Artificial intelligence (AI) involves programming machines to simulate human thinking and actions. AI has reached full maturity by offering scalable, reliable, and connected services. The next step is scaling AI up into production. With the power of AI, industrial businesses can implement more effective quality controls, predict system failures, streamline inventory tracking, or implement autonomous logistics processes.
AI can work in real-time, improving throughput, whilst simultaneously being able to recognize early indicators for quality issues, within the production process to prevent potential failures. By combining AI in production, challenges in logistics can also be simplified. Having control of processes and inventory is important for logistics management as well as identifying potential inefficiencies. By combining cameras with AI, we can analyze transition areas, inventory and even their state. Displayed on a Digital Twin, this makes for easy access to information, enabling warning systems for sensitive areas of the production area.
AI is still in its early stages of being rolled out across industries. The International Data Corporation (IDC) estimates more than half of AI projects don’t move beyond the planning stages. Some issues why AI projects fail are:
1. Integration issues with business processes, IT systems and data sources
2. Not having a clear path from proof of concept (PoC) to a productive environment
3. Reliability, security, and total cost of ownership.
Our AI Solution Factory helps to overcome these challenges. We discuss business goals through common workshop for stakeholders to develop a clear understanding about data and its availability for usage, which are fundamental in AI projects. Followed by a discussion on how to put the company’s data to use in an AI environment.
The next step is the proof of concept (PoC) and modelling, which involves training algorithms and training the customer. But that’s where lies the real challenge to move to production. The stumbling block involves issues such as monitoring, logging, edge hardware and computing capabilities, and is something a partnership with T-Systems can help businesses overcome with effective implementation of the rollout stage from PoC to a productive environment.
Another concern is cost. Retraining models and testing deployed AI functions can get very expensive. T-Systems can help businesses cut costs by reusing platforms from previous projects.
The AI Solution Factory from T-Systems enables AI application creation from development to an integrated application in a cloud or edge environment. We do this in three stages.
T-Systems ensures that your AI project is reliable, successful, and secure.
