AI in the Enterprise: Challenges & Opportunities | Bài thảo luận tiếng anh thương mại

THUONG MAI UNIVERSITY TOPIC
AI IN THE ENTERPRISE: CHALLENGES AND OPPORTUNITIES Group 5 Class: 251_EFIN3311_31
Hà Nội, 09/2024
TASKS ASSIGNMENT TABLE Name Sudent ID Task Nguyễn Ngọc Linh 23D190074 III. Recommendations 3.1. For Businesses Nguyễn Thị Hải Linh 23D105060 III. Recommendations 3.2. For Individuals / Employees Vũ Khánh Linh 23D105063
II. Main contents 2.2. Opportunities Nguyễn Thành Luân 23D105023
I. Introduction & Overview Nguyễn Trung Đức Minh 23D190029 IV. Conclusion Hoàng Thị Bích Ngọc 23D105066
II. Main contents 2.1. Challenges 1
TABLE OF CONTENTS
I. INTRODUCTION & OVERVIEW........................................................................- 1 -
1.1. RATIONALE FOR CHOOSING THE TOPIC...............................................................- 1 -
1.2. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?..........................................................- 2 -
1.3. THE IMPORTANCE OF AI IN THE CURRENT CONTEXT..........................................- 2 -
II. CHALLENGES & OPPORTUNITIES................................................................- 3 -
2.1. CHALLENGES........................................................................................................- 3 -
2.2. OPPORTUNITIES....................................................................................................- 4 -
III. RECOMMENDATIONS......................................................................................- 5 -
3.1. FOR BUSINESSES..................................................................................................- 5 -
3.2. FOR INDIVIDUALS / EMPLOYEES..........................................................................- 5 -
IV. CONCLUSION......................................................................................................- 7 -
REFERENCES............................................................................................................- 8 - 2
I. Introduction & Overview
1.1. Rationale for Choosing the Topic
AI has shifted from a theoretical concept to a strategic imperative. By 2025, 78%
of global companies use AI daily, over 90% are adopting or exploring it, and 92% of
executives plan to increase spending within three years. AI is no longer experimental but
a core driver of business strategy.
The global AI market is projected to reach $1.85 trillion by 2030 with a CAGR of
37.3%. WTO estimates AI could boost global trade by nearly 40% and raise GDP by 12– 13% by 2040. In this sense, AI is “as transformative as the steam engine” for
productivity, with McKinsey projecting a long-term boost of up to $4.4 trillion. However,
business adoption is still nascent: nearly all companies invest in AI, but only about 1%
consider their deployment mature. This gap—between AI’s immense promise and its
real-world integration—makes it a critical area of study for enterprises.
1.2. What is Artificial Intelligence (AI)? According to IBM, AI refers to technologies that simulate “human learning,
comprehension, problem solving, decision making, creativity, and autonomy.” At its
core, AI encompasses systems that can learn, reason, and act like humans. Its primary business applications rely on machine learning and deep learning, which allow
prediction, automation, and insight extraction.
Artificial intelligence is a broad, interdisciplinary field of science dedicated to
building computers and machines that can perform tasks that would typically require
human intelligence. At its core, AI is a set of technologies that empower computers to
reason, learn, and act in ways that mimic human cognitive functions. These capabilities
include the ability to see, understand and translate spoken and written language, analyze
data, and make recommendations. While AI encompasses many disciplines—including computer science, data analytics, neuroscience, and engineering—its operational
application in business is primarily based on machine learning and deep learning.
Machine learning, a popular subset of AI, involves training algorithms on vast
datasets to identify patterns and relationships that humans may miss, enabling the system
to make predictions or categorize information. The application of these technologies is
revolutionizing numerous business processes. For example, generative AI is used for
content creation and personalized marketing, while business intelligence (BI) platforms
leverage AI to analyze large volumes of data and provide actionable insights for data-
informed decision-making. Similarly, Optical Character Recognition (OCR) uses AI to 3
extract text and data from unstructured documents, transforming them into structured, business-ready information. These and other
applications demonstrate AI's power to
automate workflows, reduce human error, and accelerate research and development.
1.3. The Importance of AI in the Current Context The contemporary importance of AI extends far beyond a simple list of applications; it fundamentally redefines how businesses establish and maintain a
competitive advantage. In a dynamic market, traditional differentiators such as cost
leadership, brand strength, or operational scale are becoming standard expectations rather
than unique strengths. The new competitive edge is a company's speed of detection and response to change.
