AI Act: what are the implications for sensitive sectors in Europe?

When Ama­zon’s AI recruit­ment tool faced scruti­ny for sys­tem­at­i­cal­ly dis­crim­i­nat­ing against women can­di­dates¹, or when Hire­Vue’s facial analy­sis algo­rithm sab­o­taged neu­ro­di­ver­gent job seek­ers², the tech world got a real­i­ty check of what unreg­u­lat­ed AI is capa­ble of. Today, Europe is lead­ing the legal way for­ward in the AI leg­is­la­tion domain, one risk-crit­i­cal sec­tor at a time. Europe’s Arti­fi­cial Intel­li­gence Act (or « AI Act »), the world’s first com­pre­hen­sive AI reg­u­la­to­ry frame­work, claims to avoid such fail­ures while also trans­form­ing AI deploy­ment in com­pa­nies, espe­cial­ly in risk-crit­i­cal sectors.

The stakes are sub­stan­tial. French enter­pris­es alone invest­ed well over €1bn in AI tech­nolo­gies in 2023, 35% of French com­pa­nies active­ly deploy­ing AI sys­tems accord­ing to Busi­ness France³. The trend is obvi­ous; indus­try lev­el AI deploy­ment is increas­ing. While the AI Act’s bind­ing oblig­a­tions took effect, only this month⁴, the fun­da­men­tal choice lies with com­pa­nies, either treat com­pli­ance as a reg­u­la­to­ry bur­den on the backs of legal depart­ments or trans­form it into a dis­tinc­tive capa­bil­i­ty, help­ing you to thrive in the AI world.

“Com­pa­nies that inte­grate legal require­ments as design prin­ci­ples can trans­form com­pli­ance into a strate­gic advan­tage,” explains Jean de Bod­i­nat, founder of Rakam AI and teacher at Ecole Poly­tech­nique (IP Paris). The reg­u­la­tion estab­lish­es a risk-based clas­si­fi­ca­tion sys­tem, with “high-risk” AI sys­tems those affect­ing health, safe­ty, or fun­da­men­tal rights fac­ing the strictest require­ments. Some cri­te­ria that gov­ern the foun­da­tion of these clas­si­fi­ca­tion sys­tems include manda­to­ry risk man­age­ment, data gov­er­nance, tech­ni­cal doc­u­men­ta­tion, human over­sight, and qual­i­ty man­age­ment sys­tems. Oper­a­tional per­for­mance and mar­ket posi­tion­ing just got an upgrade, just anoth­er feath­er in the cap of the EU-AI Act. In the arti­cle, we out­line four sec­tors where AI “high-risk” sys­tems are par­tic­u­lar­ly affect­ed by the legislation.

In French class­rooms and online learn­ing plat­forms, AI-dri­ven assess­ment tools are not only trans­form­ing edu­ca­tion but fac­ing a reg­u­la­to­ry bur­den, now more than ever. Edu­ca­tion­al AI sys­tems evi­dent­ly fall under the Act’s “high-risk” cat­e­go­ry due to their direct influ­ence on aca­d­e­m­ic per­for­mance. Con­sid­er Lingueo’s e‑LATE plat­form⁵, spe­cial­is­ing in tai­lored lan­guage assess­ments using speech recog­ni­tion and auto­mat­ed scor­ing. Stu­dents receive tar­get­ed feed­back while teach­ers main­tain over­sight through ded­i­cat­ed dash­boards. The sys­tem exem­pli­fies how edu­ca­tion­al tech­nol­o­gy can meet AI Act require­ments while deliv­er­ing edu­ca­tion­al value.

Stu­dents and teach­ers need to under­stand how auto­mat­ed deci­sions are made.

