August 2025 – Evergy, a leading U.S. utility serving 1.7 million customers across Kansas and Missouri, has renewed its commitment to digital asset management through an extended collaboration with eSmart Systems. Building on the success of a 3.5-year digitalization and inspection program powered by Grid Vision^®, Evergy will continue to expand its digital asset strategy to create a future-ready, data-driven grid.

The original program transformed over 10,100 miles of Evergy’s transmission network into a centralized, visual asset repository, creating a full digital inventory of every transmission asset down to the component level. Over 1 million high-resolution images were linked to asset records, while 70,000+ annotated defects were cataloged to prioritize maintenance and repairs. This complete and searchable asset inventory has enabled engineering, real estate, and operations teams to access reliable, up-to-date data, dramatically improving data accuracy and situational awareness.

With this renewal, Evergy will continue to assess transmission inspections while expanding the use of Grid Vision^® to its distribution grid and digitalizing new asset types, including the full digitalization of inspections and imagery for underground vaults. By consolidating these asset types into a unified platform, Evergy will gain a deeper understanding of asset health across its entire network, enabling more accurate risk-based analysis and investment decisions.

Through this renewal, Evergy and eSmart Systems will continue working together to advance virtual inspections, enhance safety by minimizing unnecessary fieldwork, and support the transition toward a modernized, fully digital grid.

The continued collaboration will enable Evergy to expand its digital asset inventory, streamline inspection workflows, and unlock deeper insights to support the growing demands of a modern utility grid.

About Evergy

Evergy (NASDAQ: EVRG) provides electricity to 1.7 million customers across Kansas and Missouri. Headquartered in Kansas City, Evergy is a vertically integrated utility committed to delivering safe, reliable, and affordable energy. The company is actively investing in grid modernization and sustainable infrastructure to support a resilient, future-ready energy system. For more information, visit https://www.evergy.com/

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.

12 August 2025 – eSmart Systems’ Grid Vision^® has been named a winner in the Best SaaS Solution for Energy, Utilities or Telecoms at The 2025 SaaS Awards.

Operated by global cloud computing awards body The Cloud Awards, The SaaS Awards judges have completed their final round of assessment of the finalists, resulting in winners being selected in each category.

The SaaS Awards celebrates outstanding levels of innovation and excellence in the software-as-a-service industry, from business process improvement solutions to cutting-edge uses of AI, and both niche and generalized services. The program received entries from organizations of all sizes across the globe, including the USA and Canada, the UK and Europe, the Middle East, and APAC.

The program will return to welcome new submissions in 2026, continuing to recognize the latest advancements in software-as-a-service.

To view the full list of winners, visit The Cloud Awards website.

Entries are now open for the long-running Cloud Awards program, which recognizes organizations at the forefront of cloud computing. Submissions are open until October 24, with earlybird entries accepted until August 29. Entries to other Cloud Awards programs, including The FinTech Awards and The Security Awards, both concluding in 2026, are also open.

About The Cloud Awards

The Cloud Awards is an international program which has been recognizing and honoring industry leaders, innovators and organizational transformation in cloud computing since 2011. The Cloud Awards comprises five awards programs, each uniquely celebrating success across cloud computing, software-as-a-service (SaaS), cloud security, artificial intelligence (AI), and financial technologies (FinTech).

Winners are selected by a judging panel of international industry experts.

About The Cloud Awards Program

The Cloud Awards identifies and celebrates the most innovative organizations, technologies, individuals and teams in the world of cloud computing. The program spans 36 categories, including ‘Best Cloud Infrastructure’ and ‘Best Cloud Automation Solution’. 

About The SaaS Awards    

The SaaS Awards focuses on software-as-a-service, with categories segmented into overall SaaS excellence, by sector, business operational processes, or outstanding uses of certain technologies (such as AI).

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.

May 2025 – Fingrid, Finland’s transmission system operator, has launched a strategic initiative to enhance grid maintenance in collaboration with eSmart Systems, a leader in critical grid asset management solutions. The project focuses on the conditional analysis of Fingrid’s Extra High Voltage (EHV) portfolio, leveraging advanced aerial data capture and virtual inspections using GridVision^® software to improve grid reliability, safety, and sustainability.

Modernizing data capture and virtual inspections

As part of this initiative, Fingrid partnered with eSmart Systems and Airpelago. The project utilized automated flight patterns to capture data across 400 kV towers and spans along three powerlines. This approach introduced virtual inspections, significantly reducing the need for physical fieldwork and planned outages.

The captured data is processed in Grid Vision^®, enabling Fingrid’s maintenance service providers to conduct comprehensive inspections more efficiently and with less reliance on on-site visits. Grid Vision^® delivers a complete overview of each transmission tower’s condition, supporting proactive maintenance decisions and strengthening Fingrid’s overall asset management strategy. By digitalizing inspections, Fingrid is achieving improved data accuracy, new asset insights, enhanced safety, and high-quality decision-making data.

