That’s why we helped build a first-of-its-kind wildfire technology framework. In partnership with Smart Electric Power Alliance (SEPA), Overstory, Pano AI, Rhizome, and Technosylva, we contributed to publish the Wildfire Technology Landscape: A Framework for U.S. Utilities.

One Framework. Every Stage.

The report introduces a Six-Stage Wildfire Risk Reduction Framework that maps the full lifecycle of utility wildfire management, from long-range capital planning all the way through post-event recovery and learning.

Source: SEPA / PNNL (2026)

Wildfire risk management doesn’t happen in silos, but utility technology stacks often do. What makes this framework valuable is that it reflects how decisions actually flow across an organization. Risk intelligence from pre-season planning informs operational thresholds during high-risk weather windows. Detection alerts feed real-time fire spread simulations. Post-event data retrains ignition probability models and updates capital priorities before the next cycle begins. The framework makes those handoffs clear, because the value of any single technology is multiplied when the data it produces reaches the right team at the right time.

Where eSmart Systems Fits In

Our role sits at the foundation. eSmart Systems analyzes drone, aerial, and ground imagery using AI to assign each asset component-level ignition risk score based on actual component condition, not just age. That data replaces outdated age-based guesswork that has historically driven capital decisions and replaces them with something defensible: real evidence at the asset level.

We also correct GPS coordinate errors in utility GIS records, surface gaps in asset data, and bring asset condition, detection coverage, and vegetation risk together in a single operational dashboard. We operate across all six stages, because good asset intelligence is relevant before, during, and after a wildfire event.

The Policy Picture Is Moving Fast

The regulatory landscape is accelerating alongside the technology. As of May 2026, thirteen states require utilities to file Wildfire Mitigation Plans, and more are watching closely.

Source: NARUC / PNNL / SEPA (2026)

Whether your state requires a formal WMP or not, the planning frameworks, investment justification tools, and performance metrics in this report give utilities a practical foundation to build from now. Waiting for a mandate is a strategy, but it’s rarely the most cost-effective one.

The Takeaway

The report’s core finding is simple but important: individual technologies deliver value, but the greatest impact comes from connecting them into a unified operational picture. Utilities that do this well reduce ignition risk, make better operational decisions, and build the evidentiary record they need with regulators, insurers, and their boards.

We’re building toward that future with every inspection, every corrected data record, and every integrated dashboard we put in front of a utility team.

Full framework report: DOWNLOAD HERE

The improvement cycle in traditional AI can run months long, requires thousands of labeled examples, and still doesn’t guarantee results. For industries like energy infrastructure, where the most critical defects are also the rarest, that’s not a minor inconvenience. It’s a dead end.

So we built our own patent-pending AI to solve it.

This whitepaper covers our Adaptive AI approach – which flips the model entirely: instead of collecting data, labeling it, training for weeks, and hoping the output reflects what your experts actually meant, domain experts simply show the system a few examples. It learns. It adapts in real time. And when better foundation models come out, performance improves automatically: no retraining, no re-labeling, no waiting.

Utilities have long understood that no single threat defines resilience. Wildfire, wind, ice, flooding, heat, and even routine conductor fatigue all share a common truth: the grid’s performance during extreme events often hinges on the smallest components. Cotter pins, C-hooks, splices, connectors, insulators, shackles, and other pieces of “minor” hardware have an outsized influence on ignition potential, mechanical integrity, and outage risk – particularly when hazards are escalating faster than traditional inspection cycles can keep up.

Recent wildfire and storm investigations show that catastrophic failures frequently originate not from core grid equipment but rather from the fasteners, attachments, clearances, and conductor interfaces designed to keep assets stable under stress. When one of these components loosens, corrodes, cracks, or is installed incorrectly, the result can create risks that scale quickly under extreme weather or high-fire conditions. This isn’t speculative; stats are well documented, with 10% of California wildfires ignited by utility assets, and nationally known utility-caused fires burning 104,000-390,000 acres annually.

At the same time, hazards themselves are intensifying. Pacific Northwest National Labs (PNNL) notes wildfire exposure is expanding geographically and seasonally, with longer durations of high-risk weather and increased fire potential even in regions that historically saw little wildfire activity. Meanwhile, climate-driven heat, severe storms, and changing wind patterns are stressing transmission and distribution assets outside traditional high-risk prone areas. Extreme weather caused $131B in global losses in the first half of 2025 alone, and customers experiencing an average of 18.2 hours of outages in the Southeast US.

