HUG 2026 Event Brief
Honeywell hosted its 50th Annual User Group at the JW Marriott Desert Ridge in Phoenix, AZ. More than 600 customers, partners and industry leaders attended the event from 8-11 June. Company executives outlined a clear vision for the company’s future following the separation of its Specialty Chemicals business in 2025 and the planned separation of its Aerospace business. The result will be Honeywell Technologies, a company fully focused on automation across process industries, buildings, and industrial sectors.
In his opening presentation, Chairman and CEO Vimal Kapur described Honeywell’s transformation as a strategic shift toward becoming one of the world’s largest automation focused companies. “We are in the process of building probably the world’s largest pure play automation company.” He explained that Honeywell’s focus spans process industries, buildings, and industrial automation, with an emphasis on mission critical operations where reliability, safety, and uptime are essential. “What we have learned over a period of time, is that automation is a very large industry and we have chosen to play in the space which is mission critical.”
A significant portion of Kapur’s presentation focused on Honeywell Forge, the company’s cloud-based industrial platform. Built with nearly $1 billion in investment, Forge connects industrial assets, enables real time performance monitoring, and supports the shift from reactive to predictive operations.
It also serves as the foundation for AI driven applications, cybersecurity, and operational intelligence. Kapur described autonomy as a long-term evolution where AI augments human decision making by preserving and scaling operational knowledge that would otherwise be lost through workforce turnover.
For life sciences professionals, the takeaway is direct: AI is not replacing validated human oversight, but extending institutional knowledge in environments where compliance, consistency, and traceability are critical. From a practical standpoint, AI value is already here, but only for organizations that can operationalize data and governance. The winners are not those with the most advanced AI models, but those who can connect fragmented systems, trust their data, and embed AI into validated workflows.
Lastly, in life sciences, AI is not a “technology project” anymore. It is becoming a quality, compliance, and operational transformation effort. This means success depends less on experimentation alone and more on disciplined use case selection, strong governance, and human in the loop design.
Integrated, Autonomous Operations are the Future
James Masso, President of Honeywell Process Automation, emphasized the scale of Honeywell’s installed base, noting “over 24,000 systems globally,” including widespread adoption across energy, manufacturing, and life sciences industries.
He highlighted the integration of AI directly into control systems, enabling “unparalleled gains in performance” across regulated and complex environments. Masso described a future of integrated, autonomous operations spanning the enterprise, supported by acquisitions and platform integration that have driven significant growth.
For life sciences organizations, this signals increasing convergence between operational technology, quality systems, and AI driven decision support across manufacturing and regulated production environments.
AI Delivers Immediate Value in Life Sciences
Martin Dowdall, Chief Product Officer for Honeywell Life Sciences, shifted the focus from future vision to current impact. “We’re not going to talk about futuristic roadmaps… we’re going to talk about things we’ve done in the real world.” He highlighted a key operational reality in life sciences: rising complaint volumes and constrained quality resources. In response, Honeywell developed a complaint processing agent inside TrackWise Digital that automates triage, classification, coding, and reportability assessments with human oversight.
Processing time that previously took up to an hour can now be reduced to seconds, allowing quality teams to focus on higher value work. The most important takeaway for life sciences leaders is not just efficiency, but scalability. AI is already reducing operational burden in regulated workflows without removing compliance controls.
On validation concerns, Dowdall noted: “The single biggest barrier I see to getting started – is the decision to get started.” He encouraged a controlled, evidence driven approach where early experiments inform formal validation efforts.
Demonstrating AI in Action
Sujatha Sharma, Solution Engineering Manager for Honeywell Life Sciences, demonstrated AI-powered complaint processing within TrackWise Digital.
She described the system as “semi-autonomous because we are augmenting the users to make a decision.” The AI identifies duplicates, supports classification, assists with regulatory coding, summarizes investigations, and translates submissions across languages. A key differentiator is transparency. “It doesn’t just stop from the decision making. It actually provides you rationale.”
For life sciences professionals, this is critical: explainability and auditability are becoming as important as automation itself in regulated decision making.
The Need for OT Cybersecurity
Paul Smith, Director of Cybersecurity Offering Management at Honeywell discussed OT cybersecurity in the context of increasing convergence between industrial operations and security monitoring. The focus was on improving visibility across distributed industrial environments by bringing security data, operational systems, and remote access activity into more unified platforms. This reflects a broader shift in OT environments toward earlier detection of suspicious activity and better coordination between operational teams and security functions across large-scale industrial footprints.
A key theme from the discussion was the scale and complexity of building security baselines in real industrial environments. As Smith put it, “we have such an interesting data set to build models and baseline data around.” He also emphasized how that visibility supports practical security operations: “we can pull that telemetry and take a look at what normal operation looks like.” Together, these points highlight the core OT cybersecurity challenge of distinguishing normal industrial behavior from abnormal or potentially malicious activity in complex, always-on environments.
