Challenges of Artificial Intelligence: Risks and Solutions

Artificial intelligence is an advanced technology that changes our lives every day. We can talk for hours about the benefits this invention provides for people and businesses. However, while these tools are designed to improve business efficiency, their implementation is challenging, especially at the initial stage of integration.

Gartner research has shown that more than 85% of AI projects experience delays or difficulties during the integration phase due to a lack of necessary infrastructure and personnel.   

Top challenges of the AI technology 

As AI advances, increasingly complex challenges arise that touch on technological, ethical, and social aspects. Let’s look at the key AI challenges and discuss possible solutions.  

Data protection 

One of the biggest challenges of artificial intelligence is data protection. As AI systems process vast amounts of personal information, compliance with privacy standards such as the GDPR in Europe and the DSGVO in Germany is critical. These regulations require companies to take strict measures to protect data and be transparent about how it is used. In the US, a more flexible standard known as CCPA also imposes requirements on companies regarding the handling of personal information, but with its own twist. All of these regulations pose challenges for companies looking to implement AI, as they need to ensure data security without breaking the law.  

To address this AI challenge and data privacy issues, companies should take several practical steps: 

• First, it is worth conducting a data audit to understand what information is being collected and how it is being used. This will help identify potential risks and areas for improvement.  

• Second, it is important to implement a privacy policy that clearly explains to users how their data is being processed and provides an opportunity for them to consent. It is also worth considering the use of data anonymization and other security technologies to minimize the risk of leakage.  

• Finally, regular training of employees on AI and data protection issues will help create a security culture within the organization and ensure that all required standards are met. 

Liability for damage 

Here, the main AI problem is who should be held liable for damage caused by AI-powered devices or services. For example, if a self-driving car crashes, the question arises: who is responsible for covering the damage – the car owner, the manufacturer, or the software developer? 

If manufacturers are relieved of all liability, they may lose the incentive to create high-quality and safe products, which will reduce user trust in new technologies. However, requirements that are too strict may, on the contrary, slow down the development and implementation of innovations.  

To avoid the problem of liability for damages, it is necessary to implement legal regulations that clearly distribute responsibility between the manufacturer, developer, and owner of the device based on their role in managing AI. It is also important to implement mandatory insurance programs to cover possible losses, ensuring the protection of both users and manufacturers. 

Ethical challenges of AI 

Ethics in AI is a key element in its development. To ensure that people trust technology, it is important to show that AI operates fairly and transparently. If algorithms make accurate and unbiased decisions, users will be more willing to accept them and see them as useful. 

Setting clear ethical boundaries is an important task for businesses. AI systems can impact people’s lives, and companies must set boundaries in advance to avoid negative consequences, errors and ethical risks, such as algorithmic bias or loss of trust. These boundaries must be clear and applied at all stages of the technology’s creation and implementation. 

As we have already mentioned above, the issue of responsibility is also critical. To minimize risks and ensure trust, businesses need to build AI systems with an emphasis on transparency and accountability, as well as ethical standards in working with data. 

Regulatory and legal AI challenges 

One of the key challenges in the development of artificial intelligence is the lack of clear legal regulation and standardization. Different countries are developing their own approaches to legislation, which creates difficulties for companies operating in the international market.  

The European Union has proposed the EU AI Act, the world’s first comprehensive regulation of artificial intelligence. This bill establishes a legal framework for the use of AI, classifying AI systems based on risk level and introducing strict requirements for high-risk applications. The law aims to ensure safety, transparency, and accountability for both developers and users of AI, which will help reduce the risks associated with its use. 

To address the regulatory issues and legal challenges of artificial intelligence, it is important to create global standards and cooperation mechanisms between countries to ensure a unified approach to regulating AI. It is also important to invest in the development of training programs for regulators and companies so that all market participants are aware of new regulations and requirements. 

Intellectual property in AI 

One of the most pressing AI problems is the issue of intellectual property (IP). When AI generates new ideas, works, or even inventions, it is not always clear who owns these results. Traditional patent and copyright laws often fail to keep up with the rapid development of technology, which creates legal uncertainty for developers and companies. For example, if an AI creates a work of art, who is considered the author – the AI developer, the owner of the software, or the AI itself? 