If resilience is the ability to withstand, adapt to, and recover from extreme conditions, then the foundation of resilience is understanding, at a granular level, which components are most likely to fail when conditions are at their worst.

A Lifecycle Framework for All-Hazards Resilience

Utilities increasingly anchor their resilience plans around a lifecycle model that spans Pre-Event, Peri-Event, and Post-Event phases. This approach maps directly to how utilities plan, operate, and restore, and aligns with the “Triple Line of Defense” model highlighted in PNNL’s wildfire and resilience best-practices work.

Figure 1 — Triple Line of Defense

Each phase is driven by different engineering questions:

• Pre-Event: Which segments, assets and components are approaching critical condition?
• Peri-Event: Which structures or spans are most vulnerable under today’s weather?
• Post-Event: What failed, where, and why — and how do we rebuild stronger?

Critically, each phase depends on trusted asset condition intelligence, especially at the component level.

Why Component-Level Inspections Are Foundational to Resilience

1. Turn hazard modeling into actionable engineering risk

Risk emerges from the interaction of hazard and asset vulnerability. Wildfire and extreme-weather risk forecasting improves when combined with reliable, current component condition data. This makes long-duration hazard events more predictable in terms of likely failure points.

2. Support targeted, defensible investment

Patchwork mitigation rarely delivers sustained improvement without common frameworks and measurable risk reduction. Component-level inspection data, as part of a triple line of defense approach, provides exactly that:

• directing hardening where degraded hardware and environmental hazards intersect,
• targeting vegetation management to spans with combined condition and encroachment risk, and
• sequencing capital projects based on failure-mode likelihood rather than geography alone.

3. Enable precision operations during elevated threat windows

Peri-event operations (such as switching, fast-trip settings, sectionalization boundaries, and PSPS alternatives) depend on knowing which assets are least likely to withstand the day’s wind, heat, or lightning. Operators armed with the knowledge of certain hardware configurations or degraded fittings that historically fail under high-wind loading, can more precisely tailor protective schemes.

4. Accelerate post-event restoration and long-term improvement

Accurate pre-event condition data allows utilities to triage restoration based on known vulnerabilities. Post-event, high-resolution component imagery supports root-cause analysis, enabling utilities to refine engineering standards and mitigate recurring failure modes.

When Conditions, Components, and Consequences Align

A Mountain West utility sought to modernize its inspection workflow to address rising wildfire exposure and strong seasonal wind events. However, the driving problem wasn’t a lack of findings, but rather prioritization. Using Grid Vision® to bring inspection observations, component classifications, and environmental context into a single risk-ranking approach so that defects could be evaluated not only by what was wrong, but by how likely it was to fail and how severe the consequence would be if it did. In practice, this meant scoring findings across three dimensions:

• Condition risk – loose, upside‑down, corroded, cracked, contaminated, or missing elements
• Component type and failure-mode risk – with particular attention to cotter pins, C‑hooks, chain shackles, splices and connectors, insulators, and conductor hardware such as armor rods, dampers, and attachment fittings
• Location/environment risk – wind corridors, dense vegetation, steep or inaccessible terrain, elevated fire-weather zones, and wildland–urban interface adjacencies.

Figure 2 – Aerial View of Lattice Tower and Zoom of Upside-Down Cotter Pin Detected by AI

When the results were analyzed, the utility discovered that about 7% of observed cotter pins were installed upside‑down, alongside other recurring issues like worn shackles and compromised insulators. Conditions that can materially increase the likelihood of conductor movement, arcing/flashover, or mechanical failure under high-wind and high-fire-risk conditions.

Importantly, the highest-risk findings were not evenly distributed; they clustered in wind-exposed corridors, tight drainages, and other high-consequence segments, enabling the utility to produce a focused work plan that directed maintenance and hardening resources to the places where component degradation and environmental hazard intersected most acutely. The shift was simple but significant: away from “fix everything” and toward “fix what matters most.”

How Component-Level Intelligence Enables the Full Resilience Flow

Component-level grid intelligence is most valuable when it doesn’t live in a silo. When inspection findings are consistently attributed to specific assets and component types, they become the connective tissue that links resilience planning, operations, and restoration into a single, closed loop.

Pre-event, component condition data strengthens risk modeling and forecasting by grounding hazard exposure in real asset vulnerability. It also sharpens capital and maintenance prioritization – helping utilities target hardening programs and vegetation work where degraded hardware and high-consequence environments overlap.

