Ioan-F anc, Vale iu; Gâ -Deac, Ioan I.
A icle
Pa icipa ion o a i icial in elligence in economic g ow h
in Romania
Am i ea u Economic
P o ided in Coope a ion wi h:
The Bucha es Uni e si y o Economic S udies
Sugges ed Ci a ion: Ioan-F anc, Vale iu; Gâ -Deac, Ioan I. (2024) : Pa icipa ion o a i icial in elligence
in economic g ow h in Romania, Am i ea u Economic, ISSN 2247-9104, The Bucha es Uni e si y o
Economic S udies, Bucha es , Vol. 26, Iss. 67, pp. 944-956,
h ps://doi.o g/10.24818/EA/2024/67/944
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Pa icipa ion o A i icial In elligence in Economic G ow h in Romania
944 Am i ea u Economic
PARTICIPATION OF ARTIFICIAL INTELLIGENCE
IN ECONOMIC GROWTH IN ROMANIA
Vale iu Ioan-F anc1 and Ioan I. Gâ -Deac2
1)2)“Cos in C. Ki ițescu” Na ional Ins i u e o Economic Resea ch - Romanian
Academy, Bucha es , Romania
Please ci e his a icle as:
Ioan-F anc, V. and Gâ -Deac, I.I., 2024. Pa icipa ion o
A i icial In elligence in Economic G ow h in Romania.
Am i ea u Economic, 26(67), pp. 944-956.
DOI: h ps://doi.o g/10.24818/EA/2024/67/944
A icle His o y
Recei ed: 2 Ma ch 2024
Re ised: 8 May 2024
Accep ed: 15 June 2024
Abs ac
The pu pose o his a icle is o demons a e ha i is necessa y o model he connec ions o
he Romanian economy o g ow h wi h he help o A i icial In elligence (AI). Tha is why
i is use ul o align wi h he ends in he EU economy and on a global le el o use inno a i e
echnologies, wi h he domes ic economy ha ing he oppo uni y o become excellen . I is a
momen o oppo uni y o commi men in his ega d, conside ing ha Romania al eady has
he IT in as uc u e and he human esou ces wi h a eal p edisposi ion o AI. The a icle
epo s on he esea ch ca ied ou , by way o example, on a numbe o AI companies, and
om he answe s ecei ed and p ocessed, eal alues eme ge ha e lec he po en ial o
con ibu ing o economic g ow h h ough AI. Ou a icle p esen s a i s -o -i s-kind iew o
how AI in es men and pa icipa ion ela e o domes ic economic ou comes. I is es ima ed
ha , mainly, in Romania in he coming yea s, he expec ed economic g ow h can be
egis e ed on accoun o he ac i i ies coming om small and medium en e p ises domina ed
by AI. As such, i is conside ed ha he new AI economy will be able o be buil and engaged
in Romania o he ex en ha small and medium en e p ises will show “cons uc i e
beha iou , wi h e olu ions based on inno a ion wi h he help o AI”.
Keywo ds: A i icial In elligence (AI), economic g ow h, AI a iables, AI g ow h modelling
JEL Classi ica ion: C15, E17, O11, O47
Co esponding au ho , Vale iu Ioan-F anc – e-mail: [email p o ec ed]
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons
A ibu ion License, which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in
any medium, p o ided he o iginal wo k is p ope ly ci ed. © 2024 The Au ho (s).
Am i ea u Economic Recommends
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Vol. 26 • No. 67 • Augus 2024 945
In oduc ion
Cu en ly, he idea o an almos con inuous in luence on he economy coming om he anks
o A i icial In elligence (AI), which can be de ined as a compe i o o au ho i y, is
sugges ed. The p omo e s o gene al economic science belie e ha “ he economy canno be
sepa a ed om AI” which p o es, mainly, use ul in iden i ying alignmen s o minimise isks,
inc ease p oduc i i y and incomes. Romania has an annual economic g ow h in he las pe iod
(“in 15 yea s, he GDP inc eased 4 imes, and he GDP pe inhabi an inc eased 2.3 imes”
(Geo gescu, n.d.)), bu i lags behind he le els eached in he EU by de eloped coun ies o
mos OECD coun ies. The le els o po e y and non- ul ilmen a e isible, and he speed o
p og ess owa ds uppe limi s is s ill low (inc eased budge de ici , high ex e nal deb , low
p oduc i i y, e c.). Added o his is he la ge emig a ion ou side he coun y, he lack o
easible and sus ainable s a egies in he medium and long e m in some b anches o
economy, he lack o compe i i eness o indus y and ag icul u e, e c. Howe e , we belie e
ha he e is a chance o econcep ualise he economic g ow h o he coun y wi h he
consolida ed eme gence and mani es a ion o AI in all sec o s o ac i i y na ionally and, by
ex ension, globally.
