{"id":1105,"date":"2025-10-23T08:44:27","date_gmt":"2025-10-23T08:44:27","guid":{"rendered":"https:\/\/kalkinmaguncesi.izka.org.tr\/?p=1105"},"modified":"2025-10-23T08:44:28","modified_gmt":"2025-10-23T08:44:28","slug":"yapay-zeka-destekli-proje-yonetimi","status":"publish","type":"post","link":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/2025\/10\/23\/yapay-zeka-destekli-proje-yonetimi\/","title":{"rendered":"Yapay Zek\u00e2 Destekli Proje Y\u00f6netimi"},"content":{"rendered":"\n<p class=\"has-black-color has-cyan-bluish-gray-background-color has-text-color has-background\"><strong><strong>\u0130pek KOCAO\u011eLU<\/strong><\/strong><br><em>Uzman<\/em><br><em>Proje Uygulama ve \u0130zleme Birimi<br><\/em><a href=\"mailto:ipek.kocaoglu@izka.org.tr\"><em>ipek.kocaoglu@izka.org.tr<\/em><\/a><\/p>\n\n\n\n<p>Proje y\u00f6netimi, karma\u015f\u0131k ve \u00e7ok boyutlu projelerin etkin bir \u015fekilde planlanmas\u0131, uygulanmas\u0131 ve tamamlanmas\u0131na y\u00f6nelik sistematik bir yakla\u015f\u0131m\u0131 ifade eder. Projelerin ba\u015far\u0131ya ula\u015fmas\u0131, bu a\u015famalar\u0131n etkin bir \u015fekilde y\u00f6netilmesi ve izlenmesine ba\u011fl\u0131d\u0131r. Bu ba\u011flamda, PMI (Project Management Institute) ve IPMA (International Project Management Association) gibi kurulu\u015flar\u0131n t\u00fcm d\u00fcnyada kabul g\u00f6ren metodolojileri, projelerin ba\u015far\u0131yla y\u00f6netilmesine katk\u0131 sa\u011flamaktad\u0131r. Son y\u0131llarda, bu s\u00fcrece yapay zek\u00e2 teknolojilerinin entegre edilmesi, proje y\u00f6netimini daha \u00e7evik, \u00f6ng\u00f6r\u00fclebilir ve verimli bir yap\u0131ya kavu\u015fturmu\u015ftur.<\/p>\n\n\n\n<p>G\u00fcn\u00fcm\u00fcz\u00fcn proje y\u00f6netim s\u00fcre\u00e7lerinde yapay zek\u00e2, stratejik bir ara\u00e7 olarak \u00f6n plana \u00e7\u0131kmakta, proje ya\u015fam d\u00f6ng\u00fcs\u00fc boyunca karar alma s\u00fcre\u00e7lerini iyile\u015ftirmek ve kaynak kullan\u0131m\u0131n\u0131 optimize etmek amac\u0131yla yapay zek\u00e2n\u0131n sundu\u011fu veri analizi, tahminleme ve otomasyon gibi yeteneklerden yararlan\u0131lmaktad\u0131r (Y\u0131ld\u0131r\u0131m ve \u015eahiner, 2020).<\/p>\n\n\n\n<p>Yapay zek\u00e2 destekli proje y\u00f6netimi \u00f6zellikle veri odakl\u0131 karar alma s\u00fcre\u00e7lerinde \u00e7arp\u0131c\u0131 etkiler yaratmaktad\u0131r. Geleneksel y\u00f6ntemlerin \u00f6tesine ge\u00e7erek, b\u00fcy\u00fck veri setlerini h\u0131zl\u0131 ve etkili bir \u015fekilde analiz eden yapay zek\u00e2 sistemleri, proje y\u00f6neticilerine hem stratejik \u00f6ng\u00f6r\u00fcler sunmakta hem de \u00e7e\u015fitli alternatif senaryolarla karar alma s\u00fcrecini desteklemektedir (PMI T\u00fcrkiye, 2023).<\/p>\n\n\n\n<p><strong>Proje D\u00f6ng\u00fcs\u00fc A\u015famalar\u0131nda Yapay Zek\u00e2 Uygulamalar\u0131<\/strong><\/p>\n\n\n\n<p>Proje d\u00f6ng\u00fcs\u00fc; ba\u015flatma, planlama, y\u00fcr\u00fctme, izleme ve kapan\u0131\u015f olmak \u00fczere be\u015f temel a\u015famadan olu\u015fur. Yapay zek\u00e2, proje ya\u015fam d\u00f6ng\u00fcs\u00fcn\u00fcn her a\u015famas\u0131na etki edecek \u015fekilde geni\u015f bir uygulama yelpazesi sunmaktad\u0131r.