While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

“Hello, Marks & Spencer. How may we help you?”

As one of the biggest and best-loved retail brands in the UK, Marks & Spencer (M&S) is known for the personalized service it provides to its 30 million loyal customers. For 135 years and in 57 countries around the world, M&S has worked to meet and exceed customer expectations for quality and service. Since the advent of the telephone, this has meant cheerfully servicing customers who call in to M&S branches or into their contact centers, no matter what their request might be.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win. Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

—Akash Parmar, Enterprise Architect (Digital Customer Engagement), Marks & Spencer

The company now has a goal of bringing one third of its business online by 2022. In order to engage more customers online, it has opened a new voice channel hosted on Google Cloud.

The company previously had switchboards in 13 different stores across the UK and Ireland (UKI), handling up to nine million calls a year. But as its retail offering evolved across multiple channels, it was becoming increasingly difficult to quickly and effectively answer customer service requests using an outdated switchboard model.

Customers might call in to order an outfit they had seen in a store, to inquire about returning a dress they bought online, or to recover a lost umbrella in a food hall. Each of these different requests required a different routing response from staff, and if the company didn’t act soon, it knew that the cost of managing the increase in call volume would lead to a significant cost impact. M&S decided it was time to make a technological leap forward to meet customers’ expectations in the new, omnichannel retail environment.

“Retail is in a state of flux and M&S is transforming to better serve our customers so that we can compete and win,” says Akash Parmar, Enterprise Architect for Digital Customer Engagement at M&S. “Automating calls into our stores with Google voice recognition gives us every opportunity to get things right for our customers and keep them coming back again and again.”

Boosting opportunities for customer engagement with voice recognition

M&S customers were used to dealing with their local store for anything they needed. But as stores were completely separate from the online business, customers weren’t able to purchase something they’d seen online by calling stores because the store staff didn’t have access to platforms needed to place an online order securely. For a company that places a very high importance on customer experience, this was unacceptable.

Akash Parmar, Enterprise Architect for Customer Engagement, was set a the goal by Chris McGrath, M&S Programme Manager, to ensure that the right channel and the right level of assistance was available to customers at any point before, during, or after purchase. Akash set himself the challenge of building a platform that could adapt to all of these channels and scale very quickly.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle. It understood what we wanted to achieve and how Google Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

—Akash Parmar

In 2018, Akash reached out to Google Cloud partner DVELP, one of the UK’s leading experts on the Twilio programmable contact center platform and Google speech recognition technology. DVELP recommended a Google Cloud-based natural language speech recognition platform that leverages the audio stream intent detection functionality in the Contact Center AI solution, Dialogflow, as the heart of an inbound-call-handling strategy. This strategy was designed to improve routing accuracy, give customers more self-service options, and increase analyst visibility into customer journeys.

“We didn’t have the resources to build a speech recognition platform. DVELP removed that obstacle,” says Akash. “They understood what we wanted to achieve and how Google Cloud Voice APIs and Twilio could get us there. Whatever we want, DVELP builds it for us. DVELP always presents options, never problems.”

Using Google speech recognition to improve customer experience

M&S wanted to use natural language to enable customers to speak and state what help they required rather than choose from a list of options. This would help them answer the millions of calls coming in and figure out what customers needed quickly.

In order to do that, DVELP needed to consider how best to address tying customer intent to actions, while maintaining flexibility. DVELP recommended the unconventional choice of not referencing intent in the application layer, but mapping the available actions to the information required to perform them. These actions were then used to build a “declarative dictionary” for the customer service team.

By focusing on actions rather than intents, the solution enables the customer services team to configure actions to intents in virtually any combination of key-value pairs. Leveraging the fact that Dialogflow can detect and respond to customer intents in real time, M&S has already reached 92% accuracy in translating customer declarations to actionable intents.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of. No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

—Akash Parmar

Akash recalls that once customers became comfortable with the prompt, “in a few words, how may we help you?” they started providing simple, concise responses, and the customer learning curve quickly leveled out. At that point, Google Cloud speech recognition and Dialogflow took over. “The technology worked perfectly and the result was like magic,” says Akash.

“With Google Cloud speech recognition and Contact Center AI solutions such as Dialogflow, there’s no information that we can’t make sense of,” he says. “No matter where you call from, who you are, your age, your gender: you speak, and we understand.”

Enabling self-service contact center improvements with Dialogflow

It was important to M&S that contact center employees be self-sufficient in updating the platform to reflect changes in demand. They needed to be able to easily react to a spike in inquiries about a special offer, for example, without relying on the engineering team. At the same time, neither Akash nor the DVELP team wanted the staff to have to learn error-prone JSON inside contexts, or write responses in order to get necessary information from customers.

