Some of the challenges of AI applications are:
● Lack
of understanding: AI is still a relatively new field, and there is much we
have yet to comprehend about its inner workings. This lack of understanding can
impede the development of reliable and accurate AI systems. To address this
challenge, companies can invest in research and development efforts to advance
the understanding of AI algorithms, models, and techniques2.
● Data
quality and availability: An inherent problem with AI systems is that they
are only as good – or as bad – as the data they are trained on. Bad data is
often laced with racial, gender, communal or ethnic biases. This can lead to
unfair or discriminatory outcomes for certain groups of people. Moreover, some
domains may lack sufficient or relevant data to train AI systems effectively.
To address this challenge, companies can ensure that their data is diverse,
representative, and unbiased. They can also use techniques such as data
augmentation, synthetic data generation, and transfer learning to overcome data
scarcity or limitations4.
● Ethical,
legal, and social issues: AI systems can raise various ethical, legal, and
social issues that require careful consideration and regulation. For example,
AI systems can pose risks and challenges for human rights, privacy, security,
democracy, and social justice. They can also create moral dilemmas, such as who
is responsible or liable for the actions and impacts of AI systems. To address
this challenge, companies can adopt ethical principles and values that guide
the design, development, and deployment of AI systems. They can also
participate in the governance and regulation of AI, as well as hold AI systems
and their creators accountable for their actions and impacts2 3.
● Skills
gap and talent shortage: AI and emerging technologies are creating new
opportunities and challenges for education and skills development. As AI
automates many tasks and jobs, especially those that are routine and
repetitive, the demand for higher-order cognitive and socio-emotional skills
will increase. These skills include critical thinking, creativity,
communication, collaboration, and emotional intelligence. They also include
digital skills, such as coding, data analysis, and AI literacy. To address this
challenge, companies can invest in upskilling and reskilling their workforce to
prepare them for the future of work and learning. They can also collaborate
with educational institutions and other stakeholders to foster a culture of
lifelong learning and innovation2 3.
Source(s)
1. Top
10 Challenges In Artificial Intelligence In 2023 - Dataconomy
2. Artificial
Intelligence: Key challenges and opportunities - Forbes India
3. Top
10 AI Development and Implementation Challenges
4. Challenges
of using artificial intelligence | Deloitte US
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