Navigating the Build vs. Buy Conversation in Service and Manufacturing Spaces – with Leaders from Aquant, Generac, Lexmark, Electrolux, Danaher, and Comfort Systems USA

This article is sponsored by Aquant and was written, edited, and revealed in alignment with our Emerj sponsored content guidelines. Learn extra about our thought management and content material creation providers on our Emerj Media Services page.

In high-stakes subject service sectors, reminiscent of manufacturing heavy equipment or essential medical gadgets like hospital ventilators, gear failure is each expensive and probably life-threatening. The NHS recorded over 3,900 gear malfunctions between 2022 and 2025, ensuing in 87 deaths and quite a few accidents, underscoring the devastating penalties of downtime in healthcare settings. 

According to the new Value of Reliability survey from ABB, over two-thirds of commercial companies expertise unplanned outages at the very least as soon as a month, ensuing in a typical enterprise incurring near $125,000 in prices per hour. 

A 2025 Harvard Business Review article highlights how Sterling Crane utilized AI-driven preventive upkeep to avoid wasting over $3 million, whereas enhancing security and operational effectivity throughout its large-scale development and logistics operations.

Emerj not too long ago hosted a particular sequence of the ‘AI in Business’ podcast with executives from throughout the manufacturing and subject providers areas to deal with these challenges.

Executives featured in the sequence embrace 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 by way of data-driven, human-centered, and hybrid approaches that stability pace, value, experience, and enterprise impression.

The dialog highlights that profitable AI adoption requires balancing know-how, information, and human elements, selecting the proper build-buy method, prioritizing high-impact use instances, and leveraging vendor experience to realize measurable enterprise worth.

This article examines numerous key insights from their conversations for leaders aiming to implement AI successfully, optimize information, and drive measurable enterprise impression:

• Adopting a hybrid AI method: Blend in-house and vendor AI to stability pace, value, and experience, making certain quicker ROI and clean adoption.
• Prioritizing AI use instances by impression and ease: Mapping journeys, gathering suggestions, and rating alternatives utilizing an impression–ease matrix for optimum worth with minimal effort.
• Leveraging knowledgeable AI distributors for dependable impression: Partnering with AI distributors delivering high-confidence predictions to drive value financial savings and improve buyer expertise at scale.
• Ensuring robust vendor improvement hygiene: Buying AI-powered safety instruments from distributors that preserve sturdy improvement hygiene, correct risk intelligence, and proactive vulnerability testing. 
• Building AI strategically round core benefits: Prioritizing purpose-built, modular, human-centric AI that reinforces the firm’s aggressive benefit whereas managing information and compliance.
• Leveraging exterior companions to get rid of bias: Leveraging unbiased exterior companions to construction information and implement AI, avoiding inner jargon and making certain clear, repeatable processes for worth supply.

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 practically 4 years of expertise, Tim heads Product Marketing at Aquant. His earlier roles embrace positions at Market Logic Software, Oracle, and RAPP. He holds a bachelor’s diploma in Information Design Systems from Kingston University.

Tim explains that as organizations mature in their AI journeys, they’re shifting previous hype and considering extra strategically about enterprise worth. Beyond the polarities between “constructing” and “shopping for,” he identifies a 3rd, hybrid method to AI adoption, notably in specialised areas reminiscent of service for manufacturing and heavy {industry}. 

Instead of constructing or shopping for fully, organizations at the moment are contemplating the stability of in-house constructing operations and the place to buy experience to maximise effectiveness. 

Key concerns Tim emphasizes in these choices embrace understanding the finish customers, long-term dedication to upkeep, time to worth, and whole value of possession. Companies are navigating a center floor, combining inner improvement with exterior experience to strike a stability between pace, value, and specialised data.

Tim explains that purchasing AI options provides two key benefits:

• Faster time to worth and ROI – distributors allow organizations to deploy shortly in comparison with constructing in-house.
• Experience with adoption and change administration – established distributors have experience in making certain know-how is successfully used, which is essential for realizing returns.

Additionally, buying helps handle prices and venture timelines, and leverages vendor expertise to navigate widespread pitfalls in AI implementations.

“This is actually what shopping for brings to the desk: The potential to rise up and operating actually shortly. While there are extra structured prices, a lot of the latest press discusses what number of AI tasks are over price range or not on time. It’s about time to worth, it’s about managing prices, however it’s additionally about how shortly you may get staff to start out utilizing techniques.

The actual query is: How can I deploy the adoption that I must ship goal-based worth? And you want individuals who have gotten expertise of getting lived and breathed that that will help you by way of that course of.”

