Blue Mountain Summit 2026 Event Brief
Blue Mountain hosted its 2026 Summit from September 15–17 at the JB Duke Hotel in Durham, NC. Over 180 attendees, representing 90+ companies, participated in the three-day event.
The company brought together life sciences professionals focused on enterprise asset management (EAM), computerized maintenance management systems (CMMS), maintenance, calibration, quality, engineering, and digital transformation. More than 40 sessions and workshops addressed the operational challenges facing regulated manufacturers.
Across the keynote, product sessions, customer discussions, and technical presentations, a consistent theme emerged: life sciences organizations are accumulating more data and more digital tools, but the real value comes from turning that information into trusted, actionable decisions.
Artificial intelligence was naturally part of the conversation, but the Summit’s broader message was clear: AI is not a substitute for sound processes, reliable data, or human judgment. Speakers repeatedly positioned intelligence, connectivity, and automation as ways to help organizations understand what is happening, determine what matters, and act with greater confidence.
Companies from across the life sciences ecosystem, including PCI, RCM Technologies, BW Design Group, Eupry, Kneat, Quantus, USDM Life Sciences, and Verista sponsored and partnered with Blue Mountain for this years’ Summit. Stormbit also demonstrated its VEYA platform, showcasing its approach to delivering validated manufacturing data at the point of use, which was a theme that aligned closely with the event’s focus on connected operations and actionable information.
Better Decisions begin with Better Information
The Summit opened with a leadership keynote emphasizing the company’s focus on helping customers make better decisions in increasingly complex operating environments. For regulated manufacturers, the challenge is not simply collecting more information. It is making that information useful, trusted, and actionable.
AI was as an amplifier of an organization’s existing information foundation rather than a replacement for the systems that establish that foundation.
Blue Mountain also stressed an organization’s need to approach AI with discipline. The objective is not to deploy AI simply because it is available. That distinction is particularly relevant in life sciences, where digital technologies must operate within environments shaped by validation, data integrity, quality requirements, and regulatory oversight.
Four new leaders were also introduced as part of Blue Mountain’s next chapter: Savannah Coyne, Chief of Staff; CJ Simmons, Chief Sales Officer; Greg Spaulding, Chief People Officer; and Kent Malmros, Chief Commercial Officer. The new leadership team adds dedicated executive focus across the company’s operations, sales organization, people strategy, and commercial activities.
Building for Scale: Managing Risk in Life Sciences’ Mega-Projects
The life sciences industry is entering an unprecedented era of capital investment, bringing with it equally unprecedented challenges around talent, complexity, scope, schedule, and operational readiness. During an industry panel, leaders from PCI, Clarus Biologics, Amgen, AbbVie, Sequence, Inc., and Blue Mountain examined what it will take to execute these projects successfully and why organizations need to think beyond construction alone.
Rodney Perkins of PCI opened the discussion by reflecting on how dramatically the market has changed since he entered the industry in 1996. With project activity now reaching a scale few leaders have experienced before, he emphasized the importance of mentorship and developing the next generation of talent.
Darren Dasburg, CEO of Clarus Biologics, Inc., highlighted the challenge created by project magnitude. “Big risk, big dollar,” he said, pointing to the need for greater estimating discipline as projects move into the billion-dollar range. He also stressed that organizations must understand which capabilities to retain internally and where external expertise can close critical gaps.
For Paul Lewus, Vice President, Site Operations at Amgen, the defining challenge is complexity. “It’s hard enough to build one manufacturing facility,” he noted. Large campuses, however, bring manufacturing, utilities, laboratories, warehouses, offices, and other functions together, meaning that relatively small issues can have consequences across the entire operation.
Jonathan Wood, Chief Revenue Officer at Sequence, Inc., reinforced the importance of having “the right people, right place, right time,” particularly as supply-chain constraints force teams to make decisions earlier in the project lifecycle.
The panel ultimately underscored a common theme: operational readiness cannot wait until startup. Early involvement from the right technical, operational, and service partners can help organizations identify risks sooner, reduce costly rework, and build facilities designed not only to be completed, but to operate successfully for decades.
From the Shop Floor
Thomas Povanda, Head of Asset Management-Americas at Sanofi, addressed “The Business of Better Decisions.” He focused on the communication gap that can exist between people closest to the equipment and those responsible for broader operational and business decisions.
Image Source: Axendia, Inc.
Technicians and operators may recognize an emerging problem long before it becomes visible at the management level, but the information must be translated into language that enables action. His central message resonated with the attendees, “The people closest to the work often see the problem first and it’s our responsibility to make sure their voice makes it all the way to the top floor.”
He encouraged maintenance and reliability professionals to move beyond communicating activities and instead communicate consequences. Rather than presenting a request as a need to purchase a particular technology or perform a particular maintenance activity, he suggested framing it in terms of production risk, capacity, cost, quality, or compliance. “Don’t communicate the activity. Communicate the consequence,” suggested Povanda.
His message was echoed later in the Summit by Nate Matthews, Blue Mountain’s Senior Product Manager, who discussed the different levels of information required across an organization. He offered an example of a technician who may need to know that a pump has begun behaving differently; a site leader who needs to understand why it matters and what the risk is to the facility; and an executive who needs to understand the broader operational and business impact. The common requirement is a shared underlying source of information, giving each stakeholder the context needed to make decisions at their level.
Turning Asset Data Into Decision You Can Defend
Judy Fainor, CTO at Blue Mountain, expanded the Summit’s theme during the product and platform discussion, connecting Blue Mountain’s development priorities to the broader operational challenges discussed throughout the Summit.
Image Source: Axendia, Inc.