“The chal­lenge isn’t just tech­ni­cal accu­ra­cy, it’s about fair­ness and trans­paren­cy,” notes Solène Gérardin, a lawyer and AI Act spe­cial­ist who advis­es busi­ness­es on com­pli­ance. “Stu­dents and edu­ca­tors need to under­stand how auto­mat­ed deci­sions are made.” The plat­form address­es this by sep­a­rat­ing AI con­tent gen­er­a­tion from eval­u­a­tion pipelines, imple­ment­ing robust con­tent fil­ters, and pro­vid­ing clear inter­faces for edu­ca­tor over­sight. Most crit­i­cal­ly, it main­tains com­pre­hen­sive log­ging for auditabil­i­ty, a require­ment that is becom­ing stan­dard­ised across edu­ca­tion­al technology.

Exter­nal exam­ples rein­force this approach. Plat­forms like Grade­scope and Knew­ton are adopt­ing explain­able AI solu­tions that help both stu­dents and teach­ers under­stand auto­mat­ed grad­ing deci­sions. Their suc­cess demon­strates that trans­paren­cy require­ments can actu­al­ly improve edu­ca­tion­al out­comes by build­ing trust between learn­ers, edu­ca­tors, and AI systems.

Per­haps nowhere is the AI Act’s impact more vis­i­ble than in recruit­ment, where auto­mat­ed can­di­date eval­u­a­tion sys­tems are trans­form­ing and some­times dis­tort­ing hir­ing prac­tices. These sys­tems, which screen resumes and rank appli­cants using nat­ur­al lan­guage pro­cess­ing, rep­re­sent a text­book exam­ple of high-risk AI under the new reg­u­la­tion. The cau­tion­ary tales are well-doc­u­ment­ed. Ama­zon dis­con­tin­ued its AI recruit­ment tool after dis­cov­er­ing it penalised resumes con­tain­ing words like “wom­en’s”. Hire­Vue faced crit­i­cism for facial analy­sis algo­rithms that dis­ad­van­taged neu­ro­di­ver­gent can­di­dates. These fail­ures high­light why the AI Act requires trans­paren­cy in auto­mat­ed hir­ing deci­sions and grants can­di­dates the right to con­test AI-based outcomes.

Orange, the French telecom­mu­ni­ca­tions giant, offers a more promis­ing mod­el. Pro­cess­ing over two mil­lion appli­ca­tions annu­al­ly using AI sys­tems built with Google Cloud, Orange match­es can­di­dates to job descrip­tions while flag­ging results for human val­i­da­tion. By inte­grat­ing fair­ness-aware algo­rithms and com­pre­hen­sive audit pro­ce­dures, the com­pa­ny has improved gen­der diver­si­ty in tech­ni­cal roles. The com­pa­ny’s approach demon­strates how reg­u­la­to­ry require­ments can align with busi­ness objec­tives, where diverse teams often per­form bet­ter, and trans­par­ent hir­ing prac­tices enhance employ­er rep­u­ta­tion⁶.

The tech­ni­cal imple­men­ta­tion involves mod­u­lar sys­tems that sep­a­rate data pre­pro­cess­ing, scor­ing, and over­sight lay­ers. This archi­tec­ture, guid­ed by frame­works like SMACTR (Sys­tem, Meta­da­ta, Auditabil­i­ty, Con­text, Trace­abil­i­ty, Respon­si­bil­i­ty), enables quick iden­ti­fi­ca­tion and cor­rec­tion of bias issues. Key com­pli­ance strate­gies include using rep­re­sen­ta­tive datasets with min­i­mum 20% minor­i­ty group inclu­sion, log­ging and jus­ti­fy­ing all rank­ing out­comes, allow­ing can­di­date opt-outs, and con­duct­ing reg­u­lar bias audits. Rather than con­strain­ing hir­ing deci­sions, these require­ments are push­ing com­pa­nies toward more equi­table and defen­si­ble recruit­ment prac­tices⁷.

