AI's Role in Business and Careers—How Agentic and Generative AI Are Reshaping the Future

 

AI's Role in Business and Careers—How Agentic and Generative AI Are Reshaping the Future



The artificial intelligence revolution is no longer a distant possibility—it's happening right now, transforming how we work, make decisions, and create value in unprecedented ways. Consider this: in just the past two years, we've witnessed the emergence of ChatGPT reaching 100 million users in record time, the development of AI agents that can autonomously complete complex tasks, and the integration of AI capabilities into everything from smartphones to enterprise software. What makes this moment particularly significant is that we're not just seeing one type of AI advance, but rather two complementary yet distinct forms: generative AI, which creates new content and solutions, and agentic AI, which takes autonomous actions to achieve specific goals.

Understanding these AI capabilities has become essential for anyone who wants to remain competitive in today's rapidly evolving landscape. Whether you're a business leader trying to determine where to invest your organization's resources, a professional wondering how to future-proof your career, or an entrepreneur looking to build the next breakthrough product, the question isn't whether AI will impact your world—it's how quickly you can learn to harness its potential.

This exploration will provide you with actionable insights drawn from real-world implementations, practical frameworks you can apply immediately, and the strategic thinking needed to navigate both the opportunities and challenges that AI presents. By the end, you'll have a clear understanding of how to position yourself and your organization for success in an AI-driven future.

The Business Impact of AI: Efficiency, Innovation, and Competitive Edge

How AI Is Transforming Business Operations

The most immediate and visible impact of AI in business comes through its ability to automate repetitive tasks and optimize workflows in ways that were simply impossible just a few years ago. Think of AI as a highly capable assistant that never gets tired, never makes computational errors, and can process vast amounts of information simultaneously. However, unlike traditional automation that follows rigid rules, modern AI can adapt, learn, and make decisions in complex situations.

Consider how Google has revolutionized search by using AI to understand the intent behind queries rather than just matching keywords. When you search for "best restaurants near me for a date," Google's AI doesn't just look for those exact words—it understands you're looking for romantic dining options in your vicinity and considers factors like ambiance, reviews, and price range. This represents a fundamental shift from programmed responses to intelligent interpretation.

Microsoft's Copilot integration across their Office suite demonstrates how AI can enhance productivity by understanding context within documents, suggesting improvements to presentations, and even helping draft emails that match your writing style. The key insight here is that AI isn't replacing human creativity—it's amplifying it by handling the routine aspects of work so humans can focus on strategy, relationship-building, and innovation.

Amazon's fulfillment centers showcase AI's power in physical operations, where machine learning algorithms predict demand patterns, optimize inventory placement, and coordinate robotic systems to reduce delivery times from days to hours. What's particularly instructive about Amazon's approach is how they've created a feedback loop where AI systems continuously improve based on real-world performance data.

Apple's implementation of on-device generative AI with their Intelligence platform illustrates another crucial trend: bringing AI capabilities directly to end-users without compromising privacy. This approach reduces latency, maintains data security, and creates more personalized experiences—a strategy that's reshaping how we think about AI deployment.

The common thread across these examples is that successful AI implementation requires more than just adopting new technology. It demands a fundamental rethinking of business processes, customer interactions, and value creation. Organizations that treat AI as merely a tool to speed up existing processes miss the opportunity to reimagine what's possible.

Generative AI: Unlocking Creativity and New Business Models

Generative AI represents perhaps the most transformative force in modern business because it doesn't just process information—it creates entirely new content, solutions, and possibilities. To understand its impact, imagine having a team member who can instantly produce marketing copy in dozens of languages, generate product designs based on market research, create personalized customer service responses, and synthesize complex technical documentation—all while maintaining consistency with your brand voice and business objectives.

The marketing industry provides a compelling case study in generative AI adoption. Companies are using AI to create personalized email campaigns that adapt content based on individual customer behavior, generate social media posts that resonate with different demographic segments, and even produce video content that can be automatically localized for global markets. What's revolutionary here isn't just the speed of content creation, but the ability to test and iterate at a scale that was previously impossible.

In product design, generative AI is enabling rapid prototyping and iteration cycles that compress months of traditional development into days. Automotive companies use AI to generate and test thousands of design variations for vehicle components, optimizing for factors like aerodynamics, manufacturing efficiency, and aesthetic appeal simultaneously. This capability is fundamentally changing how innovation happens—from sequential processes to parallel exploration of multiple possibilities.