<\/p>\n\n\n\n<p>Proje y\u00f6netiminin ilk ad\u0131m\u0131 olan ba\u015flatma s\u00fcrecinde, projenin hedefleri, kapsam\u0131 ve ba\u015far\u0131 kriterleri tan\u0131mlan\u0131r; proje ekibi olu\u015fturulur ve roller belirlenir. Bu a\u015famada yapay zek\u00e2, \u00f6nceki projelerden elde edilen verileri analiz ederek risk de\u011ferlendirmeleri, maliyet \u00f6ng\u00f6r\u00fcleri ve payda\u015f beklentilerinin belirlenmesi gibi kritik alanlarda y\u00f6neticilere karar destek mekanizmas\u0131 sunar. Bu sayede, proje ba\u015flang\u0131c\u0131 daha rasyonel ve veri temelli bi\u00e7imde yap\u0131land\u0131r\u0131labilir (PMI T\u00fcrkiye, 2023).<\/p>\n\n\n\n<p>Planlama a\u015famas\u0131nda ise proje i\u00e7in ayr\u0131nt\u0131l\u0131 bir yol haritas\u0131 haz\u0131rlan\u0131r. Yapay zek\u00e2 bu a\u015famada makine \u00f6\u011frenimi algoritmalar\u0131 arac\u0131l\u0131\u011f\u0131yla ge\u00e7mi\u015f projelerden elde edilen verileri analiz ederek zaman \u00e7izelgeleri, kaynak tahsisi, i\u015f g\u00fcc\u00fc planlamas\u0131 ve maliyet tahminlerini y\u00fcksek do\u011frulukla modelleyebilir (Boudreau, 2023).<\/p>\n\n\n\n<p>Proje plan\u0131nda belirlenen g\u00f6revlerin hayata ge\u00e7irildi\u011fi y\u00fcr\u00fctme a\u015famas\u0131nda g\u00f6rev da\u011f\u0131l\u0131m\u0131 yap\u0131l\u0131r, kaynaklar kullan\u0131ma sunulur ve ekip \u00fcyeleri plana uygun \u015fekilde \u00e7al\u0131\u015fmalar\u0131n\u0131 s\u00fcrd\u00fcr\u00fcr (PwC, 2024).<\/p>\n\n\n\n<p>\u0130zleme a\u015famas\u0131nda ise projenin hedeflere uygunlu\u011fu ve kaynak kullan\u0131m\u0131 takip edilir. Yapay zek\u00e2, \u00f6zellikle b\u00fcy\u00fck projelerde ger\u00e7ekle\u015fen performans\u0131 \u00f6l\u00e7erek zaman, b\u00fct\u00e7e ve kalite sapmalar\u0131n\u0131 \u00f6nceden tespit edebilir. Otomatik bildirim sistemleri sayesinde, proje y\u00f6neticileri gerekli m\u00fcdahaleleri h\u0131zl\u0131ca ger\u00e7ekle\u015ftirebilir (PwC, 2024). Yapay zek\u00e2 destekli proje y\u00f6netim ara\u00e7lar\u0131 aras\u0131nda Forecast, Clarizen, Monday, Asana ve Trello gibi platformlar yer almaktad\u0131r. Bu ara\u00e7lar, i\u015f ak\u0131\u015flar\u0131n\u0131 otomatikle\u015ftirerek proje \u00e7\u0131kt\u0131lar\u0131n\u0131 tahmin etmeye, planlamaya ve ekip i\u00e7i i\u015f birli\u011fini g\u00fc\u00e7lendirmeye katk\u0131 sa\u011flamaktad\u0131r (Sahadevan, 2023).<\/p>\n\n\n\n<p>Son olarak, kapan\u0131\u015f a\u015famas\u0131nda projenin tamamlanmas\u0131n\u0131 takiben, proje \u00e7\u0131kt\u0131lar\u0131 teslim edilir, ekip da\u011f\u0131t\u0131l\u0131r ve \u00f6\u011frenilen dersler kurumsal haf\u0131zaya aktar\u0131l\u0131r. Yapay zek\u00e2 bu a\u015famada, proje boyunca toplanan verileri analiz ederek ba\u015far\u0131s\u0131zl\u0131k nedenlerini, s\u00fcre\u00e7lerdeki sapmalar\u0131 ve iyile\u015ftirme alanlar\u0131n\u0131 otomatik olarak tespit edebilir (Taboada vd., 2023). B\u00f6ylece kurumlar, yaln\u0131zca mevcut projenin de\u011fil, gelecekteki projelerin de ba\u015far\u0131s\u0131 i\u00e7in stratejik i\u00e7g\u00f6r\u00fcler elde etmi\u015f olur. Makine \u00f6\u011frenme algoritmalar\u0131 ayr\u0131ca kurumun uzun vadeli stratejileriyle uyumlu ve s\u00fcrd\u00fcr\u00fclebilirlik a\u00e7\u0131s\u0131ndan y\u00fcksek potansiyele sahip projeleri \u00f6nceliklendirebilir (Mashhood, 2023). Kapan\u0131\u015f a\u015famas\u0131nda \u00fcretilen bu bilgiler, gelecekteki projeler i\u00e7in birer rehber niteli\u011fi ta\u015f\u0131r.