AI in business operations: driving urban growth and societal sustainability

Frontiers in Artificial Intelligence, Год журнала: 2025, Номер 8

Опубликована: Март 24, 2025

Approximately 30% of smart city applications will use artificial intelligence (AI) by the end 2025, thereby radically altering urban sustainability landscape in future (Yan et al., 2023). The advent AI reshaping traditional businesses into sustainable operations is evident. Whenever brought to forefront, it considered a cornerstone business domain, enabling transition towards more innovative and practices (Appio 2024). Incorporating has many facets. According Grand View Research (2023), global market size was anticipated at USD 196.63 billion 2023 expected grow CAGR 36.6% from 2024 2030. recent fanfare surrounding elevated key enabler development, prompting companies prioritize integrate their operations; hence, there stark difference between new practices. In tandem with this evolution, growth societal dynamics are experiencing profound changes as AI-driven solutions come fore various aspects modern society (Shahidi Hamedani government, transforming conventional cities efficient ones (Ortega-Fernández 2020), have significantly shifted functional systems intelligent ones. Furthermore, another perspective, role optimizing processes surpassed comparison its implication for improving logistics operational capabilities reducing environmental impacts (Jorzik 2024a) till manufacturing reduces downtime, all which contribute economics. meantime, speedy pace adoption operations, also imperative amalgamate Acting on matter requires thoughtful approach that aligns social, economic, sustainability.The intersection recently gained widespread attention. Some studies (Chen 2024;Shahzadi 2024)focused AI's supply chain management, highlighting minimizing inefficiencies utilizing often;supply chains become leaner reduced carbon footprints, paving path operations. It estimated 2026, 60% adopt AI-powered warehouse instead just 10% 2020 (MHI, 2024).In line shift, (Dilmegani & Ermut, 2025) note invest heavily robots enhance management through technology. Robots can manage efficiently accurately automating picking, packing, sorting, inventory thus saving labor costs accelerating order processing. Amazon, instance, deployed than 200,000 warehouses optimize operations.AI be used resource utilization, automate improved efficiency, enable real-time monitoring goals (Waltersmann 2021). As focuses waste enhancing traceability, technologies such machine learning big data analytics been pivotal achieving these goals. (Tsolakis 2023) Companies like eBay leverage translation, decision-making efficiency . Similarly, Vodafone employs personalize services, exemplifying transformative impact. 2024a).These help reduce forecasting errors, minimize excess inventory, lower energy consumption. (Sharma 2020) Likewise, Smart grid protection sensors detect defects up 80% sensors, losses system's reliability adjusting conditions dynamically (Mahadik, Sheetal 2025). These economic fostering technological innovation. leverages advanced techniques deep reinforcement (DRL) dynamic (Shuford, DRL improves adaptive routing optimization, demand logistics; DRL, researchers develop adapt changes, facilitate multi-objective (Dehaybe addition, enables prevent equipment failures streamlining workflows (Mohan Moreover, centers, advancements catalyze foster other words, contribution development allocation, ensuring resilient economically prosperous (Li developing cities, impact urbanization trends. Through application AI, infrastructure optimized transportation, managing housing needs; makes possible traffic congestion advance mobility transportation systems, prescriptive autonomous vehicles (Regona Singapore, manages monitors consumption, setting benchmarks (Padhiary On similar note, Tennet TSO, German transmission system operator, AI-based IBM Watson's cognitive computing platform anticipate renewable generation real time, allowing adjustments maximizing clean use. 3Nowadays, debatable topic, inevitable. Reducing food print, circular economy sprout assists environment (Onyeaka 2023); example, agriculture industry, automation, prediction model total agricultural output value (Sachithra Subhashini, 2023), yields while apparent regarding implications IoT due ability improve sustainability. Agriculture leads way 35% technologies, followed precision farming irrigation 16% each.Farming becoming smarter innovations, increase yields, waste, conserve resources (Market.Us, 2024).Similarly, distribution, lowering footprints (Bhattacharya 2022). increasingly automated, consumption minimized aligned (Garrido 2024).By placing heart sustainability, industries solving social issues. shift linear circular, innovative, models (Pathan paradigm contributes aspects; site surveying progress monitoring, power drones decision-making, green finance sector cultivation harvesting phases (Fuentes-Peñailillo While crucial implementing brings several challenges, including ethical privacy concerns (Fan 2023).In planning infrastructure, notable examples; using knowledge acquired level potential revolutionize over solutions, welfare, vitality (Herath Mittal, AI-enabled hospitality provide personalized services seamless guest experiences (Szpilko 2023).Similarly, healthcare predict diseases rapidly (Rashid Kausik, For PRAIM study Germany assessed AI-supported mammography screening versus standard double reading. Out 463,094 women screened, 260,739 were assisted AI. With screening, 6.7 cancers detected out 1,000, 17.6% higher screening. (Eisemann Policies needed protect individual settings solve (Dong Liu, gain momentum but present significant security concerns. Data an urgent concern (Saura Acknowledging consequences, particularly when shifting employment patterns consumer behaviors, important (Yu rise automation displaced jobs created AIspecialized workers (Betts 2024).AI's personalizing underscores responsibility mitigate algorithmic biases, maintaining public trust equity. Governments must work together implement reskilling programs seamlessly AIdriven world. Ethical concerns, digital divide, underscore need transparent inclusive (Bouhouita-Guermech challenges amplified areas, where disparities access marginalize vulnerable populations. issues solved only collaborative efforts design prioritizing wellbeing inclusiveness.Several exist, integration issues, literacy resistance change, availability, reliance (Uwaoma industries, getting actionable time-consuming costly. result, cannot produce satisfactory results without robust data, undermining complicated Ensuring equal technology addressed so benefits sectors society. Additionally, within organizations utmost importance. Many resist change lack understanding, making difficult them future.Moreover, (availability quality) remains hurdle accessing opaque 2024b); training DRL's models, quality datasets critical, contexts both sparse expensive collect (Saliba 2020).On hand, concern; according Choudhuri, 30 % unreliable or poor quality; having said that, incomplete fail any method analysis affect process words data-especially high-quality data-sustainable doomed falter. A further equitable since marginalized communities often face barriers taking advantage developments (Kasun highlighted here highlight balanced deployment.Without prospects adopting bleak. However, Sustainable demands involvement government sector.Governments establish policies regulations promote transparency collaboration ensure transfer private sector. kind cooperation facilitating responsibly addressing broader goals.The advancement however, hindered limitations, unwillingness difficulty integrating pre-existing HR (Madanchian Taherdoost, hampered algorithms common sense interpret properly, resulting flawed decisions (Nishant clinicians' negatively impacted (Dratsch prescribing antidepressants, clinicians less accurate following incorrect recommendations compared baseline correct advice condition (Jacobs high cost resource-intensive reach broad audience (Sommer organizational creates barrier HRM employees reluctant about security, privacy, job (Hassan 2024).Businesses witnessing driver growth, profoundly sectors. context implemented scheduling livable development. means resources, inefficiencies, embrace practices, tackle full realized if align clearly defined targets. Achieving strategic not technical tool generating short-term benefits.Policymakers reliable fairly equitably fosters range would beneficial create same time.As becomes integral presents opportunities challenges. costs; McKinsey ( 2022), reported engines 15% competitive position 50% workforce tasks. policymakers planners creating innovations thrive inclusively overstated. Integrating balancing considerations. powerful force stakeholders atmosphere, address barriers, transparency. businesses, societies, benefit. By examining manner, article introduces fresh perspective literature because comprehensively covered current literature. valuable synthesize existing trends strong foundation understanding business.