Peri-event, operators can pair real-time weather and situational awareness with known component vulnerabilities to apply more precise operational strategies. Instead of treating the system uniformly under elevated threat, utilities can focus settings, switching, and field response around the assets most likely to fail under the conditions that day.

Post-event, a high-fidelity inspection history accelerates triage and restoration by helping crews distinguish between legacy defects and event-driven damage. That same structured record supports defensible documentation, both for internal engineering review and for the reporting and learning cycles that drive better outcomes in the next season.

Over time, this intelligence feeds long-term planning by revealing recurring failure modes across component families and environments. This enables utilities to refine specifications, adjust standards, and sequence capital work around measured risk reduction rather than time-based / cyclical actions.

Most importantly, it enables continuous improvement. Sustained progress on catastrophic risk challenges requires common measures and repeatable frameworks. Component-level data provides the baseline for those measures by making risk visible, comparable, and improvable year over year.

Put simply: this “data thread” is what transforms inspections from a periodic compliance activity into the backbone of a triple line of defense resilience model. It links risk to action, action to outcomes, and outcomes back to better decisions.

Where Resilience Becomes Real

Extreme weather and wildfire will be critical in shaping utility performance (and public trust) for the next decade. The industry will of course continue to invest in hardening, automation, and vegetation programs, as those investments remain essential. But field experience keeps reinforcing a less glamorous truth: resilience often comes down to whether the smallest pieces of hardware (across the existing hundreds of thousands of miles of lines) are going to be able to do their job when conditions are at their worst.

Cotter pins, splices, connectors, insulators, shackles, and other “minor” components can be the difference between a routine disturbance and a cascading event. Between a momentary fault and a serious event. When those components are visible, measurable, and prioritized in context (condition plus component type plus environment) utilities can shift from broadly “doing more” to surgically optimizing investment towards what matters most.

That’s what component-level inspection intelligence enables across the full resilience lifecycle: smarter pre-event prioritization, more precise peri-event operations, and faster, better-informed post-event recovery. And that’s the real shift underway: a resilience operating model utilities can defend, scale, and continuously improve.

This article was originally published on T&D World on Feb. 25, 2026 – https://www.tdworld.com/reliability-and-resiliency/article/55359830/small-components-big-consequences-how-component-level-insights-reduce-catastrophic-grid-risks

November 2025 – eSmart Systems has launched Verify AI, the first feature powered by its new and patent pending Adaptive AI technology. This advancement accelerates how utilities transform asset data into trusted insights that support safe and reliable grid operations.

Traditional AI has already improved digital inspection workflows. However, utilities still face high volumes of false positives, long deployment timelines, and difficulty tailoring results to local power grid requirements. These challenges impact decision-making and risk management.

Adaptive AI introduces a step change by enabling rapid value from limited or uneven training data. It adapts to each utility’s power grid and standards with minimal effort and improves continuously based on expert feedback.

Verify AI is the first feature based on Adaptive AI and is now available in the Grid Vision^® platform. Verify AI strengthens inspection workflows by reducing false positives and elevating only credible issues for review. This creates faster, more trusted results and allows inspectors and asset managers to focus on what matters most.

Adaptive AI will support additional features over time, including new defect detection and inventory capabilities, all designed to scale utility intelligence and improve operational outcomes.

Key benefits include:

• Faster deployment and value realisation
• Reduction in false positives and manual review effort
• Trusted insights aligned to each utility’s standards
• Stronger risk mitigation and prioritisation of critical work

Verify AI is available today in Grid Vision^®. More features powered by Adaptive AI will be released in upcoming product iterations.

About eSmart Systems

eSmart Systems is a global leader in AI-powered solutions for the inspection, analysis, and digitalization of critical grid infrastructure. Through our Grid Vision^® platform, we help utilities transition from manual inspections to intelligent, image-based asset management, creating a digital inventory of their transmission and distribution networks. This enables improved asset data quality, safer operations, reduced inspection costs, and extended asset life. With more than 20 years of international experience, eSmart Systems supports utilities worldwide in building smarter, more resilient grids for the future.

For more information, visit ai.esmartsystems.com and check out our announcement on the launch of AI Studio by eSmart Systems, incorporating our Adaptive AI technology.

As wildfire risk continues to escalate, utilities and regulators are under increasing pressure to reduce risk while maintaining reliability and affordability. This whitepaper brings together insights from leaders across the wildfire ecosystem to explore how integrated, prevention-first technology is enabling smarter decision-making and measurable results.

