Navigating the IT/OT divide: Strategies for successful AI project implementation

I have previously written about the opportunities that lie in leveraging AI for the process industry. As the article covers, the process industry could reap great benefits from using AI to improve resource utilization, prevent unplanned downtime, increase capacity & quality, and minimize their environmental impact. However, there are some hurdles when it comes to succeeding with AI projects and initiatives in the industry. One of them is the “IT/OT divide”.

If you work in the industry, this is most likely a known topic. For me, it was an eye-opener when I moved into the industrial software space in 2018. I have an Information Technology (IT) degree and during my 20+ years in the IT software industry, I never really encountered the term Operational Technology (OT), until I joined an Industrial DataOps platform company. I started to delve into the OT side, and in dialogue with industrial clients, it became clear that there indeed is a divide, and that it stalled implementing and scaling new technologies and hence realized business value. Before diving into how to bridge the gap potentially, let’s first take a deeper look at what IT/OT means and why convergence is necessary.

  

The IT/OT divide

The IT/OT divide refers to the separation between IT and OT domains within an organization. The two domains have traditionally served distinct purposes, and they have different focuses, systems, and expertise.

 

 

 

Historically, these two domains operated relatively independently, each with its own set of technologies, standards, and objectives. However, with the rise of Industry 4.0 and the increasing integration of digital technologies in industrial processes, there is a growing recognition of the need to bridge the IT/OT gap. Collaboration between IT and OT is crucial for organizations looking to implement more advanced technologies like AI to enhance efficiency, productivity, and overall operational performance.

 

Technological advancements enhance IT/OT convergence possibilities

There is no playbook that guides successful interoperability and interaction between IT and OT. However, technological advancements are making IT/IO convergence more achievable than before. This convergence is not about transforming IT professionals into plant engineers or turning machine operators into data scientists. Instead, it involves implementing a strategic approach to align and unite previously isolated subject matter experts (SMEs), data, and solutions utilized by OT and IT teams.

 

Timing to execute on IT/OT convergence has never been better. Mature industrial enterprises are:

• Realizing operational maintenance and production optimization by adopting No-code Industrial AI software

• Getting out of “pilot purgatory” and scaling realized business value with Industrial Software-as-a-Service (SaaS) software

• Democratizing access to OT data with standardized and secure integrations

 

The role of no-code industrial AI SaaS solutions

The benefit of No-code Industrial AI software lies in empowering employees without technical and programming expertise to create and use machine learning models and, hence, harness the power that lies in AI. Leveraging an Industrial AI SaaS solution presents a transformative approach to overcoming challenges faced by traditional development methods.

 

Here's how:

1. Empowering Domain Experts: During my years working with industrial organizations (not saying it happens in your company), I’ve seen the IT/OT gap play out in a way that the operational side of the business does not feel IT understands the production processes well enough and hence what they need, and IT feels that the OT side does not apricate the value of what they are delivering. With No-code Industrial AI software, built for industrial processes and for industrial users, this challenge can be mitigated and provide benefits for both IT and OT. No-code Industrial AI software empowers domain experts to solve operational challenges by providing user-friendly tools, allowing them to actively build, fine-tune, and use machine learning models without requiring programming knowledge or extensive resources from IT. IT on the other hand, will be able to provide a solution that is tailored for industrial needs and rapidly enable the business to operationalize value. Letting domain experts solve operational challenges in real-time, every day ensures that the AI solution becomes a daily decision-support tool for engineers and operators.
   
   

2. Accelerating Time to Value: Most AI initiatives have historically been IT-driven “Do-it-yourself” (DIY) projects where the solutions are built from scratch, often requiring custom-built code. DIY projects can generate results; however, implementation time often stretches over years rather than months and can face scalability challenges. How do we implement and adjust the machine learning models for the next production line? And the next plant? Who takes responsibility for maintaining the developed machine learning models? The significant cost of a DIY platform is often not building it but maintaining it. A SaaS solution offers updated machine learning models and scalability without requiring IT to recode for each new scenario. It can be set up, configured, and deliver operational business value in less than a month and easily scale to the whole organization. A bonus is that it eliminates the need for expensive hardware and ongoing maintenance costs normally associated with industrial software.
   
   

3. Unlocking data: A modern Industrial SaaS solution facilitates rapid integrations with OT systems through pre-built connectors for standard industrial protocols and systems, and an accessible open Application Programming Interface (API) for integrations with existing data platforms or 3rd party tools. Building and maintaining custom data integrations require significant time and resources from IT. With an Industrial SaaS solution, IT can focus on ensuring security and data integrity throughout the enterprise, while enabling operations to realize business value by leveraging the easily accessible OT data.

 

Bridging the IT/OT divide is paramount for successfully implementing AI projects. By fostering collaboration and integrating no-code Industrial AI tools, businesses can ensure that both IT and OT perspectives are considered throughout the process. Recognizing the importance of cooperation will be instrumental in unlocking the full potential of AI in the process industry, minimizing project failure rates, and achieving seamless integration across IT and OT domains.

 

At Intelecy, our commitment is to provide a user-friendly, No-code Industrial AI SaaS solution for industrial organizations that improves operations, drives meaningful change, achieves sustainability goals, and leaves a lasting impact. The Intelecy no-code Industrial AI Platform lets the operational workforce harness the power of AI. Explore actual use cases from clients within the process industry who leverage Intelecy to achieve substantial business value.

 

Please reach out if you’d like a tailored demo, have any questions, or want to discuss whether modern technology really can help close the IT/OT gap. We’re here to help you kick-start and succeed with your AI initiatives!