To address IP issues in the context of AI, it is necessary to create a clear legal framework that takes into account the unique aspects of the technology. First, in most cases, companies should develop internal agreements and protocols to determine who owns the results of AI work and how it will be protected.   

Second, it is worth considering the creation of new types of patents or copyrights that are specifically tailored to protect the results of AI work. For example, licenses can be developed that clearly state the rights and responsibilities of all parties – this will help to avoid potential conflicts. 

AI Bias 

The data on which models are trained often reflects historical biases, and human experts may unintentionally introduce their own biases. Identifying such biases requires careful attention, as they are often deeply ingrained and only become apparent when the final results are analyzed. But how to minimize AI risks related to bias? 

To minimize bias, companies must actively employ multi-layered strategies. This includes data analysis, algorithm audits, diversification of training data, and transparent practices. It is also important to develop a culture of inclusivity, ensuring the participation of experts with different views and backgrounds. It is important to identify potential biases early on and adjust models to ensure they remain fair and unbiased. 

Continuous monitoring is key to maintaining fairness in AI. Without regular monitoring, biases can lead to negative consequences, for example, in hiring or lending. Continuous auditing and adjustment of algorithms help identify and fix problems before they become widespread, which increases trust in AI and contributes to a more equitable technological environment.

Top challenges of AI implementation  

Implementing AI into existing processes and systems is a complex task. It is necessary to identify suitable use cases, tune AI models for specific tasks, define AI opportunities and risks and ensure that they integrate seamlessly with the current infrastructure. 

Companies face the challenge of creating fully customizable AI solutions. While AI is great for automating processes, it still requires human support. To improve accuracy and efficiency, companies can train AI systems based on specific tasks and data. However, to achieve the best results, collaboration between humans and AI is necessary. Let’s take a closer look at the main challenges in AI and possible ways to overcome them. 

Lack of AI expertise 

One of the major AI implementation challenges is the shortage of skilled professionals. The AI technology market is still in its early stages, and as of 2023, according to a LinkedIn report, the demand for AI experts has grown by 74% over the past 4 years, but the number of available professionals has not kept up with this growth. This poses significant obstacles for companies looking to effectively implement and use AI technologies.  

To overcome this AI problem, companies should take the following steps: 

• Invest in employee training: Providing internal training and courses on AI will help develop the skills of existing employees. 

• Turn to external experts: Hiring freelancers or consultants with experience in AI can speed up the implementation process and help bridge knowledge gaps. 

• Create a culture of knowledge sharing: Organizing regular meetups and workshops where employees can share their experiences and ideas on using AI will increase the overall level of expertise within the company. 

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Software Aspekte helps companies understand how their businesses can benefit from AI and find specific and most optimal use cases and scenarios tailored to their needs. We talk about AI challenges and give practical advice on how to overcome them. 

Our AI experts can deliver a workshop for your business on the fundamentals of AI technology and its potential applications. So, if you need support in any AI-related process, contact us. Our team will be happy to help you.

Сhoosing AI use cases 

Companies often don’t know where to start: which processes can really benefit from AI automation, and where the implementation of the technology will not bring the expected benefits. This uncertainty leads to businesses either implementing AI in the wrong places or missing out on opportunities for significant efficiency improvements. According to Gartner, more than 60% of AI projects do not achieve expected results due to errors at the stage of choosing suitable use cases. 

To overcome this AI implementation challenge, companies can use several approaches: 

• Analyze current processes: Conduct a detailed audit of your business processes and identify those with the biggest gaps or routine tasks that AI can optimize. 

• Start with small projects: Testing AI on limited tasks will help you understand its real effectiveness without large-scale investments. 

• Learn from others: Review examples of successful AI implementation in similar industries to see where the technology has already proven its value. 