Customer service represents another frontier where generative AI is creating new paradigms. Modern AI chatbots don't just provide scripted responses—they can understand complex customer issues, access relevant company knowledge, and generate personalized solutions while maintaining the ability to escalate to human agents when needed. The result is faster resolution times, more consistent service quality, and the ability to provide 24/7 support without proportional increases in staffing costs.

Perhaps most intriguingly, generative AI is enabling entirely new business models. Companies are emerging that exist purely to provide AI-generated services—from personalized education content to custom software applications created through natural language descriptions. These businesses leverage AI's ability to scale personalized creation in ways that would be economically impossible with traditional human-only approaches.

The strategic implication for business leaders is that generative AI isn't just about efficiency—it's about expanding the realm of what's possible. Organizations that understand this distinction are using AI not just to do existing things better, but to create entirely new value propositions for their customers.

Strategic AI Adoption: Building Future-Ready Organizations

Developing an Effective AI Strategy

Creating a successful AI strategy requires a fundamentally different approach than traditional technology implementations. Think of it as building a learning system rather than deploying a fixed solution. The most successful organizations start with a clear understanding of their business objectives and work backward to identify where AI can create the most meaningful impact.

The first step involves conducting an honest assessment of your organization's current state. This means evaluating not just your technical infrastructure, but also your data quality, organizational culture, and change management capabilities. Many AI initiatives fail not because of technological limitations, but because organizations underestimate the human and process changes required for successful adoption.

Effective AI strategy begins with pilot projects that are carefully chosen for their potential to demonstrate clear business value while being manageable in scope. The goal isn't to transform everything at once, but to build organizational confidence and expertise through early wins. For example, a retail company might start with AI-powered inventory optimization in a single product category before expanding to demand forecasting across their entire operation.

Leadership buy-in is crucial, but it needs to be informed rather than superficial. This means ensuring that executives understand both the potential and limitations of AI technologies. Organizations that succeed in AI adoption typically create cross-functional teams that include business stakeholders, technical experts, and change management professionals from the beginning of the process.

The scaling phase requires careful attention to governance frameworks that ensure AI systems remain aligned with business objectives and ethical standards as they grow in complexity and scope. This includes establishing clear accountability structures, regular performance monitoring, and mechanisms for addressing unintended consequences or biases that may emerge over time.

What distinguishes successful AI strategies is their emphasis on continuous learning and adaptation. Unlike traditional IT projects with defined endpoints, AI implementation is an ongoing process of refinement and evolution. Organizations need to build capabilities for experimenting with new AI technologies, measuring their impact, and rapidly adjusting their approach based on results.

AI Integration with Cloud, IoT, and Data Analytics

The true power of AI emerges when it's integrated with other transformative technologies, creating synergistic effects that amplify the benefits of each individual component. Cloud infrastructure serves as the foundation for this integration, providing the computational resources, storage capacity, and networking capabilities that AI systems require to operate at scale.

Consider how cloud platforms enable organizations to experiment with AI technologies without massive upfront investments in hardware. A startup can access the same machine learning capabilities as a Fortune 500 company, paying only for the computing resources they actually use. This democratization of AI capabilities is reshaping competitive dynamics across industries, allowing smaller, more agile organizations to compete with established players.

The Internet of Things (IoT) provides the sensory network that feeds real-world data into AI systems, enabling them to make decisions based on current conditions rather than historical patterns alone. In manufacturing, IoT sensors throughout a production line can feed data to AI systems that predict equipment failures, optimize energy consumption, and adjust production parameters in real-time based on demand fluctuations.

Smart buildings exemplify this integration perfectly. IoT sensors monitor occupancy patterns, temperature, lighting conditions, and energy usage throughout a facility. AI systems analyze this data to optimize heating and cooling systems, adjust lighting automatically, and even predict maintenance needs before problems occur. The result is significant cost savings, improved occupant comfort, and reduced environmental impact.

Data analytics serves as the bridge between raw information and actionable insights, with AI dramatically expanding what's possible in terms of pattern recognition and predictive modeling. Traditional analytics might tell you what happened in your business, but AI-powered analytics can predict what's likely to happen and suggest optimal responses.