DVELP’s creative solution was to fill out the “Action and parameters” section of every intent. This is usually reserved for collecting information from customer declarations, but was also easily adapted to implementing custom key-value pairs. This is particularly helpful in making sure that the contact center is ready to handle new promotions as they arise. As sales and special events are communicated to the contact center from the head office, staff can program specific vocabulary directly into Dialogflow, thanks to Contact Center AI, ensuring that the M&S system is immediately ready to handle related customer calls.

Rolling out the platform to the UK and Ireland

In just a few months after going live, calls are being efficiently routed to the contact center and its existing customer service platform. At the contact center, staff can quickly and easily respond to customer requests, place orders, and process returns. Thanks to the natural language capabilities of Google Cloud, a simple customer request like “order the red children’s dress in the Bath high street window in size six” not only gets correctly routed, but provides data points for future personalized interactions.

Being able to accurately recognize customer intent 92% of the time after less than four months since deployment is an important milestone for M&S. With a concurrent 89% voice-to-text accuracy rate for Dialogflow transcriptions, M&S has rolled out the successful speech recognition platform to all of its stores in UK and Ireland and customer service contact centers.

Akash is so pleased with the performance of the new Google Cloud platform that he’s focusing on what new functionality he can add next to improve customer experience even more.

“We’re working on collecting product codes from customers using natural language so we can give them stock availability details,” he shares. “We also want to use Google Cloud to enable a more conversational experience when customers are searching for help or FAQs on our website. At the same time, we’re looking at Contact Center AI and Dialogflow to provide a virtual assistant experience for our webchat journey. Thanks to our new voice solution, we can clearly understand the key issues that our customers face on a day-to-day basis; the aim now is to start solving these issues through self-service and automation.”

The disruptions of 2020 elevated the importance of having the right data and insights to pivot quickly when necessary. Here’s a look at how businesses can use embedded intelligence to prepare for uncertainty and meet ever-changing customer expectations.

At work and in life, some unpredictability is always a part of the package. Ten years ago, your business might have experienced sudden product demand or a system outage that slowed down deliveries. Servers or data platforms might have run out of capacity earlier than expected.

In 2020, though, the concept of unpredictability in business reached new heights. These disruptions have elevated the importance of embedded intelligence—that is, having machine learning built into the tools people use every day, so that when a pivot is necessary, everyone has the data and insights they need at their fingertips. In fact, 2020 was so disruptive, with so many changes in customer behavior and so many ripple effects, a lot of historical data and forecasting assumptions may not be helpful in 2021. This only increases the onus on businesses to make the freshest data actionable for more of the workforce. 

A decade or so ago, the idea of embedded intelligence might have seemed like science fiction. You might remember hearing that analytics would be able to make predictions, and that technology would be able to take on the complex work of predicting retail demand or helping to create a responsive supply chain. But insufficient hardware, older architecture models, slow queries, and untrustworthy data often got in the way. 

Now, that concept has become reality as enterprise decision-making has moved from legacy tools to cloud-powered data intelligence services. Today, it’s possible to perform complex analytics tasks and obtain valuable, trusted outputs much faster than ever before. That speed and scale has allowed businesses to tackle entirely new projects and release new features and products very quickly. In addition, APIs have become a lot more intelligent, making it easy to connect siloed solutions. No matter the industry, businesses can access the technology to get to the bottom of what customers need.

Related: Top 5 trends for API-powered digital transformation in 2021

Meeting ever-changing customer expectations with embedded ML

Bringing embedded analytics to real-world uses continues to evolve, with a number of inspiring examples surfacing in the past year. As a result of the pandemic and shifting public health guidelines, many businesses didn’t know month by month if they’d be interacting with customers primarily through in-person or digital channels. And even if both channels were available, it wasn’t obvious how changing customer behaviors would net out.

At patient engagement platform Force Therapeutics, for example, daily activity on their virtual care platform went up by over 140% during the pandemic. With such a large influx of incoming data, it would have been difficult—if not impossible—for a team of humans to gather, organize, and draw insights from all of that information, especially in a timely enough manner to be of use to healthcare providers. 

To deliver the necessary care when and how it was needed, Force Therapeutics required a machine learning solution that could identify patient needs based on a wide range of data. Using an embedded analytics platform, they created an application that allowed them to monitor the progress of post-op patients, answer questions, or triage concerns remotely. The platform also enabled providers to check for spikes and anomalies, in order to identify patients who needed to come in due to a critical issue.

