How AI Is Reshaping Industrial System Integrators

• AI is accelerating repetitive SI work but not replacing engineering expertise.
• Industrial System Integrators are expanding from implementation-focused services toward advisory and operational optimization roles.
• Governance, validation, and data contextualization are becoming major differentiators.
• AI may expand recurring revenue opportunities for SIs through monitoring, optimization, and AI-enabled services.

I started my career as a software developer, spending the first 15 years writing code. At the time, innovations like Microsoft Foundation Classes (MFC) felt transformative - structured ways to improve productivity, enforce consistency, and accelerate delivery.

But what we’re experiencing with AI is fundamentally different.

Across industrial automation, MES, SCADA, and operational technology (OT) environments, AI is beginning to reshape not only software delivery, but the role of the System Integrator itself. As industrial organizations look to accelerate digital transformation initiatives, System Integrators are increasingly evaluating how AI can improve engineering efficiency, operational insight, and customer value.

This isn’t just another step-change in tooling. It’s a shift in how software - and increasingly, solutions themselves - are conceived, created, and evolved. AI is compressing timelines, lowering barriers to entry, and fundamentally redefining where value is generated.

Tasks that once required deep specialization and significant time investment - from writing code to configuring systems - can now be completed in minutes. In many cases, AI is not just assisting developers - it’s operating alongside them, and at times, ahead of them.

Naturally, that raises an important question:

What happens to the role of the System Integrator?

To move beyond theory, I spoke directly with several leading System Integrators in our program to understand how AI is impacting their businesses - not in the future, but today, inside active projects and customer environments.

What I heard was not fear - but pragmatism, urgency, and a clear recognition that the rules of value creation are changing.

SIs aren’t debating if AI matters. They’re actively figuring out how to use it to win. We’re still in the early innings but the potential is huge.

That pragmatism came through clearly in my conversation with Brian Beitler of Kline Process Systems, who grounded the discussion in real-world application:

“I view AI as a tool to address specific issues or goals, not a replacement for an SI. It still needs someone to understand what you are trying to achieve and give it the necessary input parameters and ask specific output questions. I’m not too worried about AI replacing SIs until it can ingest a P&ID and create the PLC code and HMI screens on its own, and someone still must generate the design.”

This perspective reinforces a key theme emerging across the SI community: AI is powerful - but it is not autonomous value.

Across every conversation, one theme was consistent: AI is accelerating delivery - but not replacing expertise.

As Don Rahrig, President & CEO at Rain Engineering, put it:

“At Rain Engineering, we see AI as a set of very practical tools that help manufacturers get more value from the MES systems they already bought. We’re not trying to replace engineers or operators; we’re using AI to make their jobs easier and their decisions faster.”

This distinction is critical. The most successful SIs are not approaching AI as disruption to be resisted - but as leverage to be applied.

Bill Kapusta of Automatech reinforced that view:

“AI is very much an enabler for the SI community… helping our teams move faster through routine or repetitive tasks.”

And as Bob Clark from INS noted:

“Our engineers can be tremendously more efficient.”

AI is increasingly absorbing the repeatable layers of delivery - code scaffolding, documentation, testing support, even elements of configuration. But the moment complexity enters the equation - legacy environments, operational constraints, cross-system dependencies - that’s where AI alone falls short.

That’s where expertise, judgment, and real-world experience still matter.

AI doesn’t eliminate the need for System Integrators. It amplifies the importance of the best ones.

If AI were only about productivity, this would be a simple story. But it’s not. AI is changing how System Integrators are valued. For decades, the SI model was fundamentally tied to execution:

• Hours billed
• Code delivered
• Systems deployed

AI is putting pressure on all three. Value is now expanding toward:

• Insight over effort
• Outcomes over outputs
• Advisory over implementation

As Chris Monchinski, CTO at InflexionPoint, described:

“The role shifts from manually generating everything to supervising, governing, validating, and orchestrating increasingly autonomous systems.”

This is a profound shift. The SI of the future is not just building systems - they are:

• Designing solution architectures that integrate AI into operations
• Governing how AI is used, ensuring safety, compliance, and reliability
• Translating business problems into AI-enabled outcomes

System Integrators are evolving from builders… to orchestrators of intelligence. AI is giving System Integrators the opportunity to add significantly more value to their customers.

And importantly, customers are starting to expect this shift - whether SIs are ready or not.

This transformation isn’t theoretical. It’s already showing up inside SI organizations. Don Rahrig highlighted practical, near-term impact:

“Cleaning up and organizing requirements and documentation so projects move faster and with less rework.”

