{"id":7542,"date":"2026-07-02T16:33:47","date_gmt":"2026-07-02T14:33:47","guid":{"rendered":"https:\/\/industrynews.ro\/?p=7542"},"modified":"2026-07-02T16:33:48","modified_gmt":"2026-07-02T14:33:48","slug":"un-an-de-cercetare-asupra-ai-aproape-toate-regulile-generale-despre-recomandarile-ai-sunt-gresite","status":"publish","type":"post","link":"https:\/\/industrynews.ro\/en\/un-an-de-cercetare-asupra-ai-aproape-toate-regulile-generale-despre-recomandarile-ai-sunt-gresite\/","title":{"rendered":"Un an de cercetare asupra AI: aproape toate regulile generale despre recomand\u0103rile AI sunt gre\u0219ite"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Fiecare industrie are propriul mecanism prin care un brand ajunge s\u0103 fie recomandat de inteligen\u021b\u0103 artificial\u0103. Aceast\u0103 este concluzia principala a unui program de cercetare derulat de Reveal Marketing Research \u00eencep\u00e2nd din august 2025, bazat pe reverse engineering aplicat pe e\u0219antioane reprezentative de utilizatori reali \u2014 analiz\u0103 direct\u0103 at\u00e2t calitativ\u0103 c\u00e2t \u0219i cantitativ\u0103 \u2014 a r\u0103spunsurilor ob\u021binute de la sistemele AI pe care ace\u0219tia le folosesc efectiv, \u00een condi\u021bii reale.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Concluzia contrazice una dintre cele mai r\u0103sp\u00e2ndite convingeri despre AI: c\u0103 ar exist\u0103 un set de reguli generale dup\u0103 care func\u021bioneaz\u0103 recomand\u0103rile, valabil indiferent de categorie sau pia\u021b\u0103.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI devine primul punct de contact \u00een decizia de cump\u0103rare<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Schimbarea vine \u00eentr-un moment \u00een care inteligen\u021b\u0103 artificial\u0103 \u00eencepe s\u0103 joace un rol direct \u00een procesul de informare \u0219i alegere a consumatorilor. Potrivit celui mai recent studiu Reveal Market Resources, utilizarea AI \u00een Rom\u00e2nia a crescut de la 47% \u00een 2025 la 68% \u00een 2026. 91% dintre utilizatori spun c\u0103 folosesc instrumente AI pentru a caut\u0103 informa\u021bii despre produse sau companii, iar 65% afirm\u0103 c\u0103 recomand\u0103rile primite influen\u021beaz\u0103 decizia final\u0103 de cump\u0103rare.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">La nivel global, ChatGPT a dep\u0103\u0219it \u00een luna mai 2026 pragul de un miliard de utilizatori activi lunar, devenind cea mai rapid\u0103 aplic\u0103\u021bie din istorie care atinge aceast\u0103 performan\u021b\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pentru tot mai mul\u021bi consumatori, \u00eentrebarea nu mai este introdus\u0103 \u00eentr-un motor de c\u0103utare, ci adresat\u0103 direct unui sistem AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201dCompaniile nu mai concureaz\u0103 doar pentru aten\u021bia consumatorilor. Concureaz\u0103 \u0219i pentru locul pe care \u00eel ocup\u0103 \u00een ecosistemul de informa\u021bii din care AI \u00ee\u0219i construie\u0219te r\u0103spunsurile. F\u0103r\u0103 date despre acest ecosistem, deciziile de comunicare devin tot mai greu de fundamentat \u0219i tot mai greu de transformat \u00een rezultate\u201d, spune Marius Luican, Fondator &amp; Director General, Reveal Marketing Research.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Programul de cercetare a \u00eenceput \u00een august 2025 pornind de la o \u00eentrebare aparent simpl\u0103: cum decide AI ce brand recomand\u0103?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen lipsa unor r\u0103spunsuri validate, echipa Reveal Marketing Research a construit un program care a inclus sute de interac\u021biuni analizate \u00eentre utilizatori reali \u0219i modele AI, teste comparative \u00een categorii diferite de produse \u0219i servicii, analiz\u0103 surselor citate de sistemele AI, experimente privind modificarea ecosistemului informa\u021bional \u0219i observarea efectelor asupra recomand\u0103rilor generate. Concluziile au fost confirmate \u0219i aprofundate \u00een proiecte implementate pentru companii din FMCG, retail, servicii financiare, construc\u021bii, imobiliare \u0219i B2B.