Imagine a production line where machines not only operate but also learn on their own. Where sensors detect anomalies even before a malfunction occurs. Where the warehouse doesn’t wait for someone to notice that an item is out of stock, but places an order with the supplier on its own.
These are current and emerging use cases in supply chain and operations. An InfoBrief from IDC, a global IT market research firm, published in April 2024, describes automated replenishment, predictive maintenance, and visual quality control, among other things, as promising areas of application.
Companies expect artificial intelligence to lead to, among other things, more efficient processes, faster responses to disruptions, and higher employee productivity. The actual impact varies depending on the use case, data quality, and implementation.
From Data to Decision
Every manager in the industry knows this: there’s plenty of data. From production figures to inventory levels, from transportation data to financial figures. But often, that data is locked away in different systems. Or worse yet: in Excel spreadsheets and paper documents. IDC emphasizes that data quality, sufficient data, and mature data management are key prerequisites for successful AI implementation.
Still, there is hope. Companies that clean up, centralize, and effectively make their data accessible increase the likelihood that AI will actually deliver value. For example, planners can make more accurate predictions based on up-to-date data, and operators can identify maintenance needs earlier through predictive maintenance. The right data, governance, and architecture form the foundation for this, while AI outcomes will always require human judgment.
Pioneers and Followers
A common question among many managers is: Should I get on board now, or would it be better to wait until the technology is more advanced? IDC refers to this as the “leader” versus “fast follower” dilemma.
The answer: those who start now will learn faster. Of course, AI tools will continue to evolve, but the knowledge you gain by experimenting today will give you a head start tomorrow. Companies that wait too long risk falling behind in an industry that is becoming increasingly smarter and more flexible.
Strategic Questions for Managers
IDC neatly summarizes end-users’ concerns in three questions:
- Is our data good enough to start using AI?
- Should we lead the way, or is it better to be a fast follower?
- Do we view AI as a standalone tool, or as an integral part of our processes?
This is exactly where you, as a manager, can make a difference. By making the right choices, you can use AI as a catalyst rather than just another trend.
What this means in the workplace
Consider a logistics manager struggling with delivery reliability. Routes are becoming more complex, drivers are in short supply, and customers are demanding shorter delivery times. AI helps by recognizing patterns in traffic data, weather conditions, and historical deliveries. The system suggests routes that are not only faster but also more sustainable. The result: satisfied customers and lower costs.
Or consider a production manager who struggles with quality issues on a daily basis. AI-powered, trained cameras can detect deviations earlier and more consistently. Reliability depends on image quality, training data, validation, and human quality control. This can reduce waste and support quality processes.
Even the CFO benefits: more accurate demand forecasts lead to lower inventory levels and better control over working capital. Instead of millions tied up in excess raw materials, inventory is better balanced with demand.
Not without risks
AI sounds promising, but it also carries risks. Bias in algorithms, concerns about data privacy, and the question of who is responsible for the decisions AI makes. IDC therefore emphasizes the importance of governance: clear rules, transparency, and collaboration with partners who understand both the technology and the industry.
This aligns with SAP’s vision of the Autonomous Enterprise: people set the parameters, while AI assistants and agents support processes based on your organization’s authorized business data and process context. Public web information can only be used as a supplement when that option is available and has been explicitly enabled. This ensures that your data remains secure while still allowing you to work with AI.
Quinso as a trusted advisor
That’s exactly where Quinso comes in. We know the world of industry from the inside out and combine that with in-depth SAP expertise. That means we don’t view AI as a standalone project or a passing trend, but as part of the bigger picture: making your business processes smarter and more future-proof.
We help organizations start small, choose the right use cases, and simultaneously build a foundation of reliable data. That way, AI evolves from an experiment into a strategic driver for your business.
Now is the time to get started
IDC’s message is clear: AI is no longer something for “later.” Companies that start today can reap the benefits tomorrow. This isn’t just about automation, but primarily about applications that support and empower employees. At the same time, job roles, responsibilities, and required skills may change. IDC therefore advises organizations to explore now where AI can deliver demonstrable value.
Would you like to see how supply chain and production processes currently work and how they can be optimized with SAP Business AI? During our digital masterclass, we’ll turn common pain points into concrete, practical use cases.
Curious about the full IDC study? You can read it here:
