Business is being completely revolutionized by artificial intelligence. René Bohnsack, Associate Professor of Strategy and Innovation at Católica-Lisbon, warns us about this reality. And the industrial sector is no exception to this revolution.

“Artificial intelligence has incredible potential to transform how we work”, says René Bohnsack. However, from the conversation that Revista M had with this expert in business model innovation, digital transformation, and sustainable innovation, there is much more that companies operating in the global industrial landscape should consider.

5 essential steps in adopting Artificial Intelligence processes in the industry MCG

Here are some key insights that René Bohnsack considers essential when adopting Artificial Intelligence processes in the industry, outlined in five essential steps taken from the full interview (available in issue #10 of Revista M):

5 essential steps in adopting Artificial Intelligence processes in the industry

1 | Expand the IA range from pilot projects to large-scale implementations

Many companies start with small AI-focused projects to test the ‘ground’. The next step is to expand these initiatives throughout the organization.

This means taking successful pilot programs in areas such as quality control or predictive maintenance and implementing them in all relevant parts of the manufacturing process.

Large-scale implementation requires a robust infrastructure, data in good quantity, a clear strategy to ensure consistency and reliability, and, crucially, the acceptance of the company’s employees..

2 | Investir em talento e formação em IA

As AI becomes more present in the industry, the demand for qualified professionals who can develop, manage, and maintain these systems will grow.

Companies should invest in training programs to improve the skills of their current employees and attract new talent with expertise in AI and machine learning.

This could involve partnering with educational institutions, setting up in-house training programs, or hiring experts to run regular workshops with staff.

3 | Develop the data infrastructure

AI systems rely heavily on data, so ensuring that data is collected, stored, and managed effectively is vital.

Companies need to invest in advanced management systems that can handle large volumes of data and provide the insights needed for AI applications. This includes developing comprehensive and well-thought-out data management policies that guarantee data quality, privacy, and security.

4 | Establish a culture of innovation and collaboration

Establishing a culture of innovation and collaboration within the organization is also important.

Encouraging teams to experiment with AI tools and collaborate across departments can generate innovative solutions and give rise to a more cohesive implementation strategy.

5 | Focus on continuous improvement and interaction

AI technology is always evolving, and staying ahead means staying committed to continuous learning and improvement.

Companies should regularly review and refine their AI strategies, incorporate feedback from AI implementations, and stay updated with the latest advancements in AI technology.

This iterative approach helps in making incremental improvements and adapting to new challenges and opportunities as they arise.

These five topics were created based on the interview with René Bohnsack, published in Revista M #10::

René Bohnsack

René Bohnsack is an expert in business model innovation, digital transformation and sustainable innovation, and he has published his research in the leading international management and innovation journals (e.g. Research Policy, Journal of Business Venturing, Journal of International Business Studies).

Received the Best Paper Award at the Academy of Management ONE Division in 2019, in Sustainable Entrepreneurship in 2016, and the 3rd prize for the Best Innovation Management Paper 2013.

René’s current research focuses on token-based business models, competition in digital business ecosystems, and digital technologies’ role in sustainable development in business modeling. He shared the findings of his research in an acclaimed TEDx talk in 2019.