In health­care, where AI sys­tems han­dle sen­si­tive med­ical data and influ­ence patient care deci­sions, the reg­u­la­to­ry stakes reach their high­est point. Health insur­ance claim man­age­ment sys­tems exem­pli­fy this chal­lenge, falling under both the AI Act’s high-risk clas­si­fi­ca­tion and GDPR’s strict med­ical data pro­tec­tions⁸. Lola Health’s AI-pow­ered Claim Man­age­ment Agen­til­lus­trates how health­care organ­i­sa­tions can nav­i­gate this com­plex reg­u­la­to­ry land­scape. The con­ver­sa­tion­al agent oper­ates with­in Lola Health’s dig­i­tal plat­form, assist­ing mem­bers and insur­ance pro­fes­sion­als around the clock with cov­er­age ques­tions, claim sub­mis­sions, and sta­tus updates.

Com­pli­ance becomes a frame­work for oper­a­tional excel­lence rather than a bureau­crat­ic burden.

The sys­tem’s archi­tec­ture reflects com­pre­hen­sive com­pli­ance think­ing. Back-end inte­gra­tion enables real-time retrieval of per­son­alised con­tract data while secure authen­ti­ca­tion pro­tects sen­si­tive infor­ma­tion. Most impor­tant­ly, the sys­tem main­tains clear esca­la­tion path­ways to human advi­sors for com­plex claims, a require­ment that actu­al­ly improves cus­tomer service.

Han­dling large vol­umes of health data increas­es breach risks, but it also cre­ates oppor­tu­ni­ties for bet­ter patient sup­port. The agent pro­vides 24/7 per­son­alised assis­tance, speeds up case res­o­lu­tion times, and reduces sup­port costs while main­tain­ing high cus­tomer sat­is­fac­tion through clear guid­ance and pri­va­cy assurance.

Risk mit­i­ga­tion strate­gies include explain­able AI for deci­sion trans­paren­cy, strong pri­va­cy safe­guards with authen­ti­cat­ed access and secure encryp­tion, and reg­u­lar audit­ing of chat­bot advice to improve ser­vice qual­i­ty and pre­vent bias. These mea­sures, man­dat­ed by reg­u­la­tion, simul­ta­ne­ous­ly enhance oper­a­tional per­for­mance and user trust. The peri­od­ic reviews required for reg­u­la­to­ry com­pli­ance have an unex­pect­ed ben­e­fit: they con­tin­u­ous­ly improve sys­tem respons­es and main­tain high ser­vice stan­dards. Com­pli­ance becomes a frame­work for oper­a­tional excel­lence rather than a bureau­crat­ic burden.

Finan­cial ser­vices rep­re­sent per­haps the most mature exam­ple of AI Act com­pli­ance, where cred­it eval­u­a­tion sys­tems direct­ly influ­ence indi­vid­u­als’ access to finan­cial prod­ucts. These sys­tems must nav­i­gate com­plex require­ments for fair­ness, trans­paren­cy, and account­abil­i­ty while main­tain­ing com­mer­cial via­bil­i­ty. Mod­ern cred­it eval­u­a­tion plat­forms use machine learn­ing to analyse appli­cant data and pre­dict cred­it risk, con­sid­er­ing vari­ables from income and debt his­to­ry to employ­ment sta­tus and trans­ac­tion records. The chal­lenge lies in ensur­ing these sys­tems don’t repli­cate or ampli­fy exist­ing soci­etal bias­es, a require­ment that’s push­ing the entire sec­tor toward more sophis­ti­cat­ed fair­ness testing.

Lead­ing French banks have devel­oped three-lay­er fair­ness test­ing approach­es: pre­pro­cess­ing to bal­ance train­ing data, real-time mon­i­tor­ing to flag demo­graph­ic dis­par­i­ties in approvals, and post-deci­sion cal­i­bra­tion to cor­rect resid­ual bias while main­tain­ing pre­dic­tive per­for­mance. Banks also estab­lish cus­tomer appeal process­es and con­duct reg­u­lar inde­pen­dent audits. Research by Christophe Pérignon at HEC Paris has con­tributed sta­tis­ti­cal frame­works now used by major banks to iden­ti­fy and mit­i­gate dis­crim­i­na­tion in cred­it mod­els. Banks employ­ing these fair­ness-aware sys­tems have reduced approval gaps between demo­graph­ic groups to under 3% while main­tain­ing or improv­ing risk pre­dic­tion accuracy.

