Interview with Predictive Intelligence Mastermind Uwe Seebacher

During different MVP project meetings in Munich we had the great opportunity to meet up with Mr. Predictive Intelligence himself - Prof. Dr. Uwe Seebacher (MBA). We grasped the chance and invited him for a coffee talk about the advancements, challenges, pitfalls and his very own experience in the context of AI and PI.

Predictores.ai: Uwe, what are the most exciting trends in AI and Predictive Intelligence that you're currently observing? How do you see these technologies evolving in the next 5-10 years?

Uwe: The rapid pace of development in the AI industry is both exciting and challenging, with new apps and companies frequently emerging, many of which claim to be game-changers. However, we also see well-funded and established companies struggling to navigate the noise and overhyped promises that often accompany new technologies. As with any trend, after the initial hype subsides, we move towards a consolidation phase. In my book, 'Predictive Intelligence for Data-Driven Managers,' published by Springer Nature Nature in 2021, I predicted this consolidation as a necessary step to cleanse the market. This process will help companies and managers identify reliable partners and make more informed decisions about who to collaborate with moving forward.

In this context, I observe a growing trend towards the strategic combination of carefully selected AI technologies and tools, a strategy we've employed at Predictores.ai since the beginning. By integrating these toolsets and tech stacks in a smart, sequential and parallel manner within economic and industrial value chains, we can transition from what I call 'gadgetary' AI usage to applications that genuinely add economic value. This approach is crucial for developing use cases that move beyond novelty and drive real impact in the marketplace.

Predictores.ai: What are the biggest challenges organizations face when implementing AI and PI solutions?

Uwe: This is a simple yet significant challenge: enabling managers and their organizations to truly understand what AI is—and what it isn’t. Contrary to popular belief, AI is neither entirely new nor inherently innovative. Its origins can be traced back to the 1950s, with the current hype largely driven by the rapid advancements in computer and processor performance in recent years. In March 2024, I published an article titled 'AI Reimagined: A Pioneering Formula for the Modern Era,' where I discussed our mathematical modeling work that demonstrates AI is essentially a more advanced and sophisticated calculator. Understanding this fundamental concept is crucial for managers and organizations to effectively utilize these tools in their operations.

Without a clear and strategic understanding of how and where to allocate AI, organizations risk repeating the mistakes made during the ERP and CRM hypes. Many of these systems have failed to achieve their anticipated ROI, largely because their implementation was treated as an IT project rather than an initiative for organizational learning and change management.

Predictores.ai: How can they overcome these hurdles?

Uwe: Overcoming the challenges of AI implementation requires a comprehensive and strategic approach. First and foremost, it’s crucial to treat AI initiatives as more than just IT projects. They need to be integrated into the broader strategy of organizational learning and change management. This ensures that the technology aligns with the company’s overall goals and that employees are ready to adapt to new processes. In all our PI projects I see how important this is and this is why our MVP roadmap is aligned to these organizational and learning needs. It must not be about ′buying a tool′ because it is about changing organizational ways of working and thinking.

Education and training are also key. By investing in programs that equip managers and employees with the necessary knowledge and skills, organizations can demystify AI and make it more accessible across all levels of the business.

Another important strategy is to start small. Implementing AI through pilot projects allows companies to test specific use cases in a controlled environment. This approach helps in identifying what works and provides valuable lessons that can be applied when scaling successful initiatives.

Cross-functional collaboration is essential as well. Bringing together IT, data science, and business units ensures that AI solutions are tailored to meet real business needs, rather than being developed in a vacuum. This collaboration often leads to more practical and value-adding AI applications.

Change management should not be overlooked. Implementing AI often requires significant shifts in culture and operations, so it’s important to have a robust change management strategy in place. Clear communication, leadership support, and involving employees in the transition process can greatly reduce resistance and ensure a smoother adoption of AI.

Additionally, it’s important to continuously measure the performance of AI implementations against predefined KPIs and be willing to adapt as necessary. Regular assessments help in identifying areas for improvement and ensure that AI tools contribute positively to the organization’s ROI.

Finally, engaging with AI experts or consultants can provide invaluable guidance. Their experience can help navigate the complexities of AI integration and avoid common pitfalls, making the implementation process much more efficient and effective.

By taking these steps, organizations can not only overcome the hurdles associated with AI but also fully realize the benefits it has to offer.

Predictores.ai: Which industries do you believe will be most transformed by AI and Predictive Intelligence? Can you share examples of where these technologies are making the most significant impact?

Uwe: With over 75 international MVP projects across various verticals and horizontals, I can confidently say that no industry will remain untouched by the transformative power of AI and Predictive Intelligence (PI). The key factor will be how managers, as leaders, choose to harness these incredible opportunities. I draw a parallel to my PhD work, where I examined the impact of ISO 9000 certifications in technical contexts and their sustainable influence on the service industry through ISO 9004 certifications. The findings were clear: when managers viewed ISO 9004 accreditation as a genuine opportunity for growth and optimization, their organizations demonstrated tangible improvements within 6 to 8 months. Conversely, when the focus was merely on obtaining the certificate, there were no significant gains in efficiency or effectiveness. The same principle applies to AI and PI—success hinges on the leadership’s vision and commitment to leveraging these technologies for real organizational advancement.