For life sciences professionals, Honeywell’s OT cybersecurity approach is relevant because it helps protect GMP manufacturing and lab environments by improving visibility and response across connected production systems, reducing cyber risk without disrupting critical operations or product quality.
Axendia’s Perspective on AI Adoption
I was thrilled to present in the Life Sciences track and lead an executive roundtable at the invitation-only HLS Customer Advisor Board meeting. On the third day of the event, we explored how ongoing change is shaping the industry from business, regulatory, and technology perspectives.
Image Source: Axendia, Inc.
Axendia’s new market research, “AI in Life Sciences: What the Industry Is Really Saying – The Pulse on Adoption, Opportunities and Impact,” highlights a 20–60–20 split in AI readiness. In simple terms, 20% of organizations are leading the way, 60% are following closely behind, and 20% are at risk of being left behind.
Leaders are moving beyond pilots to scalable use cases and proven business value, followers are still learning and experimenting, and laggards face increasing pressure to catch up.
During the presentation, I mentioned the Dunning–Kruger effect which suggests that people with limited knowledge often overestimate their understanding, while those with deeper expertise become more aware of complexity, limitations, and uncertainty.
The adoption of AI often follows a similar progression:
Stage 1: “AI Will Solve Everything” (High Confidence, Low Experience)
At the beginning, enthusiasm is high and expectations are often unrealistic. Influenced by impressive demonstrations and tools such as ChatGPT, organizations may believe AI can quickly automate major activities such as validation, document review, or SOP development with minimal effort.
Stage 2: Reality Sets In (Confidence Drops, Understanding Increases)
As pilot projects begin, organizations encounter the practical challenges of implementation. Data quality issues, system integration, validation requirements, regulatory expectations, governance needs, and resistance to change quickly become apparent. Teams realize that AI itself is often not the biggest challenge—data, processes, and trust are.
Stage 3: Strategic Adoption (Growing Expertise, Better Judgment)
With experience, organizations become more selective and strategic. They gain a clearer understanding of which use cases create meaningful value, where human oversight remains essential, and how governance frameworks should be applied. The focus shifts from deploying AI everywhere to solving the right business problems.
Stage 4: Mature AI Integration (High Competence, Justified Confidence)
At the most mature stage, organizations have a realistic understanding of both AI’s capabilities and its limitations. AI initiatives are aligned with business objectives, supported by appropriate governance, and implemented intentionally within regulated processes. Confidence returns, but it is now based on experience and evidence rather than assumptions.
Cythia Babb-Honeywell, Shawn Opatka-Honeywell, Sandra K. Rodriguez- Axendia, and Martin Dowdall-Honeywell.
Image Source: Axendia, Inc.
The Honeywell Life Sciences Customer Advisory Board (CAB) brought together customers and Honeywell leaders to discuss the technologies, regulatory trends, and operational challenges shaping the future of the industry. The agenda focused on AI strategy, agentic frameworks, cybersecurity, quality management, supply chain optimization, and regulatory compliance. Through executive discussions, product roadmap reviews and technology demonstrations, participants explored practical pathways for adopting AI while maintaining validation, inspection readiness, security, and operational excellence across life sciences organizations.
During the CAB meeting it became clear to me companies are moving beyond the “AI will solve everything” mindset and toward a more pragmatic understanding of where AI can deliver meaningful value, where appropriate guardrails are required, and where human expertise remains essential. I joined Sriram Hemmige, Director of Product Management at Honeywell, to facilitate a discussion on validation requirements for AI enabled technologies supporting quality systems. The conversation reinforced that customers and regulators alike expect AI driven solutions to meet the same rigorous validation standards applied to any other regulated technology. Organizations are focused on establishing trust through documented evidence, transparency, and a clear demonstration that these solutions perform as intended within their intended use cases.
Several practical lessons emerged from HUG and CAB:
- Start before data is perfect, using focused use cases to build learning loops and improve data over time.
- Focus on measurable business and quality outcomes rather than technology deployment.
- Connect siloed data across enterprise and operational systems.
- Maintain human oversight in regulated and mission critical workflows.
- Build trust through governance, transparency, auditability, and compliance.
In Brief
The event reinforced that AI is no longer a future concept in life sciences. It is already delivering measurable value in regulated workflows, particularly in quality and complaint management.
As Honeywell continues its transformation into a focused automation company, the consistent theme was partnership and co-creation with customers.
For life sciences organizations, the implication is straightforward: the next phase of competitiveness will depend on how effectively AI is embedded into validated processes, trusted data environments, and human-centered decision systems.
To discuss how these initiatives impacts your organization or to obtain analyst coverage for your next event, click on this link to schedule a call with a member of our analyst team.
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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.