What types of Augmented Reality deployment and use modes are the most appropriate for maximum cost impact? 

In a previous article on Service in the wake of COVID-19 and the associated recession, we discussed the need for drastically reducing the cost of uptime for customers. To do that both the cost of providing service and the need for service, either by predicting and preventing, i.e. minimizing, downtime, have to be reduced. Technology in its different iterations must play a significant role in this -and here we look at Augmented Reality (AR).

The conventional operating model of (field) service is field engineers visiting customer sites to diagnose, troubleshoot, and repair equipment problems. This requires building up know-how and expertise, capacity, and, importantly, logistics. All come with significant costs, both fixed and variable.

Augmented Reality (and related forms of digital visual environments) are changing this picture. In futuristic, though already partially existing, scenarios, engineers access digital representations of equipment (Digital Twins) and intervene on the corresponding physical equipment remotely -with or without the help of AI-based algorithms. This fundamentally alters the cost structures of Uptime (and possibly the competitive space) by impacting the cost of logistics, information processing, and the required capacity for the same levels of service. While such a scenario may require some years to become mainstream, some constituent elements can already be used.

The current key areas of AR impact include training, remote guidance or assistance, instructions and task management, as well as insights through data overlay.

By freeing the user from the constraints of two-dimensional text and images (screens, paper documents), AR has been shown to drastically improve the efficiency of learning (the brain learns better 3-dimensionally). People can be trained in new skills or in applying existing skills to new equipment in a fraction of the time it takes conventionally -and internalize the material far better through simulations and real-time case studies.

Remoting in an expert can save significant amounts of time and helps avoid costly mistakes -as does the availability of step by step instructions in the field of view -whether accessed manually or provided algorithmically. Experience and outcomes can be captured and reused. Collaborative problem solving can be supercharged without experts having to visit the site or access the physical equipment.

Finally, the availability of realtime contextual data on equipment and processes -in the field of view- can significantly improve problem-solving, decisions, and interventions.

Most companies implementing AR focus on the low hanging fruit of remoting in experts and providing step by step instructions, as the technology is more widely available, inexpensive and investments required for implementation (e.g. digitization of content, integration with IoT) quite low.

However, the cost impact of AR deployment will depend heavily on context and circumstances: the existing operating model and the nature of equipment served as well as managements’ ability to follow through with necessary changes to realize potential savings.


A company makes complex made-to-order machinery distributes around the world. For Uptime it relies on a fairly small number of highly experienced engineers who spend most of their time at customer sites, possibly with a regional or type of equipment focus. The key limiting factor is clearly the high cost of expert capacity (quantity and the marginal cost of engineers, the time required to develop expertise and imbalances between supply and demand for expertise leading to prolonged downtime and decline in customer satisfaction). Such a business needs to break the expert capacity limitation.  Diagnostics and troubleshooting are probably far more capacity constraining than actual repairs. And problem types usually follow some form of Pareto rule where infrequent problems (10-20%) consume most (80-90%) problem-solving resources and account for the bulk of the costs.

On the other hand, a company making standardized equipment may have problems centered far more on repair processes and logistics. Failure modes are fewer and more predictable. The key cost drivers are Time- To-Fix and MTTR. Engineers need not have significant expertise. For cost impact, standard metrics need to improve and unit costs (cost of engineering-hour) need to reduce.

Given the different circumstances and context and hence the very different cost drivers in these cases -the tactical approach to AR implementation should be different.

In the latter case, the biggest impact can probably be achieved by providing field service personnel with step by step 3-D instructions with experts remoted in when necessary if a problem is an outlier. This can have significant and rapid quantitative capacity effects, but also impact the engineers’ profile mix reducing unit costs. In addition, AR-based training can be used to more rapidly equip novice engineers with the necessary skills. To succeed, such an implementation would require management to follow up with rapid organizational and workflow changes and capacity adjustments.

In the former case, the cost of Uptime can clearly be impacted by expanding productive utilization of existing expert capacity through remoting in experts on demand and utilizing collaborative problem-solving. Direct cost impact can be achieved by offloading repairs to customer technical personnel or outsourcing to local subcontractors supported by guidance/instructions. In addition, integration with IoT to access real-time data can further speed-up the intervention process, reduce errors, and improve optimization. Follow through management action, in this case, would be to change both the operating model for cost reductions and the business model for monetization.

Other, more nuanced, cases are of course possible.