-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 throughout Danaher’s acquisition of Abcam, delivered $60M+ in AI-driven funnel progress, and constructed international IT and digital platforms throughout a number of working corporations. He has over 25 years of expertise driving IT, AI, and digital transformation throughout 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 method, explaining that organizations ought to keep away from a one-size-fits-all technique. Instead of reinventing the wheel, they need to begin by buying core platforms from established distributors like Salesforce or Adobe, leveraging their innovation as a strong basis. 

Companies can then construct their customized use instances, combine information, and develop AI algorithms on high of those platforms which are higher tailor-made to their particular enterprise wants. Amit’s 70-30 method ensures a stability of pace, agility, and cost-effectiveness by combining the effectivity of pre-built options with the flexibility of in-house customization.

Sharing his expertise, Amit explains that finest practices start with a business-first mindset, emphasizing that AI shouldn’t be applied solely for its personal sake, however moderately as a method to realize enterprise targets. 

He emphasizes that it’s important for groups to start by mapping the buyer journey, from discovery to buy to utilization, alongside the industrial journey, encompassing advertising and marketing, gross sales, and service, to establish essential enterprise drawback areas with the most vital alternatives. 

The course of is guided by buyer suggestions, together with voice-of-customer surveys and insights from gross sales groups.

“We then took these issues and ran a prioritization workshop. What I imply by that may be a easy two-by-two framework.

So take into consideration the x-axis as the ease of implementation, and the y-axis is the enterprise impression, whether or not you measure that in income or another key enterprise metric.

And then, once you map these issues or alternatives on this straightforward priority-impact matrix, what’s it that bubbles as much as the high proper? So we checked out these, and began to unpack the query of the way to go about leveraging improvements in AI to allow use instances.”

– 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 powerful background in management and operational excellence, with prior roles at Dynex Technologies and Cattron Global. Before getting into the company world, he served 5 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 relating to the construct versus purchase determination, he tends to lean towards recommending shopping for moderately than constructing for service leaders. His reasoning is that whereas their information is easy, masking merchandise, points, elements used, and notes, creating an AI answer internally would seemingly be clunky and inefficient as a result of they don’t seem to be an AI firm. 

Instead, he prefers to companion with specialised distributors who focus fully on AI, pilot their options, and consider which has the finest method or “secret sauce.” In brief, outsourcing to consultants ensures higher high quality and effectivity than making an attempt to construct in-house capabilities.

He says that whereas AI options can successfully illustrate use instances, the actual problem lies in reaching a excessive stage of worker confidence in system predictions. Their information typically consists of free-text inputs from technicians, variability in elements utilization, and a number of potential options, which makes prediction accuracy tough. 

The objective is to make sure AI can reliably predict the appropriate half and process for a given situation. However, thus far, the instruments they’ve piloted have delivered low success charges, which is why they haven’t absolutely dedicated to any platform. Ultimately, the concern is avoiding incorrect suggestions simply because “AI mentioned so,” highlighting the want for accuracy and trustworthiness in predictions.

Eric explains that calculating ROI for his or her instruments is easy as a result of service leaders are acquainted with the prices, reminiscent of technician time, truck rolls, or name heart engagements. Any enchancment or discount in these actions interprets into incremental financial savings:

“There are quite a lot of different elements that play into the determination to construct or purchase based mostly on ROI. But I believe Incrementally, even for those who’re dealing with about 5,000 calls per day, and you’re capable of save a smaller p.c of that — even for those who might save and enhance 1,200 buyer experiences — there’s a giant value financial savings there, however then additionally the enhanced buyer expertise.”

– 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 safety, Data privateness, Risk Analysis & Gap Remediation

Brief Recognition:  Just after recording his podcast, Bryan concluded a 28-year profession at Lexmark, the place he served as the firm’s first — and most up-to-date— Chief Information Security Officer, main international IT safety, information privateness, inner audit, and bodily safety throughout 140+ websites. He constructed Lexmark’s enterprise safety and privateness applications from the floor up, earned ISO 27001 and SOC 2, and lifted the firm to an industry-leading BitSight score, with CSO50 Awards recognizing his privateness (2019) and provide chain safety (2021) initiatives.

Bryan explains that almost all organizations lack the in-house experience to construct their very own AI fashions for monitoring, as they typically depend on advanced and essential techniques, reminiscent of safety instruments. In these instances, it’s higher to purchase from trusted companions who’ve mature AI fashions able to detecting related alerts throughout instruments like endpoint detection and response (EDR) techniques, firewalls, and community monitoring. 

