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Redefining Industrial Decision-Making
In an industry where reliability, safety, and operational efficiency define performance, the ability to transform data into actionable insight is becoming a decisive advantage. AsInt has positioned itself at the forefront of this transformation, moving beyond traditional asset integrity frameworks toward a more dynamic and intelligent approach to asset management.
In this interview, Rohan Patel, CEO of AsInt, shares his perspective on how digital platforms, artificial intelligence, and integrated data environments are reshaping the way industries manage critical assets. His vision highlights a shift that is not only technological, but strategic — where information becomes the foundation for smarter, faster, and more resilient decision-making across asset-intensive sectors.
- AsInt positions itself as a company redefining asset integrity through a digital lens. How would you describe the evolution from traditional “asset integrity” to what you now define as “asset intelligence”?
Asset integrity, as the industry has practiced it for thirty years, is fundamentally about answering one question: is this asset safe to operate today? It is calculation-driven, standards-bound, and engineer-owned; and AsInt has spent three decades helping operators answer it well, primarily on SAP and alongside SAP APM.
Asset intelligence raises the question. It is not just “is it safe today?”, but what should we do about it tomorrow, next quarter, and across the fleet. That requires the same engineering rigor — the calculations, the codes like API 581 and API 579 do not go away — but it also requires those calculations to live somewhere connected, governed, and accessible to more than one specialist at one desk.
The evolution is not replacement. It is elevation. The integrity engineer’s judgment becomes more valuable, not less, because it now scales.
- In sectors such as Oil & Gas, petrochemicals, and energy — where mechanical integrity is critical — what role are digital platforms playing today in operational and strategic decision-making?
In sectors like oil and gas and petrochemicals, the daily decisions — inspection scope, turnaround timing, run-or-repair — are still made by engineers using methods rooted in API, ASME, and proprietary corporate standards. What has changed is the speed at which those decisions need to be made and defended.
Digital platforms do two things well: they remove the friction between data and decision, and they make every decision traceable. An integrity or a reliability plan built on a platform is not just faster to produce — it is auditable, repeatable, and portable from one site to the next.
Strategically, the platform is what lets a single corporate engineering standard reach every plant in a global operation without being reimplemented locally and quietly drifting. That is not a technology benefit. That is a governance benefit.
- AsInt has developed solutions integrated with enterprise environments such as SAP and data-driven platforms. What impact does this integration have on efficiency, traceability, and reliability in industrial asset management?
This is where AsInt’s heritage is most relevant. We have built and delivered engineering applications on SAP BTP and integrated deeply with SAP APM since both platforms existed. That experience shapes how we think about integration today.
The honest reality is that integration with the enterprise system of record — whether that is SAP, IBM Maximo, or another EAM — is non-negotiable for industrial customers. Work orders, notifications, master data, and cost roll-ups have to flow. What we have learned, though, is that integration alone does not make decisions better. It moves data; it does not elevate engineering judgment.
The shift we are making with AsInt Edge is to treat the enterprise platform as the system of record and the engineering platform as the system of decision. The integrity engineer works in an environment built for engineering — calculations, standards, governed libraries, defensible audit trails — and the results land cleanly in the EAM as notifications, work orders, and inspection plans.
The efficiency gain is real, but the more important gain is traceability: every risk number, every remaining-life calculation, every inspection interval can be traced back to the standard, the inputs, and the engineer who approved it. That is what reliability programs have always wanted and what enterprise systems alone have never quite delivered.
- One of the industry’s greatest challenges is turning large volumes of data into actionable insights. How does AsInt address this challenge within inspection, maintenance, and reliability programs?
I would push back gently on the framing — the industry’s problem is not really a data volume problem. It is a decision-quality-at-scale problem. Most asset-intensive operators have more inspection data than they can use, and most of it is correctly captured. The harder question is: what does this data mean for the next decision, and who is allowed to make that decision?
Our approach in inspection, maintenance, and reliability is to start with the calculation, not the data lake. A risk-based inspection program is, at its core, a calculation defined by API 581. A fitness-for-service assessment is API 579. The data feeds those calculations; the calculations drive the decisions.
When the calculation is encoded properly — versioned, governed, with the engineer’s assumptions captured — the path from data to decision is short and defensible. When it is not, you can have all the dashboards in the world and still end up with inconsistent calls between two plants.
So we focus on the engineering layer first: get the calculations and standards right, governed in a central library, then let data flow through them. AI sits across this layer — assistive, not autonomous — to flag anomalies, surface precedent, and speed the engineer through the work.