Manufacturers have more assets, regulatory scrutiny, and systems than ever before. The question organizations are asking themselves today is whether they can trust the information flowing between those systems. As Fainor put it, “It’s not really a data problem. It’s a trust problem.”
That distinction provides an important lens for understanding the Summit’s emphasis on connected operations. She described Blue Mountain’s objective as helping organizations move beyond a collection of disconnected systems and data toward a connected operational model. The goal is to turn asset data into decisions that organizations can defend within their teams and management structures, as well as when facing auditors and other stakeholders.
She also made one of the summit’s most tangible offers to attendees! Blue Mountain announced a promotion for the first 10 customers that engage with the company and go through a project involving RAM Discover. Those early participants would receive one year of RAM Discover licenses at no cost as part of the longer-term project.
The offer was presented as more than a software discount. She encouraged attendees who were interested to engage directly with the Blue Mountain team. That would ensure they understand their customers business needs and use cases and work with them towards a successful implementation.
Conditioned Based Maintenance Moves Beyond The Calendar
Traditional preventive maintenance often relies on time or calendar intervals. Nick Sturniolo, Product Manager at Blue Mountain, addressed the practical application of a data-driven approach through condition-based maintenance (CBM).
CBM uses information about the actual condition and usage of an asset to determine when intervention is warranted. He described examples involving vibration, temperature, usage, and other condition parameters. The objective is to identify a meaningful signal before failure occurs and use that signal to inform the maintenance decision.
At its core, CBM can help reduce unnecessary work. If equipment has been offline for an extended period, for example, a calendar-based maintenance interval may not accurately reflect its actual use. The underlying principle is consistent with the broader Summit message: collect the right information, understand what it means, and use it to determine the appropriate action.
AI in EAM: Purpose Before Technology
Bill Lucas, Chief Platform Architect at Blue Mountain explored the implications of AI more directly in his session, “Exploring the Potential of AI in EAM.” He asked the audience how many organizations were currently running AI pilots and then posed a more challenging question: how many felt AI was being treated as a solution without a clearly defined problem?
The response reflected a challenge that many organizations are experiencing as AI adoption accelerates. There can be pressure to experiment with new technology simply because others are doing so, even when the business problem has not been clearly defined.
Lucas argued for a more deliberate approach. One practical consideration is whether organizations should build AI capabilities themselves, buy them, or work with a partner. Lucas noted that creating a proof of concept can be relatively easy, but sustaining a homegrown application is a different proposition. A successful prototype still requires ongoing maintenance, security updates, resources, and ownership. The initial technical accomplishment therefore needs to be evaluated alongside the long-term cost and organizational commitment required to operate it.
He also emphasized that AI implementations in regulated environments require appropriate validation, governance, data quality, and user involvement. The people affected by an AI-enabled process need to be part of its development and adoption rather than being brought in only after the technology has been selected. For EAM organizations, that creates a practical framework: identify the problem first, understand the data required to address it, establish the appropriate controls, and then determine whether AI is the right tool.
Axendia’s 2026 market research, AI in Life Sciences: What the Industry Is Really Saying, provides further context for this approach. 60% of respondents reported using Generative AI, while more advanced AI architectures remain largely experimental. When asked where AI could create the most value in asset management, respondents identified predictive equipment maintenance and calibration (44%) as the leading opportunity. Axendia’s findings also reinforce the importance of getting the foundations right. Data readiness and interoperability were identified as the top barrier to AI adoption in asset and facilities management, cited by 56% of respondents. Data quality and integrity, along with integration with existing CMMS and EAM systems, followed at 44%.
For EAM leaders, the takeaway is clear: AI should be evaluated against a defined operational problem, supported by reliable data, and implemented with the governance and human involvement needed to sustain it. The opportunity is to move beyond the proof of concept and demonstrate how AI can improve equipment reliability, maintenance execution, and the performance of the facilities that support life sciences operations.
A Connected Operating Model
The product announcements and discussions throughout the Summit reinforced a larger narrative. Blue Mountain highlighted developments spanning resource planning and scheduling, smart scheduling, asset discovery, RAM Connect, condition-based maintenance, and RAM Insights. Rather than presenting these capabilities as isolated features, the Blue Mountain team framed them as components of a broader connected operational model.
That model can be summarized through four questions:
The progression from connection to understanding, action, and assurance, captures the underlying message of this years’ Summit.
In Brief
The biggest takeaway from Blue Mountain Summit 2026 was the emphasis on decision quality rather than technology adoption for its own sake. The event demonstrated that AI, connected assets, condition-based maintenance, predictive analytics, and intelligent scheduling are becoming increasingly practical components of the life sciences digital landscape. But the presentations also made clear that technology alone does not resolve the underlying challenges.
Three elements need to develop together:
- Data. Organizations need reliable, contextualized information that can move across operational silos.
- People. Operators, technicians, maintenance professionals, quality teams, IT, and executives need a shared understanding of what the information means and how decisions should be made.
- Process. AI and analytics need to be embedded in controlled workflows that support (not bypass) existing quality and compliance requirements.
Together, these perspectives suggest that the next phase of digital transformation in life sciences will depend less on simply acquiring new capabilities and more on connecting data, people, processes, and technology into a trusted operational model. For regulated manufacturers, that may ultimately be the most important role of AI and advanced analytics: not replacing human decision-making, but giving the people responsible for assets, operations, quality, and compliance better information with which to make those decisions.
Until next year! Thank you for the hospitality!
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The opinions and analysis expressed in this post reflect the judgment of Axendia at the time of publication and are subject to change without notice. Information contained in this post is current as of publication date. Information cited is not warranted by Axendia but has been obtained through a valid research methodology. This post is not intended to endorse any company or product and should not be attributed as such.