Efficiencies, jobs, sustainability: How generative AI reshapes manufacturing

How will generative AI help companies transition to Industry 5.0? How can it play a vital role in logistics and sustainability? What will happen to jobs? Cognizant’s Anuj Seth, Head of IoT & Engineering, and Anne-Sofie Risåsen, Software & Platform Engineering (SPE) Commercial Leader, share their views.

Moving to the next level of manufacturing requires a new approach where data is the central point and humans and machines augment each other. Getting there is easier said than done though. What are Cognizant clients' major challenges as they move from Industry 4.0 to 5.0?

“Many companies are still in the process of adopting 4.0 and the maturity level differs greatly,” says Anuj Seth. “As AI depends on accessible, reliable and consistent data, integration is the starting point for everyone wanting to leverage generative AI.”

Siloed data hampers development

Commonly, companies deal with fragmented environments because of growth and acquisitions over the years making integration of systems, teams and processes a tough challenge. Even basic tasks across the company can be cumbersome due to siloed data. The same goes for partner and customer systems that need to be connected to allow data to flow between systems, enabling real-time insights regarding for example supplier data. There’s also some resistance regarding sharing data between systems outside company walls as this has competitive aspects.

As for now, some manufacturers use AI for internal optimization, targeting specific areas like automation of tasks, forecasting and quality control. All changes in a normal manufacturing context, like plan rearrangements, material flows, seasonal changes or other disruptions, tend to confuse the existing AI models.

New jobs will occur

How will generative AI effect existing jobs in manufacturing? An Oxford Economics/Cognizant report describes that over the next ten years, 90 percent of all jobs, independently of industry, could be disrupted in some way by generative AI.

At the same time, new skill-based jobs will emerge as the need for competence in data, cloud and analytics will increase. The demand for more business architects, analysts and a variety of IT specialists is already a reality, and as generative AI becomes more common manufacturers will need people who can control, interpret and act on the new information to align it with business needs.

“Data, again, will be at the center together with skills connected to data,” says Anne-Sofie Risåsen. “When generative AI is in full swing, we might see new positions such as data privacy officers, customization specialists, robot coordinators and learning facilitators.”

Will it affect the headcount? Time will tell, but to remain competitive it’s essential to learn how to work with new technologies as is reshaping and retraining of employees. In response to this, Cognizant has set up the Synapse initiative which strives to upskill one million people in technology, bolstering their prospects for future employment. Drawing upon our extensive experience in training and educating a diverse global workforce, Synapse centers around cutting-edge technologies such as generative AI and other top-tier tech services. The initiative's goal is to establish a fresh, employable talent pool for the digital economy of tomorrow.

Generative AI, efficiency and sustainability wins

Can generative AI benefit sustainability? Yes – in manufacturing, efficiency and environmental advantages commonly go hand in hand. This means that a strong focus on savings and productivity often comes with a lower footprint for carbon emissions. Take optimized logistics and operations through simulations, vehicle tracking, asset optimization and so on – actions that also reduce fuel consumption and energy usage. Generative AI can help reach sustainability goals faster, but, as usual, data is key.

To most manufacturers, sustainability is the new way of doing business and something investors, partners and customers demand, independently of the traditional cost and efficiency focus. This is something Cognizant meets daily.  

“We have a strong sustainability practice and are helping our clients achieve their sustainability goals through change management, technology and process improvements,” says Anuj Seth.

What are the top use cases in manufacturing then? From what Cognizant sees, the return on generative AI investments is currently in production planning, forecasting and process improvements. Beyond cost optimization, increased accuracy and productivity advantages, there is also innovation potential.

“With all this data, it’s important to think about what to do with it in a structured manner,” says Anne-Sofie Risåsen. “We have several frameworks to accelerate ideas and turn them into new solutions with clients.”

To learn more, visit our generative AI hub and our manufacturing pages.