Digital Engineering trends to watch out for in 2024 | HCLTech

Digital Engineering trends to watch out for in 2024

Navigating the future with composable platforms, cloud optimization and cutting-edge trends in AI and 5G network monetization
8 minutes read
Vittal Devarajan
Vittal Devarajan
Senior Vice President, Engineering and R&D Services
8 minutes read
Digital Engineering trends to watch out for in 2024

The challenges faced by enterprises in 2023 prompted a strategic shift in R&D toward digital engineering. Despite multiple headwinds in businesses, technology and geo-politics, enterprises intensified efforts to leverage digital technologies across products, services and business operations.

dominated the headlines, featuring buzzwords like large language models (LLMs) and GenAI. Other digital technologies also gained momentum with the expansion of 5G deployment, the mainstreaming of digital platforms and cloud computing, and the convergence of systems, hardware and sensors in digital manufacturing. These trends are set to continue their momentum into 2024, with beginning to deliver on promises and chip development pushing the boundaries of the nanometre (nm) scale, transitioning processes to cloud.

Moving into 2024, distinct digital engineering trends are emerging beyond these broad contours. Together with our clients across various sectors, we at HCLTech are integrating digital innovations across product value chains to generate new value, maintain a competitive edge, build resilient systems and develop sustainable products. Let us look at some of the distinct digital engineering trends poised to shape 2024.

Digital Engineering Trends 2024

Composable platforms are here to stay

have emerged as the foundation for transforming traditional business models into dynamic digital platforms. Applications built from business-centric modular components follow the MACH (Microservices-based, API-first, Headless, Cloud Native) architecture. This architecture is at the core of companies undergoing digital platform modernization.

Modernizing corporate digital platforms enables seamless interaction among various components of the business, significantly enhancing business productivity. Advances in data engineering in consuming and correlating unstructured data and use of AI and analytics have helped in creating new cognitive customer experiences.

Time to optimize cloud costs

As an increasing number of enterprise applications and workloads move to cloud, the management of cloud operations becomes increasingly intricate. The use of cloud infrastructure for cutting-edge technologies such as 5G, AI/ML and GenAI has increased the cost of cloud operations.

has become essential for enterprises, particularly those that have transitioned their workloads and platforms to the public cloud. In 2024, the demand for specialized financial operations (FinOps) services to optimize cloud costs will grow further.

Telecommunication companies (telcos) have made significant investments in 5G with an eye to exploiting the potential of its enterprise applications. To enhance average revenue per user (ARPU), it is crucial to enable new services. Telcos can achieve this by securely exposing the application programming interfaces (APIs) of the 5G network (network-as-a-service), allowing the development of innovative applications and use cases. By doing so, the telcos can capitalize on their network investments.

AI: Actualizing intelligence

In 2024, businesses are set to implement their AI strategies, focusing on execution and integration of various AI forms like GenAI. These diverse forms, including text, code and non-textual forms like images, audio, video, computer vision and natural language processing (NLP), have carved distinct places across different sectors.

The integration of these diverse AI forms to harness their combined potential will drive innovation and improve customer experiences, business growth and operational efficiency. The year will also underscore the importance of developing a sturdy framework for trustworthy and responsible AI as it becomes intertwined with business processes and decision-making.

meets GenAI

GenAI emerges as a powerful tool for application modernization, assisting in code reverse engineering, extracting business rules and uncovering domain models. It automates the generation of cloud-native code, UI code from design images and infrastructure as code (IaC), which ensures that applications are tailor-made for modern cloud environments. This not only reduces development time but also ensures seamless operations in cloud. GenAI chatbots also leverage NLP techniques and deep learning to engage in human-like conversations, consuming available data/information on varied subjects to respond to queries and generate context-sensitive content.

Smaller LLMs to power smarter edge devices

Industry 4.0, propelled by smart edge devices, is expected to pick up momentum in 2024. These devices, previously powered by traditional machine learning models or computer vision-based models, are set to evolve with the advent of smaller LLMs. This will result in smarter devices capable of swift decision-making. The potential use cases include predictive maintenance to prevent machine downtime, real-time data utilization for self-repair or reconfiguration of machines to boost productivity and decentralized quality control to improve product quality.

Sustainable manufacturing gathers steam

As focus shifts to , it becomes important to comprehensively analyze the entire value chain and employ appropriate tools to effectively manage emissions throughout the manufacturing process. Digital manufacturing provides the necessary foundation for effectively measuring, monitoring, predicting and optimizing sustainable operations while helping to create hyper-personalized experiences powered by smart factories.

Making the Metaverse mainstream: Engineering adoption for a tech major

Digital twining with AR/VR, AI and IoT

The combination of VR and digital twins will enrich the visualization aspect and provide a more comprehensive understanding of replicated objects. By employing GenAI techniques, digital twins can accurately simulate and predict behavior, enabling innovative ways of synthetic data generation.

This can be especially useful in simulation for asset intensive manufacturing, where it can accelerate new product development while optimizing the cost of product development. Digital twins connect to IoT systems to replicate the behavior of physical objects using real-time data, enhancing various aspects such as design, manufacturing, operations and maintenance.

Silicon joins ‘as-a-service’ bandwagon

Driven by the expansion of 5G technology and the advancements in AI, the market is poised to experience a rise in IP-centric custom . These services will be executed comprehensively and efficiently, catering to the specific needs of applications. Applications can range from domain specific use cases in automotive, IoT and edge devices to data-centric applications across industries. Custom System on Chips (SoCs) with best-of-breed modules from multiple suppliers are becoming key differentiators in high volume use cases.

Post silicon validation reaches for the cloud

Cloud-based (PSV) labs have emerged as a viable solution for capital and infrastructure-intensive validation tasks such as emulation and system validation. This migration to cloud offers small and medium integrated device manufacturers (IDMs) the opportunity to access advanced platforms on a pay-per-use basis. This shift toward cloud-based PSV labs signifies a significant advancement in the semiconductor industry, enabling greater accessibility and flexibility for IDMs of all sizes.

In conversation with – Abhishek Divekar

Grateful for contributions from our experts – Gunamani Rajagopal, Vasudevan Vijayaragavan, Atul Jain, Shantanu Rai, Sreekanth Jayanti, Biswadeep Chatterjee, Sathish Kumar M, and Ankur Agarwal.

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