MBA 510 Week Two discussion 4 replies. Please reply to the TWO students discussion post. 50 word min.

Reply to the TWO students' discussion posts—minimum 50 words.

Contributions must display original thinking and good knowledge of the subject matter, including links and references to sources to support your arguments. Additionally, make sure you cite sources you reference in-text and under a “References” section in APA format. 

Frank Vealencis

Artificial intelligence has fundamentally altered how companies design and manage their technology systems, shifting from manual processes toward integrated, predictive platforms. The benefits of integrating AI become most evident in organizations that have transformed entire operational systems rather than treating AI as a simple add on tool. Research shows that firms that reengineer workflows around AI experience higher productivity, stronger customer outcomes, and enhanced innovation (McKinsey & Company, 2025).

A strong example of this transformation is Walmart, which redesigned its supply chain technology to incorporate AI for demand forecasting, inventory planning, and distribution. Walmart integrated machine learning into warehouse operations and real-time store data, enabling the system to anticipate shifts in product demand before they occurred. During implementation, the company reported improved shelf availability, shorter replenishment times, and significant reductions in food and product waste (Walmart, 2024). What began as a logistics upgrade evolved into a full digital transformation that affected how products moved through the system, how purchasing decisions were made, and how stores served customers. Additionally, competitive pressures from firms such as Amazon accelerated the need for Walmart to adopt AI, demonstrating that technological change is often not optional, but essential for continued survival in the retail sector.

Another clear example is Netflix, where AI reshaped both the recommendation engine and content delivery systems. The platform continuously analyzes what viewers watch, how long they view it, scrolling patterns, and even pause or rewind behavior to predict interest. Some viewers may perceive these systems as intrusive, but the design intent is to remain nearly invisible, enhancing convenience rather than disrupting it. Today, the recommendation system influences more than 80% of what users choose to watch, dramatically increasing engagement (Gomez-Uribe & Hunt, 2015). This capability is embedded across the entire value chain, influencing content acquisition, marketing decisions, and the original programming. Its strategic importance is demonstrated by competitors adopting similar systems simply to keep pace.

Both cases highlight a deeper trend. Technology systems are no longer passive record keepers. They have become active decision participants, offering predictions, recommendations, and automated actions. However, these benefits depend on strong data governance and workforce capability. Studies show that employees in AI enabled organizations spend less time on routine tasks and more time interpreting insights, developing ideas, and ensuring ethical use of technology (Aspen University, 2024). In this way, AI augments people rather than replaces them.

In simple terms, AI changes how systems work—and how people work with systems. Organizations that thrive treat AI not as a gadget, but as a strategic partner that helps the business operate faster, think smarter, and innovate with intention.

References

Aspen University. (2024).  The future of work: How AI and automation are changing business.

Gomez-Uribe, C., & Hunt, N. (2015). The Netflix recommender system: Algorithms, business value, and innovation.  ACM Transactions on Management Information Systems.

McKinsey & Company. (2025).  The state of AI: Insights from the front lines of adoption.

Walmart. (2024).  How AI is transforming supply chain visibility and demand forecasting.

Will McMahan

AI fundamentally transforms company technology systems by shifting them from static, rulebased infrastructures into dynamic, adaptive platforms that continuously learn and evolve. When organizations integrate artificial intelligence into their technology systems, the most immediate change is the move from episodic updates to realtime intelligence. Traditional IT systems often rely on periodic upgrades and manual interventions, but AI enables continuous monitoring, predictive analytics, and automated decisionmaking. For example, AI can detect anomalies in network traffic instantly, reducing cybersecurity risks and downtime. This shift allows companies to operate with greater agility and resilience, ensuring that systems adapt proactively rather than reactively to challenges (Narain et al, 2025).

Another major change is the restructuring of workflows and team interactions. AI systems often automate routine tasks, such as log monitoring, regulatory updates, or customer service queries, freeing human employees to focus on highervalue activities. Kaplan et al. (2024) notes that generative AI is reshaping enterprise technology by introducing new patterns of collaboration between humans and AI agents, such as “factory” models for predictable processes and “artisan” models for creative, customized work. This fundamentally alters how IT teams are organized, requiring new skill sets in AI oversight, ethical governance, and data management.

AI also drives architectural modernization. Legacy systems built on siloed databases and rigid workflows are increasingly replaced by cloudbased, AIenabled platforms that integrate multiple data types, text, images, voice, and sensor data, into unified decision frameworks. This multimodal capability enhances efficiency and opens new opportunities for innovation, such as predictive supply chain management or personalized customer experiences. Finally, AI adoption requires companies to rethink risk management and compliance. While AI offers efficiency gains, it also introduces challenges around transparency, bias, and data privacy. Organizations must implement governance frameworks to ensure ethical use of AI, balancing innovation with accountability.  In summary, AI transforms company technology systems by enabling continuous reinvention, smarter workflows, modernized architectures, and enhanced risk management. Companies that embrace these changes position themselves for sustained competitive advantage in a rapidly evolving digital economy.

References

Kaplan, J., Gu, M., & Sinha, M. (2024, December 2).  Enterprise technology’s next chapter: Four gen AI shifts that will reshape business technology. McKinsey & Company.  https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/enterprise-technologys-next-chapter-four-gen-ai-shifts-that-will-reshape-business-technology

Narain, K., Ghosh, B., Wilson, H. J., & Shukla, P. (2025, January 27).  3 ways AI is changing how companies work.  Harvard Business Review.  https://hbr.org/2025/01/3-ways-ai-is-changing-how-companies-work