AI's ability to process and analyze massive volumes of real-time data allows
businesses to move from a reactive to a proactive strategic posture. Rather than relying
on static, retrospective reports, AI-driven insights continuously evolve, reflecting real-
time market conditions and emerging patterns. This enables leadership to make faster,
more confident, and data-backed decisions. A company that can leverage AI for market research and trend analysis can capitalize on opportunities before competitors even
recognize them, securing a first-mover advantage. This transformation is not uniform,
however; a significant divide exists in AI adoption. Data shows that larger companies are
twice as likely to have adopted and deployed AI technologies as small companies. In
2021, 69% of companies with more than 5,000 employees used AI, a figure that climbed
to 60% for companies with over 10,000 employees. This disparity highlights a potential
risk of further market concentration as larger enterprises widen their technological lead.
II. Challenges & Opportunities 2.1. Challenges
Challenges of AI Adoption in the Enterprise
Skills Gap and Lack of Training
One of the biggest barriers to AI adoption is the lack of skills. The 2025 AI Skills
Report shows 65% of firms abandoned AI projects due to talent shortages, while 47% of
executives cite skills gaps as the top obstacle. About 38% of employees feel unprepared
to work with AI, though most are eager to learn if training and transparency improve. This gap is also unequal: men report 42% higher AI proficiency than women, and
younger workers have far more access to AI training than older ones.
In simple terms, many workers do not have enough AI knowledge, so they feel worried or
confused when using it. Without good training, it is hard to use AI effectively.
Rapid Pace of AI Development 4
AI evolves at breathtaking speed, creating both technical and financial challenges.
Poor data quality, legacy systems, and costly integration often stall projects. The pace of
change makes ROI uncertain, with platforms at risk of obsolescence within three to five years.
In other words, AI changes very fast. New tools and updates appear all the time,
and companies struggle to keep up because it takes a lot of money and time.
Risk of Job Displacement AI and
automation pose risks to jobs in
programming, accounting, customer
service, and more. Goldman Sachs estimates 6–7% of U.S. jobs could be displaced,
especially as “agentic AI” can handle entire workflows once done by several people. Yet
history shows technology often creates new roles: over 85% of today’s jobs did not exist in 1940.
Put simply: some jobs—especially simple or repetitive ones—may be replaced by machines, such as robots
in factories
or chatbots in customer service. This makes
workers afraid of losing their jobs.
Ethical and Responsibility Concerns AI introduces risks of bias, opacity, and accountability gaps. Discriminatory
training data can lead to unfair outcomes in lending, hiring, policing, or healthcare. Deep
learning “black boxes” reduce transparency, while legal liability remains unclear. AI can
also amplify misinformation through deepfakes or biased recommendation systems.
To simplify: AI can make mistakes or unfair decisions. The difficult question is
who should be responsible—the company, the programmer, or the AI system itself. 2.2. Opportunities Despite the hurdles enterprises face when adopting artificial intelligence, the opportunities it offers are equally transformative. One of the biggest advantages is
increased productivity and operational efficiency. By automating repetitive and time-
consuming tasks, AI enables employees to focus on more strategic and creative work, helping businesses save time, reduce errors, and accelerate decision-making. Many
organizations are already reporting measurable gains—ranging from thousands of hours
saved to efficiency improvements of over 50%.
At the same time, AI is reshaping the job market in constructive ways. While some
routine roles may be replaced, AI is also creating entirely new positions such as AI specialists, data analysts, and machine learning engineers. More importantly, it is
transforming existing jobs by supporting employees in working smarter and encouraging
the development of critical soft skills like problem-solving, communication, adaptability,
and strategic thinking. This shift moves human labor toward higher-order tasks while
ensuring that uniquely human capabilities remain central. 5 However , these opportunities cannot be realized without overcoming key
challenges. The most pressing is the skills gap: many workers lack adequate knowledge and training in AI, leaving them uncertain about its use. The rapid pace of AI
development adds another layer of complexity, as companies often struggle to keep up
with constant updates. Concerns about job displacement also persist, particularly in roles
involving repetitive tasks, such as factory work or customer service. Finally, ethical and
accountability issues—such as bias in algorithms or responsibility for AI errors—remain
unresolved and demand careful consideration.
Taken together, this shows that the future of AI in business is not defined by
replacement but by transformation. To thrive, organizations must strike a balance: seizing
AI’s opportunities for growth and innovation while actively addressing its challenges
through training, responsible integration, and strong ethical frameworks. III. Recommendations
3.1. For Businesses
Businesses should first focus on internal training, reskilling, and upskilling. As AI
changes the nature of work, employees need to learn how to use new tools effectively.
Reskilling helps workers adapt when some of their tasks are replaced by automation. For
example, a call center agent whose routine questions are answered by a chatbot can be trained to handle complex customer problems or manage customer relationships. Upskilling, on the other hand, allows employees to improve in their current roles. Marketing staff, for ins tance, can learn to use AI-powered analytics to understand
customer behavior better and design stronger campaigns. A good example is Amazon,
which has invested heavily in training programs to prepare warehouse employees for new roles
in IT support and data analysis. This not
only increases productivity but also reduces the fear of job loss.