Pérignon’s research demon­strates that eth­i­cal com­pli­ance and com­mer­cial objec­tives can align. This align­ment rep­re­sents the AI Act’s broad­er promise: that reg­u­la­to­ry require­ments can dri­ve inno­va­tion toward more effec­tive, trust­wor­thy systems.

Solène Gérardin notes that it is rarely black and white to know if an AI sys­tem is “high-risk.” She argues that the best deci­sion amidst this ambi­gu­i­ty is to be proac­tive, by build­ing AI with com­pli­ance from the begin­ning. Clas­si­fi­ca­tion is sim­ple if a sys­tem is on Annex III of the AI Act. For any­thing out­side that list, busi­ness­es must deter­mine if their prod­uct is includ­ed under har­mon­i­sa­tion leg­is­la­tion and requires (3rd par­ty) con­for­mi­ty assess­ment as set forth in Arti­cle 6(1) of the Reg­u­la­tion. She has also indi­cat­ed that the Euro­pean Union has plans to pub­lish detailed guid­ance, includ­ing con­crete exam­ples for bor­der­line cas­es, by the begin­ning of 2026. Once the guid­ance is avail­able, com­pli­ance will be expect­ed across all industries.

The Gen­er­al-Pur­pose AI Code (GP-AI) of Prac­tice was pub­lished last month. Accord­ing to the offi­cial EU-AI act web­site, its pro­vi­sions will take effect on the act from 2 August 2025⁹. It was draft­ed in col­lab­o­ra­tion with near­ly 1000 stake­hold­ers, as an inclu­sive doc­u­ment that trans­lat­ed the act’s gen­er­al pur­pose mod­el require­ments into action­able and prac­ti­cal guid­ance on prin­ci­ples includ­ing but not lim­it­ed to trans­paren­cy, sys­temic risk-mit­i­ga­tion and copy­right com­pli­ance. The code is built to fos­ter val­ues of trust and account­abil­i­ty across Europe’s AI ecosys­tem. It also inter­sects ESG (envi­ron­men­tal and social gov­er­nance) and sus­tain­abil­i­ty goals, mak­ing AI com­pli­ance more than just a legal oblig­a­tion for busi­ness­es. It is a defin­i­tive strat­e­gy to rein­force gov­er­nance and be com­pet­i­tive in the long-term.

These case stud­ies reveal a com­mon pat­tern: organ­i­sa­tions treat­ing AI Act oblig­a­tions as design prin­ci­ples rather than con­straints achieve supe­ri­or mar­ket posi­tion­ing and oper­a­tional per­for­mance. Ear­ly com­pli­ance offers com­pet­i­tive advan­tages that extend far beyond legal adher­ence. Trans­par­ent AI sys­tems build cus­tomer trust, espe­cial­ly in sen­si­tive sec­tors where deci­sions sig­nif­i­cant­ly impact indi­vid­u­als’ lives. Pro­cure­ment process­es increas­ing­ly favour com­pli­ant ven­dors, cre­at­ing busi­ness oppor­tu­ni­ties for pre­pared orga­ni­za­tions. Access to ESG-con­scious investors improves as com­pli­ance sig­nals robust governance.

The Act’s scope will like­ly expand to cov­er new sec­tors includ­ing trans­porta­tion, ener­gy, and pub­lic admin­is­tra­tion. With tech­ni­cal stan­dards still devel­op­ing and enforce­ment mech­a­nisms tak­ing shape, orga­ni­za­tions face a choice: invest ear­ly in com­pli­ance infra­struc­ture or scram­ble to meet require­ments as dead­lines approach. The EU AI Act trans­forms com­pli­ance from reg­u­la­to­ry bur­den into strate­gic assets. For com­pa­nies nav­i­gat­ing this tran­si­tion, the mes­sage is clear: the future belongs to those who build com­pli­ance into their com­pet­i­tive strat­e­gy from the start.