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.

Collaboration is the key to making AI and IoT work
Companies have to collaborate and ensure buy-in to be successful with transformational tech like artificial intelligence and Internet of Things.

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 to getting 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 Svetlana Sicular 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.

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:

  1. Get on board with boring AI
  2. Rethink upskilling
  3. Lead on risk and responsibility
  4. Operationalize AI — integrated and at scale
  5. Reinvent your business model

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