reassess my role, and adapt my approach — not by clinging to what I already knew, but by focusing on where I could best contribute next.
The first and hardest transition was moving from being a deeply technical individual contributor to becoming a manager. Success no longer came from knowing every detail myself or completing the work myself, it came from trusting others to deliver high‑quality work without telling them exactly how to do it. That shift forced me to let go of control, focus on outcomes instead of methods, and learn how to lead through influence rather than expertise alone.
**Another pivotal moment came after spending many years at the same company.**
I still had strong subject‑matter knowledge, but newly hired architects didn’t know my background and questioned my role. Instead of trying to re‑prove myself at the technical level, I reframed my value. As the release train engineer (RTE), I focused on building and empowering a strong leadership team, letting them lead their respective disciplines, while I concentrated on the broader portfolio — aligning work across teams, managing financials, and putting the right building blocks in place for long‑term success.
**The most overwhelming shift was moving to a new company in a similar role, but in a completely different industry and organizational structure.**
What had worked before didn’t automatically translate. I handled it by getting very clear on what my function was expected to deliver, identifying where I needed to learn, and building enough understanding across disciplines to add value without becoming a burden to the team. Over time, that learning mindset helped me become a trusted partner rather than an outsider.
**Across all of these shifts, the common thread was redefining how I created value.**
Each technology or organizational change required me to step back, reassess my role, and adapt my approach — not by clinging to what I already knew, but by focusing on where I could best contribute next.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
When I first started my job, AI was not as big as it was today. I mainly had to do research and writing from my scratch with just generic search engines. Once the firm decided to be AI forward, I was quite skeptical for that AI was a good tool, but I was able to streamline my work by learning how to prompt who the audience is and what information must be used. I also made bots that only utilized certain information inputs to guarantee that information used is the correct source.
When I first started out, working with data meant rolling up my sleeves and doing everything the hard way. I was writing scripts, managing ETL jobs by hand, and spending late nights chasing down why a batch load failed halfway through. It was tedious, but that’s just how things were done. We got used to the grind.
Then cloud data platforms started taking off—Snowflake, Databricks, Azure Data Factory—and suddenly the whole game changed. Instead of weeks of manual work, you could stand up pipelines in hours. Real-time streaming analytics went from being a dream to something you could actually deliver. I’ll be honest, my first instinct was to treat these new tools like the old ones—basically just trying to recreate my manual processes in the cloud. And it didn’t work.
The turning point came when I stopped clinging to my old way of doing things and started asking, “What’s possible now that wasn’t possible before?” Once I leaned into automation, scalability, and real-time insights, it completely shifted how I approached data. It was humbling to admit that my old playbook wasn’t enough, but it was also freeing—because I realized I didn’t have to spend nights fixing broken jobs anymore. That experience taught me a big lesson: adaptability isn’t about protecting the old way; it’s about being curious enough to embrace the new one.
When I first started out, working with data meant rolling up my sleeves and doing everything the hard way. I was writing scripts, managing ETL jobs by hand, and spending late nights chasing down why a batch load failed halfway through. It was tedious, but that’s just how things were done. We got used to the grind.
Then cloud data platforms started taking off—Snowflake, Databricks, Azure Data Factory—and suddenly the whole game changed. Instead of weeks of manual work, you could stand up pipelines in hours. Real-time streaming analytics went from being a dream to something you could actually deliver. I’ll be honest, my first instinct was to treat these new tools like the old ones—basically just trying to recreate my manual processes in the cloud. And it didn’t work.
The turning point came when I stopped clinging to my old way of doing things and started asking, “What’s possible now that wasn’t possible before?” Once I leaned into automation, scalability, and real-time insights, it completely shifted how I approached data. It was humbling to admit that my old playbook wasn’t enough, but it was also freeing—because I realized I didn’t have to spend nights fixing broken jobs anymore. That experience taught me a big lesson: adaptability isn’t about protecting the old way; it’s about being curious enough to embrace the new one.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
On one occasion, we had to organize a large number of documents and prepare technical reports for a client. Artificial intelligence helped us better structure our ideas, and by always following best practices, we were able to generate the reports efficiently. Furthermore, with the support of ChatPwC, we were able to automate most of the repetitive tasks, allowing us to focus on analysis and adding more value to the final product.
Having to learn the functionality and purpose of a database management processing tool on the first day of starting new client work and handling it by finding training curriculum from the tool's website and learning hands-on from the beginning.
I am a heavy user of the firm's predictive analytics technology for revenue. At first, the technology was I say, not so great and I was not a true believer and thus maybe "shy" about "selling" the benefits to our PwC teams. However, in less than a year, a number of enhancements have been made making the technology "more of a success"....thus, making me a believer and now I am a "big seller" to our internal PwC teams.
With AI into picture, I think we should rethink all our approaches. There is a vast pool of knowledge that makes humans smarter. AI does not reinvent the wheel, it gives you all the possibilities based on the data that is trained upon. Humans, have limitations about memory but AI is really efficient in guessing the next token and come up with multiple approaches.
Having to learn the functionality and purpose of a database management processing tool on the first day of starting new client work and handling it by finding training curriculum from the tool's website and learning hands-on from the beginning.
I am a heavy user of the firm's predictive analytics technology for revenue. At first, the technology was I say, not so great and I was not a true believer and thus maybe "shy" about "selling" the benefits to our PwC teams. However, in less than a year, a number of enhancements have been made making the technology "more of a success"....thus, making me a believer and now I am a "big seller" to our internal PwC teams.
