Hugo Dozois-Caouette, CTO and Co-founder at MaintainX chats about some of the common hurdles faced by tech teams and modern CIOs when adopting AI powered processes across business units in this CIO Influence Interview:
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Tell us about your role at MaintainX?
As co-founder and CTO of MaintainX, I’ve been driven by a vision that has taken us from an early-stage app to a $2.5B platform redefining frontline operations. From the start, my founding insight was clear: the 80% of the global workforce without a desk deserved consumer-grade software experiences, and that belief has shaped MaintainX’s mobile-first architecture from day one. I’ve led the technical evolution of a platform that now unifies maintenance workflows, asset management, parts inventory, purchase orders, and safety compliance in a single, intuitive system. My engineering philosophy has always centered on building software that frontline professionals actually want to use, driving the organic adoption that has been central to MaintainX’s growth.
What are the top highlights of MaintainX’s latest AI-powered reporting capabilities?
Manufacturing teams don’t operate like commercial enterprise IT. Data can more often be incomplete, decisions are more time‑sensitive, and reports need to translate directly into action on the floor. Report Builder AI is built for that reality. It lets teams ask questions in plain language and instantly surface reliability, downtime, and asset insights without manual reporting or BI bottlenecks. For example, a plant supervisor can ask why a production line slowed during a shift and immediately see downtime events, asset issues tied to that period and missed preventative maintenance tasks, enabling them to act in real time instead of waiting on a report. Unlike traditional dashboard tools, it’s designed to work with messy, real-world maintenance data and connect frontline activity to executive-level visibility so leaders can see what’s happening and act before issues turn into downtime.
What prevents modern manufacturing organizations from achieving complete digital transformation?
The biggest barrier isn’t technology. It’s the gap between the systems that companies buy and the way frontline teams actually work. Most digital transformation efforts stall because they’re designed top-down with clean data models, rigid workflows, and reporting tools that assume perfect inputs. Manufacturing doesn’t work that way. Data is incomplete, environments are messy, and the people closest to the work are often the last ones considered in the rollout.
Three things consistently get in the way:
Data fragmentation:
Critical maintenance and asset data lives across disconnected systems, paper logs, tribal knowledge, and spreadsheets. Without a single source of truth that frontline teams actually use, digital initiatives produce dashboards nobody trusts.
Workflow mismatch:
Tools built for office environments get forced onto shop floors. If the software adds friction to a technician’s day instead of removing it, adoption dies. Transformation only sticks when digital tools are embedded into existing work patterns, not layered on top of them.
Pilot purgatory:
Organizations run proof-of-concept after proof-of-concept without committing to operational change. Real transformation requires connecting frontline execution data to decision-making at scale, not just demonstrating what’s possible in a controlled environment.
The organizations that break through treat digitization as an operational discipline, not an IT project. They start with the work itself by capturing what’s happening on the floor, making that data useful in real time, and building from there.
How can modern CIOs, CISOs, and IT teams become more proficient in adopting and deploying the right AI-powered processes and workflows in ways that actually support operations, reliability, and frontline execution?
In industrial environments, AI has to support execution, not experimentation. Unlike commercial enterprise IT, where delays can be simply inconvenient at best, manufacturing teams are managing physical assets, safety risk, and downtime where failures almost always have immediate operational and financial impact.
The most effective CIOs and CISOs start by embedding AI directly into maintenance and reliability workflows, focusing on removing friction that slows frontline teams down. That means prioritizing AI that reduces manual reporting as a daily burden, surfaces insights on demand and improves data quality over time, rather than relying on standalone analytics tools that assume clean data or require heavy interpretation.
Just as important, industrial AI must be trustworthy and resilient. Security, access controls, and auditability matter, but so does reliability in messy, real-world environments. When AI reinforces disciplined execution, that is, helping teams spot risk early, act faster, and keep work moving, it becomes a foundation for operations, not another stalled initiative.
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How is AI currently influencing the day-to-day work standards of IT and networking teams responsible for keeping systems, equipment, and networks running?
The most immediate impact is on how teams find and act on information. In traditional environments, diagnosing an equipment issue or understanding failure history means digging through work order logs, maintenance records, and asset documentation manually. AI is compressing that cycle from hours to seconds by surfacing relevant context at the point of work.
For IT and networking teams supporting industrial operations, this shows up in a few ways:
Faster triage:
AI can correlate asset history, recent work orders, and failure patterns to help teams prioritize what to fix first, rather than treating every alert with equal urgency.
Reduced documentation burden:
Frontline teams spend a significant portion of their day on manual data entry and reporting. AI that auto-generates summaries, flags anomalies, and pre-fills records lets teams spend more time on the work and less time documenting it.
Proactive maintenance signals:
Instead of waiting for failures, AI identifies patterns across equipment performance and maintenance history that point to emerging risk. Teams can intervene before unplanned downtime hits.
Knowledge continuity:
When experienced technicians leave, institutional knowledge walks out the door. AI trained on historical work data helps preserve and surface that knowledge for newer team members.
The shift isn’t dramatic on any single day. It’s cumulative. AI is raising the baseline for how fast teams can respond, how much context they have when they do, and how consistently work gets executed across shifts and sites.
Five thoughts on the future of IT system maintenance and security in an AI-powered future for always‑on manufacturing environments
1. Maintenance becomes predictive by default, not by project.
Today, predictive maintenance is a special initiative with dedicated budgets and long implementation timelines. As AI becomes embedded in core maintenance platforms, pattern detection and failure prediction will be standard operating capabilities, not a separate workstream. Teams won’t “do predictive maintenance.” They’ll just maintain equipment with better information.
2. Security and operational technology converge.
As maintenance systems become more connected and AI-driven, the attack surface expands. IT security teams will need to treat CMMS and asset management platforms with the same rigor as production infrastructure. Access controls, audit trails, and data governance aren’t nice-to-haves. They’re operational requirements when AI is making recommendations that affect physical safety.
3. The data quality problem solves itself through use.
The traditional approach is to clean your data before you can use AI. That’s backwards for manufacturing. The future is AI that works with imperfect data from day one and improves data quality as a by-product of adoption. Every work order completed, every asset interaction logged, and every insight acted on to feed the system and make the next recommendation better.
4. AI agents handle routine coordination; humans handle judgment.
Scheduling preventive maintenance, dispatching work orders, routing approvals, and generating compliance reports: these are coordination tasks that consume enormous amounts of human time today. AI agents will handle the routine orchestration so that skilled technicians and reliability engineers can focus on the decisions that actually require expertise.
5. Always-on operations demand always-on intelligence.
Manufacturing doesn’t stop at 5 PM. AI that supports 24/7 operations needs to be resilient, auditable, and capable of operating with minimal human oversight during off-shifts. That means investing in AI systems that explain their reasoning, escalate appropriately, and fail gracefully, not just ones that produce impressive demos during business hours.
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Hugo Dozois-Caouette, is CTO and Co-founder at MaintainX
MaintainX is an AI-powered computerized maintenance management solution (CMMS) and enterprise asset management (EAM) platform that helps frontline teams reduce unplanned equipment downtime and increase operational efficiency. MaintainX turns real-time asset and work data into proactive insights that drive operational excellence for organizations in manufacturing, energy, facilities management, and other physical-asset-driven industries.
