This article is sponsored by Aquant and was written, edited, and published in alignment with our Emerj sponsored content guidelines. Learn more about our thought leadership and content creation services on our Emerj Media Services page.

In high-stakes field service sectors, such as manufacturing heavy machinery or critical medical devices like hospital ventilators, equipment failure is both costly and potentially life-threatening. The NHS recorded over 3,900 equipment malfunctions between 2022 and 2025, resulting in 87 deaths and numerous injuries, underscoring the devastating consequences of downtime in healthcare settings. 

According to the new Value of Reliability survey from ABB, over two-thirds of industrial businesses experience unplanned outages at least once a month, resulting in a typical business incurring close to $125,000 in costs per hour. 

A 2025 Harvard Business Review article highlights how Sterling Crane utilized AI-driven preventive maintenance to save over $3 million, while enhancing safety and operational efficiency across its large-scale construction and logistics operations.

Emerj recently hosted a special series of the ‘AI in Business’ podcast with executives from across the manufacturing and field services spaces to address these challenges.

Executives featured in the series include Tim Burge, Director at Aquant; Neil Bhandar, Chief Data Analytics Officer at Generac; Bryan Willett, Chief Information Security Officer at Lexmark; Eric Rivas, Director for Service Repair for North America at Electrolux; Amit Gupta, Chief Digital Officer at Danaher; Joe Lang, Vice President of Service Technology and Innovation at Comfort Systems USA. 

During these conversations with Emerj Editorial Director Matthew DeMello, leaders dived deep into strategically implementing AI through data-driven, human-centered, and hybrid approaches that balance speed, cost, expertise, and business impact.

The conversation highlights that successful AI adoption requires balancing technology, data, and human factors, choosing the right build-buy approach, prioritizing high-impact use cases, and leveraging vendor expertise to achieve measurable business value.

This article examines various key insights from their conversations for leaders aiming to implement AI effectively, optimize data, and drive measurable business impact:

• Adopting a hybrid AI approach: Blend in-house and vendor AI to balance speed, cost, and expertise, ensuring faster ROI and smooth adoption.
• Prioritizing AI use cases by impact and ease: Mapping journeys, gathering feedback, and ranking opportunities using an impact–ease matrix for maximum value with minimal effort.
• Leveraging expert AI vendors for reliable impact: Partnering with AI vendors delivering high-confidence predictions to drive cost savings and enhance customer experience at scale.
• Ensuring strong vendor development hygiene: Buying AI-powered security tools from vendors that maintain robust development hygiene, accurate threat intelligence, and proactive vulnerability testing. 
• Building AI strategically around core advantages: Prioritizing purpose-built, modular, human-centric AI that reinforces the company’s competitive advantage while managing data and compliance.
• Leveraging external partners to eliminate bias: Leveraging unbiased external partners to structure data and implement AI, avoiding internal jargon and ensuring clean, repeatable processes for value delivery.

Adopting a Hybrid AI Approach

Episode – What Smart Manufacturing Leaders Consider Before Adopting AI

Guest: Tim Burge, Director, Aquant

Expertise: AI, Strategic Consulting, Product Marketing

Brief Recognition:  With nearly four years of experience, Tim heads Product Marketing at Aquant. His previous roles include positions at Market Logic Software, Oracle, and RAPP. He holds a bachelor’s degree in Information Design Systems from Kingston University.

Tim explains that as organizations mature in their AI journeys, they are moving past hype and thinking more strategically about business value. Beyond the polarities between “building” and “buying,” he identifies a third, hybrid approach to AI adoption, particularly in specialized areas such as service for manufacturing and heavy industry. 

Instead of building or buying entirely, organizations are now considering the balance of in-house building operations and where to purchase expertise to maximize effectiveness. 

Key considerations Tim emphasizes in these decisions include understanding the end users, long-term commitment to maintenance, time to value, and total cost of ownership. Companies are navigating a middle ground, combining internal development with external expertise to strike a balance between speed, price, and specialized knowledge.

Tim explains that buying AI solutions offers two key advantages:

• Faster time to value and ROI – vendors enable organizations to deploy quickly compared to building in-house.
• Experience with adoption and change management – established vendors have expertise in ensuring technology is effectively used, which is critical for realizing returns.

Additionally, purchasing helps manage costs and project timelines, and leverages vendor experience to navigate common pitfalls in AI implementations.

“This is really what buying brings to the table: The ability to get up and running really quickly. While there are more structured costs, much of the recent press discusses how many AI projects are over budget or behind schedule. It’s about time to value, it’s about managing costs, but it’s also about how quickly you can get employees to start using systems.

The real question is: How can I deploy the adoption that I need to deliver goal-based value? And you need people who have got experience of having lived and breathed that to help you through that process.”

