Overcoming the barriers of AI-led digitization with human intelligence

people working at a computer in a lab
Image: Nicol Ritchie/DataProphet

Implementing AI on the road to Fourth Industrial Revolution (4IR)-readiness offers unprecedented opportunities for manufacturers. Manufacturing lighthouses are the trailblazing businesses adopting 4IR technologies at scale in their plants.

These industries are already sustainably capitalizing on AI’s ability to enable manufacturing lighthouses to make predictions and decisions, realizing many competitive, financial and operational advantages and efficiencies.

Predictive maintenance, for example, already makes possible increases in asset productivity of up to 20%. With AI offering so much scope for growth in manufacturing, what is holding businesses back from adopting the Industrial Internet of Things (IIoT)?

While AI technology is driving the revolution in manufacturing, human intelligence is the biggest decider between success and failure. Increasingly, companies realize they’re going to need more advanced technical, cognitive, social and emotional skills. You’ll get further faster if you have greater understanding, buy-in and collaboration from your people as the business transitions to 4IR.

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Lighthouse manufacturers share an approach to change management that embraces human capital at all stages of the digital maturity journey. As that road moves toward digital transformation, common barriers, such as misaligned communication, lack of buy-in, shortage of key skills and rigid company culture can be broken down by focusing on people first.

Misaligned communication

Many manufacturers function with traditional communication styles, where silos and management chains don’t connect fluidly. But, seamless communication is needed during complex change-management processes.

In AI-driven projects, vast amounts of information are communicated and analyzed between different stakeholder groups. When that information is gathered and categorized properly, a broad view of operations emerges that can be seen and understood by everybody.

Santhosh Shetty, a technical sales engineer who specializes in AI for manufacturing, says this business-wide view enables new insights that connect silos and hierarchies.

“What is happening is that the teams on the ground, at the plant, they’re working in silos. They’re responsible for a particular process and for the instrument of that particular process, alone,” said Shetty. “Whereas, in reality, the plant’s system is connected and consists of multiple different processes.

“What we’re doing is enabling the business to see the plant in its entirety in a single view and highlight the operating regimes of the plant on the singular view. We can show the client where they are operating in both good and bad quality regions and the durations during which they operate in such a region. That is valuable to our customers because they’ve never seen the plant depicted in that holistic fashion before—across the manufacturing plant and all the interconnecting processes at the same time, in one view. And once everyone sees this, they’re suddenly on the same page and start talking about what’s possible.”

Lack of buy-in

Maximizing buy-in at all levels of the business increases the likelihood that projects will get support and deliver on objectives. For example, as well as providing guidance and resources, how a sponsor engages with a project will dictate how seriously the business’s population will regard it. From the plant floor to IT, management and C-suite, everyone has to see value for the business and themselves, along with what the digital maturity journey will look like.

People are naturally resistant to change, especially if previous change projects have underperformed or failed, which statistically many do.

In a 2019 article at The Innovator, chief digital officer at Michelin, Eric Chaniot, said of the company’s success in digital transformation, that only 5% depends on technology. The remaining 95% of success is about winning over those you need to make the new environment work.

“No one ever tells you ‘no,’” Chaniot said, “but you can see in their eyes that they feel like saying it.”

One of the key credibility factors in successful AI projects is integrity of data. Modern data science methods provide greater transparency into the AI pipeline and offer the ability to transform raw data into what machine learning models need to make prescriptions for optimization. Even before sharing the data, explaining the reasoning and methods the models use to plant engineers and operators builds greater trust in the resulting business insights.

Shortage of key skills

The road to digital maturity is only beginning for most manufacturers—but so is the stream of talent needed to design and implement digital maturity programs. AI projects demand multi-skilled teams of data scientists, business intelligence analysts, machine learning engineers and software architects. These roles are complex and require diverse capabilities to integrate critical technologies.

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McKinsey reports that “manufacturing C-suites are already well aware that talent gaps are the biggest barrier to digital transformation: 42% of industrial companies report they are already experiencing a shortage of labor with 4IR capabilities, and only 32% feel prepared for the 4IR’s potential impact on roles and skills.”

Naturally, this shortage makes the transition more difficult across the manufacturing landscape.

The AI consultants you appoint will have experience in dealing with change management issues, especially the more systemic challenges that come in the early stages of digital transformation. Invite these experts to share their perspective on change from leading other manufacturers through the journey.

As well as being able to assess a plant’s state of readiness, they’ll bring knowledge that helps guide strategists, project leads and stakeholders through the human aspects of change that will be necessary.

Rigid company culture

Long-held beliefs that “the old way is the right way” are difficult to shift, especially in manufacturing. The manufacturing industry is characterized by tradition, and tradition is a pillar of culture that doesn’t change easily.

When transforming an operational process to be more digital and data-driven, organizational dynamics in a plant may shift toward confusion, misunderstanding and resistance.

Forbes reports that “digital transformation doesn’t begin with technology. What we see is that the companies that succeed and lead in transformation are the ones that can adapt their culture.”

The change you wish to see must be envisioned and enacted by leaders who can influence behavioral shifts and make the case for greater productivity through digital transformation.

This means creating a culture where individuals at every level know how to interpret data and act on it. A data-driven culture enables its members to discern and comprehend the facts, ignore bias, identify problems and grasp opportunities.

For example, a feature of manufacturing lighthouses is making executive sponsorship at the highest level mandatory, ensuring that corporate culture changes, so 4IR disruptions can be successful.

A people strategy for manufacturing change management

Lighthouse manufacturers know the barriers to digital maturity fall away when risks are mitigated through careful change management. That means putting the organization’s human contingent front and center.

This approach integrates otherwise disparate views and behaviors, empowers staff and encourages a culture of continuous improvement through integrated change and innovation. However, it is crucial to first establish the extent to which the 4IR technology is innately facilitating this change, especially when connecting silos and hierarchies that will form a holistic view of a plant.

For now, this is a challenge faced by most manufacturers in diverse verticals. Work closely with your AI partners on 4IR change projects and you’re more likely to harness the complexities of IIoT, sooner.

Taking all your people along for the journey will create a smoother transition to AI-driven digital maturity, which means you’ll spend less time fighting fires. This saves everyone’s attention for a new era of manufacturing excellence.

Nicol Ritchie
Nicol Ritchie, technical writer at DataProphet

Nicol Ritchie, technical writer at DataProphet, heads up written content creation for DataProphet. He has extensive corporate experience in technical long-form writing across a range of industries—including financial services, digital advisories and corporate social responsibility. Nicol holds master’s degrees in both Applied Linguistics and Creative Writing.

Roy Walsh

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