This may sound incremental - but at scale, these gains are meaningful. Requirements clarity, documentation quality, and knowledge reuse have historically been major sources of inefficiency. As a former GE employee this looks a lot like Lean - reducing the amount of non-value work that the customer isn’t willing to pay for.

Bill Kapusta built on this:

“AI helps us accelerate development and improve consistency… allowing engineers to spend more time focused on architecture and delivering better overall solutions.”

Industry research supports these observations. According to McKinsey, leading AI-driven software organizations are reporting productivity gains of 16–30%, while some studies have shown AI-assisted development can significantly reduce time spent on coding and other knowledge-work activities.

• Up to 56% reduction in programming time (MIT/Microsoft research)
• 16–30% faster time-to-market (McKinsey)
• 16–30% productivity gains (McKinsey)
• 31–45% quality improvements (McKinsey)

One of our system integrator (SI) partners recently shared an example of how a team member used Claude to build a dashboard entirely through AI prompts. By leveraging this approach, they were able to create a fully functional dashboard in a fraction of the typical development time. This has the potential to significantly reduce both development and debugging efforts for SIs, while also enabling rapid prototyping.

The impact extends to end customers as well. Faster development cycles mean customers can realize value sooner, while SIs benefit from increased efficiency - allowing them to take on more projects and serve a broader client base.

This trend is not limited to dashboards. Across the broader software development landscape, I’ve seen demonstrations of AI-generated applications that reach near production-level quality much more quickly than traditional methods. As a result, development timelines are shrinking, and teams can deliver products at an accelerated pace. While organizational impacts will vary, I see AI primarily as a complement to existing talent rather than a replacement, enabling teams to focus more on higher-value work.

Ultimately, this represents a significant advantage for organizations: faster innovation, lower costs, and quicker realization of value. For those concerned about job displacement, history offers important perspective. While technological innovation has always reshaped the workforce, it has consistently created new opportunities for higher-value work. Progress is inevitable - after all, we didn’t stop the advancement of automobiles to preserve the buggy whip industry.

These examples illustrate why many SIs view AI as a force multiplier rather than a replacement for engineering expertise. By automating portions of development, documentation, and configuration work, AI allows engineers to focus more of their time on architecture, problem-solving, customer engagement, and other higher-value activities.

That reallocation of time - from execution to thinking - is where real leverage emerges. This will allow SIs to spend more of their time with value driving tasks - making their expertise even more valuable to end customers.

But this acceleration is not without risk - and here again, Brian Beitler offered a critical and often overlooked perspective:

“AI can be too agreeable, and it has a desire to please you, to keep you coming back for more. You must validate its responses and not blindly assume what it says is true.”

This highlights an emerging competency for SIs: not just using AI - but validating, governing, and challenging it. I am guilty of trusting AI more than I probably should at times. Evaluating and trusting results is clearly needed.

Chris Monchinski emphasized the same need for discipline:

“AI-generated output can be fast, but not necessarily correct, safe, or compliant… governance becomes a critical differentiator.”

And Bob Clark captured the competitive tension:

“We can be more efficient - but so can our customers.”

That dynamic matters. As customers gain access to the same tools, the baseline expectation shifts. Efficiency stops being a differentiator - and becomes table stakes.

While much of the conversation focuses on efficiency, the more important story is growth. AI is expanding what System Integrators can offer - not just how they deliver.

Don Rahrig described this evolution clearly:

“Design new ‘AI on top of MES’ services… and build more recurring services around monitoring, tuning, and continuous improvement.” This gives SIs the opportunity to offer additional value to their customers.

This is a fundamental expansion from project-based work to lifecycle-based value. Chris Monchinski added an important nuance:

“The highest-value use cases center around measurable operational outcomes… but AI is most valuable when embedded directly into operational workflows.”

That distinction matters.

Standalone AI dashboards don’t drive transformation. Operationalized AI - embedded into MES, SCADA, and decision workflows - does. We’re seeing this in Proficy products and Roadmaps today.

This creates entirely new categories of SI value:

• AI-enabled operational optimization
• Continuous performance improvement services
• Embedded decision support systems
• Managed AI/OT environments

And critically, it shifts revenue models toward:

• Recurring engagement
• Outcome-based relationships
• Long-term partnerships

So, AI is expanding the value equation that SIs are offering to their customers. As this continues to happen, they will need to evolve.

Despite all the momentum around AI, one constraint remains constant: Data readiness.

As Bob Clark put it:

“If you want to use AI, you not only need a ton of data - you need it organized and contextualized. That’s a big opportunity for SIs.”

