As the value potential of AI has been realized, it has resulted in rapid expansion and development of real-world applications. The rapid adoption isn't because AI will replace the human workforce but enhance it by taking care of the boring and time-consuming activities that, while essential, aren't favored by colleagues for one reason or another.
There are a couple of common examples of this already, such as:
- Predictive maintenance: Recording data from sensors and equipment to predict failures before they occur, reducing downtime and maintenance costs. The benefit being an improvement in operational efficiency and extending asset lifespans.
- Quality checks: Machine vision automates quality checks by analyzing images to detect defects and ensure consistency. It's cost-effective, scalable, and enhances precision compared to manual inspections.
Thanks to advancements in cloud computing, such as SAP BTP, AI is no longer bound by technical constraints such as computational power or data processing limitations incumbent on a manufacturer's infrastructure. This means they can now harness AI-driven solutions with unprecedented speed and scale. These advancements empower rapid innovation, enabling smarter production processes, enhanced quality control, and better resource management—key factors that form real business agility.
The Trust Deficit
Trust has become the critical barrier to AI adoption because, despite its technical capabilities, AI often operates as a "black box," making its decision-making processes opaque. Manufacturers usually don't feel confident that AI outputs are accurate, unbiased, and aligned with business goals. Without the ability to understand or verify these results, leaders, consciously or not, hesitate to rely on AI for critical decisions, fearing negative operational impacts.
Now, if you've invested in AI to keep up with competitors, this trust is essential to succeed. Trust can be built by understanding and reconciling AI outputs, which is simpler than you might think. The key is "robust data governance."
The Role of Robust Data Governance
All AI is only as good as the data behind it. It's a classic GIGO (garbage in, garbage out) scenario. Thus, if you want to trust your AI is making good decisions, you need to start with your data. Enter data governance. A good master data management solution centralizes, standardizes, and cleanses data, eliminating duplicates and inconsistencies that could distort AI outputs.
By providing a single source of truth, MDM ensures that AI training data is accurate, representative, and free from biases that could lead to skewed results. This is particularly critical in manufacturing, where AI applications depend on precise, consistent data to perform effectively.
Ethics and Human Oversight
New hires are typically unaware of your company's policies and protocols, so they must be trained to do the job well and be trusted. AI is the same. It can't just come in and do the job instantly; it needs time and collaboration to learn right from wrong.
Without careful oversight, biases in that data can result in unfair or harmful outcomes. For example, AI models trained on skewed datasets may perpetuate gender, cultural, or operational biases, producing results that undermine inclusivity or decision-making fairness. This could mean flawed quality control decisions or biased workforce optimization recommendations in manufacturing. It's important to remember that the technical side of AI is so important, but it ultimately affects your people.
This isn't something you can get out of the box. A one-size-fits-all approach will not work as ethics and organizational policies differ for everyone. To prevent such issues, businesses must align AI outputs with their core values, such as fairness, transparency, and sustainability. This requires deliberate retraining of AI models and the unique contexts of their operations, ensuring that the technology supports, rather than contradicts, the company's mission.
While trust is being built and tested, human oversight is indispensable. While AI excels at processing large volumes of data, it cannot understand nuance, context, or the ethical implications of its outputs. Manual hand-holding is vital in validating AI decisions, providing the "common sense" that technology cannot replicate.
It might sound counter-intuitive to run manual processes alongside AI automated ones—it's supposed to cut the workload in half, not double it, right? Well, yes, and although there is a cost implication, overlapping the two is a dead-certain way to know if it's doing the job, i.e., if it can be trusted.
A Roadmap to Trusted AI
- Implement a Robust Data Governance Solution: Start by creating a solid foundation for AI models to make reliable, objective decisions by ensuring the data you are feeding it is correct.
- Prioritize Ethics and Human Oversight: Train AI with company-specific policies to align with organizational values like fairness and transparency. Regular retraining and human oversight are crucial to keep AI updated with the latest evolutions in your company's ethics.
- Parallel Manual and AI Processes for Verification until you are happy: Test if the system's outputs are reliable. Although this incurs additional costs, it provides confidence that AI performs as expected, reinforcing accountability and trust.