The strategic advantage comes from creating integrated systems where these technologies work together seamlessly. Cloud infrastructure provides the processing power, IoT devices generate real-time data streams, and AI systems analyze this information to make autonomous decisions or provide recommendations to human operators. Organizations that successfully integrate these technologies create competitive advantages that are difficult for competitors to replicate quickly.

AI's Impact on Careers: Skills, Opportunities, and Challenges

Job Market Disruption: Automation and Job Displacement

The relationship between AI and employment is far more nuanced than the simple narrative of robots replacing human workers. While AI will indeed automate certain tasks and roles, it's simultaneously creating new types of work and augmenting human capabilities in ways that make many jobs more interesting and valuable.

Understanding which roles are most vulnerable to AI automation requires looking at the underlying tasks rather than job titles. Work that involves highly repetitive processes, clear rules, and minimal need for human judgment is most susceptible to automation. This includes certain data entry positions, basic customer service interactions, and routine analytical tasks. However, even within these categories, the impact varies significantly based on the specific context and requirements.

Consider the evolution of accounting as an illustrative example. Basic bookkeeping tasks have been largely automated, but this hasn't eliminated accountants—it has elevated their role toward strategic financial advisory, complex problem-solving, and regulatory compliance. AI handles the routine calculations and data processing, while human accountants focus on interpretation, planning, and client relationships.

The concept of job augmentation is perhaps more significant than job replacement. Many roles are being enhanced by AI capabilities rather than eliminated. Doctors use AI to analyze medical images and suggest diagnoses, but they retain responsibility for patient care and treatment decisions. Marketing professionals use AI to generate content ideas and analyze campaign performance, but they provide the strategic thinking and creative direction that gives campaigns their impact.

Future-proofing your career in an AI-dominated landscape requires developing a portfolio of capabilities that complement rather than compete with AI systems. This means cultivating skills that are inherently human: emotional intelligence, creative problem-solving, ethical reasoning, and the ability to work effectively with AI tools rather than being replaced by them.

The most successful professionals are those who learn to leverage AI as a powerful assistant while maintaining their uniquely human contributions. This might mean using AI to handle research and data analysis while focusing your expertise on strategy and relationship building, or using AI to generate initial drafts of content while applying your judgment and creativity to refine and personalize the output.

Essential Skills for the AI Era

The skill landscape for the AI era encompasses both technical competencies and human capabilities that become more valuable as AI handles routine tasks. Understanding this dual nature is crucial for anyone planning their professional development in an AI-driven future.

On the technical side, machine learning literacy has become as fundamental as basic computer skills were a generation ago. This doesn't mean everyone needs to become a data scientist, but professionals across all industries benefit from understanding how machine learning works, what types of problems it can solve, and how to evaluate the quality and reliability of AI-generated outputs.

Data science skills remain highly valuable, particularly the ability to clean and prepare data for AI systems, interpret results, and communicate findings to non-technical stakeholders. As AI systems become more sophisticated, the role of data scientists is evolving toward higher-level problem formulation and model evaluation rather than routine algorithm implementation.

Natural language processing expertise is increasingly important as AI systems become more conversational and integrated into everyday business processes. This includes understanding how to design effective prompts for AI systems, evaluate the quality of AI-generated text, and integrate natural language interfaces into business applications.

Prompt engineering has emerged as a critical new skill—the ability to communicate effectively with AI systems to achieve desired outcomes. This involves understanding how different AI models respond to various types of instructions, how to structure queries for optimal results, and how to iterate and refine prompts based on system responses.

However, the most significant opportunities may lie in developing distinctly human skills that become more valuable as AI handles routine tasks. Communication skills become paramount when your role involves interpreting AI outputs for diverse audiences, facilitating human-AI collaboration, and ensuring that AI-driven decisions align with organizational values and objectives.

Adaptability and continuous learning are essential in a rapidly evolving technological landscape. The specific tools and platforms you use today may be obsolete in five years, but the ability to quickly master new technologies and adapt to changing requirements will remain valuable throughout your career.

Critical thinking and ethical reasoning become increasingly important as AI systems make more decisions that affect people's lives. Someone needs to evaluate whether AI recommendations align with organizational values, identify potential biases or unintended consequences, and ensure that AI implementations serve human needs rather than simply optimizing metrics.

