Analysis
In the spirit of embracing advanced technology, 57% of our surveyed manufacturing firms have started incorporating artificial intelligence (AI) into their operations. It’s a no brainer. AI-powered robots are taking over repetitive tasks or analyzing large amounts of data to deliver insights and predictions that help management make informed decisions about their operations.
However, from our survey in September, we discovered the stages of development and integration vary across manufacturers. From green to well-established, assess the current state of AI implementation across manufacturing.
Experimenting with AI
One of the notable benefits of AI in manufacturing is increased efficiency and productivity. AI-powered systems streamline numerous processes, reduce the scope of human error, and amp up the speed of productivity. They can quickly analyze data, predict trends, ensure optimal resource utilization, and automate routine tasks. Yet, despite the recognition of the implementation benefits, only 2% of respondents have employed the advantages of AI technologies extensively.
There’s a long way to go and a list of barriers in the path.
AI is not restricted to performing tasks alone – it also serves as a valuable tool for employees to make more informed decisions – if they have the proper tools and knowledge. The top three limitations all stem from the lack of information, skills, and understanding.
Primary Challenges or Barriers Preventing Adoption of AI
So, what are manufacturers doing to bridge those gaps? Just under half of the companies surveyed are teaming up with third-party sources to approach adoption. Reliance on only third parties or only internal expertise is limited, with a respective 7% and 6% of respondents. Teamwork looks to make the cyber dream work, as leading AI innovation at a company is an upward battle and often a second hat for the internal folks.
Who Is Leading AI Efforts?
CIO is the most common AI leader among those surveyed. More interestingly is the disparity among the rest of the organizations. AI falls into a relatively senior-level leader, under the departments of IT, engineering R&D, or operations for most enterprises. Among the other write-in responses, several companies had no one named yet, a few listed collaborations of departments identified above, one company had created an AI sub-team, one was being led by the CEO’s office, and one company named the Chief Compliance Officer as the lead.
Additionally, one company reported that they had appointed a VP-level leader to own the roadmap. With the growth in Chief Digital or Chief Data Officers, spurred by growing trends and opportunities, what leaders will be emerging with the Chief AI Officer title?
The Wide Breadth of Uses
Manufacturing is no stranger to artificial intelligence. The incorporation of advanced algorithms and machine learning has led to smarter and faster operational abilities on the facility floor for decades. Manufacturing execution systems (MES) are hopefully near perfected. Modern AI brings a new level of synergy between machine and human, expanding its applications from finance to human resources to more operational uses. By harnessing the power of data, AI can offer valuable insights across the organization.
Current AI Use Cases
In the midst of the industrial internet of things (IIoT) and growth in digital twin, the road to machine and digital autonomy shrinks. Advancements are compounding on each other, as seen with the launch of ChatGPT and subsequent competitors in 2023 alone. And the industry is following suit. 87% of surveyed companies are exploring the use of AI technologies across various departments. Not factoring AI into business planning will lead to disadvantages.
What Areas Are Next for AI Tool Integration?
- Finance: Analytics, trends, and forecast-related tools
- Legal & HR: Job descriptions and contract management
- Operations: Data digestion and risk & trend management
Staying Ahead of Potential Issues
Nearly half (46%) of responding companies have yet to establish AI guidance. No matter in which department AI control lies – which vastly varies across member companies – surveillance mechanisms that can quickly identify and mitigate potential malfunctions, bugs, or breaches in ethical conduct are concerns across the entire organization.
When working with employees, dealing with sensitive information, or having to recalibrate sensors, the easy fix to plug that data into a machine isn’t always direct. Preventive strategies like ethical programming, auditing, monitoring, and prioritizing human-AI collaboration are strategic considerations to have at the forefront of any technological adaptations. Additionally, establishing routine checks on the security concerns helps address the #1 barrier to AI adoption.
Report cited: Manufacturers Alliance survey, Assessing the Current State of AI Implementation in Manufacturing, Fall 2023.