How manufacturers can amplify intelligence with AI

For many manufacturing companies, the potential of artificial intelligence (AI) is easier to envision than the reality, and the journey between current and future states has begun only in earnest. The transformative power of the technology remains on the horizon. Yet, a pioneering EY and Microsoft study shows why there’s no time like the present to start capitalizing on AI.

The survey of 86 manufacturing companies across Europe has implications for the sector across the world, revealing that 81% agree that AI has become more important for their business over the past 12 months, but just 10% say they have a detailed plan for it with initiatives and defined responsibilities. Meanwhile, 16% say that they are still developing and implementing AI on an ad hoc basis. Just 12% have managed to scale AI company-wide, and these leaders have been on their journeys for over five years, on average, reporting lower costs, sharper decision-making and greater customer engagement.

Becoming a leader who integrates AI within their business is more than installing new software and reaping the rewards. It requires time and careful planning — but the payoffs are substantial. Our survey (described in more detail in the full report) shows where the opportunities lie and how manufacturers can replicate some of the leading practices.

Opportunities and hurdles

Going back to 2014, manufacturing companies were involved in just five M&A deals focusing on AI, according to EY Embryonic. That number shot up to 59 in 2019, totaling 179 transactions over that time period, with a com­pounded annual growth rate (CAGR) of 64% and a total transaction value of €1.4 billion.

It’s easy to see why manufacturers are enthusiastic about AI. In factories, smart sensors, the internet of things and AI enable predictive maintenance — used by 68% of survey respondents — to save costs and extend the lifespan of important assets. Then there are digital twins, which are virtual replicas of a product, process or piece of equipment to use in simulations. In the survey, 62% say they have adopted digital twins — for example, in making supply chains more resilient. AI can play a role at the other side of the value chain as well by enabling chatbots — used by 66% of survey respondents — to respond to inquiries quickly through text analysis, and cybersecurity intrusion identification was a popular response as well (69%).

Other use cases are more nascent but also powerful — for instance, AI can help forecast customer demand (37%) and manage inventory (32%) for seamless fulfillment. Analytics also can drive better decision-making and more effective utilization of labor, and AI visual analytics can be used in maintenance for faster inspections and verifications.

Yet, “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. For instance, respondents in the EY/Microsoft survey note hurdles such as how data must be collected and cleansed to be easily connected to AI solutions in production. Too often, siloed functions and unintegrated platforms don’t forge the needed links 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 gaps (pdf). And technology is just as much about humans as it is about computers and digitization: how your people work, the tasks they fulfill and the culture that enables that work will all take on new dimensions.

What to do: the road map

Shaping, accelerating and optimizing your AI journey requires six steps, regardless of whether you’re just starting out or are trying to strengthen your plan to make it holistic.

1. Acknowledge AI’s 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 KPIs aligned with your 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 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 your AI solution. Data storage through databases such as data lakes guide the data flow and strengthen your 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 startups, academia, consultancies and other tech leaders — can be tapped, adding perspective to your understanding of the business and use cases.

5. In-house AI skills

Even with partners, your existing workforce will need to learn new skills and fulfill 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.