Most industrial organizations still struggle with:

• Siloed systems
• Inconsistent data models
• Lack of operational context
• Poor data quality

AI doesn’t solve these problems - it exposes them. Before AI can generate value, someone must:

• Integrate systems
• Structure and contextualize data
• Align data with operational meaning

This is not a new problem - but it is now a more urgent one. And it plays directly into the core strengths of System Integrators. In many ways, AI is increasing demand for foundational SI capabilities - not reducing it.

Despite the excitement, none of the SI leaders I spoke with were naïve about the challenges. Chris Monchinski summarized the balance well:

“The firms that resist AI will struggle. The firms that embrace it without governance will create risk. The firms that succeed will blend AI acceleration with engineering discipline.”

Bob Clark offered a clear, time-phased perspective:

“Short term, AI is a competitive advantage… medium term, customers will expect faster delivery… long term, it’s still evolving.”

There are real questions still to be answered:

• How is ROI consistently measured?
• Where does AI introduce risk in regulated environments?
• Which parts of SI work become commoditized?

But none of these uncertainties change the core reality: AI is not optional. It is foundational to the next phase of the industry.

These changes will have a profound impact on end customers as well. Organizations will be able to realize value significantly faster, shortening the time between investment and measurable outcomes. This acceleration allows them not only to improve internal efficiency but also to deliver enhanced products and services to their own customers more quickly and consistently. In highly competitive markets, this speed becomes a critical differentiator.

The scale of investment in AI reflects the magnitude of the opportunity. Research from NVIDIA and other industry analysts shows organizations are increasingly using AI to improve productivity, accelerate innovation, reduce costs, and drive revenue growth.

From an executive standpoint, AI represents a dual lever for value creation. On one side, it drives revenue growth by enabling faster innovation cycles - more products, features, and enhancements can be brought to market in less time. On the other side, it reduces operating costs by streamlining development processes, minimizing manual effort, and improving productivity across teams.

This combination fundamentally reshapes the economics of software and product development. Organizations can operate with greater agility, deploy resources more effectively, and scale their capabilities without a proportional increase in headcount. The result is improved margins, stronger cash flow, and a more resilient business model that can adapt quickly to changing market demands.

Beyond financial impact, AI also transforms how organizations think about experimentation and risk. With dramatically lower costs and faster iteration cycles, companies can test ideas, prototype solutions, and refine offerings at a pace that was previously impractical. This fosters a culture of continuous innovation, where learning cycles are shorter and breakthroughs can be achieved more frequently.

In many ways, we are at the beginning of a structural shift in how value is created across industries. The impact of AI is not incremental - it is foundational. Much like the introduction of the internet redefined communication, commerce, and access to information, AI is redefining how work gets done and how value is generated. It is compressing timelines, lowering barriers to entry, and enabling a new class of capabilities that were previously unattainable.

Organizations that embrace this shift early will be positioned to lead, capturing disproportionate value through faster execution and smarter operations. Those that hesitate risk falling behind as the competitive baseline itself evolves.

Read about our take on Practical Generative AI Use Case for Industrial Operations here.

The System Integrator community is at an inflection point. AI is not eliminating the need for SIs. It is raising the bar - significantly.

As Bob Clark put it:

“The people that figure out how to use it to be more efficient are going to be better integrators.”

The next generation of SI leaders will not be defined by how much work they deliver - but by how effectively they translate technology into measurable outcomes.

The winners will:

• Embed AI into their own operations
• Build AI-native service offerings
• Lead with domain expertise and governance
• Focus relentlessly on outcomes, not outputs

This is not a gradual evolution. It is a structural shift in how value is created and delivered. The real question is no longer whether AI will reshape the SI industry.

It’s: Who will use it to lead - and who will be forced to catch up?

So to paraphrase Mark Twain: “The reports of [System Integrator] death [due to AI] are greatly exaggerated”.

Special thanks to the industrial automation and System Integration leaders who contributed insights and perspectives for this article:

• Don Rahrig, President and CEO Rain Engineering
• Bill Kapusta, Sr. Director, MES & Enterprise Solutions, Automatech
• Chris Monchinski, CTO, InflexionPoint
• Bob Clark, Director of Operations, INS3
• Brian Beitler, System Integration Director, Kline Process Systems

The research and statistics referenced in this article were informed by the following resources:

• How Is Your Team Spending the Time Saved by Gen AI? (Harvard Business Review 2025)
• Leading AI-driven software organizations show the way (McKinsey & Company 2025)
• How AI Is Driving Revenue, Cutting Costs and Boosting Productivity for Every Industry in 2026 (NVIDIA 2026)