To tackle AI implementation, manufacturers need a mature data collection and analysis system as well as internal support.

Artificial Intelligence (AI), or the ability for a computer to think and learn, presents a wide range of opportunities across multiple industries to drastically change — and even disrupt — how they do business.

This promise is leading companies to implement AI into their enterprise processes, as they hope to glean some of the key benefits: increase efficiency, improve customer service and uncover additional revenue streams.

In the face of this enthusiasm — and the progress some companies and industries are making with AI projects — what is often underreported is that AI initiatives are complex and require a unified approach from internal teams and external partners.

A new report, “Breaking Ground on Implementing AI,” uncovers how manufacturing companies are tackling AI implementation, the hurdles they encounter along the way, and how far along they are on obtaining full data intelligence.

According to the report, nearly two-thirds (61%) of the companies said their company has good intentions but needs to reevaluate the way it implements AI projects. To help manufacturing companies look back on their AI implementation processes with more favorable results, here are three tips for successful AI implementation.

Establish a Mature Data Infrastructure

Data is an essential component of implementing AI. Without a reliable stream of data from IoT sensors, it’s difficult for manufacturers to initiate an AI program that provides consistent and dependable insights. It’s important for companies to have an effective data collection system to fully take advantage of AI technology, or to even begin testing AI capabilities.

Despite the importance of this step in implementing AI, a majority (84%) of manufacturing companies said their company cannot automatically and continuously act on their data intelligence. This means that the data they are collecting now does not provide insights that lead to proactive, real-time decisions to improve manufacturing processes.

In fact, many companies are still in the assessment phases of AI implementation. One-quarter (25%) said their company was in the pre-implementation phase, or using AI but only on a small scale to assess its value. Another 24 percent said their company was getting familiar with AI and assessing the potential business and financial value AI could bring.

In order to successfully implement AI initiatives, and to get the most out of the efforts, prioritizing the establishment of a mature data collection and analysis system is a key step. It’s also important to set aside ample time to achieve this goal. Nearly three-quarters of manufacturing companies (72%) said it took more time than anticipated for their company to implement the technical/data collection infrastructure needed to take advantage of AI.

Define Specific Company Outcomes

Another vital step toward implementing successful AI projects is identifying specific business outcomes and goals. When companies are on the same page about how they define success with AI, they are better positioned to achieve those objectives.

However, just over half (57%) of manufacturing companies said their company implemented AI projects with a clear goal. On the other hand, industry pressure and restricted timelines are influencing some manufacturing companies to implement AI before they are ready. According to the report, 17 percent of manufacturing companies implemented AI initiatives due to industry or peer pressure to utilize the technology and another quarter (26%) said their company implemented AI projects even though other contingencies (e.g. IT infrastructure, market readiness, etc.) were outstanding.

Manufacturing companies are looking to AI to improve a number of business outcomes. While goals will vary from company to company, many are looking to AI to help improve efficiency and to save money.

The top business problems companies are trying to solve with AI are:

  • Cost savings (54%)
  • Automating tasks (49%)
  • More productive workforce (49%)
  • Efficiency in business processes (49%)
  • Improve the quality of our products or customer experience (49%)

Obtain Internal Buy-In

A final key step manufacturing companies should take when implementing AI is starting early when obtaining buy-in from internal stakeholders. Sixty-two percent said it took more time for their company than anticipated to acquire internal buy-in and commitment in implementing AI. In fact, only 28 percent said obtaining internal buy-in was a clear priority for their company and was successfully addressed at the onset of the process of implementing AI. When internal stakeholders are on board with AI implementation, it is easier to obtain the resources needed to be successful. Important steps include securing the necessary budget, gaining access to the necessary expertise and keeping engagement and confidence high that AI can achieve agreed-upon business outcomes and objectives.

AI use within the manufacturing industry is ramping up and showing no signs of slowing down. The technology has the potential to revolutionize the industry, but in order for manufacturing companies to realize the full potential AI can bring, it’s important to make sure that the proper data infrastructure is in place, clear goals and objectives are outlined ahead of time and that internal buy-in is a priority at the outset of the AI implementation process.

Prateek Joshi Plutoshift, Industry Today
Prateek Joshi is the Founder and CEO of Plutoshift.

Prateek Joshi, Founder of Plutoshift, is a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and more. He has also been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Sensors Expo. You can learn more about him on his personal website at

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