-Tim Burge, Director at Aquant

Prioritizing AI Use Cases by Impact and Ease

Episode – Moving from Pilot to Profit in Service AI Deployments:

Guest: Amit Gupta, Chief Digital Officer, Danaher

Expertise: Digital Transformation, IT Strategy, AI

Brief Recognition: As Chief Digital and Information Officer at Abcam and Danaher Life Sciences, Amit led digital integration during Danaher’s acquisition of Abcam, delivered \$60M+ in AI-driven funnel growth, and built global IT and digital platforms across multiple operating companies. He has over 25 years of experience driving IT, AI, and digital transformation across the Life Sciences, Biotech, CPG, Pharmaceuticals, Medical Equipment, and Industrial Manufacturing Sectors. He holds an MBA from the University of California, Berkeley’s Haas School, Wharton, and Nanyang Business School.

Throughout his podcast, Amit emphasizes a novel 70-30 ratio for a buy-versus-build approach, explaining that organizations should avoid a one-size-fits-all strategy. Instead of reinventing the wheel, they should start by purchasing core platforms from established vendors like Salesforce or Adobe, leveraging their innovation as a solid foundation. 

Companies can then build their custom use cases, integrate data, and develop AI algorithms on top of these platforms that are better tailored to their specific business needs. Amit’s 70-30 approach ensures a balance of speed, agility, and cost-effectiveness by combining the efficiency of pre-built solutions with the flexibility of in-house customization.

Sharing his experience, Amit explains that best practices begin with a business-first mindset, emphasizing that AI should not be implemented solely for its own sake, but rather as a means to achieve business goals. 

He emphasizes that it is essential for teams to begin by mapping the customer journey, from discovery to purchase to usage, alongside the commercial journey, encompassing marketing, sales, and service, to identify critical business problem areas with the most significant opportunities. 

The process is guided by customer feedback, including voice-of-customer surveys and insights from sales teams.

“We then took those problems and ran a prioritization workshop. What I mean by that is a simple two-by-two framework.

So think about the x-axis as the ease of implementation, and the y-axis is the business impact, whether you measure that in revenue or any other key business metric.

And then, when you map those problems or opportunities on this simple priority-impact matrix, what is it that bubbles up to the top right? So we looked at those, and started to unpack the question of how to go about leveraging innovations in AI to enable use cases.”

– Amit Gupta, Chief Digital Officer at Danaher

Leveraging Expert AI Vendors for Reliable Impact

Episode – Balancing Efficiency and Trust in Field Service Operations – with Eric Rivas of Electrolux

Guest: Eric Rivas, Director for Service Repair for North America at Electrolux

Expertise: Analytics, Efficiency, Customer Experience

Brief Recognition: He brings a strong background in leadership and operational excellence, with prior roles at Dynex Technologies and Cattron Global. Before entering the corporate world, he served five years as an Air Traffic Control Radar Repair Technician in the U.S. Marine Corps. He holds an MBA from Colorado State University.

Eric explains that when it comes to the build versus buy decision, he tends to lean toward recommending buying rather than building for service leaders. His reasoning is that while their data is straightforward, covering products, issues, parts used, and notes, creating an AI solution internally would likely be clunky and inefficient because they are not an AI company. 

Instead, he prefers to partner with specialized vendors who focus entirely on AI, pilot their solutions, and evaluate which has the best approach or “secret sauce.” In short, outsourcing to experts ensures better quality and efficiency than attempting to build in-house capabilities.

He says that while AI solutions can effectively illustrate use cases, the real challenge lies in achieving a high level of employee confidence in system predictions. Their data often includes free-text inputs from technicians, variability in parts usage, and multiple possible solutions, which makes prediction accuracy difficult. 

The goal is to ensure AI can reliably predict the correct part and procedure for a given issue. However, so far, the tools they’ve piloted have delivered low success rates, which is why they haven’t fully committed to any platform. Ultimately, the concern is avoiding incorrect recommendations just because “AI said so,” highlighting the need for accuracy and trustworthiness in predictions.

Eric explains that calculating ROI for their tools is straightforward because service leaders are familiar with the costs, such as technician time, truck rolls, or call center engagements. Any improvement or reduction in these activities translates into incremental savings:

“There are a lot of other factors that play into the decision to build or buy based on ROI. But I think Incrementally, even if you’re handling about 5,000 calls per day, and you’re able to save a smaller percent of that — even if you could save and improve 1,200 customer experiences — there’s a big cost savings there, but then also the enhanced customer experience.”

– Eric Rivas, Director for Service Repair for North America at Electrolux

Ensure Strong Vendor Development Hygiene

Episode – How AI Partnerships Make Security a Strategic Advantage

Guest: Bryan Willett, Chief Information Security Officer, Lexmark

Expertise: IT security, Data privacy, Risk Analysis & Gap Remediation

Brief Recognition:  Just after recording his podcast, Bryan concluded a 28-year career at Lexmark, where he served as the company’s first — and most recent— Chief Information Security Officer, leading global IT security, data privacy, internal audit, and physical security across 140+ sites. He built Lexmark’s enterprise security and privacy programs from the ground up, earned ISO 27001 and SOC 2, and lifted the company to an industry-leading BitSight rating, with CSO50 Awards recognizing his privacy (2019) and supply chain security (2021) initiatives.

Bryan explains that most organizations lack the in-house expertise to build their own AI models for monitoring, as they often rely on complex and critical systems, such as security tools. In these cases, it’s better to buy from trusted partners who have mature AI models capable of detecting relevant alerts across tools like endpoint detection and response (EDR) systems, firewalls, and network monitoring. 