<\/p>\n\n\n\n<p><strong>Proje Y\u00f6netiminin Evrimi<\/strong><\/p>\n\n\n\n<p>Ruiz, Torres ve Gonz\u00e1lez Crespo (2020), \u015fekilde g\u00f6r\u00fcld\u00fc\u011f\u00fc \u00fczere 1983\u2019ten 2050\u2019ye uzanan s\u00fcre\u00e7te, otomasyondan otonomiye proje y\u00f6netiminin ge\u00e7irdi\u011fi ve ge\u00e7irmesi beklenen d\u00f6rt evre tan\u0131mlam\u0131\u015ft\u0131r:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"758\" height=\"225\" src=\"https:\/\/kalkinmaguncesi.izka.org.tr\/wp-content\/uploads\/2025\/10\/y.png\" alt=\"\" class=\"wp-image-1106\" srcset=\"https:\/\/kalkinmaguncesi.izka.org.tr\/wp-content\/uploads\/2025\/10\/y.png 758w, https:\/\/kalkinmaguncesi.izka.org.tr\/wp-content\/uploads\/2025\/10\/y-300x89.png 300w\" sizes=\"auto, (max-width: 758px) 100vw, 758px\" \/><\/figure>\n\n\n\n<ol start=\"1\" class=\"wp-block-list\">\n<li><strong>Entegrasyon ve Otomasyon (1983):<\/strong> Bilgi teknolojilerinin entegrasyonu Microsoft Project ve Oracle Primavera gibi yaz\u0131l\u0131mlar arac\u0131l\u0131\u011f\u0131yla ba\u015flam\u0131\u015f proje y\u00f6netiminde g\u00f6revlerin otomatikle\u015ftirilmesine \u00f6nc\u00fcl\u00fck etmi\u015ftir.<\/li>\n\n\n\n<li><strong>Chatbot Asistanlar\u0131 (2016)<\/strong>: Toplant\u0131 hat\u0131rlat\u0131c\u0131lar\u0131 ve \u00f6zetleyici ara\u00e7lar olarak sohbet robotlar\u0131 proje y\u00f6netimine dahil olmaya ba\u015flam\u0131\u015ft\u0131r.<\/li>\n\n\n\n<li><strong>Makine \u00d6\u011frenmesine Dayal\u0131 Proje Y\u00f6netimi (2023\u20132035):<\/strong> Makine \u00f6\u011frenmesi, ge\u00e7mi\u015f projelerden edinilen verilerle kaynak planlamas\u0131 ve risk y\u00f6netimi alan\u0131nda karar alma s\u00fcre\u00e7lerinde kullan\u0131lmaya ba\u015flam\u0131\u015ft\u0131r.<\/li>\n\n\n\n<li><strong>Otonom Proje Y\u00f6netimi (2035\u20132050):<\/strong> Bu a\u015famada, yapay zek\u00e2n\u0131n karar alma s\u00fcre\u00e7lerini tamamen devralabilece\u011fi \u00f6ng\u00f6r\u00fclmektedir.<\/li>\n<\/ol>\n\n\n\n<p><strong>Faydalar<\/strong><\/p>\n\n\n\n<p>Yapay zek\u00e2 entegrasyonunun proje y\u00f6netimine sa\u011flad\u0131\u011f\u0131 temel faydalar \u015f\u00f6yle \u00f6zetlenebilir (Eaton Business School [EBS], 2024):<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Verimlilik Art\u0131\u015f\u0131:<\/strong> Rutin ve tekrarlayan i\u015flerin otomasyonu, ekiplerin daha stratejik g\u00f6revlerle ilgilenmesine olanak tan\u0131makta; b\u00f6ylece genel proje s\u00fcresi ve i\u015f g\u00fcc\u00fc maliyeti azalmaktad\u0131r.<\/li>\n\n\n\n<li><strong>H\u0131z ve \u00d6l\u00e7eklenebilirlik:<\/strong> Ayn\u0131 anda birden fazla karma\u015f\u0131k projenin y\u00f6netilebilmesi, yapay zek\u00e2n\u0131n \u00e7ok b\u00fcy\u00fck veri k\u00fcmeleriyle h\u0131zl\u0131 analiz yapabilme yetene\u011fi sayesinde m\u00fcmk\u00fcn olmaktad\u0131r.