Challenges for leaders during AI implementation

The history of artificial intelligence (AI) is decades old but only in the past few years it has made its way back to the spotlight in business life: due to technological advancements and abundance of data, it has become possible to utilize this technology effectively in business. Many industries have been busy adopting AI but only few have made significant financial gains with it.

Over the course of recent years, the hype for AI’s utilization in the field of business has soared. It has regained interest of the public as a direct result of an abundance of new breakthroughs [1]. A Global survey conducted by McKinsey Analytics suggested that approximately fifty percent of research participating companies have adopted AI as a component of business ventures [2].

The scientific research community and the public currently converse about terms such as companies’ data strategies, Big Data, Machine Learning and Deep Learning – terminology relevant to understanding the topic sphere. As an example of the vast emphasis AI already has on society, a report called State of AI in Finland 2020, disclosed that AI would become an official item of Finland’s national strategy, in an aspiration to recover growth opportunities for the technology sector [3]. Currently, businesses, and people alike, are facing a situation where adaptation to the new technology is imminent and inevitable, whether they like it or not.

I am Shahzaib, I started working in the field of AI in 2015, creating solutions for machine learning, deep learning, reinforcement learning, and computer vision. In the spring of 2018, I became interested in Natural Language Processing (NLP), which is about making computers understand and work with human language. By the end of 2018, I started learning about Large Language Models (LLMs), even though they weren't very popular at the time. After that, I switched from doing research to working directly with clients. Over the past five years, I have had many meetings to understand how AI can be used in different areas and what are the challenges that arise during AI implementation. Now lets start with applications.

Applications in Diverse Sectors

AI is not a futuristic vision, but rather something that is here today and being integrated with and deployed into a variety of sectors. This includes fields such as finance, national security, health care, criminal justice, transportation, and smart cities. There are numerous examples where AI already is making an impact on the world and augmenting human capabilities in significant ways.

 One of the reasons for the growing role of AI is the tremendous opportunities for economic development that it presents. A project undertaken by PriceWaterhouseCoopers estimated that “artificial intelligence technologies could increase global GDP by $15.7 trillion, a full 14%, by 2030” [4]. That includes advances of $7 trillion in China, $3.7 trillion in North America, $1.8 trillion in Northern Europe, $1.2 trillion for Africa and Oceania, $0.9 trillion in the rest of Asia outside of China, $0.7 trillion in Southern Europe, and $0.5 trillion in Latin America. China is making rapid strides because it has set a national goal of investing $150 billion in AI and becoming the global leader in this area by 2030.

Meanwhile, a McKinsey Global Institute study of China found that “AI-led automation can give the Chinese economy a productivity injection that would add 0.8 to 1.4 percentage points to GDP growth annually, depending on the speed of adoption.” [5]. Although its authors found that China currently lags the United States and the United Kingdom in AI deployment, the sheer size of its AI market gives that country tremendous opportunities for pilot testing and future development.

Sale Generation

Sales of AI applications are expected to rise from about $93 million in Europe in 2016 to nearly $7,876 million in 2025 (see Fig 2). This corresponds to a growth rate of over 8400% and illustrates the potential of using software supported by AI. Worldwide it is expected to increase from nearly $360 million in 2016 to over $31,000 million in 2025, a growth rate of nearly 8730%. Furthermore, a Deloitte-study revealed that about 80% of 200 companies questioned in Germany count AI to their crucial success factors.

Challenges

AI is becoming a major topic in the business world, with companies like Google, Netflix, Amazon, and others benefiting greatly from AI and machine learning algorithms. Small and medium-sized businesses, as well as huge corporations, are all affected. Businesses have been under pressure to adopt AI technologies in order to stay competitive. There are numerous publications demonstrating the need of incorporating AI into business procedures. Because artificial intelligence has proven to be advantageous to the successful operation of enterprises. According to an Accenture analysis, artificial intelligence may raise corporate efficiency by 40% and profitability by 38%.

However, we can't ignore the difficulties that organizations have faced in implementing AI. These obstacles make the prospect of successful AI integration appear far-fetched, if not impossible. There are several challenges in adopting AI in the business sector, for example:

•  Knowing Which Tasks Are Best Left to People
•  Over-Relying on Third-Party Integrations
•  Losing Empathetic Customer Service
• Leaders Not Having a Clear Understanding of How AI Is Being Used on Their Organization
•  Identifying The Correct Problem to Address
•  Not Adding a Human Element
•  Finding Developed Services to Manage AI Computing Power
•  Trust Deficit, Limited Knowledge
•  Human-level
•  Data Privacy and Security
•  The Bias Problem
•  Data Scarcity

AI Strategy

As AI technology weaves into the societal fabric, global AI regulations emerge as a crucial, yet complex topic. The release of technologies like ChatGPT has amplified public interest, urging the need for harmonized regulation. This article delves into the diverse approaches to AI governance worldwide, exploring their implications for innovation and ethical standards in the digital age. Understanding the landscape of global AI regulations involves familiarizing oneself with the specific requirements and restrictions imposed by different jurisdictions. It requires keeping up-to-date with the latest developments and regulation changes to ensure compliance, avoid legal issues, and mitigate potential risks including possible reputational harm.

