Artificial Intelligence (AI) plays a pivotal role in revolutionizing the manufacturing sector by enhancing processes and driving innovation. Discover how AI’s transformative capabilities can elevate your business to new heights.
While incremental advances in production efficiency have sustained global enterprises’ competitiveness, traditional methods are reaching their limits. New pathways, powered by data and advanced technologies like AI, now enhance productivity, flexibility, and sustainability while empowering the workforce. While many manufacturers have introduced first AI use cases into their operations, the majority struggle to realize their AI aspirations due to insufficient organizational and technological foundations crucial for scaling AI solutions across production networks.
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Meanwhile, AI continues to evolve, with innovations like generative AI pushing the boundaries of what’s possible. As emphasized in the World Economic Forum Report, implementing and scaling AI is not a one-time effort. With AI constantly progressing, it’s vital to approach AI integration as a continuous journey that keeps your operations at the forefront of innovation.
87%
believe that AI will
have a positive
impact on productivity
91%
see Time Loss and
Reduced Productivity
as the biggest
organizational risks
82%
of organizations are
held back by
challenges
98%
face challenges when
it comes to their data
Are businesses truly leveraging the full potential of technology? Our latest study, featuring insights from 500+ global decision-makers, uncovers the biggest challenges organizations face when adopting new technologies. From time loss and productivity risks to AI-driven opportunities and partner ecosystems, this executive summary provides data-driven insights to help you make smarter technology decisions.
1. Driving Profits Through Strategic Decision-Making
Leverage data to make informed, profit-driving decisions.
2. Boosting Efficiency
AI elevates efficiency by automating tasks, reducing errors, and enabling machines to operate 24/7, allowing human operators to focus on complex challenges.
3. Predictive Maintenance
Preemptively addressing equipment issues reduces downtime, cuts repair costs, and extends machinery lifespan.
4. Improved Product Quality
AI-enhanced quality control reduces waste and enhances customer satisfaction with precise real-time data analysis.
5. Reducing Energy Consumption
Optimize energy use to achieve sustainable manufacturing processes.
6. Supply Chain Optimization
Real-time tracking and forecasting streamline supply chains, ensuring minimal inventory and timely deliveries while at the same time evaluating and mitigating possible supply chain disruptions.
Martin Schiesser
Head Manufacturing Industries
Senior Partner, Detecon
Focus: Deploy collaborative robots (cobots)
on shop floors.
Impact: Boost workplace safety by mitigating accident risks and automating arduous tasks.
Focus: Harness AI, machine learning, and big data analytics for smart supply chains.
Impact: Enhance decision-making by adapting to fluctuating demands, reducing inefficiencies.
Focus: Automate mundane tasks and provide
actionable insights.
Impact: Free employees for complex, creative endeavors, enhancing job satisfaction.
Focus: Analyze machinery data to preemptively
address issues.
Impact: Minimize downtime and reduce
maintenance costs.
Focus: Use image recognition for defect detection.
Impact: Ensure superior product quality,
reduce waste, and avoid recalls.
Embrace AI for Sustainable Growth