Creative problem-solving remains a uniquely human capability that becomes more valuable as AI handles routine analysis. This involves the ability to frame problems in new ways, synthesize insights from multiple sources, and develop innovative solutions that go beyond what existing data and patterns suggest.

Practical Applications: AI in Action

Case Studies from Leading Companies

Examining how leading organizations have successfully implemented AI provides concrete examples of the principles and strategies we've discussed. These cases demonstrate that successful AI adoption isn't just about technology—it's about reimagining business processes and customer experiences.

In the financial services sector, JPMorgan Chase's implementation of AI for fraud detection illustrates how machine learning can enhance both security and customer experience. Their AI systems analyze millions of transactions in real-time, identifying suspicious patterns that would be impossible for human analysts to detect at scale. The system learns from each transaction, continuously improving its accuracy while reducing false positives that inconvenience legitimate customers. The key insight is that AI doesn't just automate existing fraud detection processes—it enables entirely new approaches that consider far more variables and patterns than traditional rule-based systems.

Healthcare provides another compelling example through IBM Watson's oncology applications. Rather than replacing doctors, Watson serves as a research assistant that can quickly review vast amounts of medical literature, patient records, and treatment outcomes to suggest potential treatment options. The AI system can process information from thousands of medical journals and clinical trials in minutes, providing doctors with evidence-based recommendations that would take human researchers weeks to compile. This augmentation approach allows healthcare professionals to focus on patient care while leveraging AI's ability to synthesize complex medical knowledge.

In retail, Amazon's recommendation engine demonstrates how AI can create personalized experiences at scale. The system doesn't just suggest products based on purchase history—it considers browsing behavior, seasonal patterns, similar customer preferences, and even real-time factors like current events or weather conditions. What makes this particularly effective is how the AI learns from every interaction, continuously refining its understanding of individual preferences while identifying broader patterns across customer segments.

Netflix's content recommendation and production strategies showcase how AI can transform entire business models. Their AI systems not only suggest content to individual viewers but also inform decisions about which original content to produce. By analyzing viewing patterns, completion rates, and engagement metrics, Netflix can predict which types of shows and movies are likely to succeed with different audience segments, reducing the risk of expensive content investments.

These examples share several common characteristics that contribute to their success. They focus on solving specific business problems rather than implementing AI for its own sake. They integrate AI capabilities into existing workflows rather than creating entirely separate systems. They maintain human oversight and decision-making authority for critical outcomes. Most importantly, they treat AI implementation as an ongoing process of learning and refinement rather than a one-time technology deployment.

Step-by-Step: Building Your First AI-Powered App

Creating your first AI-powered application might seem daunting, but breaking it down into manageable steps makes the process much more approachable. Think of this as learning to cook—you start with simple recipes and basic techniques before attempting complex dishes.

The first step involves clearly defining the problem you want to solve. This sounds obvious, but many AI projects fail because they start with the technology rather than the business need. Spend time understanding exactly what outcome you want to achieve, who will use the application, and how you'll measure success. For example, rather than saying "we want to use AI for customer service," specify "we want to reduce response time for common billing questions while maintaining customer satisfaction scores above 90%."

Data sourcing and preparation typically consume the majority of time in AI projects, so it's crucial to understand this phase thoroughly. Start by identifying what data you need, where it exists, and how you'll access it. Clean, well-organized data is far more valuable than large amounts of messy information. If you're working on a customer service application, you might need historical chat logs, customer account information, and resolution outcomes. The data needs to be formatted consistently, with clear labels and minimal errors.

Tool selection depends on your technical expertise, budget, and specific requirements. Google Vertex AI provides excellent integration with other Google services and strong support for custom machine learning models. Microsoft Azure AI offers comprehensive tools that integrate well with existing Microsoft infrastructure. AWS SageMaker provides powerful capabilities for organizations already using Amazon's cloud services. OpenAI's APIs offer quick access to advanced language models without requiring deep machine learning expertise.

For beginners, starting with pre-built AI services often makes more sense than building custom models. These services handle the complex machine learning infrastructure while allowing you to focus on application logic and user experience. You can always migrate to custom solutions as your expertise and requirements grow.

Rapid prototyping is essential for validating your approach before investing significant time and resources. Create a basic version of your application that demonstrates core functionality, even if it's not production-ready. This allows you to test your assumptions, gather user feedback, and identify potential issues early in the development process.

