Solutions30’s AI Strategy for Connectivity and Energy

When a company manages 80,000 service calls daily across 10 countries with a workforce of 16,000 technicians, manual oversight becomes impossible. 

This reality has driven Solutions30, the European field services company, to embrace AI.

Solutions30 provides installation and maintenance services for telecommunications infrastructure, energy grids and digital equipment across Europe. 

From fibre optic installations to smart meter exchanges, its technicians handle everything from routine maintenance to complex safety-critical work that can’t afford mistakes.

Jerzy Badowski, the company’s Group Chief Information Officer (CIO), has spent over 20 years in IT across banking, insurance, healthcare and aviation before landing at Solutions30. 

His perspective on AI reflects this broad experience: it’s a tool that must complement existing frameworks rather than replace them entirely.

“AI is a layer on top of all the controls and all the frameworks that already exist,” he explains. 

This philosophy has guided Solutions 30’s approach to stand out in the market.

How Solutions30 uses AI to tackle quality control and route optimisation

Solutions30’s approach to AI is about implementing AI systems that enhance rather than disrupt established operations.

Outwardly, the company’s growth through acquisitions across Europe has created both opportunities and challenges. 

While the scale provides rich data sets for AI applications, integrating systems from multiple acquired companies requires careful standardisation. 

This backdrop makes its AI deployments particularly interesting – as Solutions30 is not starting with a clean slate but rather layering intelligence onto complex, multi-country operations.

Solutions 30’s most compelling AI implementation addresses a problem that’s both safety-critical and scale-intensive: verifying the quality of smart meter installations. 

When technicians exchange traditional gas meters for smart devices, the connections must be perfect. 

On the flip side, poor installation can create dangerous situations for both workers and customers.

In response, the company developed what it calls the “Deepomatic” system, using computer vision to analyse installation photographs in real time. 

During the project, this AI system processed over 1.3 million images, automatically verifying that connections were properly secured and safety protocols followed.

“We have implemented an image recognition system, which allows our technicians to verify the quality of a job before they do it,” Jerzy says. 

The system addresses work that is “quite risky and dangerous, not only for the technician doing the job but also for the customer.”

The AI system identifies specific connection points and components, comparing them against predetermined standards and flagging deviations immediately. 

Technicians get instant feedback before leaving a job site, eliminating the need for separate quality inspections and reducing the risk of callbacks.

The second major AI deployment tackles route optimisation: a challenge that becomes exponentially more complex when Solutions30 is coordinating thousands of vehicles across multiple countries. 

The system processes variables including traffic patterns, appointment schedules and geographic constraints to calculate optimal travel paths daily.

The results demonstrate how AI can deliver multiple benefits simultaneously. 

Reduced travel time improves customer satisfaction, lower fuel consumption supports environmental targets and increased efficiency allows technicians to complete more appointments per day.

“We can optimise the time of travel, which is good for our end customer because the customer doesn’t need to wait a lot of time,” Jerzy explains.

“From an ESG point of view, we are reducing carbon footprint and we are reducing fuel use as well.”

For a company operating thousands of vehicles, small efficiency improvements generate substantial aggregate benefits. 

Even saving a litre of fuel per vehicle per day adds up to significant environmental and cost impacts across their European operations.

The results of data quality becoming the foundation

What makes Solutions 30’s AI story particularly relevant is its emphasis on data quality as a prerequisite for effective machine learning (ML). 

Operating across multiple countries with different languages, currencies and regulatory frameworks creates data complexity that many companies underestimate.

“We are living in a big data world,” Jerzy says.

“We have hundreds of different databases in use, which generates a lot of data.” 

But volume without quality creates problems: “If we don’t have enough quality in our data, we will make wrong decisions, we’ll go in the wrong direction,” he adds.

“So one of our preliminary goals is to keep the quality of data, then to give the good data to our decision makers to properly manage the company.

“That's the kind of foundation not touching AI, but if we include AI into this part, then it can be easier to keep this quality – that's something on our roadmap.”

This focus on data foundations reflects hard-won experience. 

AI systems learn from historical patterns, so errors or inconsistencies in training data will produce unreliable algorithms. 

For a company handling safety-critical work, this isn’t just about efficiency: it’s about preventing dangerous mistakes.

The company’s approach involves standardising data collection and storage processes across its acquired companies. 

This creates consistent formats that AI systems can process reliably while supporting broader business intelligence needs.

Jerzy’s team has several AI proof-of-concept projects in development, but he emphasises the importance of comprehensive testing before production deployment.

“AI has two sides, so I would compare it to a knife. It can ease your daily activities, but it can also be very dangerous,” he warns.

“We need to approach AI very carefully. We need to test it, we need to prepare it in such a way that it'll not harm us or any of our partners.”

The role of NinjaOne

In environments where AI decisions affect safety, customer relationships or regulatory compliance, the testing phase becomes crucial.

This is where the company’s partnership with NinjaOne, the endpoint management platform provider, comes in.

NinjaOne illustrates how large enterprises balance functionality with budget constraints. 

“We are using this platform for monitoring and for managing our endpoints like PCs and mobile devices,” Jerzy says.

Unlike large banks or telecoms with substantial IT budgets, field services companies must evaluate solutions based on both capability and cost.

“We look at the solutions very comprehensively and not only from the functionality point of view, but also from the cost of the licences,” Jerzy explains.

“NinjaOne was the most cost efficient as well as valuable option for Solutions30.”

The NinjaOne platform manages endpoints across Solutions 30’s workforce, providing centralised visibility into hardware and software status while automating security patching and system updates. 

The platform’s AI algorithms offer predictive insights about infrastructure problems, though Jerzy admits they haven’t fully exploited these capabilities yet.

The partnership has evolved over four years, expanding from a limited pilot to comprehensive coverage of their infrastructure.

“The bigger infrastructure you migrate into NinjaOne, the bigger advantages you get,” Jerzy observes.

AI advice for long term success 

Solutions30’s AI strategy must account for an increasingly complex threat landscape where attackers also use AI tools. 

This creates a defensive requirement for AI-enabled security systems while ensuring their own AI deployments don’t create new vulnerabilities.

“The hackers use AI technology to attack us. So we must be very careful,” Jerzy notes. 

This arms race dynamic means companies can’t simply implement AI for efficiency gains. They must also consider how these systems affect their security posture.

The regulatory environment adds another layer of complexity. Operating across European jurisdictions means complying with GDPR, the NIS2 Directive and various industry-specific standards. 

AI systems must operate within these frameworks while maintaining operational consistency across different countries.

Perhaps most striking is Jerzy’s view on the competitive implications of AI adoption. 

He suggests that companies delaying AI implementation may face existential challenges: “If the company will not use AI technologies within the next few years, I think that it’ll not exist anymore.”

For Solutions30, the scale of operations makes this particularly relevant. 

“We have a huge scale of operations and a huge scale of the workforce, so AI can bring a lot of efficiency and cost reduction in our future,” Jerzy says.

The company’s experience demonstrates that successful AI implementation in traditional industries requires balancing innovation with caution, functionality with cost and efficiency with safety. 

Its approach – treating AI as a layer that enhances existing frameworks rather than replacing them –  offers a practical model for companies navigating similar transformations.

“Use it widely but use it wisely as well,” Jerzy advises.

“There are a lot of different advantages of AI technologies, but there is also a kind of threat behind it.”