<\/li>\n\n\n\n<li><strong>Hata Oranlar\u0131n\u0131n Azalmas\u0131:<\/strong> Manuel hatalar b\u00fcy\u00fck \u00f6l\u00e7\u00fcde azalt\u0131larak daha g\u00fcvenilir proje \u00e7\u0131kt\u0131lar\u0131 elde edilmekte; tahminler, b\u00fct\u00e7eler ve performans de\u011ferlendirmeleri daha isabetli yap\u0131labilmektedir.<\/li>\n\n\n\n<li><strong>Ger\u00e7ek Zamanl\u0131 \u0130zleme:<\/strong> Proje ilerleyi\u015fi anl\u0131k verilerle takip edilebilmekte; bu da m\u00fcdahale s\u00fcre\u00e7lerini h\u0131zland\u0131rmakta ve projenin ba\u015far\u0131 oran\u0131n\u0131 art\u0131rmaktad\u0131r.<\/li>\n<\/ul>\n\n\n\n<p><strong>Uygulama Alanlar\u0131 ve Ba\u015far\u0131 \u00d6rnekleri<\/strong><\/p>\n\n\n\n<p>Yapay zek\u00e2n\u0131n proje y\u00f6netiminde etkin bi\u00e7imde kullan\u0131ld\u0131\u011f\u0131 uygulama alanlar\u0131, sekt\u00f6rel olarak farkl\u0131l\u0131k g\u00f6stermekle birlikte; in\u015faat, bili\u015fim, finans, sa\u011fl\u0131k ve savunma sanayi gibi y\u00fcksek risk, y\u00fcksek sermaye ve \u00e7ok payda\u015fl\u0131 yap\u0131lar\u0131n yer ald\u0131\u011f\u0131 sekt\u00f6rlerde belirginle\u015fmektedir (EBS, 2024).<\/p>\n\n\n\n<p>\u0130n\u015faat sekt\u00f6r\u00fc, yapay zek\u00e2 temelli zaman \u00e7izelgeleme, i\u015f g\u00fcc\u00fc tahsisi ve maliyet sapma \u00f6ng\u00f6r\u00fclerinin en erken benimsendi\u011fi alanlardan biridir. \u00d6rne\u011fin, ProPlanner, Deltek ve Buildots gibi yaz\u0131l\u0131mlar; sahadan al\u0131nan g\u00f6r\u00fcnt\u00fcler \u00fczerinden yapay zek\u00e2 destekli analizler yaparak ilerleme durumu, kaynak kullan\u0131m\u0131 ve olas\u0131 gecikmelere dair verileri proje y\u00f6neticilerine ger\u00e7ek zamanl\u0131 olarak iletebilmektedir (Branscombe, 2018). \u201cDubai Metro Uzatma Projesi\u201d, b\u00fcy\u00fck \u00f6l\u00e7ekli altyap\u0131 projelerinde yapay zek\u00e2 uygulamalar\u0131n\u0131n etkileyici bir \u00f6rne\u011fini sunmaktad\u0131r. 2,4 milyar dolar b\u00fct\u00e7eli projede, 1.000\u2019den fazla IoT sens\u00f6r\u00fcnden toplanan ger\u00e7ek zamanl\u0131 veriler, geli\u015fmi\u015f makine \u00f6\u011frenimi algoritmalar\u0131 ile analiz edilerek proje s\u00fcresinin %23 k\u0131sald\u0131\u011f\u0131 ve maliyetlerin %15 azald\u0131\u011f\u0131 rapor edilmi\u015ftir. Yapay zek\u00e2 tabanl\u0131 lojistik y\u00f6netim sistemi, 27 farkl\u0131 \u015fantiye b\u00f6lgesinde malzeme ve ekipman hareketlerini ge\u00e7mi\u015f veriler, hava durumu ve trafik ko\u015fullar\u0131na g\u00f6re optimize ederek kaynak kullan\u0131m\u0131nda <strong>%30 verimlilik art\u0131\u015f\u0131 sa\u011flam\u0131\u015ft\u0131r<\/strong> (Build News, 2024).<\/p>\n\n\n\n<p>Bili\u015fim ve yaz\u0131l\u0131m sekt\u00f6r\u00fcnde, Agile ve Scrum gibi \u00e7evik y\u00f6ntemler ile entegre \u00e7al\u0131\u015fan yapay zek\u00e2 \u00e7\u00f6z\u00fcmleri, sprint planlamas\u0131ndan hata tahminine kadar bir\u00e7ok a\u015famada aktif kullan\u0131lmaktad\u0131r. Atlassian, ClickUp, Microsoft Project gibi ara\u00e7lar, makine \u00f6\u011frenmesi sayesinde, ge\u00e7mi\u015f verilere dayanarak daha ger\u00e7ek\u00e7i s\u00fcre tahminleri sunmakta ve tak\u0131m i\u00e7i i\u015f y\u00fck\u00fc dengesizliklerini tespit edebilmektedir. Ayr\u0131ca, do\u011fal dil i\u015fleme (NLP) teknikleri sayesinde m\u00fc\u015fteri talepleri daha h\u0131zl\u0131 analiz edilmekte ve dok\u00fcmantasyon s\u00fcre\u00e7leri otomatikle\u015ftirilmektedir (Shamim, 2024).