By Tim Finerty
Across industrial automation environments, a quiet transformation is under way.
It’s not happening in the PLC cabinet or on the plant floor. It’s happening in shared drives, desktop scripts, low-code tools and AI applications that never passed through IT governance. Over time, well-intentioned engineers, analysts and operators build small automations to solve immediate problems — a data pull here, a reconciliation macro there, a workflow trigger built in a browser-based tool.
Individually, these tools may seem harmless. Collectively, they create what many integrators are now seeing firsthand inside client environments: undocumented automation ecosystems that operate outside formal control structures.
The result is operational risk hiding in plain sight.
When “Helpful” Automation Becomes Hidden Infrastructure
Shadow automation doesn’t usually begin as misconduct. It begins as initiative.
An engineer needs faster production reporting.
A project manager builds a macro to reconcile job costs.
A technician uses an AI assistant to clean up maintenance logs.
These solutions solve real problems. But because they sit outside approved systems — and are rarely documented — they introduce new layers of complexity:
- No version control
- No change management
- No access oversight
- No backup or recovery plan
- No validation of outputs
Over time, these informal tools become embedded in daily operations. They start feeding data into dashboards, influencing production decisions and even shaping financial reporting.
And yet, no one officially “owns” them.
Why Control System Integrators Are Seeing This More Often
Integrators working inside manufacturing and industrial environments are often the first to notice inconsistencies:
- Data discrepancies between systems
- Manual overrides that no one can fully explain
- AI-generated outputs embedded in reports without attribution
- Critical workflows dependent on a single individual’s desktop
As automation expands beyond the plant floor into analytics, forecasting and operational optimization, the boundaries between IT, OT and business systems blur. That convergence increases the likelihood that undocumented automations will interact with core systems in unpredictable ways.
The risk is not that automation exists. The risk is that no one knows where it exists — or how it works.
The AI Acceleration Effect
The rise of generative AI and low-code tools has amplified the issue.
Today, creating a workflow automation or data transformation script requires minimal technical expertise. An engineer can generate code with a prompt. A supervisor can build a data model without formal training. A finance analyst can deploy a bot in hours.
This accessibility is powerful — and potentially destabilizing.
AI-assisted automation often lacks:
- Testing against edge cases
- Bias review
- Data validation standards
- Documentation trails
- Regulatory or compliance consideration
In regulated industrial sectors, that gap can become more than a nuisance. It can become a liability.
The Operational Consequences
When shadow automation remains undiscovered, organizations may face:
1. Data Integrity Issues: Unverified scripts and macros can alter or transform data in ways that are not transparent, creating reporting inconsistencies.
2. Business Continuity Risk: If a critical process lives in one employee’s personal file structure, what happens when that employee leaves?
3. Cybersecurity Exposure: Unvetted tools and external AI platforms can introduce new entry points for sensitive operational data.
4. Compliance Gaps: In industries subject to regulatory oversight, undocumented processes undermine audit readiness and internal controls.
For control system integrators, these risks often surface during system upgrades, ERP transitions or digital transformation initiatives — moments when organizations expect clarity, not hidden dependencies.
A Governance Problem, Not a Technology Problem
It’s important to note: This is not an argument against automation or AI. Both are essential to modern industrial competitiveness.
The issue is governance.
Organizations that successfully manage automation growth tend to:
- Inventory automation assets (formal and informal)
- Establish clear ownership for workflows and scripts
- Implement change management standards
- Define acceptable AI use policies
- Create cross-functional visibility between IT, OT and finance
When automation is treated as infrastructure — rather than individual productivity enhancement — it can be governed appropriately.
What Integrators Can Do
Control system integrators are uniquely positioned to help clients address this challenge because they operate at the intersection of operations, technology and business process.
Practical steps may include:
- Asking targeted discovery questions about informal workflows
- Reviewing data handoffs between systems
- Evaluating where AI tools are influencing operational decisions
- Encouraging documentation standards during implementation projects
- Helping clients build automation inventories during modernization efforts
In many cases, simply surfacing the conversation is the most valuable first step.
Bringing Shadow Automation into the Light
Automation inside industrial environments is no longer limited to control systems and robotics. It now extends into analytics, forecasting, reporting and AI-assisted decision-making.
The organizations that thrive in this new environment will not be those that suppress automation. They will be those who understand it — fully, transparently and strategically.
Undocumented automation is not a sign of failure. It is a signal that innovation is happening faster than governance.
The challenge — and opportunity — is bringing that innovation into the light before it becomes an operational blind spot.
Tim Finerty, CPA, is a partner with Wipfli Advisory LLC. WIPFLI partners with CSIA to provide members with business and tech advisory services from a firm that understands your unique business needs.
Photo by Guillaume Issaly on Unsplash