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pe parcursul cercet\u0103rii, una dintre ipotezele ini\u021biale a fost infirmat\u0103: c\u0103 ar exist\u0103 un set unic de reguli care explic\u0103 recomand\u0103rile generate de AI indiferent de industrie. Datele au ar\u0103tat exact contrarul.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Diferen\u021bele dintre industrii s-au dovedit mai mari dec\u00e2t diferen\u021bele dintre modelele AI analizate.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201dAm \u00eenceput cercetarea convin\u0219i c\u0103 vom descoperi un set de reguli generale dup\u0103 care AI recomand\u0103 brandurile. Dup\u0103 sute de teste, concluzia a fost exact opus\u0103: fiecare industrie func\u021bioneaz\u0103 dup\u0103 propriile reguli. Din acest motiv, orice strategie bazat\u0103 pe re\u021bete universale este fundamental gre\u0219it\u0103\u201d, adaug\u0103 Marius Luican.&nbsp;&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Programul nu \u0219i-a propus s\u0103 testeze performan\u021b\u0103 modelelor AI \u0219i nici acurate\u021bea r\u0103spunsurilor acestora. Obiectivul a fost identificarea mecanismelor prin care sistemele AI construiesc recomand\u0103rile \u0219i selecteaz\u0103 sursele utilizate.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong><em>Ce a descoperit cercetarea<\/em><\/strong><em><\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&nbsp;<strong>Fiecare industrie are propriul mecanism de recomandare<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Nu exist\u0103 o regul\u0103 universal\u0103 dup\u0103 care func\u021bioneaz\u0103 AI. \u00cen unele categorii, sistemele acord\u0103 o important\u0103 major\u0103 informa\u021biilor publicate pe site-ul oficial al companiei. \u00cen altele, recomand\u0103rile sunt construite aproape exclusiv din publica\u021bii independente, platforme specializate sau con\u021binut generat de utilizatori. Acela\u0219i tip de strategie poate produce rezultate foarte diferite \u00een dou\u0103 industrii aparent apropiate.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI stabile\u0219te criteriile \u00eenainte s\u0103 caute informa\u021bii<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Analiz\u0103 r\u0103spunsurilor arat\u0103 c\u0103 modelele AI \u00ee\u0219i construiesc mai \u00eent\u00e2i un cadru de evaluare specific categoriei \u2014 abia dup\u0103 aceea selecteaz\u0103 sursele care r\u0103spund acelor criterii. Cu alte cuvinte, sursele nu determina singure recomandarea. Ele sunt filtrate printr-un mecanism de evaluare construit de AI, invizibil pentru orice analiz\u0103 superficial\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Num\u0103rul surselor care conteaz\u0103 este surprinz\u0103tor de mic<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen majoritatea categoriilor analizate, recomand\u0103rile AI au fost construite pornind de la 6 p\u00e2n\u0103 la 15 surse dominante. Aceast\u0103 \u00eenseamn\u0103 c\u0103 vizibilitatea unui brand poate depinde de prezen\u021ba \u00eentr-un num\u0103r restr\u00e2ns de publica\u021bii \u0219i platforme relevante, nu de sute de site-uri.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00centr-un caz analizat, surs\u0103 num\u0103rul unu citat\u0103 de AI nu era niciun portal de specialitate, ci un site local pe care brandul nici nu \u0219tia c\u0103 exist\u0103 \u2014 \u0219i care influen\u021b\u0103 recomand\u0103rile mai mult dec\u00e2t site-ul oficial al companiei.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Topurile globale spun foarte pu\u021bin despre fiecare pia\u021b\u0103<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">O publica\u021bie aflat\u0103 printre cele mai citate surse AI la nivel interna\u021bional poate avea o influen\u021b\u0103 minim\u0103 \u00eentr-o categorie din Rom\u00e2nia. \u00cen acela\u0219i timp, platforme locale foarte specializate pot avea un rol decisiv \u00een recomand\u0103rile generate pentru utilizatorii rom\u00e2ni.