<\/p>\n\n\n\n<p>Finans sekt\u00f6r\u00fc, projelerin hem zaman hem de risk hassasiyetinin y\u00fcksek oldu\u011fu bir aland\u0131r. HSBC ve Barclays gibi bankalar, yapay zek\u00e2 destekli risk analiz sistemlerini dijital d\u00f6n\u00fc\u015f\u00fcm projelerinde karar destek ara\u00e7lar\u0131 olarak kullanmaktad\u0131r. \u00d6rne\u011fin, RiskWatch program\u0131, proje ba\u015flang\u0131c\u0131nda potansiyel senaryolar\u0131 de\u011ferlendirip bu senaryolar alt\u0131nda maliyet ve zaman sapmalar\u0131n\u0131 sim\u00fcle ederek y\u00f6neticilere \u00f6nerilerde bulunabilmektedir (PwC, 2024).<\/p>\n\n\n\n<p>Sa\u011fl\u0131k sekt\u00f6r\u00fc, dijital hastane sistemleri veya e-sa\u011fl\u0131k platformlar\u0131n\u0131n kurulumunda yapay zek\u00e2 teknolojilerinin uygulanmaya ba\u015fland\u0131\u011f\u0131 stratejik bir aland\u0131r. Yapay zek\u00e2, proje s\u00fcre\u00e7lerinde hasta verisi g\u00fcvenli\u011fi, kaynak planlamas\u0131 ve kullan\u0131c\u0131 deneyimi gibi farkl\u0131 kriterlerin birlikte ele al\u0131nmas\u0131na imkan tan\u0131maktad\u0131r. Bu sayede hem hasta memnuniyeti hem de yat\u0131r\u0131m verimlili\u011fi a\u00e7\u0131s\u0131ndan olumlu sonu\u00e7lar elde edilmektedir (EBS, 2024).<\/p>\n\n\n\n<p>Savunma ve havac\u0131l\u0131k gibi stratejik sekt\u00f6rlerde ise yapay zek\u00e2, b\u00fcy\u00fck \u00f6l\u00e7ekli ve y\u00fcksek hassasiyetli projelerde zaman\u0131nda teslimat ve hatas\u0131zl\u0131k oran\u0131n\u0131 art\u0131rmak amac\u0131yla kullan\u0131lmaktad\u0131r. \u00d6zellikle ABD Savunma Bakanl\u0131\u011f\u0131n\u0131n yapay zek\u00e2 destekli proje izleme sistemleri geli\u015ftirmesi ve bu sistemleri siber g\u00fcvenlik projelerine entegre etmesi dikkat \u00e7ekici bir \u00f6rnektir. Bu uygulamalar, operasyonel verimlili\u011fin yan\u0131 s\u0131ra stratejik \u00f6ng\u00f6r\u00fc kabiliyetini de g\u00fc\u00e7lendirmektedir (Ahmed, 2023).<\/p>\n\n\n\n<p>Kurumsal \u00f6l\u00e7ekte incelendi\u011finde, Tesla, \u00fcretim ve tedarik zinciri projelerinde yapay zek\u00e2 ile entegre \u00e7al\u0131\u015fan sim\u00fclasyon sistemlerini aktif olarak kullanmakta, \u00fcretim band\u0131ndaki de\u011fi\u015fkenleri analiz ederek sistemsel kararlar\u0131 otonom bi\u00e7imde alabilmektedir (EBS, 2024). Benzer \u015fekilde Amazon, b\u00fcy\u00fck \u00f6l\u00e7ekli depo y\u00f6netim sistemi \u201cSequoia\u201d \u00fczerinden yapay zek\u00e2, robotik ve bilgisayarl\u0131 g\u00f6r\u00fc teknolojilerini birle\u015ftirmi\u015ftir. 2023\u2019te devreye giren ve 30 milyondan fazla \u00fcr\u00fcn\u00fc i\u015fleyebilen sistem, kaynak planlamas\u0131n\u0131 optimize ederek hem maliyetleri d\u00fc\u015f\u00fcrm\u00fc\u015f hem de teslimat s\u00fcrelerini k\u0131saltm\u0131\u015ft\u0131r (Stone, 2025).<\/p>\n\n\n\n<p>T\u00fcm bu \u00f6rnekler, yapay zek\u00e2n\u0131n proje y\u00f6netiminde yaln\u0131zca teknolojik de\u011fil, ayn\u0131 zamanda stratejik bir enstr\u00fcman haline geldi\u011fini ortaya koymaktad\u0131r.<\/p>\n\n\n\n<p><strong>Zorluklar<\/strong><\/p>\n\n\n\n<p>Yapay zek\u00e2n\u0131n proje y\u00f6netimine entegrasyonu, \u00f6nemli f\u0131rsatlar kadar baz\u0131 zorluklar\u0131 da beraberinde getirmektedir. \u00d6ncelikle, kaliteli ve yap\u0131land\u0131r\u0131lm\u0131\u015f veri eksikli\u011fi, yapay zek\u00e2 sistemlerinin do\u011fru analiz yapmas\u0131n\u0131 engellemektedir. Ayr\u0131ca, y\u00fcksek maliyetli kurulum, e\u011fitim gereksinimi ve de\u011fi\u015fime kar\u015f\u0131 diren\u00e7, uygulamay\u0131 zorla\u015ft\u0131ran di\u011fer unsurlar aras\u0131ndad\u0131r (EBS, 2024).<\/p>\n\n\n\n<p><strong>Gelece\u011fe Y\u00f6nelik \u00d6ng\u00f6r\u00fcler<\/strong><\/p>\n\n\n\n<p>Yapay zek\u00e2 entegrasyonu, proje y\u00f6netiminin do\u011fas\u0131n\u0131 yeniden tan\u0131mlayan, b\u00fct\u00fcnc\u00fcl ve d\u00f6n\u00fc\u015ft\u00fcr\u00fcc\u00fc bir paradigma de\u011fi\u015fimini beraberinde getirmektedir. Gartner (2019) ara\u015ft\u0131rmas\u0131na g\u00f6re, 2030 y\u0131l\u0131na kadar veri toplama, izleme ve raporlama gibi geleneksel proje y\u00f6netimi g\u00f6revlerinin %80&#8217;inin yapay zek\u00e2 taraf\u0131ndan yerine getirilmesi beklenmektedir. Ayr\u0131ca proje y\u00f6netiminde yapay zek\u00e2 teknolojilerinin pazar b\u00fcy\u00fckl\u00fc\u011f\u00fcn\u00fcn 2023\u2019te 2,5 milyar dolardan 2028\u2019de 5,7 milyar dolara \u00e7\u0131kmas\u0131 beklenmektedir (Sahadevan, 2023).<\/p>\n\n\n\n<p>Yapay zek\u00e2n\u0131n proje y\u00f6netiminde yol a\u00e7mas\u0131 beklenen en k\u00f6kl\u00fc de\u011fi\u015fim, otonom proje y\u00f6netimi olacakt\u0131r. Otonom proje y\u00f6netimi, insan m\u00fcdahalesine asgari d\u00fczeyde ihtiya\u00e7 duyan ve proje hedeflerini kendi kendine tan\u0131mlay\u0131p y\u00f6neten sistemleri ifade etmektedir. S\u00f6z konusu sistemler, yapay zek\u00e2 destekli senaryo analizleriyle kaynaklar\u0131 dinamik bi\u00e7imde yeniden tahsis edebilecek, riskleri erken tespit ederek proaktif \u00f6nlemler alabilecek ve geli\u015fmelere g\u00f6re faaliyet planlar\u0131n\u0131 revize edebilecektir (PwC, 2024).<\/p>\n\n\n\n<p>Bununla birlikte, bu d\u00f6n\u00fc\u015f\u00fcm\u00fcn insan\u0131n rol\u00fcn\u00fc tamamen ortadan kald\u0131rmas\u0131 beklenmemektedir. Yapay zek\u00e2n\u0131n teknik analizlerde \u00fcst\u00fcn olmas\u0131na kar\u015f\u0131n insan ili\u015fkileri, liderlik, empati ve payda\u015f y\u00f6netimi gibi sosyal becerilerde yetersiz kald\u0131\u011f\u0131 g\u00f6r\u00fclmektedir (PwC, 2024). Sonu\u00e7 olarak, proje y\u00f6netiminin gelece\u011finin insanla teknolojinin rekabetinden \u00e7ok, i\u015f birli\u011fine dayal\u0131 hibrit bir model olarak \u015fekillenece\u011fi \u00e7\u0131kar\u0131m\u0131 yap\u0131labilir.<strong><br><\/strong><\/p>\n\n\n\n<p><strong>Kaynak\u00e7a<br><\/strong>Ahmed, M. (2023, May\u0131s 16). <em>The rise of AI \u2013 How AI is revolutionising project management<\/em> [Video]. <a href=\"https:\/\/youtu.be\/angzLwP2mDk?si=7Y0yKVYMfXWqn4IB\">https:\/\/youtu.be\/angzLwP2mDk?si=7Y0yKVYMfXWqn4IB<\/a><\/p>\n\n\n\n<p>Boudreau, P. (2023, Nisan 10). <em>How to apply AI to your project<\/em> [Video]. <a href=\"https:\/\/www.youtube.com\/watch?v=cgx3G7Ww0IE\">https:\/\/www.youtube.com\/watch?v=cgx3G7Ww0IE<\/a><\/p>\n\n\n\n<p>Branscombe, M. (2018, Ocak 12). <em>How AI could revolutionize project management.