How Generative AI Became My Job-Search Partner.

9

October

2025

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I first started using GenAI tools for internship search during my bachelor’s studies, mainly to save time when preparing applications. What began as a small shortcut quickly turned into something much more valuable. Over time, I learned to use AI not only for writing but for analysing vacancies, identifying missing skills, and tailoring my applications to what employers were really looking for. I often asked it to highlight key competencies and keywords that could make my CV or motivation letter stand out. Combined with insights from HR professionals and career influencers I follow online, I developed a method that feels strategic and personalised, where AI acts as a sparring partner rather than a content generator.

GenAI also became part of my preparation for interviews and assessments. I’ve used it to simulate common behavioural and technical interview questions, structure my answers, and gain feedback on how clearly my reasoning comes across. When preparing for the TestGorilla assessment, I applied the same study habits I used for university exams by asking ChatGPT to create logical reasoning and situational judgment exercises and to explain how recruiters might evaluate responses. It made me feel more confident, structured, and aware of my performance.

Looking back, I’d say GenAI has become a real career companion by helping me work smarter, reflect more deeply, and stay organised throughout the job-search process. However, this experience changed how I view AI in the recruitment process. While it saves time and helps formulate ideas clearly, it doesn’t remove the need to put effort into each application. The outcome still depends on how well I can express my own story, experiences, and motivation. AI can refine my words, but it can’t replace authenticity or individuality. In the end, standing out still comes from the human touch behind the screen. Do you think AI will ever fully understand what makes a candidate truly unique?

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How is AI Becoming a Game-Changer in Pharma?

11

September

2025

5/5 (3)

The pharmaceutical industry is known for its slow and expensive drug development cycle, often taking 10–15 years and billions of dollars to bring a single drug to market (Hamilton, 2024). AI is rewriting this equation. By simulating molecular interactions, predicting promising compounds, and automating lab work, AI can cut discovery timelines by up to 50% (Baur & Fath, 2024). Instead of screening millions of molecules in the lab, algorithms instantly narrow the field, allowing researchers to focus only on the most viable candidates. This is not just efficiency, it’s a revolution in how R&D decisions are made (Malesu, 2025; Baur & Fath, 2024).

But speed alone isn’t the only breakthrough. AI empowers scientists to make smarter decisions by analysing vast datasets, genomic sequences, clinical trial data, and chemical libraries, to detect patterns invisible to the human eye (Hamilton, 2024; Baur & Fath, 2024). This drastically reduces the number of failed trials, improves the accuracy of predictions, and enables the design of more targeted therapies (Suri et al., 2024). In other words, AI doesn’t just help scientists work faster, it helps them work smarter.

The financial impact is equally transformative. By streamlining trials, automating lab tasks, and cutting down on costly failures, AI reduces operational costs while accelerating time to market (Baur & Fath, 2024; Walch, 2025). For pharma companies, this means higher ROI and a stronger competitive edge in an industry where every day counts. For patients, it translates to faster access to life-saving drugs.

Yet, alongside the promise, ethical challenges remain significant. Many AI models operate with limited transparency, making it difficult for scientists and regulators to fully understand how decisions are reached. Bias in training data can reinforce health disparities, and the risk of mishandling sensitive patient data is ever-present. Regulators are racing to keep up, but the pace of innovation often outstrips policy (Suri et al., 2024; Malesu, 2025). This tension between innovation and accountability may ultimately determine how much trust society places in AI-driven healthcare.

How much trust would you place in AI to guide critical decisions in healthcare, and what safeguards would make you feel confident in its use?

References:

Baur, M., & Fath, S. (2024, October 8). Why AI is a game changer for the pharmaceutical industry. Roland Berger. https://www.rolandberger.com/en/Insights/Publications/Why-AI-is-a-game-changer-for-the-pharmaceutical-industry.html

Hamilton, C. (2024, December 17). Reinventing pharma: How AI is revolutionizing drug discovery. BioLife Health Center. https://www.biolifehealthcenter.com/post/reinventing-pharma-how-ai-is-revolutionizing-drug-discovery

Malesu, V. K. (2025, June 11). Why drug discovery needs robots and artificial intelligence. News-Medical.net. https://www.news-medical.net/health/Why-Drug-Discovery-Needs-Robots-and-Artificial-Intelligence.aspx

Suri, G.S., Kaur, G. & Shinde, D. Beyond boundaries: exploring the transformative power of AI in pharmaceuticals. Discov Artif Intell 4, 82 (2024). https://doi.org/10.1007/s44163-024-00192-7                                       

Walch, K. (2025, March 2). How AI is transforming the pharmaceutical industry. Forbes. https://www.forbes.com/sites/kathleenwalch/2025/03/02/how-ai-is-transforming-the-pharmaceutical-industry/

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