Testing and iteration are crucial phases that many first-time AI developers underestimate. AI systems often behave differently in production than in controlled testing environments. Start with a small group of users, monitor performance carefully, and be prepared to make adjustments based on real-world usage patterns.

Deployment considerations include not just the technical aspects of hosting your application, but also user training, support processes, and ongoing monitoring. AI applications often require different support approaches than traditional software because their behavior can be less predictable and their failures less obvious.

Addressing "Why It Matters": Strategic and Personal Implications

Strategic Implications for Businesses

The strategic implications of AI extend far beyond operational efficiency or cost reduction—they fundamentally reshape how organizations create and capture value in their markets. Understanding these implications is crucial for business leaders who want to position their organizations for long-term success rather than simply optimizing current operations.

AI has become table stakes for competitive positioning in many industries. This doesn't mean every organization needs to implement cutting-edge AI research, but rather that basic AI capabilities—such as data-driven decision making, automated customer interactions, and intelligent process optimization—are rapidly becoming minimum expectations rather than differentiators. Organizations that fail to develop these capabilities risk being unable to compete on speed, personalization, or cost-effectiveness.

The cost of inaction compounds over time because AI systems improve through learning and data accumulation. Organizations that start implementing AI capabilities today will have more data, better-trained models, and deeper organizational expertise than those who wait. This creates a virtuous cycle where early adopters gain advantages that become increasingly difficult for competitors to overcome.

Market positioning changes when AI enables new forms of value creation. Companies that traditionally competed on product features can now compete on personalized experiences. Organizations that previously focused on operational efficiency can now offer predictive services. The key insight is that AI doesn't just improve existing value propositions—it enables entirely new ones.

Talent acquisition and retention become critical strategic factors in an AI-driven landscape. Organizations need people who can work effectively with AI systems, interpret their outputs, and ensure they align with business objectives. The competition for AI-literate talent is intensifying, making it essential for organizations to develop internal AI capabilities rather than relying entirely on external expertise.

Risk management takes on new dimensions when AI systems make decisions that affect customers, employees, and business outcomes. Organizations need frameworks for ensuring AI systems remain aligned with company values, comply with evolving regulations, and maintain acceptable levels of performance over time. This requires new types of governance structures and oversight mechanisms.

The most successful organizations treat AI as an enabler of transformation rather than just an efficiency tool. They use AI capabilities to reimagine customer experiences, create new revenue streams, and develop competitive advantages that are sustainable over time. This strategic approach to AI adoption positions organizations to thrive in an increasingly AI-driven marketplace.

Career Growth in the Age of AI

The career landscape in an AI-driven world offers unprecedented opportunities for professionals who understand how to position themselves strategically. Rather than viewing AI as a threat to employment, forward-thinking professionals can leverage AI capabilities to enhance their value and create new career paths.

New roles are emerging that didn't exist five years ago and are becoming increasingly important across industries. AI ethicists help organizations ensure their AI systems operate fairly and transparently. Prompt engineers specialize in communicating effectively with AI systems to achieve desired outcomes. AI product managers bridge the gap between technical capabilities and business requirements, ensuring AI implementations create real value for customers and organizations.

The most significant career opportunities often arise at the intersection of AI capabilities and domain expertise. A marketing professional who understands how to leverage AI for content creation and campaign optimization becomes more valuable than someone with only traditional marketing skills. A financial analyst who can work with AI systems to identify patterns and generate insights can handle more complex analyses and provide greater strategic value.

Project portfolios become crucial for demonstrating AI-related capabilities to potential employers or clients. This means actively seeking opportunities to work with AI tools in your current role, even if they're not formally part of your job description. Document your experiences, quantify the results, and develop case studies that showcase your ability to achieve business outcomes through AI implementation.

Thought leadership in AI-related topics can significantly accelerate career growth. This doesn't require becoming a technical expert, but rather developing a perspective on how AI impacts your industry or functional area. Writing about your experiences, speaking at conferences, or participating in professional discussions about AI implementation helps establish credibility and visibility in the field.

Networking becomes particularly important in a rapidly evolving field where best practices are still emerging. Connecting with other professionals who are implementing AI in similar contexts provides opportunities to learn from their experiences, collaborate on projects, and stay current with emerging trends and technologies.