<\/em> <a href=\"https:\/\/www.cio.com\/article\/228200\/how-ai-could-revolutionize-project-management.html\">https:\/\/www.cio.com\/article\/228200\/how-ai-could-revolutionize-project-management.html<\/a><\/p>\n\n\n\n<p>Build News. (2024, Temmuz 18). <em>AI predictive analytics is transforming construction project management: The Dubai Metro Extension case<\/em>. <a href=\"https:\/\/www.build-news.com\/uncategorized\/ai-predictive-analytics-is-transforming-construction-project-management\/\">https:\/\/www.build-news.com\/uncategorized\/ai-predictive-analytics-is-transforming-construction-project-management\/<\/a><\/p>\n\n\n\n<p>Stone, M. (2025, \u015eubat 11). <em>How Amazon uses robots to sort and transport packages in warehouses<\/em>. Business Insider. <a href=\"https:\/\/www.businessinsider.com\/how-amazon-uses-robots-sort-transport-packages-warehouses-2025-2\">https:\/\/www.businessinsider.com\/how-amazon-uses-robots-sort-transport-packages-warehouses-2025-2<\/a><\/p>\n\n\n\n<p>Eaton Business School (EBS). (2024). <em>Artificial intelligence in project management: Scope, use cases, and benefits<\/em>. <a href=\"https:\/\/ebsedu.org\/blog\/artificial-intelligence-ai-in-project-management\">https:\/\/ebsedu.org\/blog\/artificial-intelligence-ai-in-project-management<\/a><\/p>\n\n\n\n<p>Gartner, Inc. (2019, 20 Mart). <em>Gartner says 80 percent of today\u2019s project management tasks will be eliminated by 2030 as artificial intelligence takes over<\/em> <a href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2019-03-20-gartner-says-80-percent-of-today-s-project-management\">https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2019-03-20-gartner-says-80-percent-of-today-s-project-management<\/a><\/p>\n\n\n\n<p>Project Management Institute (PMI) T\u00fcrkiye. (2023). <em>Proje y\u00f6netiminde yapay zek\u00e2 uygulamalar\u0131<\/em>. <a href=\"https:\/\/www.pmi.org.tr\/tr\/blog\/proje-yonetiminde-yapay-zeka-uygulamalari\">https:\/\/www.pmi.org.tr\/tr\/blog\/proje-yonetiminde-yapay-zeka-uygulamalari<\/a><\/p>\n\n\n\n<p>PricewaterhouseCoopers (PwC). (2024). <em>AI will transform project management\u2014are you ready?<\/em> <a href=\"https:\/\/www.pwc.ch\/en\/publications\/2019\/ai-will-transform-project-management-en2019-web.pdf\">https:\/\/www.pwc.ch\/en\/publications\/2019\/ai-will-transform-project-management-en2019-web.pdf<\/a><\/p>\n\n\n\n<p>Ruiz, J. G., Torres, J. M., &amp; Gonz\u00e1lez Crespo, R. (2020). The application of artificial intelligence in project management research: A review. <em>International Journal of Interactive Multimedia and Artificial Intelligence, 6<\/em>(6), 54\u201364. <a href=\"https:\/\/doi.org\/10.9781\/ijimai.2020.12.003\">https:\/\/doi.org\/10.9781\/ijimai.2020.12.003<\/a><\/p>\n\n\n\n<p>Taboada, I., Daneshpajouh, A., Toledo, N., &amp; de Vass, T. (2023). Artificial Intelligence Enabled Project Management: A Systematic Literature Review.