AI in manufacturing is stalled by lack of data infrastructure and internal buy-in

61% of survey respondents said their company needs to re-evaluate how AI projects are implemented.

According to Gartner, artificial intelligence is still in the early phases of the hype cycle.

Among the 37 types of AI on its chart, only speech recognition and GPU accelerators have reached the plateau of productivity.

Despite the fact that many AI technologies are too new for mainstream adoption, manufacturing leaders are already stuck in a rut with these projects, according to a new survey from Plutoshift.

Plutoshift found that manufacturing professionals are having trouble with almost every aspect of AI projects from defining realistic outcomes and collecting data to getting internal buy-in.

The Gartner Hype Cycle for Artificial Intelligence 2019 examines the stream of innovations and trends in the AI sector and scopes AI plans.

Artificial general intelligence is at the very start of the curve with quantum computing and chatbots at the peak of inflated expectations and autonomous vehicles in the trough of disillusionment, according to Gartner.

Gartner analyst Laurence Goasduff said that there are many new technologies in this year’s hype cycle but only a few are fully understood and even fewer are seeing mainstream adoption.

SEE: Prescriptive analytics: An insider’s guide (free PDF)

Plutoshift surveyed 250 manufacturing professionals in October 2019. The blind survey was completed online and responses were random, voluntary, and anonymous.

According to the survey, some of the worrying signs about AI in the manufacturing sector include:

61% said their company has good intentions but needs to reevaluate how AI projects are implemented
60% said their company struggled to come to a consensus on a focused, practical strategy for implementing AI
26% said their company implemented AI projects even though other contingencies (e.g.IT infrastructure, market readiness, etc.) were outstanding

Only 17% of respondents said their company was in full implementation stage with AI projects. Most companies are still in the preparation phase:

25% said their company was in pre-implementation phase
24% said their company was getting familiar with AI and assessing the potential business and financial value
20% said their company was assessing the internal resources needed to implement AI

Only 13% said they were building a business case for AI, which is the phase fast followers should be in, according to Gartner.

The survey respondents aren’t ready to integrate technologies across business units and revamp business processes, two keys to AI success according to Accenture. Survey respondents are using AI to accomplish relatively narrow goals:

Cost savings 54%
Automating tasks 49%
More productive workforce 49%
Efficiency in business processes 49%
Improve quality of products or customer service 49%

Several problems cited in the survey sound familiar: 34% struggled due to lack of technical expertise in the planning phases and lack of engagement due to low confidence levels in the technology.

On the bright side, 47% of respondents said AI projects were staying in scope and returning clear deliverables.

A PwC report found that only a few companies are using AI at scale and seeing financial benefits from these projects. The report recommends these five broad priorities for AI projects in 2020: