MxD’s 22,000 square-foot manufacturing floor

ML Journal

ML Journal

How Manufacturers Can Amplify Intelligence with AI

Manufacturers struggling to scale value from investments in digital technologies and AI need to take a more holistic approach that ensures C-suite buy-in, addresses new skills requirements, and creates an AI-friendly culture for the future 

For many manufacturing companies, the potential of artificial intelligence (AI) is easier to envision than the steps needed to integrate it into their business. Leaders can see the transformative power of the technology as it continues to evolve and they begin to imagine what it would mean for their business, from end-to-end supply chain visibility to powerful new insights from predictive analytics to the ability to respond to sudden demand shifts more quickly. It’s all out there, and yet, the strategy and execution needed to bring these tools to life continue to be elusive.

As companies struggle to scale the value from their investments in digital technology, many often find themselves in a state of pilot purgatory. They experiment with AI and machine learning, the Internet of Things (IoT), augmented reality, virtual reality, and other solutions that may have no clearly defined purpose. It’s a fundamental flaw that keeps many manufacturing companies from leveraging the benefits that new technology can bring to the table. They could have the best data scientists in the world and a whiteboard full of bold ideas that would forever reshape the company’s future. But if they don’t have an empowering business vision tied to clear business outcomes and a viable business case, it’s going to be very difficult to find success.

Leaders need the right mindset, the right skill set, and the right culture to effectively scale digital innovation in a company and create a functioning unit that can maximize the advantages that it brings to an organization. This effort requires time and careful planning, and there will, no doubt, be setbacks along the way. Do it right, however, and the payoff for the company and its customers can be substantial.

Opportunities and Hurdles

It’s easy to see why manufacturers are enthusiastic about AI. In factories, smart sensors, IoT, and AI enable predictive maintenance¹ to save costs and extend the lifespan of important assets. Then there are digital twins², which are virtual replicas of a product, process, or piece of equipment, which they can use in simulations to help make supply chains more resilient³. AI can play a role on the other side of the value chain as well by enabling chatbots to respond to inquiries quickly through text analysis. Cybersecurity intrusion identification is a popular response as well.

Other use cases are more nascent, but also powerful. For example, AI can help forecast customer demand and manage inventory for seamless fulfilment. Analytics4 can also drive better decision-making and more effective utilization of labor, and AI visual analytics can be used in maintenance for faster inspections and verifications.

“If companies don’t have an empowering business vision tied to clear business outcomes and a viable business case, it’s going to be very difficult to find success.”


One key is understanding that doing AI is not just a matter of implementing the technology. A focused approach on business outcomes first, followed by a robust data quality and governance process, are critical to drive business value at scale. Hurdles, such as how data must be collected and cleansed to be easily connected to AI solutions in production, must be addressed. Too often, siloed functions and unintegrated platforms don’t forge the links needed to make AI effective.

Then, there are manual processes to digitize and sensor-based data points to be linked. Governance is also crucial for company-wide implementation and utilization to bridge AI’s trust gaps6. Technology is just as much about humans as it is about computers and digitization. How people work, the tasks they fulfil, and the culture that enables that work will all take on new dimensions.

From Concept to Implementation

AI is not a single technology, but a set of methods and tools with subdomains applied to countless situations. One way to look at it is that technology can be implemented, bots can be built, but AI must be applied. Value from AI doesn’t come from putting it in, at least not yet. But AI is maturing and being embedded in enterprise systems, as well as becoming more accessible for nontechnical users.

It’s an evolving platform with plenty of room to keep growing. The lesson for manufacturers struggling to develop a plan for AI is to begin with the end goal in mind, but that end goal needs to be much broader and longer-term than companies typically think of in a business setting. It’s not always looking at where the company is now and what the next two steps it needs to take should be. It’s also about where the company wants to be 5, 10, or 20 years from now. What does that world look like?

It may not be easy to see, especially in a world where everything can change so suddenly. But companies still need to develop a road map, set priorities, and make a plan so they can become more agile and more responsive to changes in consumer wants and needs. They acknowledge that they may need to revisit this plan from time to time, and perhaps make big changes to it. But it’s still a plan, and it gives the company valuable direction to help it move to where it wants to go. In Manufacturing 4.0, companies need a different mindset about the pace of change and how they’re going to adapt to it.

“Corporate cultures that have become rigid and narrowly focused on the needs of today rather than the possibilities of the future must be challenged.”


One lesson that has been validated again and again across all industries, including manufacturing, is the idea that simply spending money is almost never the answer for what ails the business. There are companies that have spent millions of dollars on new equipment, new factories, and new software to expand their ability to serve customers. But when the leader of that company brings a product to the conference room and asks his or her team to explain how much it would cost to make more of it, or explain the path that product took from inception to its delivery to the customer, there is often no answer. Digital can solve so many problems in business, but only if there is a clear implementation strategy and a defined purpose to it.

