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.