When I speak with clients today, one thing is clear: they are no longer asking how to “go digital.” They are asking how to stay relevant as markets, customer expectations, regulation and AI all change at once. That is why I believe digital engineering has moved far beyond a modernization program. It is now a strategic business capability and increasingly, the end-to-end operating backbone for product and business innovation, industrial resilience and sustainable growth. At HCLTech, we see this shift every day across the organization value chain, from software, cloud, data and AI to silicon, connectivity, digital twins and immersive technologies. Our experience is grounded in scale and real-world impact: more than 375 active customer engagements in the digital product engineering space, over 250 digital products developed for Fortune 500 companies, more than 400 digital engineering patents filed and 65% of our workforce certified in emerging technologies.

The digital engineering urgency is real. A 2025 survey from McKinsey found that 88% of organizations now use AI in at least one business function, yet only 23% say they are scaling an Agentic AI system somewhere in the enterprise. Capgemini’s 2026 Engineering and R&D Pulse shows the pressure this creates: 78% of leaders report rising engineering costs, while more than 75% expect AI to improve productivity, time to market and cost performance by 20% to 50%. At the same time, the IEA projects data-center electricity consumption will rise to around 945 TWh by 2030. The message is unmistakable: the next era of digital engineering will not be won by experimentation alone, but by how effectively enterprises scale it as an end-to-end engineering backbone for innovation, resilience and measurable business value.

The new engineering core

• Trend 1: AI becomes the default layer of engineering
• Trend 2: Legacy modernization shifts from migration to AI-led reinvention
• Trend 3: Platform engineering becomes the control plane for scale
• Trend 4: Cloud engineering becomes AI-ready and energy-aware

From my perspective, these four shifts belong together. AI is no longer just a productivity tool; it is changing how requirements are interpreted, code is generated, systems are tested and operations are optimized. However, that only scales when enterprises move beyond fragmented point tools. Platform engineering is becoming foundational here and Gartner has said that by 2026, 80% of large software engineering organizations will establish platform engineering teams. This is also why open, production-grade ecosystems matter. HCLTech’s expanded collaboration with Google Cloud around Agentic AI and our full-stack AI approach with AMD both reflect the market’s move from pilot activity to industrialized execution. 

• Trend 5: Data engineering becomes the bridge from data to business value

One of the biggest execution gaps I see is not a lack of data, but a lack of usable, connected and contextualized data. Enterprises are sitting on fragmented information across engineering, operations, manufacturing and service environments. IDC expects manufacturing industries alone to accumulate 92 exabytes of data by 2030. That makes data engineering a boardroom issue. The winners will be the organizations that treat data quality, lineage, semantics and observability as engineering disciplines, not analytics clean-up work. At HCLTech, this is exactly why data engineering and AI sits at the center of the digital engineering stack. 

From digital products to living systems

• Trend 6: Software-defined products rewrite lifecycle economics
• Trend 7: PLM and digital twins become closed-loop orchestration engines

Products are becoming software-defined, continuously evolving and increasingly service-led. That changes the economics of engineering. It means lifecycle traceability matters more, over-the-air change becomes strategic and PLM must evolve from system of record to system of orchestration. At HCLTech, we see PLM as the enterprise engine that connects design, manufacturing, compliance, service and monetization. Digital twins advance that same shift: they are no longer only for visualization but for validation, simulation and decision support across the product lifecycle. 

• Trend 8: AIoT moves intelligence to the point of action
• Trend 9: 5G Advanced and private networks become engineering fabric
• Trend 10: Spatial computing finds industrial utility

These trends are converging faster than many enterprises realize. AIoT is pushing decision-making closer to devices, plants and field environments. 5G is maturing from a connectivity upgrade into a runtime layer for real-time operations; Ericsson forecasts 6.4 billion 5G subscriptions by 2031. Spatial computing is moving beyond novelty into practical use cases such as immersive training, remote assistance, guided maintenance and simulation. IDC says the XR market rebounded 44.4% in 2025, driven by smart glasses alone. What matters now is not these technologies in isolation, but their convergence. AI/GenAI-led development, AIoT, 5G Advanced and spatial computing are collectively creating a new engineering fabric; one that is transforming how products and operations sense, respond, adapt and deliver value in real time.

• Trend 11: Semiconductor engineering becomes a strategic differentiator

The more intelligence shifts to the edge, the more silicon matters. WSTS says global semiconductor sales reached $795.6 billion in 2025 and forecasts the market to approach $975 billion in 2026. That is not just a chip industry story; it is a digital engineering story. Enterprises increasingly need system-aware thinking across embedded software, compute architecture, power efficiency, packaging and AI acceleration. The strategic value of semiconductor engineering will keep rising because performance, latency, cost and energy are now inseparable design choices. 

Trust, resilience and responsibility by design

• Trend 12: Quality engineering becomes continuous trust engineering
• Trend 13: Security and compliance shift left into the lifecycle
• Trend 14: Sustainability becomes a product-definition constraint

This is one of the most important shifts of all. Capgemini’s World Quality Report shows that 89% of organizations are already piloting or deploying GenAI-augmented quality-engineering workflows, but only 37% have them in production. That tells me quality is not disappearing; it is becoming more strategic. In parallel, the EU AI Act becomes fully applicable from August 2, 2026, while the Cyber Resilience Act’s reporting obligations start on September 11, 2026 and its main obligations apply from December 11, 2027. The Digital Product Passport is also emerging as a core mechanism for sharing data on product sustainability, durability and environmental attributes. Put together, these shifts mean trust, traceability, cybersecurity and sustainability can no longer sit outside engineering. They must be designed within digital engineering from day one.

The operating model for the next era

• Trend 15: Future-ready talent, human-in-the-loop governance and ecosystem-led delivery become the real source of scale

If I had to choose one constraint that will decide who wins, it is not tool access. It is operating-model readiness. The World Economic Forum says 170 million new roles could be created by 2030 while 92 million will be displaced and 63% of employers already cite skills gaps as a major barrier to transformation. Capgemini also found that only 15% of executives believe AI can replace the creativity and problem-solving of human engineers. I agree with that view. The future is not human versus AI. It is human judgment, domain expertise and responsible oversight working with intelligent systems. That is why I believe enterprise should invest less in “vanilla” capability-building and more in future-ready skills, domain-rich engineering talent and partner ecosystems that help them scale with speed and discipline.

Engineering the next era of value

The takeaway is straightforward. The next five years in digital engineering will not be defined by who launched the most pilots or migrated the most workloads. They will be defined by who built the strongest engineering operating model: AI-native, platform-led, data-disciplined, secure, sustainable and outcome-oriented. Ay HCLTech, this perspective is shaped not only by market observation, but by the scale of impact we help deliver: 2.5 billion-plus human lives touched across the globe and more than $150 billion in client revenue influenced through our engineering-led transformation work.

For me, that is the real story of digital engineering. It is no longer about digitizing functions in isolation. It is about engineering connected systems of value. Systems that enable enterprise to keep adapting, keep competing and keep creating growth in a world that will only become more intelligent, more distributed and more dynamic as we traverse into the future.