He emphasizes that every software has a selected objective; endpoints analyze detailed system exercise, whereas firewalls want quick, high-volume detection, and distributors ought to present correct risk intelligence and preserve robust improvement hygiene. The key takeaway is to companion with consultants moderately than making an attempt to construct essential AI safety fashions internally.

Bryan explains that improvement hygiene is essential for safety instruments. What he means by “good hygiene” consists of having a strong safety improvement lifecycle, processes to detect and repair dangerous code earlier than launch, and utilizing instruments to establish vulnerabilities.

“Vendors want to make use of the proper instruments to establish dangers in their code base and remediate them shortly. They also needs to have proactive groups operating pink teaming and penetration testing in opposition to their merchandise.

This is very essential for firewalls and VPN concentrators, the place poor code hygiene has led to critical vulnerabilities in latest years. No matter how superior the AI or instruments inside a tool could also be, if the underlying code hygiene is weak and stuffed with vulnerabilities, it merely 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 twenty years of expertise driving data-driven transformations throughout numerous industries and features. At Generac, he leads the improvement of information technique and the buildout of analytics platforms and capabilities throughout manufacturers reminiscent of Consumer Power, Energy Technology, Ecobee, DR Power, and PRAMAC. He holds a grasp’s diploma in Industrial and Systems Engineering from Lehigh University.

Neil explains that fashionable fashions have billions of parameters and require giant, high-quality datasets for sturdy coaching. Obtaining and managing these information is difficult as a consequence of privateness rules, compliance necessities, and sensitivity concerns. 

Beyond information, constructing fashions additionally calls for vital compute energy, storage, human assets, and mental capability. 

Therefore, Bhandar argues, the determination to purchase or construct an AI mannequin shouldn’t be simple; it requires cautious planning round information sourcing, storage, compliance, and infrastructure. For smaller organizations, making an attempt to develop such fashions internally will be overwhelming and dangerous, making it a fancy, high-stakes endeavor.

Neil emphasizes a number of key ideas for implementing AI successfully:

• Fitness for objective: Don’t chase the shiniest new software; give attention to options that straight meet your wants.
• Prioritization and focus: You can’t resolve every part without delay; keep centered on essential issues.
• Modular design: Build AI options like Lego blocks moderately than monoliths, making them simpler to debug, change, and preserve.
• Human-centric method: Remember that each service suppliers and customers are people; AI ought to really feel intuitive and relatable to cut back resistance.

“You’ve received to know what your sustainable aggressive benefit is earlier than you get into any of those strategic, long-term investments that may very well be pivotal to the place you land, three, 5, or 10 years from now.

You’ve additionally received to mainly be sure that any partnerships you get into — any instruments, platforms, decisions that you find yourself making — proceed to leverage that sustainable aggressive benefit.

This is very true in the world of Gen AI, the place these fashions are extraordinarily information hungry. Oftentimes, the information that they use may very well be your opponents’ information, which suggests they can not distinguish between what’s distinctive to you versus to someone else that you simply’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 practically twenty years. He has offered the firm with service management to develop and develop the group whereas creating long-term, strategic targets and expectations for the company. He can be an advisory board member for Field Service USA, The Service Council, and Aquant.

During his podcast look, Joe emphasizes the significance of utilizing exterior companions with out {industry} bias when implementing AI or digital options. Instead of retraining inner groups steeped in tribal data and jargon, his group skilled these exterior companions and outfitted them with the proper instruments to feed correct information. 

Relying on exterior companions for coaching, Joe notes, avoids inner bias that may corrupt information, ensures clear, constant, and structured info, and creates a repeatable course of for delivering worth again to the subject.

He emphasizes that inflexible, one-size-fits-all processes don’t work in advanced service environments as a result of there are a number of methods to realize the identical objective, fixing gear and satisfying the buyer. The {industry} is evolving quicker than folks will be skilled, so corporations should give attention to the fundamentals and construct versatile, data-driven techniques that assist technicians, even those that aren’t consultants, to allow them to nonetheless resolve buyer points successfully.

He explains that Comfort Systems addressed this problem by creating the Fixed Support Center. The superior platform not solely assists 2,700 subject technicians in actual time but in addition generates invaluable information for coaching priorities and useful resource allocation:

“We’ve additionally constructed a technician assist app to allow them to really go self-service, get their very own handbook, get their very own options, that are all AI-driven. 

We’re making an attempt to shift it left in the truck for the technician, as a result of clients’ expectations have modified. They don’t count on the technician they get to be the knowledgeable on every part. What clients do count on them to do is know the place the assets are to go discover the answer to their drawback.”

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