With AI into picture, I think we should rethink all our approaches. There is a vast pool of knowledge that makes humans smarter. AI does not reinvent the wheel, it gives you all the possibilities based on the data that is trained upon. Humans, have limitations about memory but AI is really efficient in guessing the next token and come up with multiple approaches.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
Internet at home without using the phone. That gave me instant access to a lot of information, and I had to learn how to disregard the unnecessary information and focus without distractions. Before, time was of the essence, because it was paid or others needed the phone. With instant access, I started to procrastinate, so I had to start focusing again
Another example is with GenAI, now I force myself to use it everyday, even with minor things. In that way, now my mind is set up to: how can the chat help me with this? And a lot of ideas are coming just changing that mindset.
One major technology shift that pushed me to rethink my approach was the rapid adoption of generative AI tools. Initially, my instinct was to treat them as just another efficiency booster — something that could automate tasks here and there. But very quickly, it became clear that this wasn’t just an incremental improvement; it was a fundamental shift in how we ideate, create, and deliver value.
Instead of resisting the pace of change, I stepped back and reframed my approach: rather than asking ‘How do I use this tool?’, I asked ‘How does this change the way I think about the work itself?’ That meant experimenting hands-on, building small proofs of concept, and most importantly, engaging my team so we could learn together.
The result was that we moved from cautious adoption to confidently integrating AI into our business processes. It taught me that in moments of big tech shifts, the right response isn’t just to learn the tool, but to rethink the mindset and processes around it. And that right there is my North Star.
Another example is with GenAI, now I force myself to use it everyday, even with minor things. In that way, now my mind is set up to: how can the chat help me with this? And a lot of ideas are coming just changing that mindset.
One major technology shift that pushed me to rethink my approach was the rapid adoption of generative AI tools. Initially, my instinct was to treat them as just another efficiency booster — something that could automate tasks here and there. But very quickly, it became clear that this wasn’t just an incremental improvement; it was a fundamental shift in how we ideate, create, and deliver value.
Instead of resisting the pace of change, I stepped back and reframed my approach: rather than asking ‘How do I use this tool?’, I asked ‘How does this change the way I think about the work itself?’ That meant experimenting hands-on, building small proofs of concept, and most importantly, engaging my team so we could learn together.
The result was that we moved from cautious adoption to confidently integrating AI into our business processes. It taught me that in moments of big tech shifts, the right response isn’t just to learn the tool, but to rethink the mindset and processes around it. And that right there is my North Star.
PwC Impact
884
Answers
Orlando, Florida
Updated
PwC’s Answer
AI has been fun, exciting and challenging. When I first got introduced to prompting, it was a 50/50 experience. In other words, 50% of the time, AI did not seem to understand what I was asking it to do, causing me frustration. Then as I learned better prompting techniques and continuously practiced it, the results were better. I am still learning and growing with AI, but now I am more confident in using it and therefore want to learn new techniques and capabilities that I can use every day. I am constantly looking for opportunities in my daily work where AI can help - for example, identifying possible uses of AI in situations where my project team is stuck or where the current processes we are using seem inefficient!
AI has shifted the way I work in a huge way. Taking advantage of all of the internal and external tools and finding out how they can make my life easier.
Adaptability is key to navigate such shift. Trainee's are encouraged to attain trainer subject knowledge and guidance.
Always love to learn new things, like the smart phones and apps, cloud adoption, AI. I treat it as an exciting time to learn and be in front of these changes.
AI helped me to rethink on writing emails, putting new ideas into a plan.
As someone that loves crafting comms, taking a lot of info and data and making a story, I was hesitant to use AI to help, but I realized AI is not stopping me from being able to do that, just now providing ANOTHER tool to make my ultimate deliverable even better, and I can produce it faster.
AI has shifted the way I work in a huge way. Taking advantage of all of the internal and external tools and finding out how they can make my life easier.
Adaptability is key to navigate such shift. Trainee's are encouraged to attain trainer subject knowledge and guidance.
Always love to learn new things, like the smart phones and apps, cloud adoption, AI. I treat it as an exciting time to learn and be in front of these changes.
AI helped me to rethink on writing emails, putting new ideas into a plan.
As someone that loves crafting comms, taking a lot of info and data and making a story, I was hesitant to use AI to help, but I realized AI is not stopping me from being able to do that, just now providing ANOTHER tool to make my ultimate deliverable even better, and I can produce it faster.
Mark Blitzer
Learning / HR Technology
4
Answers
Dallas, Texas
Updated
Mark’s Answer
You'll likely hear a lot about the current AI changes, so let me take you back a bit to the shift to remote work during the Covid-19 pandemic. This was a unique time because it pushed rapid improvements in technology and how we use it, with tech companies responding to needs rather than leading innovations like with AI.
Here are a few key points:
1. Human-centered design became crucial. Understanding user behavior and business needs was essential. It wasn't just about working from home with a laptop; it was about rethinking how work could be done remotely.
2. Diverse perspectives were important. Learning from a company's history and involving different teams helped create and scale solutions. I worked with departments I hadn't collaborated with before, which was vital during sudden changes.
3. Quick failures were valuable, and short-term solutions were okay even if they didn't fit long-term plans. We set up many temporary fixes to keep things running, and while they weren't perfect for the future, they were necessary. Sudden changes often take time to understand, and companies can't always wait to act. Covid highlighted this need for quick adaptability.
Here are a few key points:
1. Human-centered design became crucial. Understanding user behavior and business needs was essential. It wasn't just about working from home with a laptop; it was about rethinking how work could be done remotely.
2. Diverse perspectives were important. Learning from a company's history and involving different teams helped create and scale solutions. I worked with departments I hadn't collaborated with before, which was vital during sudden changes.
3. Quick failures were valuable, and short-term solutions were okay even if they didn't fit long-term plans. We set up many temporary fixes to keep things running, and while they weren't perfect for the future, they were necessary. Sudden changes often take time to understand, and companies can't always wait to act. Covid highlighted this need for quick adaptability.