The competition in artificial intelligence applications is increasing, but upcoming regulations, standards, and norms bring uncertainty. To gain a competitive advantage as a company and to attract more AI experts as employees, data science must now be connected to the core business, and AI must be scaled. To do this, it is essential to understand the risks of AI applications and their effects comprehensively and in-depth.
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Why Do Companies Now Need To Understand The Risks In Detail To Make Far-Reaching Decisions?
The excellent business potential of AI applications has long since arrived on the management floors. At the same time, far-reaching changes are pending with AI, which leads to uncertainties: In compliance, the “EU AI Act” will bring regulations to companies that do not only affect high-risk applications. The self-regulatory concept of trustworthy AI is still quite vague. This is about complying with various aspects, from fairness to security to reliable robustness. In addition, industrial standards (DIN, VDE, IEEE) are currently being developed in multiple places, which set long-term guidelines but cause a lot of uncertainty in far-reaching decisions regarding practical design and implementation.
In addition, there are requirements from stakeholders from various areas. These can be customers, customers’ buyers, rating agencies, auditors, and the press. You will all be interested in how AI companies balance the “S” (social) and “G” (governance) criteria in their AI decisions. In the future, large companies’ purchasing and specialist departments will increasingly ask about measures such as compliance and trustworthiness of the AI applications in their tenders.
Why AI Now Needs To Be Closely Connected To Business
How will the new framework conditions affect the development and success of AI applications; what risks arise and how can managers make the right decisions? Only when the AI is firmly anchored in the organization, is part of the overall process, and is deeply integrated into risk management can appropriate adjustment screws be found for making decisions in risky gray areas.
To do this, it is necessary to consider the AI application holistically over its entire life cycle. This includes the processes of AI creation, software integration, and deployment. External and neutral service providers with high standards help assess and optimize your data scientists’ statements.
Better Manage Individual AI Risks With Solid Risk Management
Anyone who manages a high budget always has the risk on their radar anyway: What can happen there? Who is responsible for that; how quickly do we notice the mistake; Has something like this happened before, and what does it cost us if it doesn’t work? These questions often only generate a shrug of the shoulders when it comes to new technologies because the risk landscape of AI is complex.
Risks are hidden within the four primary areas of tension: robustness, fairness, transparency, and security, especially in the interaction with each other. Corporate management always has to consider: How transparent should we be, how fair? What risk arises when we consciously reduce one area to strengthen another? Such a consideration only works if the risks and their impact are clearly understood.
Completely Illuminate And Dig Deep Into The Risk Landscape Of AI
Suppose all risks of the AI application are systematically recorded and evaluated holistically at all levels. In that case, the risk management in the company is no longer understood only as a skeptic, spoilsport, and brake but as a clutch for shifting up to the next gear.
Because only when the risk landscape of AI is carefully examined can the risks of AI become manageable. This also includes, for example, the planning of mitigation and communication measures – an enormous competitive advantage over companies that only drive by sight in the semi-dark.
And: If you understand the unique risk landscape for your AI product, you will be able to cope more quickly with changing framework conditions and challenges with scaling. After all, when you scale up, the risks can quickly multiply. A trustworthy AI you want to scale up must be set up cleanly and integrated into the organization.
Detailed Investigation And AI Risk Heat Map Expand Room For Decision-Making
Most companies in the AI sector feel comfortable with robustness. Still, they face significant difficulties in assessing the risks in the other levels and then individually with the new requirements and regulations for the use case. The dangers of AI should be continuously examined and evaluated about the use case. For this purpose, risk management and data science expertise must be bundled so that companies gain greater transparency about AI risks and their impact on the business. It is crucial that the AI is examined in detail and that these details are examined with particular questions from experts who have specialist knowledge but also understand the business connection.
A heatmap should capture the individual risks of the AI applications and serve as a solid basis for scenarios and decisions. In this way, the risk management experts and the AI department in the company move even closer together, increasing their decision-making scope and strengthening the company’s competitiveness in the long term.