Another important recommendation is to build a culture of continuous learning,
where AI and humans work together. AI should not be seen as a replacement but as a
partner that supports human decision-making. Companies can encourage this culture by
organizing knowledge-sharing sessions where employees exchange ideas about using AI
in daily work. They can also provide innovation labs or workshops where staff test new AI solutions, such
as chatbots, predictive maintenance tools, or automated reporting
systems. Managers should reward creativity and recognize employees who combine AI
insights with human judgment to improve outcomes. Microsoft is a good example of this approach. It promotes a workplace culture where employees are encouraged to
experiment with AI, leading to new ideas and more effective problem-solving. Finally, businesses should ensure that they are implementing responsible AI
principles. To build trust among customers and employees, AI systems must be used
fairly, transparently, and responsibly. Fairness means avoiding bias in decision-making.
For example, an AI hiring tool should not unfairly favor or exclude candidates based on 6
gender or ethnicity. Transparency requires companies to explain how AI decisions are
made. A bank using AI for loan approval, for instance, should make clear the main
criteria used in the process. Protecting data privacy is also essential, especially for e-
commerce platforms that collect customer information to offer product recommendations. Accountability is equally important, as companies must have policies and teams responsible for checking AI systems regularly. IBM has already introduced strong
guidelines for trustworthy AI that focus on fairness, explainability, and data protection.
Such practices help businesses maintain public confidence and avoid reputational risks
3.2. For Individuals / Employees
Acquiring AI Literacy (AI & Digital Literacy)
In today’s rapidly changing digital environment, employees need more than just
basic computer proficiency. They must develop a solid understanding of how artificial
intelligence works, what it is capable of, and where its limitations lie. Being “AI-literate”
means being able to evaluate AI-driven outcomes critically, recognize biases or errors in
automated systems, and integrate AI tools effectively into daily workflows. This literacy
is not simply about technical know-how; it also involves developing the ability to ask the right questions, challenge assumptions, and make more informed decisions in collaboration with AI-powered systems. In essence, AI literacy is becoming a new
foundational skill—similar to reading, writing, and numeracy—for thriving in the digital economy.
Developing Soft Skills (Critical Thinking, Emotional Intelligence, Leadership)
As AI systems increasingly take over repetitive, technical, or routine tasks, the
uniquely human abilities of employees gain greater importance. Critical thinking enables employees to assess complex problems, interpret data responsibly, a nd avoid blind
reliance on machine outputs. Emotional intelligence (EQ) fosters empathy, resilience, and stronger interpersonal relationships, which are essential for teamwork and customer interaction. Leadership skills, meanwhile, go beyond traditional management—they
equip employees to guide teams through rapid changes, manage uncertainty, and inspire
others in environments where digital disruption is constant. Together, these soft skills act as differentiators that AI cannot easily replicate, ensuring human workers remain
indispensable contributors to organizational growth. Proactively
Learning via Open Programs (Coursera, LinkedIn, IBM SkillsBuild, etc.)
Learning in the digital era does not end with a university degree. Employees are
expected to pursue lifelong learning and continuously reskill to remain relevant in a dynamic labor market. Fortunately, the rise of open and affordable online learning
platforms has made professional development more accessible than ever. For example:
- Coursera offers world-class courses from top universities, covering fields such as
AI, data science, business management, and more. 7
- LinkedIn Learning provides career-oriented training in technology, leadership, and professional development.
- IBM SkillsBuild delivers hands-on programs on cutting-edge technologies such as
artificial intelligence, cloud computing, and cybersecurity.
By engaging in these flexible and self-paced learning opportunities, employees can stay ahead of technological trends, expand their skill sets, and enhance their
employability. This proactive learning mindset empowers them not only to adapt to
technological disruption but also to take charge of their own career trajectories. IV. Conclusion
The spread of AI in enterprises is both an immense opportunity and a significant challenge. Rather than replacing
humans, AI augments them—boosting productivity, streamlining operations, and shifting human work toward creativity, strategy, and oversight. At the same time, enterprises face major hurdles: skills gaps, integration
complexity, and ethical risks such as bias and accountability. A passive approach could
deepen the digital divide and intensify talent shortages.
The future of work lies in collaboration, not competition, between humans and
machines. Organizations that thrive will treat AI as a supportive tool, invest in workforce
training, and uphold strong ethical principles. Likewise, individuals must adopt lifelong learning, building both AI literacy and uniquely human skills. Through this dual investment in people and technology, enterprises can unlock AI’s full potential for sustainable growth.
In conclusion, artificial intelligence offers businesses both great opportunities and
major challenges. While it can improve efficiency and create new roles in businesses, it
also raises concerns about job loss, skills gaps, and ethics. But in the end, it can't be
denied that AI is a supportive tool that empowers people rather than replaces them. 8 REFERENCES
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