Why some businesses hesitate to adopt AI-based simulation
Artificial intelligence has the potential to accelerate design processes, yet many engineering and manufacturing teams remain hesitant. This reluctance often stems from cost concerns—traditional simulation-based design is already perceived as efficient. Typically, data from one simulation run is used to inform the next design iteration, then discarded once minimum requirements are met.
Simulation data is treated as a transient byproduct.
AI-driven simulation, however, requires large and carefully curated datasets. Companies that do not routinely store or organize their simulation data may see the extra effort and cost of generating comprehensive training data as outweighing the benefits. Without structured data retention, AI adoption appears more like an obstacle than an opportunity.
Barriers to AI adoption
Lack of data infrastructure
Even organizations that recognize AI’s potential often lack the infrastructure needed to capture and retain simulation data. Without scalable storage, consistent labeling practices, or centralized repositories, data remains scattered across teams and software tools. This fragmentation makes it difficult to compile high-quality datasets for machine learning. Incomplete or disorganized data provides limited value in model development and can hinder future AI initiatives.
Building a strong data infrastructure is essential. Companies can start by implementing systematic data management practices, ensuring simulation data is properly stored, labeled, and readily accessible for AI applications.
Inconsistent data formats
Most simulation software generates data in specialized or proprietary formats that do not easily integrate with machine learning applications. Converting files, aligning naming conventions, and merging results into a single dataset can be time-consuming and error-prone. These challenges add to the cost and complexity of AI adoption, especially for companies that are already uncertain about the return on investment.
Standardization is key to overcoming this challenge. By adopting universal data formats and interoperability standards, organizations can streamline AI integration and facilitate smoother workflows across different tools and teams.
Limited ownership of AI models
Many off-the-shelf AI tools offer little competitive advantage if companies cannot retrain or customize them. Third-party solutions often lock users into rigid frameworks that do not accommodate specific product requirements or manufacturing processes. To maximize value, engineering teams need the flexibility to adapt AI models to their own data and design constraints. Otherwise, they risk obtaining generic results that fail to justify the expense and effort of implementation.
The ability to modify and refine AI models based on proprietary data is what ultimately drives real innovation and efficiency. Choosing AI solutions that allow for customization ensures companies maintain control over their technological investments.
Path to AI adoption
A path forward
Rather than overhauling existing workflows, companies can take a gradual approach to AI adoption. The first step is to capture and retain more simulation data from each design iteration. Standardizing file formats and implementing reliable data management platforms lay a strong foundation for future AI projects.
At the same time, organizations can explore in-house AI solutions that allow training and fine-tuning with proprietary datasets. This ensures that AI tools remain relevant to specific business goals. By focusing on data quality, software compatibility, and model ownership, companies can realize the benefits of AI-based simulation without disrupting existing processes.
This is where Miura’s data management services come in, offering tailored solutions that enable engineering teams to transition to AI-driven workflows seamlessly.
Miura’s data management services
Pipelines-as-code
Engineering teams often use a mix of tools for simulation and machine learning, leading to inconsistent workflows. Adopting a pipelines-as-code approach ensures that every data operation—from ingestion to transformation to delivery—is defined in a structured, version-controlled environment. Instead of introducing another platform, this strategy integrates seamlessly with existing machine learning frameworks, enabling teams to manage complex data tasks with clarity and precision.
Rapid prototyping and frictionless deployment
Experimentation is crucial in advanced engineering workflows, but scaling up from small tests to full production can be challenging. Manual storage configuration and process scheduling create bottlenecks.
A fully managed service automates both aspects, allowing engineers to prototype transformations through an intuitive interface and deploy them at scale with minimal effort. Storage becomes a background process, and pipeline execution is orchestrated automatically, freeing teams to focus on optimizing their models rather than managing infrastructure.
Cross-compatibility through domain-specific data connectors
Many organizations rely on specialized simulation software, each with unique data structures and file formats. Our domain-specific connectors bridge these gaps, enabling seamless data integration across different simulation tools. Engineers can merge data into a unified pipeline without dealing with manual file conversions or inconsistent schemas. This streamlined access to diverse data sources provides a more comprehensive view of engineering processes and enables cross-domain problem-solving.
By ensuring cross-compatibility, businesses can leverage AI-driven insights across multiple simulation environments, unlocking greater innovation potential.
Self-hosted option
Security and data ownership are critical in engineering and manufacturing, especially for proprietary designs and processes. A self-hosted deployment model allows companies to run Miura’s data management framework within their own infrastructure. This ensures that sensitive information remains within the organization’s network while still benefiting from automation, scalability, and seamless integration. By maintaining full control over their data, businesses can comply with internal policies without sacrificing AI-driven innovation.
Unlock the full potential of AI in engineering simulation
By addressing the core challenges of AI adoption—data infrastructure, compatibility, and model ownership—companies can transition to AI-driven simulation with confidence. Miura’s solutions empower engineering teams to unlock the full potential of AI while maintaining flexibility, security, and control over their data.
Get started today. Contact Miura to learn how our data management solutions can streamline your AI transition and enhance your simulation workflows.
