by Ania Kaci, Senior Principal, Responsible AI Leader

AI governance is one of the most talked about topics in energy management and industrial automation right now, and yet, some persistent misconceptions are holding organizations back from deploying AI at scale.  

That’s why we want to break down the top seven myths we encounter in our day-to-day work, debunk them, and paint a picture of the realities that should replace them. 

Myth 1: AI governance is just about compliance

Reality: It’s easy to think of governance as a box-ticking exercise, but it shouldn’t be. Instead, when approached the right way, AI governance can actually drive innovation.  

At Schneider Electric, we view our AI governance framework as a strategic advantage, not a prohibitive rulebook. In practice, AI governance drives value by helping us prioritize which AI use cases to deploy.  

So, what does AI governance look like to us? We have a checklist of 6 principles we use to assess all AI deployments. Here are some of the questions that allow us to check if we remain compliant with them:  

This builds the transparency customers need to trust and adopt AI solutions.   

Myth 2: AI governance slows down innovation

Reality: This one frustrates us most—because the opposite is true. 

Teams can spend more time innovating and less time navigating different requirements, approval processes and review criteria when we standardize all processes. When teams are navigating different processes or unclear review criteria for every new project, that’s what slows innovation. Our answer: standardize everything that can be standardized.  

We recommend starting by creating consistent gate reviews with clear requirements for the project to be able to move onto the next step. Second, we recommend assigning each project a risk level and then following a specific risk mitigation for each level. And third, create shared governance assets such as reusable tests, common templates, standard training and instructions for use. That way teams don’t have to rebuild from scratch each time and spending more time building and less time navigating bureaucracy.

Myth 3: One-size-fits-all AI policies work

Reality: A predictive maintenance algorithm and a grid management system are not the same thing. They shouldn’t be governed the same way. 

We operate a risk-based AI classification system—Low, Medium, High, and Critical—with tailored protocols, approval chains, and testing requirements at each level. Energy grid management has fundamentally different stakes than factory floor automation. Our governance framework recognizes that, with sector-specific guidelines and adaptive policies designed to evolve alongside both technology and regulation. Context-aware governance is a must-have. 

Myth 4: Technical teams can handle AI governance alone

Reality: Technology expertise is necessary, but it’s not sufficient. 

Our Responsible AI Office and Committee bring together engineering, legal, ethics, business strategy, and operations. Why? Because pure technologists can miss critical context that domain experts catch immediately.  

Our AI Hub and Bespoke Model draws on specialists from the energy sector, manufacturing, and cross-functional teams who understand the real-world stakes of the systems being governed. We’ve made mandatory AI literacy training standard across all organizational levels, ensuring that good governance is everyone’s responsibility.

Myth 5: AI equals automation

Reality: AI enables automation, but smart industrial AI is about strategic human-AI collaboration, not replacing human judgment. 

Our approach uses tiered intervention systems: routine decisions are automated, anomalies are flagged, and critical decisions require human approval.  

We can enable AI systems to operate autonomously within defined parameters to save time and money, but we must ensure humans step in when defined thresholds are met. We’ve also built self-monitoring AI systems that can detect their own performance degradation and trigger a human review. The goal is to reduce oversight burden where it’s safe to do so and increase it precisely where it matters most. 

Myth 6: AI governance is about preventing bad outcomes

Reality: Governance framed purely around risk prevention falls short. 

We’ve shifted our philosophy from only focusing on “risk prevention” to “value optimization.” That means building governance systems that actively improve AI performance with a feedback loops, rather than simply monitoring for failures. And it means implementing sustainable AI protocols that optimize for every stakeholder: customers, workers, the environment, and society at large. Governance done right doesn’t constrain AI. It makes AI better. 

Myth 7: AI’s environmental impact makes it unsustainable for industrial use

Reality: AI does consume energy. But when used responsibly, it can deliver an outsized return, greatly supporting the energy transition. 

A good example is our SpaceLogic™ Touchscreen Room Controller, which is an edge AI device that integrates HVAC, lighting, and blinds to optimize occupant comfort while delivering up to 35% energy savings.  

More broadly, industrial AI is helping reduce carbon emissions, optimize energy demand in real time, and remove barriers to renewable adoption. In well-designed applications, AI can clearly and defensibly compensate for consumption by reducing energy use and optimizing energy demand. 

The bottom line

None of this is without friction. Change management is hard and embedding a responsible AI culture takes time. The market is also maturing unevenly: organizations are at very different governance levels, and transparency in AI practices remains limited. 

But these are solvable problems. And the organizations that solve them will be the ones that earn the trust of their customers, their regulators, and their own people to deploy AI at scale. 

Learn more on AI governance in the episode of AI at Scale podcast with Ania Kaci: AI governance decoded.