Key Components of Different Country Regulations

Different countries have their key components when it comes to AI regulations. These components can vary in scope, focus, and specific requirements. Some common key components of different country regulations include:

• Data protection and privacy: Many countries have specific regulations regarding the collection, storage, and use of personal data in AI systems. These regulations aim to protect individuals' privacy and ensure that their data is handled responsibly.
• Transparency and explainability: Some countries require AI systems to be transparent and provide explanations for their decisions. This ensures accountability and helps build trust in AI technologies.
• Bias and fairness: Addressing bias and ensuring fairness in AI systems is a key concern for many countries. Regulations may require businesses to take steps to mitigate bias and ensure that AI systems do not discriminate against certain groups.
• Ethical considerations: Some countries have started incorporating ethical principles into their AI regulations. These principles guide businesses in developing and using AI systems responsibly and ethically.

Challenges Faced by Businesses in Compliance

Understanding the key components of different country regulations is essential for businesses operating in multiple jurisdictions. It helps them adapt their AI systems to comply with specific requirements and avoid potential legal risks while building and maintaining customer trust. Complying with global AI regulations can present several challenges for businesses. Some of the common challenges faced include:

• Complexity and inconsistency: The landscape of global AI regulations is complex and often inconsistent. Different countries may have different requirements and interpretations of AI regulations, making it challenging for businesses to navigate and ensure compliance.
• Rapidly changing regulations: AI regulations are evolving rapidly as technologies advance and new concerns arise. Businesses need to stay updated with the latest changes in regulations to ensure compliance and avoid penalties.
• Resource and expertise constraints: Ensuring compliance with global AI regulations requires dedicated resources and expertise. Smaller businesses or those without specialized legal or technical teams may face challenges in understanding and implementing the necessary measures.
• Cross-border operations: Businesses that operate across multiple countries face additional challenges in complying with different sets of regulations. They need to develop strategies and processes that enable them to meet the requirements of each jurisdiction.

Overcoming these challenges requires a proactive approach, investment in resources and expertise, and staying informed about the latest developments in global AI regulations.

Impact of Global AI Regulations on Innovation

Global AI regulations have a significant impact on innovation in the AI industry. While regulations aim to ensure responsible and ethical use of AI technologies, they can also introduce barriers and challenges for businesses. One of the impacts of global AI regulations on innovation is the need for increased transparency and explainability in AI systems. While this fosters trust and accountability, it can also limit the development of more complex and sophisticated AI models that may not be easily explainable. Additionally, regulations that focus on data protection and privacy can introduce limitations on the use of data for training AI models (see Fig 4). This can hinder the development of AI systems that rely on large amounts of data. However, global AI regulations also present opportunities for innovation. Setting clear guidelines, standards, and regulations can encourage the development of AI technologies that prioritize fairness, ethics, and human rights. They can also foster collaboration between businesses, researchers, and regulators to address the challenges and risks associated with AI. Overall, the impact of global AI regulations on innovation is a complex and multifaceted issue that requires balancing the need for regulation with the potential for innovation and advancement in the AI industry.

Best Practices for Navigating Global AI Regulations

Navigating global AI regulations can be daunting, but there are best practices that businesses can follow to ensure compliance and mitigate risks. Some of these best practices include:

Stay informed: Keep track of the latest developments in global AI regulations. Regularly review updates from regulatory bodies and industry associations to stay up-to-date with the evolving landscape.

Conduct thorough assessments: Assess the impact of global AI regulations on your business operations and AI systems. Identify areas where compliance may be challenging and develop strategies to address them.

Engage with regulators: Establish open lines of communication with regulatory authorities to seek clarification and guidance on compliance requirements. Proactively engage in discussions and provide feedback on proposed regulations.

Collaborate with experts: Seek advice from legal, technical, and ethical experts who specialize in AI regulations. Their expertise can help your business navigate the complexities of global AI regulations and ensure compliance.

Implement robust governance frameworks: Develop internal governance frameworks that incorporate the principles and requirements of global AI regulations. This includes processes for data protection, bias mitigation, and ethical considerations.

By following these best practices, businesses can navigate global AI regulations effectively and ensure compliance while maximizing the benefits of AI technologies.

REFERENCES

[1]   Tecuci, G. Artificial Intelligence. Wiley Interdisciplinary Reviews Computational Statistics 2021.

[2]   Balakrishnan, T., Chui, M, Hall, B. & Henke, N. Global survey: The state of AI in 2020. 2021.

[3]   Törnroth, A., Poikola, A., Hagstöm, H., Ailisto, H., Huttunen, H. Purosto, J., Himberg, J., Mäkinen, J., Kutvonen, M., Keski-Äijö, O., Floréen, P., Alanen, P., Myllymäki, P., Räsänen, P., Erkinheimo, P., Lankiniemi, S., Kaukonen, S., Seppala, T., Mucha, T., Saavalainen. State of AI in Finland 2021.

[4]   PriceWaterhouseCoopers, “Sizing the Prize: What’s the Real Value of AI for Your Business and How Can You Capitalise?” 2017.

[5]   Dominic Barton, Jonathan Woetzel, Jeongmin Seong, and Qinzheng Tian, “Artificial Intelligence: Implications for China” 2017.