• Leverage expert advice: Engaging AI consultants or partners with experience in selecting and implementing cases can significantly reduce the risk of failure. The Software Aspekte team conducts analytical assessments to identify tasks that AI solutions can improve. By analyzing current operations, we select areas where AI can be beneficial and recommend solutions for improvement. 

AI glitches 

One of the major technological challenges of artificial intelligence is the tendency of AI systems to create unreliable information. Sometimes, AI “makes up” numbers or facts when there is not enough information to draw such conclusions. This is especially critical in applications where data accuracy is of utmost importance, such as in healthcare or the financial sector. Incorrect data can have serious consequences and reduce trust in AI. 

Here are some suggested solutions for overcoming challenges: 

• Testing and validating actual data: Regularly testing AI models, comparing their responses to verified facts and hypothetical scenarios can reveal anomalies and help determine the causes of incorrect responses. 

• Performance monitoring: It is important to monitor discrepancies between expected and actual results. This helps to identify errors in time and assess where the AI gets its information. 

• Training on high-quality data: Ensuring that AI models are trained on high-quality and reliable data can significantly reduce the likelihood of errors. 

• Human oversight: Implementing processes where humans can intervene, review, and correct AI results, especially in mission-critical systems. 

The absence of capable infrastructure 

Many companies face the AI challenge of legacy infrastructure when implementing AI. Old systems and applications cannot handle large volumes of data in the required timeframe, which slows down and complicates the integration of AI technologies. The lack of powerful storage and modern processors also hinders successful implementation, not to mention the need to train employees to work with new tools.  

Here are some options for solving this technological challenge: 

• Scalable computing power: Implementing modern processors (e.g. GPUs or TPUs) that are specifically designed to handle large amounts of data and support parallel processing will help process data faster, which is critical for effective AI. 

• Cloud solutions: Switching to cloud platforms such as AWS, Google Cloud, or Azure can greatly simplify access to powerful computing resources and provide flexibility in processing big data. 

• Optimizing data storage: Invest in high-performance data warehouses (e.g. SSDs) and databases that can quickly process large amounts of data (e.g. NoSQL or distributed databases). 

• Integrating modern APIs and microservices: Modernizing legacy systems by using APIs and microservices architecture can improve interoperability between different systems and AI, making it easier to implement and operate. 

The cost and return on investment 

Implementing new technologies like artificial intelligence in a company always requires a significant financial investment. You need to consider the costs of purchasing equipment, licensing software, and hiring or training specialists. Even with the most advanced solutions and talented people, it can be difficult to estimate the return on investment. Unlike traditional investments, the results may not be immediate and may take several years to fully pay off. This is a long-term strategy, and the exact payback period is not always clear. 

Let’s look at some options for tackling the cost and ROI challenge: 

• A gradual approach: Instead of investing in a full rollout, start with pilot projects. This will help evaluate the effectiveness of solutions for AI challenges and give you the opportunity to adjust the strategy before large-scale implementation. 

• Clear KPIs and metrics: Develop specific metrics to measure the success of AI projects. These can be not only financial results, but also productivity improvements, cost reductions, process optimization, etc. 

• Long-term value assessment: It is important to consider not only short-term costs, but also potential long-term benefits, such as automation of routine tasks, improved quality of products and services, which can ultimately lead to cost reductions. 

• Reassess and adjust the budget: It is important to regularly review the budget to adapt to new conditions and get the most out of your investment. 

Conclusion 

Implementing new technologies into your business can be a real challenge, but it also brings significant benefits. By recognizing the potential AI challenges and taking proactive steps to overcome them, you can successfully overcome the obstacles that arise and make the most of the capabilities of this modern tool.  

It is important to remember that it is not just about technology – it is about creating an environment in which people and machines can successfully interact and develop. When humans and technology work together, solutions can be developed that are tailored to the unique needs and preferences of a business while maintaining flexibility and personalization. 

And if you need help with implementing AI in your business processes or you want to get advice on using AI technology in your company, contact our AI experts. Software Aspekte will analyze your company’s processes, identify where AI can create added value, and develop customized AI solutions that achieve your goals.