Fingrid’s vision for a sustainable grid

Fingrid continues to pursue technological advancements that support effective, data-driven grid maintenance. With the increasing complexity of energy transmission and rising sustainability expectations, the company remains committed to securing Finland’s energy supply while promoting a clean, market-based power system.

As Finland’s national transmission system operator, Fingrid plays a vital role in balancing electricity production and consumption. Through its extensive network, the company ensures the seamless flow of electricity across borders, connecting Finland to the Nordic and Central European power systems.

This project underscores Fingrid’s forward-thinking approach to grid management and reinforces its mission to provide uninterrupted electricity access, improve inspection safety, and accelerate digitalization in support of a more resilient and sustainable energy infrastructure.

About Fingrid

Fingrid Oyj is Finland’s transmission system operator, owned by the Finnish state and pension insurance companies. The company ensures a stable and secure electricity supply across the nation, transmitting electricity through the high-voltage network from production facilities to industrial consumers and electricity companies. As part of the joint Nordic power system, Fingrid plays a vital role in maintaining energy security and enabling cross-border electricity trade, ensuring a reliable and efficient power market.

About eSmart Systems

Smart Systems is a leading provider of AI-powered solutions for the inspection and maintenance of critical infrastructure. Through our software solution, GridVision^®, we revolutionize how utility companies operate and maintain their transmission and distribution grids. We support utilities worldwide in reducing inspection costs, making inspections safer, improving asset data quality, and prolonging asset life by providing an image-based digital inventory representing all physical grid assets. eSmart Systems has more than 20 years of international experience in establishing and operating knowledge-based IT and energy-related companies targeting global markets.

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!

With global electricity demand projected to grow by 4% in both 2024 and 2025*— one of the fastest growth rates seen in two decades — utilities face the pressing challenge of ensuring their grids can meet this surge reliably and sustainably.

Rising energy consumption and the integration of renewable energy sources make it essential not only to add capacity but to do so smartly and sustainably. We have seen first-hand there is one crucial element to meet this effectively: accurate grid asset data.  

Without this, utilities are essentially navigating blind. By understanding the true state of the grid’s condition, utilities can not only handle current loads but also plan smartly for future expansions and repairs. 

At the heart of effective grid management is accurate asset data. 

For utilities, maintaining a complete and up-to-date picture of their infrastructure is essential. Without it, effective operations management, future planning, and smart investment decisions become nearly impossible.  The first step toward improving the accuracy of grid asset data can begin with activities like grid inspections or a data quality improvement activities like grid inventory programs.

For either of these activities, utilities need to ensure that the data collection is focused on image-based asset data, collected with high-resolution cameras that capture detailed views of the infrastructure. These images, when processed through advanced tools like Grid Vision^®, provide a visual inventory of assets, cataloging them with pinpoint accuracy and linking the asset to additional information like inspection data. 

What benefits can you achieve with image-based accurate asset data? 

Once utilities start collecting and leveraging accurate image-based data, they can unlock several critical benefits that support operational efficiency and long-term grid resilience: 

1. Efficient Maintenance: Precise image-based data enables condition-based maintenance. This allows utilities to identify assets that require attention before they fail. This proactive strategy reduces outages, extends asset life, and prevents costly emergency repairs​. 
2. Better Investment Decisions: Accurate, consistent and objective insights of grid infrastructure condition allows utilities to prioritize capital investments based on risk. For example, virtual inspections enabled by AI can highlight critical areas where repairs are needed, helping to allocate resources where they will have the most impact​. 
3. Enhanced Planning for Growth: Accurate image-based asset data supports improved  forecasting for future grid expansion or reinforcement. As demand rises, utilities must not only repair existing infrastructure but also build new assets.  
4. Improved Safety and Compliance: Knowing the exact status of grid components ensures compliance and reduces the risk of incidents. This compliance aspect is particularly important as aging infrastructure and increased renewable energy integration strain the grid​. 

Data as the Cornerstone of Grid Management 

Accurate, reliable asset data is more than a convenience—it’s essential to managing current operations effectively and preparing for sustainable growth.  

By embracing advanced digital tools, like Grid Vision, and leveraging the power of AI, utilities can transform their data collection and asset management practices, ensuring they stay ahead of the curve in a rapidly evolving energy landscape​. 

*Source: International Energy Agency (IEA) report, 2024 forecasts on electricity demand growth. 

Want to see a demo of Grid Vision? Fill this form here  

Data-driven trust issues, a real-world problem for electric utilities. 

When you are responsible for the biggest machine in the world which entire societies rely upon to function, trustworthy asset data is a must. However, industry reports show that one-third of utility executives see poor asset data quality as a major challenge as it carries cross-functional impact on operations, planning and compliance. 

The lack of trust in data is due to fragmented storing across multiple systems, and in multiple formats with a lack of audit trails, causing inconsistency, inaccuracies and can lead to severe consequences for the utility such as unplanned outages, an increase in field visits, costly compliance risks and poor capital planning and investment decisions.  

Improving asset data management and ensuring high-quality data is often focused on centralizing the information, but without processes in place to validate and maintain the information these efforts quickly become a wasted opportunity, something several of our customers have reported to be true. 