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>SEO nu explic\u0103 singur vizibilitatea \u00een AI<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen unele industrii, site-ul oficial reprezint\u0103 principala surs\u0103 utilizat\u0103 de AI. \u00cen altele, contribu\u021bia acestuia scade sub 6%, iar recomand\u0103rile sunt construite predominant din articole editoriale, publica\u021bii de specialitate, recenzii sau forumuri. Acest lucru explic\u0103 de ce dou\u0103 companii cu performan\u021be SEO similare pot avea niveluri foarte diferite de vizibilitate \u00een AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00centr-unul dintre studiile implementate, un brand cu 83% vizibilitate \u00een AI nu ap\u0103rea niciodat\u0103 primul \u00een recomand\u0103ri \u2014 competitorul deschidea conversa\u021bia \u00een 76% din cazuri, de\u0219i performan\u021bele SEO ale celor dou\u0103 companii erau comparabile.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Vizibilitatea \u00een AI este rezultatul unui ecosistem, nu al unui singur canal<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Recomand\u0103rile nu sunt generate pornind de la un singur canal. Ele rezult\u0103 din combinarea autorit\u0103\u021bii publica\u021biilor, reputa\u021biei digitale, tipologiei men\u021biunilor, con\u021binutului comparativ \u0219i a altor surse relevante pentru categoria analizat\u0103. Important\u0103 fiec\u0103rui element difer\u0103 \u00eens\u0103 de la o industrie la alt\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>AI personalizeaz\u0103 recomand\u0103rile cu o acurate\u021be de segmentare remarcabil\u0103<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Una dintre cele mai importante concluzii ale cercet\u0103rii este capacitatea sistemelor AI de a adapta r\u0103spunsurile \u00een func\u021bie de profilul utilizatorului cu o precizie remarcabil\u0103. Dou\u0103 persoane care adreseaz\u0103 \u00eentreb\u0103ri similare primesc recomand\u0103ri diferite dac\u0103 au niveluri diferite de expertiz\u0103, inten\u021bii de cump\u0103rare diferite sau contexte diferite de utilizare \u2014 iar suprapunerea dintre profilul consumatorului \u0219i tipul de recomandare generat\u0103 s-a dovedit extrem de exact\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen categoriile analizate, AI a produs segment\u0103ri comportamentale \u0219i de inten\u021bie comparabile cu cele ob\u021binute prin studii dedicate de segmentare \u2014 f\u0103r\u0103 s\u0103 fi fost instruit explicit \u00een acest sens. Pentru echipele de marketing, aceast\u0103 \u00eenseamn\u0103 c\u0103 brandul t\u0103u poate fi recomandat puternic unui segment \u0219i absent complet pentru altul, chiar dac\u0103 ambele segmente pun \u00eentreb\u0103ri aparent similare.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Testarea din propriul cont ofer\u0103 o imagine incomplet\u0103<\/strong><strong><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Verificarea modului \u00een care AI recomand\u0103 un brand folosind un singur cont surprinde doar o parte foarte mic\u0103 din realitate \u2014 echivalentul analiz\u0103rii unei fotografii privind un singur pixel. Rezultatele difer\u0103 \u00een func\u021bie de utilizator, modelul AI utilizat, formularea \u00eentreb\u0103rii \u0219i istoricul conversa\u021biei. Pentru a \u00een\u021belege vizibilitatea real\u0103 este necesar\u0103 analiz\u0103 unui e\u0219antion reprezentativ de utilizatori \u2014 oameni reali, cu profiluri diferite, care folosesc sistemele AI pe care le au efectiv \u00een uz, nu simul\u0103ri de laborator.