- AsInt has incorporated advanced capabilities such as predictive, generative, and agent-based artificial intelligence. How do you envision these technologies shaping the future of reliability engineering and industrial inspection?
We have been deliberate about this. AI in engineering is not the same problem as AI in customer service or marketing, and the failure modes matter much more.
Predictive AI has the longest history in our space — degradation modeling, anomaly detection on sensor data, failure prediction. It works best where the physics is well understood and the data is dense. We use it for early warning and for prioritizing inspection scope, but the engineer always owns the decision.
Generative AI is more recent and, frankly, more often misused in industrial contexts. We do not use it to generate engineering conclusions — that would be irresponsible. We use it to compress the work around the engineering: drafting inspection narratives, summarizing decades of inspection history, accelerating standards lookups, producing the documentation that engineers traditionally spend forty percent of their time writing.
Agent-based AI is where we see the strongest near-term value: agents that orchestrate the engineering workflow — pulling the right data, running the calculation, routing to the right approver, syncing the result back to the EAM. The agent does not replace judgment; it removes the procedural overhead between judgment calls.
The unifying principle for us is that AI must be trained on, and bounded by, engineering standards. A generic model that has not been taught what API 581 means is not safe to deploy in this domain. That is why we believe the future of reliability AI belongs to the companies who have actually written and implemented these standards for decades — not the ones who treat them as training data scraped from the internet.
- From the perspective of industrial operators, how does the adoption of solutions like those offered by AsInt translate into OPEX reduction, risk mitigation, and asset life extension?
Operators evaluate us on three numbers, and they are right to.
OPEX reduction comes primarily from the inspection program itself. A defensible risk-based program typically reduces inspection volume by twenty to thirty percent while improving coverage of high-consequence assets. Fewer hours in the field, fewer scaffolds erected, less production interrupted.
Risk mitigation comes from consistency. The single biggest source of risk in mature integrity programs is inconsistent calls across plants and across decades — different engineers, different assumptions, different vintages of the standard. A governed engineering library compresses that variance. When the corporate standard updates, every plant pulls the change rather than discovers it during the next audit.
Asset life extension is the longest-tail benefit. Fitness-for-service assessments done with proper API 579 rigor routinely extend asset life by years beyond conservative blanket retirement dates — but only if the calculation infrastructure is there to do them defensibly and repeatedly.
That is the difference between knowing your assets can run longer and being able to prove it to a regulator.
These are not AI benefits, incidentally. They are engineering benefits, accelerated by AI.
- How is asset intelligence becoming a competitive advantage for asset-intensive industries, beyond just operational efficiency?
Operational efficiency is the easy part of the story. The harder, more lasting competitive advantage is decision velocity.
In a downturn, the operators who survive are the ones who can defer the right capital and accelerate the right inspections — confidently, defensibly, at speed. In an upturn, it is the ones who can bring shut-in assets back online without re-litigating every integrity assumption.
Asset intelligence — properly built — gives an organization institutional memory that does not walk out the door when a senior reliability engineer retires. Every assumption, every override, every calculation revision is captured and learnable. That is a structural advantage, not a project ROI.
The companies that treat this as an IT initiative will get the efficiency. The companies that treat it as a strategic capability — owned at the executive level, governed across the enterprise — will get the durable advantage.
We see this most clearly in clients who have gone from running integrity as a site-by-site discipline to running it as a corporate function. Different operating posture entirely.
- What message would you like to share with industry leaders who are still transitioning toward digital models of integrity and reliability?
Three things.
First, do not outsource engineering. The standards, the calculations, the corporate practice — that is your institutional capital. Digitize it, but own it. Platforms come and go; your engineering judgment is what compounds.
Second, the goal is not to replace your enterprise system of record. SAP and the major EAMs do what they do well. The opportunity is to build an engineering layer that works with those systems and lifts the quality of every decision that flows through them.
Third, this transition is one or two budget cycles long, not five. The technology is ready. The standards are stable. The AI is mature enough where it matters and honest about where it does not. The companies that move now will have institutionalized this advantage before the rest of the industry finishes its proof-of-concept phase.
The shift from asset integrity to asset intelligence is not optional anymore. The question is who leads it inside your organization.
This article was written by Rohan Patel of AsInt and published as part of the eighth issue of Inspenet Brief magazine (July 2026), dedicated to technical content in the energy and industrial sectors.