As companies develop pilots and embark on proof-of-concept initiatives with different technologies, they need to keep the big picture in mind. It’s great that they’re doing a pilot on blockchain, or how AI could dramatically change how they use warehouse space, but how do these fit into what they’re trying to accomplish from a business outcomes perspective? Here’s what they’re doing, here’s how they’re doing it, and here’s the outcome they’re ultimately going to achieve. That’s the piece many companies struggle with because many still operate in silos, and they are never able to bring that full picture together.

By combining human and AI understanding and interpretation, they can find whole new ways of approaching challenges, developing ideas, and driving growth. Rather than having to begin with a blank canvas, they can use AI to direct their creative judgement and decision-making processes to suggest new routes for innovation that are more likely to be successful, based on the available data7.

EY recently worked with one manufacturer that saw an opportunity to make a bigger version of its signature product. The company had spent $100 million on process improvement and assembled a top-notch team of data scientists but struggled to develop a cohesive plan of attack. The company’s CFO had become very skeptical about the value of digitization through this effort. Every time the company tried to change its product mix, throughput dropped significantly, and the production schedule was disrupted.

“Without an engaged C-suite, it will be a struggle to have a dialogue about how best to use AI, how to allocate resources, and how to set priorities across all business units and functions.”


The problem was planning and execution. The CFO needed a baseline understanding of throughput across the company’s factories, including when new products were introduced. He also needed a way to simulate how the plan might work before any capital allocation decisions were made. The solution was to create a business case and pilot a digital twin capability that enabled the plan and led to significant savings for the company.

The AI Road Map

Shaping, accelerating, and optimizing an AI journey requires six steps, regardless of whether a company is just starting out or trying to strengthen its plan to make it holistic.

1. Acknowledge AI potential

Without an engaged C-suite, it will be a struggle to have a dialogue about how best to use AI, how to allocate resources, and how to set priorities across all business units and functions. It’s a good idea to pick company AI agents who know about the potential of the technology and will keep it on the agenda by helping to hone robust business cases, develop metrics for a proof of concept and then move any AI solutions into production. Without leadership from the top, AI initiatives can get lost in the shuffle amid other priorities and disruptions in the market.

2. Transform and plan

An agile and open culture is a baseline need for the business to be able to effectively leverage new technologies, not just AI. A plan should include key performance indicators aligned with the organization’s business strategy, and finance allocations should be clearly set. A data unit should be established, working in tandem with AI agents and a digital committee or center of excellence, to address requirements in the current state and support the journey to the future state around items such as data collection and cleansing.

3. Data foundation and structure

The data unit or owner is vital for asserting oversight across all of the data points across the supply chain, involving many customers and processes. Non-digital data must be converted, other data sources should be cleaned, and structure should be added to boost the quality of the data, and ultimately, its effectiveness in the AI solution. Data storage through databases such as data lakes help guide the data flow and strengthen the ability to perform analytics. Data governance, processing, explainability, and transparency are all components of a successful solution that should be addressed up front.

4. External partnership ecosystem

Manufacturing companies have developed many talented resources with varied skill sets, but AI know-how can be in short supply. Thankfully, a robust ecosystem of external parties, including start-ups, academia, consultancies, and other tech leaders, can be tapped, adding perspective to the company’s understanding of the business and use cases.

5. In-house AI expertise

Even with partners, the existing workforce will need to learn new skills and fulfil new responsibilities. AI experts, data scientists, and engineers are crucial personnel to hire, but an understanding of data science must be spread throughout the organization. Corporate cultures that have become rigid and narrowly focused on the needs of today rather than the possibilities of the future must be challenged, because AI works only when skills and experiences from many disciplines unite.

6. Architecture and infrastructure

Algorithms are a core part of AI solutions. In fact, these rarely pose a struggle for most organizations to build. The complexity arises when the time comes to integrate them with the technological architecture. Smaller modules with clear guidelines and principles make this process simpler for running proofs of concept and scaling the solutions. And standardized infrastructure service offerings on the market provide agile and robust ways to enable these AI solutions with flexibility.

With great challenges come even greater opportunities. Manufacturers that create an AI-friendly culture today are positioning themselves to boost customer and employee satisfaction as costs decline and helping drive their competitive edge in a challenging and complex moment for businesses across the world.   M

1    How preventive maintenance can backfire and harm your assets,
2    Can a supply chain digital twin make you twice as agile?
3    How to navigate supply chain disruption with digital process mining and digital twins,
4    How forward-thinking organizations are becoming data-driven,
5    Why contactless field service presents an opportunity beyond COVID-19,
6    Bridging AI’s trust gaps: Aligning policymakers and companies,
7    Is AI the start of the truly creative human?

The views expressed by the authors are not necessarily those of Ernst & Young LLP or other members of the global EY organization.

Data supporting the content of this article is derived from the Microsoft and Ernst & Young LLP report to explore how AI can transform manufacturing:




View More