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&nbsp;<strong><em>De la cercetare la metodologie<\/em><\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Cercetarea a condus la dezvoltarea metodologiei AI Source Map, un cadru de analiz\u0103 care transform\u0103 rezultatele cercet\u0103rii \u00een recomand\u0103ri opera\u021bionale pentru echipele de marketing.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Spre deosebire de abord\u0103rile care simuleaz\u0103 interac\u021biuni \u00een laborator, AI Source Map aplic\u0103 reverse engineering pe e\u0219antioane reprezentative: subiec\u021bi recruta\u021bi dup\u0103 profilul relevant pentru categoria analizat\u0103 interogheaz\u0103 sistemele AI pe care le folosesc \u00een mod real, iar r\u0103spunsurile ob\u021binute \u2014 at\u00e2t calitativ c\u00e2t \u0219i cantitativ \u2014 sunt analizate direct, f\u0103r\u0103 filtre sau simul\u0103ri. Metodologia m\u0103soar\u0103 frecven\u021ba cu care brandurile sunt recomandate, identifica sursele care influen\u021beaz\u0103 aceste recomand\u0103ri, analizeaz\u0103 argumentele folosite de AI \u0219i eviden\u021biaz\u0103 diferen\u021bele fa\u021b\u0103 de competitori pentru segmente diferite de consumatori.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Rezultatul nu este un raport de diagnostic. Este un set de instrumente strategice direct ac\u021bionabile: un brief de crea\u021bie care specific\u0103 ce s\u0103 comunici \u0219i cum, astfel \u00eenc\u00e2t brandul s\u0103 fie prezent \u00een ecosistemele informa\u021bionale din care sistemele AI \u00ee\u0219i construiesc recomand\u0103rile \u2014 \u0219i un brief de media care indic\u0103 unde s\u0103 investe\u0219ti, cu un ranking clar al canalelor \u0219i platformelor care influen\u021beaz\u0103 cel mai mult vizibilitatea AI \u00een categoria analizat\u0103.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Pe baza acestor dou\u0103 documente, echipele de marketing pot construi o strategie robust\u0103 de pozi\u021bionare a brandului \u00een ecosistemul AI \u2014 astfel \u00eenc\u00e2t AI s\u0103 \u00eel aleag\u0103 \u0219i s\u0103 \u00eel recomande consumatorilor \u0219i poten\u021bialilor clien\u021bi.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u201dA verific\u0103 dac\u0103 AI \u00ee\u021bi recomand\u0103 brandul de pe propriul cont este echivalentul analiz\u0103rii unei fotografii privind un singur pixel. Cercetarea noastr\u0103 arat\u0103 c\u0103 r\u0103spunsurile se modific\u0103 \u00een func\u021bie de utilizator, context \u0219i categorie. Pentru companii, diferen\u021ba dintre aceste perspective poate \u00eensemna pierderea sau c\u00e2\u0219tigarea unei recomand\u0103ri,\u201d a conchis Marius Luican.&nbsp;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u00cen contextul \u00een care AI devine primul filtru \u00eentre consumator \u0219i brand, \u00een\u021belegerea modului \u00een care aceste sisteme construiesc recomand\u0103rile devine o component\u0103 esen\u021bial\u0103 a strategiei de marketing.<\/p>","protected":false},"excerpt":{"rendered":"<p>Fiecare industrie are propriul mecanism prin care un brand ajunge s\u0103 fie recomandat de inteligen\u021b\u0103 artificial\u0103. Aceast\u0103 este concluzia principala a unui program de cercetare derulat de Reveal Marketing Research \u00eencep\u00e2nd din august 2025, bazat pe reverse engineering aplicat pe e\u0219antioane reprezentative de utilizatori reali \u2014 analiz\u0103 direct\u0103 at\u00e2t calitativ\u0103 c\u00e2t \u0219i cantitativ\u0103 \u2014 a<\/p>","protected":false},"author":3,"featured_media":6125,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[7],"tags":[],"class_list":["post-7542","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-for-business"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO Pro 5.0.4 - aioseo.com -->\n\t<meta name=\"description\" content=\"La nivel global, ChatGPT a dep\u0103\u0219it \u00een luna mai 2026 pragul de un miliard de utilizatori activi lunar, devenind cea mai rapid\u0103 aplic\u0103\u021bie din istorie care atinge aceast\u0103 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