How do you uplift the quality of your data at scale?  

Our Grid Vision^® solution is developed by utility professionals, for utilities to not only uplift the quality of data, but to also include visual data, which is validated and maintained over time, through an established three step process ensuring you do your quality uplift right the first time:  

Qualification: Understanding the starting point & identify gaps 
To be able to uplift the data quality, you need to understand where you are and what you want to achieve with your data.  

• Validating your data rules and models 

Your asset data strategy needs to be tied to your business rules and built on the correct data model for your business to enable utilization.  

• Verify your gaps and test the methodology 

A small sample of assets are captured in the field and compared with the asset data in the system. As well as capturing the data, images of each asset are taken and linked to the asset in the field.  The data is validated through a filling rate and qualified to assess the quality level. This step also identifies gaps in the data and tests the process in practice. 

This stage qualifies gaps in your data to determine if there is a data quality issue. It provides an insight into image-based accurate asset data and tests the process end-to end so you can successfully scale.  

Scaling: Enriching data 
As your foundation is built, filling the gaps and enriching existing data is next. By using the qualification of the sampled asset data, the asset management strategy can be rolled out to all assets. In this stage, thousands of substations can be digitalized and visually documented with a clear audit trail and consistent capturing processes within a time frame so that the data remains accurate and current.  A complete, image-based digital inventory is created through high-quality data, ready to be utilized by utility teams.  

Utilization and Maintenance
Using an image-based digital inventory, utility teams can make data-driven decisions across their business processes such as network planning, maintenance and investment decisions with confidence, as the data is accurate with a clear audit trail. However, a digital asset requires maintenance to ensure it continues to represent its physical equivalent in the field. This requires continued updates to your data to reflect and validate changes to the physical asset through maintenance activities and updates.  

Grid Vision supports every stage of the asset data management journey through its software and services, supporting each step in the lifecycle of infrastructure assets.  

Listen to how Stedin used this phased approach and utilized Grid Vision to avoid potential failures among 22,000 substations: Watch webinar here

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 approach to AI-assisted virtual inspections is different. We have an asset centric approach to all of our solutions and build your image-based digital asset while you inspect as we know that data can support grid owners in their operations.

What is an image-based digital asset?
This is a digital representation of your physical grid linking your asset to your asset data, inspection data, images, meta data and grid topology. 

Why would you need this data?
You may not have easy access to this image-based data today in your core IT systems. This data gives you another dimension of insight into your physical grid, the mechanical state of your grid through structured and tagged photos linked to inspection data, grid topology, meta data and a clear audit trail. 

We hear from utilities that images taken of their grid by field crew is often not tagged, unstructured, not linked to an asset and difficult to access. If there are images, they are on a USB stick somewhere, on someone’s mobile phone or in a database that takes ages to load.

What value can an image-based digital asset give you?
From working with 50+ utilities globally we have seen our customers utilize the image-based digital asset in Grid Vision^® in various ways and have summarized the top 10 common use cases below:  

1. Improved capital planning
Our customers have now based their investment plans on accurate asset data and risk. Supporting them to prioritize their capital planning. 

2. Decreased unplanned outage time
Utilizing Grid Vision as part of the outage management process to check the impacted assets and then plan the correct tools and expertise before going on site has seen our customers decrease CAIDI by 28% (43 mins). 

3. Identifying common vulnerabilities
Our customers have saved hours from eliminating field visits by identifying assets at risk within Grid Vision at a desk within hours instead of days.

4. Supporting emergency maintenance 
Providing Grid Vision to emergency response teams to check for structure types before sending out the crew to the field has saved our customers time and shortened time to restore grid faults.

5. Wildfire mitigation 
Multiple customers are using Grid Vision to identify assets at risk from wildfire within hours by searching for components at risk at a desk and providing the visual data to field crews to go and fix.  

6. Supporting regulatory reporting and major incidents 
Our customers are using Grid Vision to confirm the state of an asset before an incident for root cause analysis and regulatory reporting.   

7. Improving situational awareness and business decision 
Our customers are using Grid Vision in virtual meetings with leaders to demonstrate challenges with projects.  

8. Maintenance & planning
Grid Vision is used by maintenance teams to identify inventory, materials, skills, right of way before going on site.  

9. Siting land rights 
For new builds, our customers are checking Grid Vision to confirm any siting land rights for new projects. One of our customers identified an undocumented family cemetery on a potential new substation site.  

10. Maintenance strategy  
Grid Vision is used to support maintenance strategies, one of our customers identified assets failing before end of design life through Grid Vision. This insight helped them to re-prioritize their maintenance strategy to avoid unplanned asset failures.  

By leveraging Grid Vision’s image-based digital asset, our customers are unlocking multiple core use cases and saving their teams time, improving safety, reducing costs across multiple departments and improving grid resiliency. 

We do more than provide virtual inspections, we start with the asset itself and link the data that is needed to support your asset management processes. 

Thinking of making the transition? Contact us today to start your journey.

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.