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Why organizational culture is critical to AI success

When implementing cutting edge technology, such as AI and machine learning, organizational culture can be a leading factor to the success and failure of AI implementations. Here are our tips for preparing your organization for AI success.


Process improvement requires being ready for change

One of the top use cases for AI in manufacturing and the oil & gas sectors is the ability to optimize processes in order to increase yield, reduce energy costs and improve productivity. Some of Canvass’s customers have increased asset utilization, reduced greenhouse-gas emissions by 10+ million pounds of CO2per year and improved quality reliability leading to reduced waste and production costs. This was only possible because the operations teams were ready for change. Where a lot of operations teams have relied on intuition and operator experience to manage their production cycles, AI ushers in a data-driven decision-making environment. Therefore, operations teams must adopt a mindset that AI and the insights it can derive will help them to enhance their capabilities, optimize their operations and will empower them to create new kinds of workflows and processes unlike the ones that they have been running before. Being ready for that is important because if you're not that means you've got the wrong expectations internally from a change management perspective.

AI success requires organization-wide commitment

Because manufacturing environments are typically fast-paced and complex, prioritizing AI projects can be challenging, particularly when there are burning issues on the operations line that need constant attention. In order to prioritize an innovation initiative, such as AI, an organization requires commitment from all levels. That is – if an executive just announces a directive that they want to implement AI and nobody else has bought into the project, 99% of the time these projects will not gain any traction or garner meaningful success. In the same vein, if a plant engineer wants to implement AI but doesn’t have support from management or IT, it will not succeed.


Instead, when investing in disruptive technology, commitment cannot just stop at investing in the infrastructure and hiring the technical expertise. Instead, the whole organization needs to stand behind the change that it’s going to bring to the business. This way, the organization will be able to identify the best place where AI will provide meaningful impact and then they can evaluate how they scale it across the business. Where AI projects can fail is when organizations look for short term benefits and do not commit the organizational effort that is required. Therefore, getting this first level of commitment is critical for success.


Canvass’s approach to change management

For enterprises embarking on their AI journey, Canvass advocates establishing a SWAT team within the organization. A SWAT team essentially becomes the change leaders for the organization that represent different areas and levels of the business. For example, a SWAT team will consist of an executive sponsor, representatives from the IT and operations business lines and an AI champion, who will have the AI technical knowledge to understand what AI can and cannot do and how to bridge the gap between AI, IT, OT and the wider organization. By having the organizational structure in place, will help as you try to scale the AI project because you have the internal champions to support. For example, if IT does not allow you to share the data but the operations side is still going out and doing the projects in siloes it will hit a dead end and remain in pilot purgatory. By bringing the entire organization together via a SWAT team, you can then start to roll out these projects in one part of your operations, achieve success and then gradually scale it to similar processes within your facilities and eventually across multiple facilities. From here, as a collective, the SWAT team will be able to determine:

· Your data strategy

· Your cloud strategy

· How to scale AI; and

· What partners you need to roll this out across several places.


Critical to implementing AI is not how to make an AI project successful, but how to set up the organization to be AI successful. This requires the organizational structure: the people, processes, assets, and everything around it, to be focused around how it can foster an AI success organization that then breeds further success and value.


If you want to learn more about how to prepare your organization for AI, contact us today.