It examines how connecting intelligence across weather, vegetation, assets, and operations helps organizations move beyond reactive approaches toward proactive, data-driven, and cost-effective wildfire mitigation strategies.

AI is no longer a distant promise for utilities. It’s already helping grid operators streamline inspections, identify defects, and generate insights from asset data. These use cases offer real value, but common AI misconceptions often lead to AI being applied too narrowly and evaluated in isolation, without thinking through the larger system it needs to serve. An IEEE report states that up to 80% of AI projects fail to deliver value, and 87% never make it into production¹. Many of these failures are tied to insufficient, siloed, or ungoverned data, these don’t get fix by only focusing on better AI models.

That’s where progress can slow down. When AI is seen as the main objective rather than a component of a broader strategy, utilities can risk investing in tools that don’t scale, models built on poor-quality data, and insights that never reach frontline operations.

This article unpacks some of the most common AI misconceptions for utilities, and shows how shifting the mindset toward developing grid-wide intelligence vs just putting a magnifying glass on AI, can drive real, operational outcomes.

Once the POC works, are we ready to scale?

Most utilities start their AI journey with a proof of concept (POC). Pilot projects often succeed because they’re run in a bubble with controlled data, clear goals, and limited variability.

In the real world, scaling means not just the AI models, but the entire system: data quality, validation loops, human workflows, and the people. POCs don’t prove readiness, they prove potential. That’s an important difference.

That’s why it’s critical to partner with a team with proven operational experience at scale, because scaling requires more than good AI. It demands a holistic understanding of data, utility operations, and what it takes to make innovative technology work under real operational pressures and changing conditions.

Does more data mean better AI results?

Utilities sit on mountains of asset data, but volume isn’t the issue. Enough data is key, but quality matters far more than quantity. According to a study, 70% of AI projects fail to meet their goals in large part due to issues with data quality². To succeed, you want to know exactly what data you’re working with, how it’s been used, and whether it’s reliable. That requires version control, structured datasets, and a clear distinction between training, testing, and validation sets.

We have proven that in practice, more data isn’t always better. At eSmart Systems, we have reduced certain datasets from 500,000 to 100,000 images and achieved better outcomes by focusing on quality over quantity. By removing noise, correcting labels, and structuring data more effectively, the team achieved more accurate and explainable results. Uplifting data quality led to stronger model performance and greater trust in the results.

Should AI be the main goal?

AI isn’t the goal, better decisions are. High model accuracy means little if it doesn’t improve how you manage risk, respond to outages, or plan investments.

The real shift happens when utilities stop implementing AI as the destination and instead focus on building grid intelligence – structured, connected data that powers the entire operation. Inspections can be a key part of this shift, by capturing consistent, high-quality asset data that feeds a central digital model of the grid. With a central digital asset, operational teams can identify defects and inventory gaps in one platform, while planners prioritize investments based on condition, not assumptions. It also enables faster responses to weather events and ensures regulatory reports are grounded in real inspection data and not estimates.

Some major utilities (read more here) have made that leap, turning inspection programs into digital asset platforms that now drive maintenance, capital planning, and regulatory reporting.

Why look at the bigger picture?

Utilities face unprecedented challenges: load growth, renewable integration, aging infrastructure, and extreme weather. The old model: manual inspections, disconnected data, reactive maintenance – can’t keep up.

Grid intelligence platforms powered by AI, like Grid Vision, don’t just modernize inspections. They turn inspections into structured intelligence that supports every layer of grid operations. With visibility of asset condition down to component level, grid operators can act before problems escalate, avoiding outages, extending asset life, and allocating resources where they matter most. Focus on decisions, not just AI hype. Treat data as a strategic asset, not a byproduct. Building the intelligence layer your grid needs means investing in technology and processes that support risk mitigation at scale. AI plays a role, but shouldn’t be the only goal. Operational value is unlocked when data, insight, and action come together to drive smarter decisions for a stronger, more resilient grid.

Want to chat with our team about AI for utilities? Fill this form here  

Source¹: https://ieeexplore.ieee.org/document/10572277/metrics#metrics Source²: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/a-data-leaders-technical-guide-to-scaling-gen-ai?

When large parts of the Iberian Peninsula were plunged into darkness, speculation ran wild about the possible cause. Cyberattack? System failure? Climate stress?
Whatever the root cause, the real takeaway is this: our power grid is vulnerable, and we need to be ready.