Amidst all of the disruption, it became clear that teamwork is essential, and that effective teamwork relies on having the right data-driven tools to get the job done.

Likewise, home delivery became a bigger part of consumers’ routines. This increased pressure on companies to adapt quickly to changes that might prevent packages from arriving on time, such as worsening weather conditions or upstream supply chain disruptions. Amidst all of the disruption, it became clear that teamwork is essential, and that effective teamwork relies on having the right data-driven tools to get the job done.

One example of this can be seen in Google Cloud customers who are using public data to accelerate their journey from data to actionable insights. Some retailers are utilizing the Google Cloud Public Datasets Program to leverage NOAA’s Global Surface Summary of the Day (GSOD) and Severe Weather Data Inventory datasets in order to better understand disruptive weather events, reroute their supply chains to prevent disruptions, and predict their in-store inventory needs to support communities as they recover from natural disasters. 

Implementing ML without the complexity

The idea of embedded ML has been hyped for years, but for many use cases, the status quo tools have not caught up to the enthusiasm. Many business intelligence tools rooted in older database architectures require intense engineering work to deliver insights, queries are often slow, and the output is not always consistent or accurate. Part of the challenge is that building ML pipelines is difficult. Data in a database or data warehouse typically needs to move to an intelligence platform so models can be trained, and the models then need to be deployed and integrated into business workflows.

But modern data warehouses such as BigQuery let users train models in the warehouse itself, without having to move the data—and once the models are created, they can be applied and integrated into business processes using simple SQL. When it comes to embedding ML into enterprise processes, these modern approaches significantly lower the barrier for entry. Plus, tools like Looker, Google Cloud’s platform for modern BI and data applications, were created specifically for modern data needs, with the assumption that data needs would constantly evolve and that iterations should be made quickly without eating up inordinate engineering resources.

Related: Google (Looker) recognized in the Gartner 2021 Magic Quadrant for Analytics and Business Intelligence Platforms.

For Commonwealth Care Alliance (CCA), Looker was originally implemented to alleviate their pain points around data bottlenecks and data chaos. But when the pandemic hit, the nonprofit, community-based healthcare organization pivoted to make use of Looker’s tools to better serve patients. CCA used BigQuery and Looker to combine numerous datasources, create a predictive model that assesses risk, and distribute that model to its clinicians. This has given response teams the insights to determine who is too high risk to come in for care so they can reach out with home care solutions.

This kind of agility is not a one-time antidote to a one-time disruption, but rather the norm to which organizations must aspire if they want to remain competitive and protect themselves against future disruptions.

This same functionality is also helping businesses like SoundCommerce. Retailers like Constellation Brands, Eddie Bauer, and FTD/ProFlowers use SoundCommerce’s out-of-the-box data platform, which is powered by BigQuery and Looker, to collect retail data from any source and build a model around the metrics and relationships that are most crucial to retail. This has saved brands hundreds of manual reporting hours each month, and reduced platform licensing costs by almost 75%. Just as importantly, during the uncertain times of 2020, brands that used SoundCommerce were able to align real-time and predictive business decisions across marketing and operations with critical retail KPIs like contribution margin and customer lifetime value (CLV). 

As 2020 showed us, we can never predict the future—but we can prepare for unpredictability by having the agility to always improve, and by positioning ourselves to make quick, intelligent pivots when the time comes. Last year was in many ways a rubicon: This kind of agility is not a one-time antidote to a one-time disruption, but rather the norm to which organizations must aspire if they want to remain competitive and protect themselves against future disruptions.

Looking for an ‘easy button’ to speed up your BI workloads running on BigQuery? Check out our latest announcement about BI Engine on the Google Cloud Blog.

Debanjan Saha

Debanjan Saha is GM of Data Analytics at Google Cloud, where he leads the strategy and execution of analytics services in GCP. Prior to joining Google, Debanjan was VP of Amazon Aurora and RDS at Amazon Web Services. Earlier in his career Debanjan held multiple executive and technical leadership positions at IBM and Tellium, an optical networking pioneer that he helped grow from an early stage start-up to a public company. 

Debanjan is a Fellow of the IEEE and a Distinguished Scientist of the ACM. He has co-authored a book, 50 patent applications, and numerous technical articles including award winning papers and Internet standards. He received MS and PhD degrees from the University of Maryland, and a B.Tech from IIT, all in Computer Science. In 2019, Business Insider named him as one of the top 10 technology executives transforming business.