Generative AI (genAI) is introducing a new era for the technology sector by driving productivity in critical areas like software development and UI prototyping. Beyond time savings, generative AI is making strategic impacts, such as by reducing cybersecurity threats and enabling cutting-edge research.
Revolutionizing Software Development: From Code Generation to Optimized Design
Generative AI is transforming the software development lifecycle, addressing challenges from code creation to debugging. Organizations with active genAI initiatives are seeing a 7%-18% improvement in productivity across the software development life cycle.
Tools like GitHub Copilot and OpenAI’s Codex can write code based on natural language descriptions, speeding up development by suggesting or completing code snippets. In fact, 95% of developers say they enjoy coding more when using Github Copilot. Organizations have seen an average of 9% time savings when using these technologies for code assistance and 10% when writing documentation.
Beyond code generation, GenAI is also efficiently detecting bugs, vulnerabilities, and security flaws that manual reviews might miss. Once it detects these bugs, it can then make tangible recommendations for the best ways to solve them. Over the next two years, it’s expected that generative AI will augment 25% of debugging and testing tasks.
AI is also enriching software design by analyzing existing codebases and user requirements to suggest optimized system architectures or automatically refactor legacy code, improving software performance and scalability. It’s anticipated that within the next two years genAI will augment 28% of software design work.
UI/UX Prototyping with GenAI
Generative AI is making waves in UI/UX design by enabling rapid prototyping and iteration. Tools like Uizard and Figma’s AI plugins allow designers to transform sketches into fully interactive prototypes in a matter of minutes. GenAI can also analyze user data to suggest optimized layouts, color schemes, and design patterns that enhance usability.
For example, Figma’s First Draft genAI tool helps designers quickly push an idea forward, by allowing them to generate a first draft of a basic design from a simple prompt. Another example: Vercel enables users to build a fully functional web app just by describing their vision in a simple text box.
Strengthening Defenses: GenAI in Cybersecurity
GenAI-powered systems can analyze vast amounts of data in seconds, identifying vulnerabilities, detecting anomalies, and responding to cyberattacks. GenAI can also simulate potential attack scenarios, helping organizations anticipate and mitigate risks before they materialize.
In fact, in a survey of industry executives of companies with annual revenue greater than $1 billion, 61% of respondents said they see genAI strengthening cybersecurity in the long term, 62% expect it to play a proactive role in vulnerability detection, and 54% said genAI can create realistic threat scenarios to enhance cybersecurity analyst training. Already, more than 40% of organizations have initiated proofs of concept using genAI for security.
Enabling Research and Innovation
Generative AI is a powerful tool for research. It’s now assisting technology teams in exploring new algorithms and analyzing large datasets. AI can aid researchers in fields like quantum computing, cybersecurity, and network optimization by generating insights and enabling faster breakthroughs. For example, researchers can ask genAI to “Explore recent breakthroughs in reinforcement learning algorithms and their applications in real-time decision-making systems,” as shown in this AlphaSense Generative Search example. The genAI tool then provides a concise and digestible response by summarizing 140 documents — including company publications, patents, academic papers, and news sources — in just a few seconds.
Empowering Real-Time Decision-Making
Generative AI, enhanced by edge AI, is transforming the technology industry by enabling real-time decision-making in critical applications. By processing data locally on devices, edge AI reduces latency, ensuring faster responses and greater efficiency for technology-driven solutions.
For example, AI-driven medical devices can analyze patient data locally to deliver instant insights, while autonomous vehicles rely on edge AI for split-second decisions that ensure safety. Apple is working on OpenELM, a group of open-source large language models and small language models that run on a single device rather than connecting to cloud servers. The language model is optimized for on-device use, marking a significant advancement in mobile AI innovation.
Edge AI enhances privacy and security by processing sensitive data locally, minimizing the risks associated with transmitting information to cloud servers. It also reduces energy consumption by limiting data transfers, making it a more sustainable AI solution.
Looking Ahead to the Future of GenAI in Tech
Generative AI is undeniably transforming the technology sector, driving a fundamental shift in how technology is designed, implemented, and maintained, while enabling more efficient workflows, smarter decision-making, and groundbreaking innovations. As this transformation continues, it’s clear that the role of technology professionals will evolve.
Software developers, for example, will no longer be limited to writing code but will also need to choose and refine AI-generated code, balancing trade-offs between security, performance, and maintainability. Across all areas of the tech industry, professionals are being called upon to think critically and adapt to emerging tools.
Looking ahead to 2025, as genAI use cases become more strategic, a mix of technical expertise and strategic insight will be more essential than ever. Critical thinking, creativity, and the ability to evaluate AI-driven outputs will become just as important — if not more so — than traditional coding proficiency. As genAI continues to shape the technology landscape, it is fostering a new era where human ingenuity and AI innovation converge to redefine roles, unlock opportunities, and push the boundaries of what is possible.
ABOUT THE AUTHOR
Sarah Hoffman
Director of Research, AI
Sarah Hoffman is Director of Research, AI at AlphaSense, where she explores artificial intelligence trends that will matter most to AlphaSense’s customers. Previously, Sarah was Vice President of AI and ML Research for Fidelity Investments, led FactSet’s ML and Language Technology team and worked as an Information Technology Analyst at Lehman Brothers. With a career spanning two decades in AI, ML, natural language processing, and other technologies, Sarah’s expertise has been featured in The Wall Street Journal, CNBC, VentureBeat and on Bloomberg TV, and she was named a Rising Star for WatersTechnology’s Women in Technology and Data Awards in 2018. Sarah holds a master’s degree from Columbia University in computer science with a focus on natural language processing, and a B.B.A. from Baruch College in computer information systems. Sarah is based in New York.
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