This massive outage has thrown a spotlight on the fragility of modern energy infrastructure and the urgent need for greater resilience, better visibility, and faster insight.

Could it have been avoided?
With smarter infrastructure management in place, we believe it’s possible.

Let’s unpack the challenges and how digital intelligence can help strengthen reliability for the future.

The grid’s growing problem (it’s not just age)

Think of the modern power grid as an aging athlete, now forced to run faster than ever, in a hurricane.

Much of the grid was built between 1950 and 2000. It wasn’t designed for today’s load profiles, intermittent renewables, or the climate extremes we now face.

As electricity demand grows, this aging infrastructure is operating under increasing stress, making visibility into its condition more critical than ever.

Why traditional grid inspection and management isn’t enough

Here’s the reality:

• Time-based inspections are reactive and often inefficient
• Outdated views of asset condition lead to ineffective maintenance planning
• With millions of components in play, even the best teams can’t catch everything

This is why grid operators need to shift from reactive to proactive, from compliance to condition, and from siloed systems to centralized intelligence.

5 hidden threats to grid reliability (and what helps)

1. Extreme weather + Aging infrastructure = Recipe for collapse

Today’s climate exposes yesterday’s infrastructure. One weak pole or c-hook in a storm can trigger cascading failure.

What helps: Condition-based inspections, driven by real-time data, flag risk before failure, not after.

2. Data overload, insight deficit

Sensors, imagery, asset data – there’s more than ever. But data without structure is just noise.

What helps: Pattern recognition, trend analysis, and context-aware insights turn data into decisions.

3. Disconnected inspections

Finding one defect in isolation may mean nothing, unless you can see the broader picture. A crack here. A hotspot there. Are they linked?

What helps: Historical trends and geospatial analysis expose systemic risks before they escalate.

4. The AI illusion

AI isn’t magic, but it is a brilliant tool, and it needs quality data, clear objectives, and thoughtful application.

Used wisely, AI accelerates inspections, improves accuracy, and frees up resources to focus on what matters most.

5. The CapEx vs OpEx dilemma

Utilities often face complex decisions when allocating budgets between maintenance and long-term upgrades. Without clear insights into asset condition and risk, it can be challenging to prioritize the right actions at the right time.

What helps: Long-term asset intelligence supports more informed investment planning, helping teams decide when to repair, replace, or defer, with confidence backed by data.

What would help avoid the next outage? A smarter, more resilient grid

We are heading toward a future where grids can self-assess, self-optimize, and in some cases, self-correct.

A truly intelligent grid will:

• Adjust its own limits based on weather and load (for example, dynamic line rating, flexible load control, and optimizing asset performance based on updated risk conditions)
• Reroute power automatically when stress is detected
• Deliver real-time condition feedback from both 2D and 3D data
• Support predictive planning based on asset history and spatial intelligence

As trust in technologies like AI grows through proven results and real-world performance, we are moving closer to achieving a smarter and more resilient grid, even for critical infrastructure.
The technology is already here. Now it’s about implementation.

The bottom line

We may never know exactly what caused the Iberian blackout.
But we do know what can help us prevent the next one:
A more resilient, better-informed grid.

That means upgrading not just our infrastructure, but the way we monitor, maintain, and manage it.

It’s time to stop waiting for failure.
Use the tools available to gain better asset data, smarter insights, and the clarity needed to manage rising demand.

Repower enhances grid reliability with AI through Grid Vision^®

Read the case study!

At eSmart Systems we have always been pushing the envelope to enable our utility customers to get the most out of technology to help solve their business problems.  We pioneered virtual inspections for critical infrastructure over 10 years ago and are working with 60+ global utilities to inspect and digitalize power grid infrastructure every day. We were the first to market the use AI to support grid inspectors, to make the inspections faster and with more objectivity.

We are now breaking ground again with a new way for our customers to get the biggest return on using AI for virtual inspections!

AI is an amazing tool for supporting virtual inspections. And honestly, the first three models can be made by anyone. Running 50+ models at industrial scale is a completely different ballgame.  

This is our turf. With our unparalleled volume of customers and data, we empower all our customers using AI. 

The subsequent challenge is that utilities have different definitions of what they consider a defect. This is the challenge we are now addressing and solving with our new patent pending AI technology. 

Our patent pending AI technology allows our AI to adapt to each utility’s specific needs while still capitalizing on global training data and models. While performing an inspection in Grid Vision^®, our AI will adjust, adapt and learn, on the fly. This will reduce the number of false positives and dramatically increase the value of the AI. 