Язык: Английский

A eficiência da Inteligência Artificial no recrutamento e seleção: uma abordagem geral e sua aplicação em pequenas e médias empresas

Caderno Pedagógico, Год журнала: 2025, Номер 22(6), С. e15489 - e15489

Опубликована: Апрель 10, 2025

A crescente adoção da Inteligência Artificial (IA) em processos seletivos tem despertado interesse tanto pela promessa de maior eficiência quanto pelos desafios éticos que emergem, sobretudo pequenas e médias empresas (PMEs). Este artigo realiza uma Revisão Sistemática Literatura para analisar como a IA impacta seleção pessoal PMEs, considerando fatores automação, viés algorítmico, regulação integração tecnológica. Foram avaliados 28 estudos publicados entre 2023 2025, identificando vantagens redução do tempo triagem precisão nas contratações. Contudo, evidenciaram-se barreiras resistência organizacional, carência infraestrutura tecnológica escassez específicos sobre PMEs. pesquisa destaca importância modelos híbridos, conciliem automação supervisão humana, solução viável garantir justiça, transparência adaptação às limitações estruturais das menor porte. análise aponta ainda lacuna na literatura à aplicação prática indicando necessidade empíricos, programas capacitação diretrizes regulatórias específicas. Conclui-se que, embora promissora, implementação PMEs requer um planejamento cuidadoso, leve conta não apenas inovação tecnológica, mas também os humanos contextuais impactam sua adoção.

Examining innovation practices in the IT sector: A socioformative and AI-driven analysis of talent development in Mexico and the United States

Social Sciences & Humanities Open, Год журнала: 2025, Номер 11, С. 101512 - 101512

Опубликована: Янв. 1, 2025

Язык: Английский