The key to career success in the AI era is developing a complementary relationship with AI technologies rather than competing with them. This means identifying the uniquely human contributions you can make—strategic thinking, relationship building, creative problem-solving, ethical reasoning—while leveraging AI tools to enhance your effectiveness in these areas.

Continuous learning is essential, but it should be strategic rather than scattered. Focus on developing expertise in areas where AI and human capabilities intersect, rather than trying to become an expert in every AI technology. The goal is to become someone who can effectively bridge the gap between AI capabilities and business value, making you indispensable in an AI-driven organization.

Conclusion

The transformation brought by agentic and generative AI represents one of the most significant shifts in how we create value, make decisions, and solve problems since the advent of the internet. What we've explored throughout this discussion demonstrates that success in this new landscape requires more than simply adopting new technologies—it demands a fundamental reimagining of how businesses operate and how professionals contribute value.

For organizations, the path forward involves treating AI as an enabler of transformation rather than just an efficiency tool. The companies that will thrive are those that use AI to reimagine customer experiences, create new revenue streams, and develop sustainable competitive advantages. This requires strategic thinking, organizational commitment, and a willingness to experiment and learn from both successes and failures.

For professionals, the opportunities are substantial for those who position themselves strategically. The key is developing a complementary relationship with AI technologies, leveraging their capabilities to enhance uniquely human contributions like strategic thinking, creative problem-solving, and relationship building. The most successful careers will be built by those who can effectively bridge the gap between AI capabilities and business value.

The time for passive observation has passed. Whether you're leading an organization or managing your own career, the question is no longer whether AI will impact your future—it's how quickly and effectively you can learn to harness its potential. Start by assessing your current AI readiness, whether that's your organization's technical infrastructure and cultural preparedness, or your own skills and knowledge gaps. Invest in continuous learning, but make it strategic and focused on areas where you can create the most value.

Most importantly, begin experimenting with AI tools and applications in your current context. The learning that comes from hands-on experience with AI systems is invaluable and cannot be replicated through reading or training alone. Start small, measure results, and iterate based on what you learn. The organizations and professionals who embrace this experimental mindset will be best positioned to capitalize on the unprecedented opportunities that AI presents.

The future belongs to those who can effectively combine human insight with artificial intelligence capabilities. The time to begin building that future is now.


General AI Market, Adoption & Trends


AI in Business Operations & Case Studies


AI Strategy, Implementation & Digital Transformation


Generative & Agentic AI


AI Skills, Careers & Upskilling


AI Tools, Platforms & Prototyping


Analytics, Data & Cloud


AI Ethics, Policy & Governance


Communities, Events & Media


Tip:
For best SEO and user experience, hyperlink the resource text (as shown above) directly in your blog copy, especially in “Further Reading” or “Resources” sections, and when referencing stats, case studies, or frameworks. This will boost authority, engagement, and SERP visibility2457.

  1. https://www150.statcan.gc.ca/n1/pub/11-621-m/11-621-m2025008-eng.htm

  2. https://explodingtopics.com/blog/ai-statistics

  3. https://www.forbes.com/sites/bernardmarr/2025/06/03/mind-blowing-ai-statistics-everyone-must-know-about-now-in-2025/

  4. https://www.venasolutions.com/blog/ai-statistics

  5. https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai

  6. https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/superagency-in-the-workplace-empowering-people-to-unlock-ais-full-potential-at-work

  7. https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html

  8. https://www.surveymonkey.com/mp/ai-marketing-statistics/

  9. https://posthumanism.co.uk/jp/article/view/1178

  10. https://theamericanjournals.com/index.php/tajmei/article/view/6259/5785

  11. https://www.journalmabis.org/mabis/article/view/862

  12. https://ieeexplore.ieee.org/document/10941275/

  13. https://www.sciencepubco.com/index.php/IJSW/article/view/33431

  14. https://www.ajol.info/index.php/ngjsd/article/view/293918

  15. https://ieeexplore.ieee.org/document/11020024/

  16. https://sol.sbc.org.br/index.php/sbsi/article/view/34336

  17. http://www.scholink.org/ojs/index.php/ibes/article/view/55973

  18. https://economdevelopment.in.ua/index.php/journal/article/view/1184

  19. https://arxiv.org/abs/2504.07139

  20. https://pmc.ncbi.nlm.nih.gov/articles/PMC11973334/

Comments

Post a Comment