By Prateek Joshi
Manufacturing companies know that Artificial Intelligence (AI) can have multiple business advantages for the frontline team and on the overall bottom line. But despite companies’ best intentions and AI’s clear potential, many companies struggle to fully utilize AI. This is leading them to reevaluate their strategy on how to leverage AI for their business, according to our new report.
The usage of AI at the enterprise level is continuing to grow, but the projects are often loosely defined and can take longer than anticipated to show returns. This has the potential to limit the progress that AI can provide. To further understand the hurdles standing in the way and opportunities AI can bring to manufacturing, we surveyed 250 manufacturing professionals who have visibility into their company’s AI strategy.
Continuing The March Toward AI Implementation
Companies have good intentions when they begin the AI implementation process, but the complexity of AI can pose problems and cause companies to reevaluate their strategy.
- 61% said their company has good intentions but needs to reevaluate the way it implements AI projects
- Only 17% of respondents said their company was in full implementation stage of their AI projects
- 34% said their company has struggled to keep its AI project(s) in scope because there was a lack of expert guidance at the planning phase of the project
What is Really Holding Companies Back
Having a mature data collection and storage system is essential for AI implementation projects. Without the ability to collect and store data in a timely manner, manufacturing companies can’t get far with AI implementation. Manufacturers are realizing it takes more time than anticipated to get their data systems up and running. 72% of manufacturing companies said it took more time than anticipated for their company to implement the technical/data collection infrastructure needed to take advantage of the benefits of AI.
Internal buy-in is important for any company project, but it’s especially important for something as complex as AI. Just like marketing, business development, or any other business function, commitment from key stakeholders is essential for success.
Internal buy-in from employees needs to be taken into account when implementing AI projects. 62% said their company took more time than anticipated to acquire internal buy-in and commitment in implementing AI. This lack of internal buy-in has also caused 34% of employees to say that there is a lack of engagement toward these projects.
Important Factors For Successful AI Projects
We found that issues with internal buy-in, decisions regarding who should use the data and lack of budget consensus related to AI projects can slow down implementation in the manufacturing industry.
It’s important to have a mature data collection and analysis infrastructure and to agree on specific business outcomes before implementing and incorporating AI into an industrial workflow. Multiple reasons are given when companies make the decision to begin implementing AI. 54% said that cost savings were the top business problem that they were trying to solve, followed by 49% that said automating tasks was the top reason.
These kinds of projects can lose focus within the company and encounter multiple problems. Less than half (47%) said their company has kept AI projects in scope and focused on deliverables. Moving to a focused approach that can manage the complex process of AI can help to eliminate these issues and help companies stay focused on the long-term goal.
The use of AI technology in the manufacturing industry has the potential and opportunity for companies to empower the frontline team with automated performance monitoring for any industrial workflow. AI can help businesses drive ROI by reducing resource consumption, operating costs, and reliably predict the current state of their business predictions, whenever they need it.
How do your company’s experiences with AI match up to the respondents? Read more about the report and the methodology here.