According to McKinsey, organizations that adopt and integrate AI technology are better positioned to double their cash flow by 2030. However, when looking at manufacturing organizations specifically, they have room to grow when it comes to AI maturity. According to an ESI Thought Lab study, over half (55%) of manufacturing organizations surveyed were categorized as either a “beginner” or “implementer” with regard to their stage of AI maturity. This means they are either developing plans and building internal support for AI or starting to pilot AI and use simple applications.
The benefits of AI in the manufacturing sector are many, but some examples include quality management (reducing the cost of quality checks and failures and optimizing maintenance and scheduling), plant maintenance to reduce overall costs, and real-time reporting and analysis. That said, how can an industry like manufacturing — which is not digital-native at its core — build strong data teams and talent that have enough impact to boost overall AI maturity?
1. Think Beyond the Data Scientist
Even today, many companies still look to hire "data unicorns” — that is, supernatural all-in-one data wizards who possess the entire range of skills that the organization needs. Not only is this an expensive and unrealistic strategy in many industries, but upon mapping out what the business needs and the skills required to fill those needs, it’s probably unnecessary as well.
Organizations are increasingly realizing and acknowledging that the data science process itself doesn’t solely revolve around data scientists — you need individuals with different skill sets to support each step of the AI lifecycle. Thus, in order to go beyond the hype and successfully implement a comprehensive and sustainable AI strategy, you need to be able to dissect each part of the AI model lifecycle, translate it into concrete organizational resources and needs, and then map those needs to the different data profiles available.
The guidebook Staffing the AI Enterprise provides a sample framework for mapping the organization’s needs. This framework allows manufacturing organizations to consider exactly what the needs of the business are and which types of data profiles would add the most value before going out and hiring a bunch of data scientists who may or may not ultimately be able to have the expected impact.
2. Turn the Business Into Data Professionals
“The self-service data initiative at GE Aviation was born out of a conversation in a conference room. The idea was that you would never be able to hire enough data professionals to meet the data demands of the business, so instead, why not turn the business into data professionals. Taking that premise we started to define what self-service meant for us and how it would work. ”
— Jonathan Tudor, Director - Data & Analytics at GE Aviation (from the white paper GE Aviation: From Data Silos to Self-Service; A Deep Dive into the Process, People, and Technology that Enabled GE’s Data Revolution)
More often than not, especially in the beginning of the AI journey where many projects are low-hanging fruit, this exercise of mapping the AI lifecycle to staffing needs shows the importance of business experts (that is, subject matter experts who know the business inside and out) over people who purely possess data skills.
The key is to empower people at the individual level in a way that also serves the collective good of the organization. If people are able to make better day-to-day decisions with data at all levels of the company, regardless of what kind of data training or education they have, there will inevitably be a visible impact for the enterprise at a more macro level.
3. Implement a Formal Upskilling Program
Despite the ever-growing number of data professionals, the talent and skills gap continues to be one of the top roadblocks to scaling data and AI efforts in manufacturing and beyond. One of the best ways to overcome this challenge and staff for an inclusive and sustainable AI strategy is through upskilling: providing existing employees with the education, training, tools, and incentives to transition into the data-driven roles required to address business needs and close the hiring gap.
Helping staff both understand how AI, data science, and machine learning fit into the larger company’s strategy can be just as important as educating people on the concepts and technology themselves. By clearly communicating the value, existing employees can more easily see how upskilling fits into the picture.
After education, the next step is actually providing the tools (like Dataiku) that allow non-data scientists to participate in the data science process. That means features that support AutoML and augmented analytics, but also that allow non-data scientists to do things like connect to data and do data cleaning or exploration — all without code. But it also means being able to work on projects where needed with people like data scientists and data engineers. This is critical because investing in upskilling without the ability to then make these employees part of the overall data science, machine learning, and AI strategy is a misplaced effort.