He emphasizes that each tool has a specific purpose; endpoints analyze detailed system activity, while firewalls need fast, high-volume detection, and vendors should provide accurate threat intelligence and maintain strong development hygiene. The key takeaway is to partner with experts rather than attempting to build critical AI security models internally.

Bryan explains that development hygiene is critical for security tools. What he means by “good hygiene” includes having a robust security development lifecycle, processes to detect and fix risky code before release, and using tools to identify vulnerabilities.

“Vendors need to use the right tools to identify risks in their code base and remediate them quickly. They should also have proactive teams running red teaming and penetration testing against their products.

This is especially critical for firewalls and VPN concentrators, where poor code hygiene has led to serious vulnerabilities in recent years. No matter how advanced the AI or tools inside a device may be, if the underlying code hygiene is weak and full of vulnerabilities, it simply doesn’t matter.”

– Bryan Willett, Chief Information Security Officer at Lexmark

Build AI Strategically Around Core Advantages

Episode – Why Service Teams Outgrow DIY AI Solutions

Guest: Neil Bhandar, Chief Data Analytics Officer, Generac

Expertise: Machine Learning, Data Management, Data Governance

Brief Recognition: Neil brings over two decades of experience driving data-driven transformations across diverse industries and functions. At Generac, he leads the development of data strategy and the buildout of analytics platforms and capabilities across brands such as Consumer Power, Energy Technology, Ecobee, DR Power, and PRAMAC. He holds a master’s degree in Industrial and Systems Engineering from Lehigh University.

Neil explains that modern models have billions of parameters and require large, high-quality datasets for robust training. Obtaining and managing these data is challenging due to privacy regulations, compliance requirements, and sensitivity considerations. 

Beyond data, building models also demands significant compute power, storage, human resources, and intellectual capacity. 

Therefore, Bhandar argues, the decision to buy or build an AI model is not straightforward; it requires careful planning around data sourcing, storage, compliance, and infrastructure. For smaller organizations, attempting to develop such models internally can be overwhelming and risky, making it a complex, high-stakes endeavor.

Neil emphasizes several key principles for implementing AI effectively:

• Fitness for purpose: Don’t chase the shiniest new tool; focus on solutions that directly meet your needs.
• Prioritization and focus: You can’t solve everything at once; stay focused on critical problems.
• Modular design: Build AI solutions like Lego blocks rather than monoliths, making them easier to debug, replace, and maintain.
• Human-centric approach: Remember that both service providers and users are humans; AI should feel intuitive and relatable to reduce resistance.

“You’ve got to know what your sustainable competitive advantage is before you get into any of these strategic, long-term investments that could be pivotal to where you land, three, five, or 10 years from now.

You’ve also got to basically make sure that any partnerships you get into — any tools, platforms, choices that you end up making — continue to leverage that sustainable competitive advantage.

This is especially true in the world of Gen AI, where these models are extremely data hungry. Oftentimes, the data that they use could be your competitors’ data, which means they cannot distinguish between what is distinctive to you versus to somebody else that you’re competing with.”

– Neil Bhandar, Chief Data Analytics Officer at Generac

Leverage External Partners to Eliminate Bias

Episode – Turning Legacy Service Contracts into First Time Fix Wins – with Joe Lang of Comfort Systems

Guest: Joe Lang, Vice President of Service Technology and Innovation, Comfort Systems USA

Expertise: Leadership, Innovation, Sales

Brief Recognition: Joe has been with Comfort Systems for nearly two decades. He has provided the company with service leadership to develop and grow the organization while creating long-term, strategic goals and expectations for the corporation. He is also an advisory board member for Field Service USA, The Service Council, and Aquant.

During his podcast appearance, Joe emphasizes the importance of using external partners without industry bias when implementing AI or digital solutions. Instead of retraining internal teams steeped in tribal knowledge and jargon, his organization trained these outside partners and equipped them with the right tools to feed accurate data. 

Relying on outside partners for training, Joe notes, avoids internal bias that can corrupt data, ensures clean, consistent, and structured information, and creates a repeatable process for delivering value back to the field.

He emphasizes that rigid, one-size-fits-all processes don’t work in complex service environments because there are multiple ways to achieve the same goal, fixing equipment and satisfying the customer. The industry is evolving faster than people can be trained, so companies must focus on the basics and build flexible, data-driven systems that support technicians, even those who aren’t experts, so they can still resolve customer issues effectively.

He explains that Comfort Systems addressed this challenge by creating the Fixed Support Center. The advanced platform not only assists 2,700 field technicians in real time but also generates valuable data for training priorities and resource allocation:

“We’ve also built a technician support app so they can actually go self-service, get their own manual, get their own solutions, which are all AI-driven. 

We’re trying to shift it left in the truck for the technician, because customers’ expectations have changed. They don’t expect the technician they get to be the expert on everything. What customers do expect them to do is know where the resources are to go find the solution to their problem.”

– Joe Lang, Vice President of Service Technology and Innovation, Comfort Systems USA