These questions are designed to stimulate discussion around various aspects of generative AI adoption in engineering and other domains, encouraging both technical and non-technical stakeholders to explore its potential, challenges, and implications.
Can genAI be deployed across processes to detect irregular patterns of data?
Can genAI be used to automate resource allocation across projects?
Can genAI alone be used to predict the stock market?
Will using genAI to develop automated testing and test case generation improve productivity?
What facets of predictive maintenance can genAI support?
How well does genAI perform in speed, efficiency, and accuracy?
can it handle large-scale or real-time applications?
what are the computational requirements and infrastructure needed to deploy and maintain the system?
If genAI is generating the code, who owns the written code?
what strategies can we employ to protect sensitive or proprietary information when utilizing models trained on proprietary datasets?
What are the skills needed to deliver genAI projects?
What are our genAI projects, how were these initiated, and where are we now?
What kind of data sources are avaliable for genAI to be trained on?
Is this data clean and reliable?
What obstacles do we anticipate in genAI implementation across the enterprise?
How are we looking to address the challenges
How will genAI adaption impact the existing systems, workflows, processes?
How might genAI projects benefit from interdisciplinary feedback loops and iterative development cycles to refine and improve solution outcomes?
Can we envision scenerios where genAI assists in generating personalized product configurations or customizations for clients?
How might genAI empower engineers to address societal challenges or contribute to the greater good beyond our organization's immediate goals?
Are there any ethical/societal considerations to be made before genAI adoption?
if yes, how do we address the concerns?"
Can genAI help with customer segmentation and personalization?
What is the best way to approach customers to initiate genAI projects?
What a specific problem or challenge are we aiming to address with gen AI
Is there a particular area or process that we are trying to improve?
Which functional areas do you see maximum utilization for genAI implementation?
What technical expertise and resources would be necessary to develop and deploy genAI solutions within our organization?
Are there existing tools, platforms, or frameworks that could accelerate the implementation of genAI in our workflows?
How can genAI be used to improve operational effeciency across your organization?
How can we ensure that genAI solutions align with our organization's values and commitmentsto sustainability and social responsibility?
How can genAI be applied to automate repetative tasks in engineering processes?
How can genAI support creative tasks such as concept generation or ideation?
What are the use cases that can be considered for genAI adaption and implementation?
How scalable are genAI solutions, and what infrastructure upgrades or investments might be required to support their deployment at scale?
How is genAI going to disrupt the way we develop the software?
What organizatinal supports and resources are avaliable to facilitate talent development and readiness for leveraging genAI?
How might genAI assist in generating personalized product recommendations or user experiences?
Are there emerging trends or advancements in genAI research that we should monitor and incorporate into our strategic planning?
Across the IT department, can genAI be used for resource allocation and supply chain visibility?"
Can genAI be looked at for employee engagement and well-being initiatives?
How do we measure the performance and effectiveness of genAI solutions once they are integrated into our processes?
What advancements in genAI research are on the horizon, and how might they impact your industry or field?
What is the roadmap for future development and improvement of the genAI technology?
Are there any emerging trends or research directions that may impact its relevance or effectiveness?
How does genAl align with long-term organizational goals or strategies?
Which processes and workflows can be considered the low-hanging fruit for genAI-influenced innovation?
Are there opportunities for upskilling or training existing employees to acquire the skills needed to work with genAI?
Can genAI be used for DevOps optimization and continuous integration/ deployment?
Can we leverage pilot projects or proof-of-concept studies to validate the feasibility and potential impact of the most promising genAI ideas before full-scale implementation?
Can genAI be used to provide enhanced IT support?
How can genAI be used to transform the customer experience of a visitor to our website?
How can genAI help across process automation and optimization?
How can genAI contribute to innovation within banking, healthcare, and other fields?
How do we balance the desire for technological innovation with the need to mitigate risks associated with data security, privacy breaches, and intellectual property protection?
How might genAI enhance collaboration between engineers, designers, and other stakeholders during the product development life cycle?
What do we need to operate genAI at scale?
What strategies can we employ to protect sensitive or proprietary information when utilizing genAI models trained on proprietary datasets?
What level of understanding and familiarity do our existing teams have with genAI concepts and technologies?
Please click the arrows to navigate to the next card.
Simply swipe to navigate to the next card
Connect with Virtusa’s Helio team to help guide your generative AI journey.