Dr. William Bain, founder and CEO of ScaleOut Software, a leader in operational intelligence through digital twins and scalable, in-memory computing, shares his predictions for the future of app development. Drawing from his 40-year career in parallel computing, he explores how the convergence of digital twins and generative AI will transform app development by delivering real-time insights, improving user interactions, and addressing AI errors. As we approach 2025, Dr. Bain highlights how these technologies will streamline development workflows, enhance decision-making, and drive the evolution of smarter, more responsive applications.
Below, Bill shares his predictions on how digital twins, combined with generative AI, will revolutionize app development in 2025 by delivering live insights, addressing AI errors, enhancing user interactions, and accelerating development.
Digital twins, software designed to model and manage complex live systems are set to revolutionize app development in 2025. These virtual representations of physical entities can provide dynamic, data-rich analytics to manage complex systems, offering real-time insights unmatched by other techniques. When combined with the power of generative AI, digital twins become an essential tool for managing operations, enhancing situational awareness, and optimizing resource allocation across a wide range of applications, including airlines, cyber and physical security systems, power grids, smart cities, and many more.
Digital twins can process live data from diverse data sources within complex systems, enabling continuous, context-based analytics, anomaly detection, and dynamic visualizations. These capabilities enable developers to create tools that offer users actionable intelligence and a deeper understanding of live operations. When paired with generative AI, digital twins will unlock advanced features like automatically detecting anomalies, generating queries from natural language input, and issuing proactive alerts to users.
For example, consider an app for managing large-scale retail operations. Implemented with an array of digital twins, it could continuously monitor inventory levels, supply chain data, and in-store activity. By integrating generative AI, the app could detect inventory shortages before they happen, generate targeted restocking suggestions, and alert managers to sales trends that require immediate attention. AI-powered data visualizations would help decision-makers quickly and precisely respond to emerging opportunities or challenges.
Generative AI will transform the creation of digital twin applications by streamlining and enhancing the development process. AI-powered tools can automate routine coding tasks, solve specialized challenges, and optimize designs. They will enable app developers to focus on strategic design and problem-solving rather than getting bogged down in technical details.
Imagine an energy management app that uses digital twins to monitor a city’s power grid. Generative AI can automate the development of algorithms that track energy consumption trends, identify anomalies, and recommend load-balancing strategies. This reduces development time and delivers powerful features for management personnel.
Incorporating generative AI will also improve how users interact with analytics results produced by digital twins. Instead of requiring users to manually construct often complex queries to extract insights, system managers can use AI to automate data tracking and visualization. Users will be able to supply natural language descriptions of the insights they need, and AI-powered applications will generate intuitive visualizations, highlight patterns, and flag anomalies for further analysis. This intelligent automation empowers app developers to create solutions that maximize the value of real-time analytics and enhance decision-making for system managers.
As the potential for generative AI grows, errors like hallucinations, biases, and data misinterpretations pose significant challenges, eroding trust and disrupting decision-making. For example, an airline app using generative AI to adjust flight schedules after unforeseen weather events may rely on legacy databases which lack the latest updates, leading to flawed recommendations.
Digital twins will solve this by providing AI models with live, accurate data. Because digital twins can maintain dynamic representations of live systems, they can feed AI models the real-time information needed to avoid mistakes. This process, called Retrieval-Augmented Generation (RAG), allows AI to query live data, refine outputs, and minimize inaccuracies.
In the case of the airline app example, digital twins can supply up-to-the-minute details about flights, passengers, crews, and gates, enabling AI to generate accurate, timely recommendations. For app developers, integrating digital twins ensures that AI-driven applications are precise, actionable, and reliable.
As live systems become increasingly complex, the integration of digital twins and generative AI will become a cornerstone of app development for real-time analytics. These technologies will enable developers to create applications that provide immediate insights, improve situational awareness, and adapt to evolving challenges. From optimizing transportation networks to running smart cities, the possibilities are virtually limitless.
In 2025, the powerful combination of digital twins and generative AI will not just enhance app development—it will define the future of how we manage and interact with live systems. Developers who embrace this trend will lead the charge in delivering smarter, more responsive applications.
Dr. William Bain is the founder and CEO of ScaleOut Software, which develops software products to provide operational intelligence for live systems using digital twins and scalable, in-memory computing. Bill earned a Ph.D. in electrical engineering from Rice University. Over a 40-year career focused on parallel computing, he has contributed to advancements at Bell Labs Research, Intel, and Microsoft, and holds patents in computer architecture and distributed computing.
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