Our customers will see our AI adapting to their own definitions of defects. By using our new patent pending technology, we enable our AI to adapt to each customer’s feedback to our AI while using Grid Vision. Which means improved performance of AI for defect detection, reduced retraining, and reducing inspection time.   

The underlying patent pending technology is not limited to powerline inspections or image recognition. This methodology is applicable for anyone who uses feature extractors whether they are working with images, sound and even text. Anywhere you meet the feature extractions limitation, our patent pending technology will be helpful in getting the quickest ROI. 

Want to learn more about our new patent pending AI technology and how it will help you get quicker ROI on your virtual inspections contact us today: Contact us today.

Our Adaptive AI technology is now available as part of AI Studio by eSmart Systems. Read the announcement or visit AI Studio at: ai.esmartsystems.com.

Repower which produces and distributes power to more than 48,000 customers in Switzerland, has partnered with eSmart Systems to digitalize and automate their grid inspection process. The program will utilize eSmart Systems’ Grid Vision^® solution, which supports the full inspection and maintenance workflow with AI.

• Transitioning Repower’s grid inspections from manual to automated powered by AI.
• Grid Vision AI-supported software will be used to inspect their HV and MV grid.
• eSmart Systems will deliver inspection services for Repower AG’s overhead lines over the course of the partnership.

This partnership will enable Repower to provide safer, more accurate and efficient inspections that will provide greater visibility into their grid assets to support their asset management strategies for grid resiliency and reliability to support growing demand and transition to low carbon generation. 

eSmart Systems will deliver the program as an inspection as a service and will utilize drones for the image capture with automated flight patterns to ensure the data is captured consistently and accurately with improved safety. For this program the actual grid inspection will be conducted within Grid Vision powered by AI and eSmart Systems will be working with regional drone operators for the image capture.

About eSmart Systems

eSmart Systems is a leading provider of AI-powered solutions for the inspection and maintenance of critical infrastructure. With our software solution Grid Vision^® we revolutionize how utility companies operate and maintain transmission and distribution networks. eSmart Systems offers a data-driven and condition-based approach to infrastructure inspections that can be managed from a single platform. We support companies worldwide by ensuring reduced costs, safer inspections, and prolonged asset life. 

About Repower

Repower is an international energy utility with its operational headquarters in Poschiavo. (Graubünden, Switzerland). Repower has been operating as an electricity producer, distribution grid operator and energy trader for more than 100 years. Its key markets are Switzerland and Italy.

Repower builds, operates and maintains distribution grids, substations, transformer stations, kiosks and supply lines right up to the house connection in Prättigau/Rhine Valley, Engadine/Valposchiavo and Surselva. The total length of the grid managed by Repower is around 3,000 kilometres.

Everyone is talking about virtual inspections, flying drones to capture grid assets and using AI to support all of this. But how do you ensure a ROI from this transition? By asking the right questions from the start.  

Lets be clear not all virtual inspections are the same! We have been delivering virtual inspections assisted by AI for over 10 years and see time and time again utilities not being successful with their approach by not comparing apple to apples when comparing vendors and not asking the right questions of their teams when they want to implement these programs.   

Questions you should be asking when transitioning to virtual inspections so you are successful.

• What problems are you trying to solve? 
• What is your success criteria?  
• What is the long-term strategy of transitioning to virtual inspections? 
• Have you got the right data capture methodology? 
• What data are you collecting when in the field – every asset or just assets impacted? 
• Will the images you capture work for the software you are using and for the inspector? 
• Have you got the right virtual inspection software? 
• Have you got the right skills inhouse to make this successful? 
• How is AI applied to improve the business process?  
• How will you train the AI?  
• How do you future proof your AI? 
• How do you ensure consistency across your inspections? 
• How will you process all the images you capture? 
• Once have your inspection results what will you do? 
• What is your strategy for the image-based data you are collecting in the field?- If you are collecting it and paying for you should use it. 
• How will you future proof your investment?  

If you apply virtual inspections to just replace your routine inspection cycle that is a great start and will definitely improve your safety, give you a great inspection report to go out and fix the issues.  You can repeat and rinse this method over and over but it is still based on time and you would be missing out on the bigger return on investment. 

If you transition to virtual inspections with your wider asset management team involved and link the program to your asset management strategy and also focus on the image-based data, that is where you see the biggest return – moving away from time-based inspections to risk of assets.  

If you are investing in going out and collecting data in the field, make it count and get your return.

Contact us today to find out more.