As part of our recently published advancements and results "Towards Next Generation Data-Driven Management Leveraging Predictive Swarm Intelligence to Reason and Predict Market Dynamics" with Routledge Taylor & Francis Group together with my colleagues and former Predictores CEO Dominik Brunner and lead faculty Prof. Dr.-Ing. Christoph Legat investigating and predicting AI/PI-related industry disruption with a newly developed Industry Disruption Index (IDI) we did not find evidence for significant differences on how AI and PI are impacting industries. However, on a macro level, we are witnessing growing disparities, particularly with the European Union falling increasingly behind Asia and the Americas. The recent announcement by Volkswagen about cancellation of job guarantees is just one example of the consequences of EU over-regulation and a lack of innovation, especially in areas like e-mobility. Unfortunately, progress has been stagnant for too long.

When asked for examples of where AI and Predictive Intelligence (PI) have the most impact, my response might be unexpected. In my view, the greatest impact occurs when leaders and decision-makers recognize that this is a top management issue and approach it as a journey of incremental progress. This isn't about technology or IT projects; it’s about raising awareness, appropriately positioning AI as an enabler and advanced calculator, and allowing time for organizational learning and adaptation. With these foundational principles, any organization—regardless of size or industry—can significantly enhance its competitive advantage.

Predictores.ai: Data is the backbone of AI and PI. What are the common pitfalls companies encounter with data quality, and how can they ensure they are working with the best possible data?

Uwe: The biggest pitfall, much like what we saw during the ERP and CRM hypes, is reducing AI and Predictive Intelligence (PI) to merely a tool, treating it as just another IT purchase. This mindset is dangerous because, while AI may be technically deployed and well-integrated into a company’s tech stack, the crucial conceptual and process dimensions are often overlooked. This oversight can result in poorly defined and inadequately structured multi-dimensional data models, leading to missing or low-quality data. Over time, this compromises the accuracy of predictions, and when entire organizations rely on this flawed intelligence, the risks can be enormous—potentially even leading to total business failure.

This is what we see day by day when we run our PI projects. Many professionals think that Pi is simply to be defined and set-up one. But it is just the opposite. In order to be able to develop a long function gradually more and more precise PI one needs to start from scratch with the essential homeworks starting by initially defining first trial prompts and questions for the PI. Based on this we define and derive the different relevant internal and external data sources. These sources are then dynamically screened by the Trust Analyzer? modul for evaluating the level of information objectivity, reliability and validity. The result is then used for dynamically adjusting the weighting of the incoming data and information as part of the multi-dimensional data model. The different data are then processed by the different defined and also emerged algorithm in the different hidden layers of the different AI applications then leading to the first predictions. These predictions are then being matched with existing reports and analyses of the project organization by running them through the Report Analyzer? modul. The smaller the divergences between the predictions and the information contained in the provided reports, the higher the reliability of the PI.

These are just two of the different moduls and steps we are using in order to start setting up and deploying a Predictive Intelligence. And then the PI starts to learn and become more and more precise. The good thing is, that once the PI delivers reliable results for example in marketing or sales, the corporate PI can then be also used for other business functions such as production or facility prediction or purchase prediction, and step by step more and more internal and external sources of information can be linked to the PI moving towards 360 degree 24/7 data and information.

Predictores.ai: Thank you for this first valuable insights!

In the upcoming interview with Prof. Dr. Uwe Seebacher, we will delve into several key aspects of his career and insights in the fields of AI and Predictive Intelligence (PI).

We’ll begin by exploring his personal journey, uncovering what initially sparked his interest in AI and PI. Prof. Seebacher will share his experiences and the path that led him to become a thought leader in these cutting-edge technologies.

Next, we’ll discuss a specific project or initiative in Predictive Intelligence that he is particularly proud of. He will highlight the key success factors that contributed to the project's success, offering valuable insights into what it takes to drive impactful AI initiatives.

Reflecting on his extensive career, Prof. Seebacher will also share critical lessons learned from working with AI and PI. He will talk about significant setbacks or learning moments that have shaped his approach and understanding of these technologies.

For those new to the field, Prof. Seebacher will offer advice for practitioners on how to navigate and succeed in the rapidly evolving world of AI and Predictive Intelligence. His insights will be invaluable for professionals looking to make their mark in this dynamic area.

Finally, we’ll look ahead as Prof. Seebacher shares his future vision for the role of AI and Predictive Intelligence in shaping the future of business and society. His thoughts on where these technologies are headed will provide a compelling glimpse into the future of AI and its potential impact.

This interview promises to be a deep dive into the mind of one of the leading experts in AI and Predictive Intelligence, offering both practical advice and forward-thinking perspectives.