Engineering Simulation in 2026—ASSESS Simulation Leadership Perspective

Simulation is entering a new phase of evolution. Global dynamics, faster product cycles, and increasingly complex systems are stretching traditional design and validation methods. Across industries, engineering teams face growing pressure to deliver better, more complex products in less time and at lower cost.

In 2026, companies will continue pushing the adoption of simulation—moving simulation further upstream into early design, reducing reliance on physical testing, and tackling increasingly complex challenges across the spectrum from EDA to PLM. Determining how AI can be leveraged to accelerate development and improve engineering efficiency will remain a top priority. Additionally, as mega-vendors consolidate through ongoing acquisitions, the long tail of emerging simulation startups and niche players will increasingly fill the gaps created by these mergers.

Here are some of the major trends for simulation in 2026:

Speeding Up Simulation Workflows

Organizations are prioritizing the acceleration of workflows to maximize impact on product development. 

This shift is especially visible in the U.S. and European automotive sector, where faster iteration directly translates to market advantage. Streamlining, automating, and accelerating workflows across the simulation toolchain remain key priorities. In 2026, AI agents will further automate setup, meshing, and results interpretation, allowing engineers to focus on engineering intent rather than manual tasks.

Effort toward the “one-button” design-to-analysis workflow continues, driven by automation and AI-powered meshing that reduce manual intervention and data translation errors. The goal is a tightly integrated end-to-end environment where geometry, physics, and analysis coexist—enabling engineers to explore design changes in real time during the creative process.

AI for Engineering Simulation

AI is rapidly emerging as a complement—and in some cases, a substitute—for compute-intensive simulations, particularly in CFD and Multiphysics. Many organizations now evaluate AI models against benchmark simulations, much like validation against physical tests in the 1990s.

In 2026, these efforts will expand as companies become more comfortable with AI and evaluate its accuracy and ability to capture physical trends. However, the “black-box” nature of large models and LLM-based tools introduces risks such as hallucination, making trust essential. Similar to the early days of simulation, AI adoption—particularly for tools that disrupt existing toolchains—will follow a familiar path, starting with extensive benchmark studies before moving into production use. As a result, expect AI solutions that complement existing processes to be adopted more quickly than those that are true disruptors, especially among companies that remain cautious and risk-averse in the face of current economic uncertainties faced in 2026.

Simulation Process and Data Management (SPDM)

SPDM remains the backbone of the digital thread, ensuring traceability and reusability of simulation data in support of Model-Based Systems Engineering (MBSE). Its “single source of truth” enables engineers to work with validated, connected models aligned with overall product definitions.

As AI/ML plays a bigger role with simulation, SPDM will play a critical role in managing the massive datasets used to train and validate these models. Organizations are beginning to see a need to certify their data sources to ensure transparency (white box AI) and quality. Fully cloud-native SPDM solutions are gaining traction for their scalability, collaboration, and accessibility across distributed teams.

Democratization of Simulation

AI is helping enable simulation democratization—making simulation accessible to non-experts. Natural-language interfaces and guided workflows let design engineers perform routine simulations without deep domain expertise, freeing specialists to focus on complex or safety-critical problems.

This shift is broadening the use of simulation beyond traditional centers of excellence and accelerating cross-disciplinary decision-making. Simplified modeling tools and lightweight applications embedded in familiar platforms such as Excel will remain popular through 2026, especially among organizations early in their simulation journey.

Cloud and HPC Accessibility

Cloud-based simulation will continue to grow steadily in 2026, driven by hybrid work practices and the need for scalable compute. Cloud access provides elastic power for large studies and global collaboration, aligning with IT strategies focused on cost efficiency.

Large enterprises will maintain hybrid approaches—retaining on-premise clusters for tightly coupled problems while using cloud HPC for standardized workloads and burst capacity. For smaller companies, cloud platforms remain attractive for flexibility and pay-per-use economics.

Vertical and Niche Simulation Tools

While major vendors dominate enterprise-scale platforms, application-specific (niche) tools are gaining traction. These focused solutions appeal to SMBs and specialized teams seeking fit-for-purpose functionality over broad multiphysics capabilities.

Smaller companies increasingly prefer consumption-based pricing—paying per simulation rather than maintaining subscription licenses. Startups and niche vendors are filling this gap through simpler business engagement, flexibility, and faster responsiveness compared with the larger incumbents.

Talent Shortage and New Skill Profiles

Demand for simulation continues to outpace the supply of skilled engineers. In 2026, talent acquisition and retention will become more competitive and challenging. Vendors that have established strong academic partnerships over the years and embedding tools into university curricula will be better served to help industry address this challenge. 

At the same time, simulation’s convergence with AI is creating new skills needs—part analyst, part data scientist. The shortage of professionals who can develop and deploy AI-augmented simulations will persist, driving companies to invest in upskilling engineers in data analytics, machine learning, and probabilistic modeling.

In Summary

As we enter 2026, simulation will continue shifting earlier in the design cycle as organizations pursue faster iteration and tighter integration between CAD, physics, and analysis. AI will play a growing role in automating workflows and augmenting traditional simulations, with adoption progressing first in areas where AI complements existing processes and can be validated through structured benchmarking. SPDM will rise in strategic importance as companies confront expanding data volumes and the need for trusted, traceable datasets to support AI and MBSE initiatives. Cloud and hybrid HPC strategies will mature, enabling more flexible access to compute resources, while niche and vertical tools will gain ground as vendors fill gaps left by industry consolidation. At the same time, skill requirements will evolve, with rising demand for engineers who can combine simulation expertise wit