AI Implementation in Industry: Overcoming Barriers to Competitiveness
Artificial intelligence (AI) presents significant opportunities for businesses, yet many struggle to successfully implement these technologies. Benjamin Massow, Head of the Center for Production, Robotics & Automation at MCI Innsbruck, explains why companies often fail to utilize existing AI solutions, the role of fear in hindering progress, and what it takes to remain competitive in an increasingly automated landscape.
Understanding Operational Reality is Key
Massow emphasizes that successful AI implementation begins with a thorough understanding of a company’s existing processes. “It always starts with a common understanding of operational reality,” he states. “We analyze processes in great detail and check where time, resources, or quality are lost. Building on this, we appear at which existing solutions – be it digitalization, partial automation, or selective use of AI – can actually bring added value.” He stresses the importance of tailoring solutions to each organization’s specific needs rather than imposing standardized approaches.
Tyrol’s Adoption of AI: A Regional Perspective
While acknowledging that innovation exists across Tyrol, Massow points to resource limitations as a primary barrier to AI adoption, particularly among small and medium-sized enterprises (SMEs). “The difference usually lies less in the openness to fresh technologies than in the available resources,” he explains. Many SMEs lack dedicated innovation departments and struggle to discover the time and structure needed to effectively explore and integrate new technologies into their operations.
AI and Long-Term Competitiveness
Massow believes AI is crucial for maintaining industrial competitiveness, but not as a standalone solution. “Artificial intelligence opens up great potential, for example, in terms of efficiency, quality, or flexibility,” he says. “At the same time, you have to be aware that these opportunities are open to everyone. International competitors also use them.” He urges companies to act quickly, embrace experimentation, and accept that not all projects will succeed, noting that a high success rate isn’t necessary for overall progress.
Prioritizing Existing Solutions
A key lever for the coming years, according to Massow, is focusing on implementing existing AI solutions rather than chasing the latest complex developments. “The gap between what would have long been technically feasible and what is actually implemented is enormous,” he observes. “If we were to simply use the current status, we would have enough potential for many years to come.” He advocates for prioritizing analysis, support, and implementation of readily available technologies.
Bridging the Implementation Gap
Narrowing the gap between potential and implementation requires companies to first understand the available options in a practical, area-specific manner. “General discussions about AI are of little help if it is not clear what it means for logistics, manufacturing, or assembly,” Massow explains. He recommends a systematic examination of internal processes to identify inefficiencies and suggests seeking external support during this phase. Creating internal innovation projects with cross-departmental involvement is also crucial.
Addressing Employee Fears
The fear of job displacement is a significant concern surrounding AI implementation. Massow draws parallels to the introduction of classic automation decades ago, noting a similar pattern of skepticism. He cautions against allowing these fears to hinder progress, as employees may withhold information about their work processes for fear of being replaced. He asserts that, in practice, new technologies typically take over monotonous tasks, allowing employees to focus on more meaningful and complex work.
Managing Change and Investing in Training
To address employee concerns, Massow emphasizes the importance of early involvement, open communication about plans, and a clear message that AI is intended to relieve workload, not replace workers. Companies should also invest in employee training to equip them with the skills needed to adapt to new roles and responsibilities. He frames AI and automation projects as processes of change that require conscious and professional support.
Case Study: STIHL Tirol
Massow’s team has been collaborating with STIHL Tirol on several AI applications for approximately a year. Current efforts focus on integrating these solutions into the company’s existing IT infrastructure. One example involves implementing AI-powered testing stations on assembly lines to automatically evaluate product parameters and visual quality. This provides employees with concrete recommendations for action, reducing manual effort and improving accuracy. “Even ten or twenty minutes of time saved every day makes a considerable difference,” Massow notes.
About Benjamin Massow
Benjamin Massow, B.Sc., M.Sc., is the Head of the Center for Production, Robotics & Automation at MCI Innsbruck. Learn more about his work at MCI Innsbruck. He is also a lecturer in the Bachelor’s program Mechatronics. Find more information about the team at the Center for Production, Robotics & Automation.
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