&nbsp;<em>Applied Sciences<\/em>,&nbsp;<em>13<\/em>(8), 5014. <a href=\"https:\/\/doi.org\/10.3390\/app13085014\">https:\/\/doi.org\/10.3390\/app13085014<\/a>&nbsp;<\/p>\n\n\n\n<p>Sahadevan, S. (2023). <em>Project management in the era of artificial intelligence<\/em>. <em>European Journal of Theoretical and Applied Sciences<\/em>, <em>1<\/em>(3), 349\u2013359. <a href=\"https:\/\/doi.org\/10.59324\/ejtas.2023.1(3).35\">https:\/\/doi.org\/10.59324\/ejtas.2023.1(3).35<\/a><\/p>\n\n\n\n<p>Shamim, M. I. (2024). Artificial Intelligence in Project Management: Enhancing Efficiency and Decision-Making.&nbsp;<em>International Journal of Management Information Systems and Data Science<\/em>,&nbsp;<em>1<\/em>(1), 1\u20136. <a href=\"https:\/\/doi.org\/10.62304\/ijmisds.v1i1.107\">https:\/\/doi.org\/10.62304\/ijmisds.v1i1.107<\/a><\/p>\n\n\n\n<p>Y\u0131ld\u0131r\u0131m, G., &amp; \u015eahiner, F. (2021). Proje Y\u00f6netiminde Yapay Zek\u00e2 Tabanl\u0131 Payda\u015f Analizi Konusu \u00dczerine Ara\u015ft\u0131rma ve \u00d6neriler. <em>Journal of Investigations on Engineering and Technology<\/em>, 4(1), 1-6. <a href=\"https:\/\/dergipark.org.tr\/tr\/pub\/jiet\/issue\/63329\/846298\">https:\/\/dergipark.org.tr\/tr\/pub\/jiet\/issue\/63329\/846298<\/a><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Proje y\u00f6netimi, karma\u015f\u0131k ve \u00e7ok boyutlu projelerin etkin bir \u015fekilde planlanmas\u0131, uygulanmas\u0131 ve tamamlanmas\u0131na y\u00f6nelik sistematik bir yakla\u015f\u0131m\u0131 ifade eder. Projelerin ba\u015far\u0131ya ula\u015fmas\u0131, bu a\u015famalar\u0131n etkin bir \u015fekilde y\u00f6netilmesi ve izlenmesine ba\u011fl\u0131d\u0131r. Son y\u0131llarda, bu s\u00fcrece yapay zek\u00e2 teknolojilerinin entegre edilmesi, proje y\u00f6netimini daha \u00e7evik, \u00f6ng\u00f6r\u00fclebilir ve verimli bir yap\u0131ya kavu\u015fturmu\u015ftur.<\/p>\n","protected":false},"author":6,"featured_media":1108,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[22],"tags":[315,316,19,317],"class_list":["post-1105","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-surdurulebilir-kalkinma","tag-proje-dongusu","tag-proje-yonetimi","tag-surdurulebilir-kalkinma","tag-yapay-zeka-2"],"_links":{"self":[{"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/posts\/1105","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/users\/6"}],"replies":[{"embeddable":true,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/comments?post=1105"}],"version-history":[{"count":2,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/posts\/1105\/revisions"}],"predecessor-version":[{"id":1109,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/posts\/1105\/revisions\/1109"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/media\/1108"}],"wp:attachment":[{"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/media?parent=1105"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/categories?post=1105"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/kalkinmaguncesi.izka.org.tr\/index.php\/wp-json\/wp\/v2\/tags?post=1105"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}