Making space for spatial computing

Start with the workflow, not the hardware

The key to realizing ROI from spatial computing begins with understanding the workflow and business processes it affects. Identify workflows where spatial context, physical relationships, complex procedures or distributed expertise create a genuine business opportunity. Ask the following questions to determine whether spatial computing could improve current organizational workflows:

• What problem is the organization trying to solve?
• What is the current baseline of success?
• What measurable outcomes should improve?
• Why does a spatial interface offer an advantage over the existing workflow?

Specific use cases for spatial computing focus on simulating challenging environments for training, design or operational purposes. Use cases include the following:

• Job training. Employees can interact with immersive simulations for hazardous, technical or highly specialized job training.
• Healthcare procedures. Provides 3D visualization and AR guidance for surgery or medical training.
• Architecture and construction. Renders full-scale 3D models for design review and on-site coordination.
• Remote equipment maintenance. Offers AR-guided diagnostics and repairs for complex machinery.
• Factory or warehouse operations. Spatial overlays for inventory and routing can optimize organization and workflows.

Business outcomes for spatial computing are faster time to proficiency, improved engineering iteration, fewer operational errors and shorter collaboration cycles.

Spatial deployments also introduce hardware, software, integration, support, security and change management requirements that might require more effort than the potential benefits justify. Conventional 2D interfaces could remain more effective for many processes. The decision is not whether organizations can use spatial technology, but whether it materially improves their workflows.

Value comes from measurable change

The key to successfully scaling a program is knowing what works and improves outcomes. Establish success criteria and baseline metrics before expanding spatial computing pilots. Treat operational improvements as hypotheses to validate, not benefits to assume.

KPIs show whether a use case should be scaled, redesigned, limited or retired. Use the breakdown below to measure different KPIs of spatial computing deployments.

Training: Measure time to proficiency

To evaluate the efficacy of immersive training, KPIs must sift engagement from novelty and demonstrate improved performance. Use the following measurable outcomes to determine how spatial computing can improve training:

• Time required to complete training.
• Time to proficiency.
• Assessments of performance or knowledge retention.
• Instructor and resource requirements.
• Training-related errors.

Engineering and design: Measure iteration speed

For engineering and design use cases, examine how 3D environments can enable spatial design reviews before physical implementation. Potential KPIs include the following:

• Design-review cycle time.
• Number of physical or prototype iterations.
• Earlier identification of design issues.
• Cross-functional review efficiency.

Measure wins, errors and coordination costs

Wider KPIs to consider are successes, errors and the cost of coordination and integration with existing systems. Use the following KPIs to assess broader organizational considerations of spatial computing systems:

• First-time-right rate.
• Error or rework time.
• Resolution time.
• Collaboration cycle time.
• Escalation requirements.

Build a path from pilot to production

Once organizations have determined their use case and measurable KPIs for spatial computing deployment, they should create a roadmap to guide them from pilot to production. Establish a deployment framework that uses the following progression:

This approach constructs a path from pilot to production that demonstrates the ROI and benefit of spatial computing technologies.

Phase 1. Focused pilot

Select a workflow with a clear operational pain point and a defined user population that could benefit from spatial computing technology. Create a measurable baseline and choose manageable business integration requirements that keep the investment realistic.

Establish success criteria before deployment and treat the spatial technology pilot as a business experiment rather than a technology showcase.

Phase 2. Production hardening

Document and codify the operating model, including ownership of the environment and technology. Assess requirements around the following:

• Device management.
• Identity and access management.
• Data integration.
• Application lifecycle management.
• User support.
• Security controls.
• Privacy and compliance.

Phase 3. Enterprise integration

Relate spatial experiences in training, development or operations to business outcomes. Shift from 3D experiences toward systems connected with operational data. Identify dependencies around data quality, APIs, integration, governance and application architecture.

Phase 4. Scale

Once spatial computing technology is justified for specific use cases, replicate successful deployments across sites or workflows. Provide a common governance framework and technology architecture, and apply measure-before-scale principles to expansion decisions. Retire pilots that fail to demonstrate measurable value or that are eclipsed by better options.

Digital twins are most successful after the foundation is ready

Digital twins bring real-world data into spatial computing environments, creating virtual models that are connected to their physical counterparts. These advanced simulated objects and environments become viable once the foundation and governance for spatial computing are in place. Digital twins require that spatial representations connect to reliable operational data and business processes. Standard prerequisites include the following:

• Data quality.
• Integration.
• Governance.
• Ownership.
• Proven business case.

If these prerequisites are not met, digital twins fail to accurately represent the platforms they are meant to replicate.

Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to TechTarget Editorial, The New Stack and CompTIA Blogs.