The pu pose o his a icle is o demons a e o o icial decision-make s, de elope s o
s a egies, ac ics, and p og ammes ha he economy in Romania will ha e eal g ow h based
on he use o AI. The lack o commi men o i m imme sion in he ield o AI can lead
Romania’s cu en economy o s agna ion, o , when adhe ing o his low esul s in alignmen
wi h ope a ional ends in he EU and he global economy, o he au och honous economy
“becoming” an ad anced ield o excellence, an example wo h ollowing by o he s a e
en i ies. In he a icle, we eso o modelling he links o he Romanian economy o g ow h
wi h he help o AI. I is a momen o oppo uni y, an occasion o commi men in his ega d,
especially since Romania al eady has he s a ing IT in as uc u e and he human esou ces
wi h a eal p edisposi ion o wo k in he ield. The esea ch is ca ied ou , o example, on 72
AI companies and om he answe s ecei ed i ollows ha he a iables (ac i i ies) a e o
high esolu ion, he subjec s/ hemes in which he companies a e engaged o p ocessing
demons a e a ac i eness o he Romanian a ea om he la ge o eign in es o s in he ield
and, equally, i e lec s he po en ial o con ibu e o economic g ow h. In ou p esen
assessmen , AI becomes a subdiscipline o gene al economic science and we conside ha
he he e ogenei y o na ional in e es s, wi h ex ension in he Eu opean one, should occupy a
cen al place in he gene al economic pic u e, including he one o Romania. Wi h he help
o AI, i is possible o mani es in he new economy ules such as: ealism, agenda wi h
con en o majo i y in e es , adap a ion o ci cums ances, p onouncemen in eal ime based
on ansmi ed in o ma ion, in luence om mul i-p inciple media ion o economic decisions.
Mo eo e , an inclusi e wo ld based on AI is en isioned, and he equi emen o a global
app oach o da a in he digi al age is eme ging, encou aging compe i ion and s abili y in he
digi al economy. I is posi i e ha he Indus ial Mone a y Fund (IMF) has al eady
es ablished an AI Readiness Index ha measu es eadiness in a eas ela ed o each coun y’s
digi al in as uc u e, human capi al and labou ma ke policies, inno a ion and economic
in eg a ion, egula ion, and e hics. So, in he p esence o AI he e is a shi owa ds global
p inciples.
Ou a icle p o ides he i s sys ema ic iew o how AI in es men and pa icipa ion ela e
o domes ic economic ou comes. I is es ima ed ha , mainly, in Romania in he nex 15-20
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Pa icipa ion o A i icial In elligence in Economic G ow h in Romania
946 Am i ea u Economic
yea s, he expec ed economic g ow h can be egis e ed due o he ac i i ies coming om
small and medium en e p ises domina ed by AI. The e o e, i is conside ed ha among he
me hods o g ow h, he new economy will be able o be buil in Romania o he ex en ha
small and medium en e p ises will show “cons uc i e beha iou , wi h e olu ions based on
inno a ion wi h he help o AI”.
1. Re iew o he scien i ic li e a u e
Mos s udies highligh ha AI has po en ial and economic impac ; in ac , h ough AI, a
echnological e olu ion is aking place, s imula ing economic g ow h and incomes globally.
Inno a i e AI echnologies will lead o an inc ease in labou p oduc i i y (up o 40%), c ea e
a i ual “in elligen au oma ion” wo k o ce and di use inno a ion, which will gene a e new
e enue s eams. (Accen u e, 2022)
Global GDP may inc ease by 14% by 2030 as a esul o accele a ing AI de elopmen and
adop ion. The new sense o digi al e olu ion al eady unleashed wi h he In e ne o Things
(IoT) he s imula ion o s anda disa ion, au oma ion, pe sonalisa ion o p oduc s and
se ices, b inging p oduc i i y gains. Au oma ion o ou ine asks, expansion o obo ics and
au onomous ehicle echnologies will p omo e a new wo k o ce acco ding o
P icewa e houseCoope s (2018), and also o o he impo an au ho s (Ag awal, Gans and
Gold a b, 2019; Acemoglu e al., 2022).
He shbein and Kahn (2018) classi y jobs as equi ing cogni i e skills only i any o hem is
ound in a leas one o he ollowing e ms: “ esea ch”, “analysis”, “decision”, “sol ing”,
“ma hema ics”, “s a is ics”, o “ hinking”. The e a e signi ican in es men s in so wa e
(app ox. 70% o companies adop a leas one ype o AI echnology), and by 2030 he global
GDP will inc ease by app ox. 1.2% annually, shows Bughin e al. (2018). On he o he hand,
Fa boodi and Veldkamp (2022) ou line ha AI will ha e posi i e di ec and indi ec e ec s
on places o wo k (40% o he o al), p oduc i i y and GDP, will op imise business p ocesses
and decisions, he eby inc easing knowledge and access o in o ma ion. The e will be
implica ions o AI in he selec ion o economic policies, e ec s on p ocessing sec o s, on
companies, indus ies, a he le el o coun ies gene a ing impac and edis ibu ion o he
labou o ce. A he same ime, Szczepanski (2019) poin s ou ha AI will be c ucial o
labou p oduc i i y, income dis ibu ion, and especially o economic g ow h i sel , as
suppo ed also by Mihe and Philippon (2019) and Fa boodi e al. (2019).
A he 2023 Wo ld Economic Fo um (WEF), i was no ed ha he use o AI o common
asks has seen an inc ease in ecen yea s, and Cha GPT de eloped by OpenAI is an example
o gene a i e AI used daily by mo e han 1 billion people. I is ecalled ha he GEF in
Oc obe 2020 concluded ha , while AI would elimina e 85 million jobs globally, by 2025 i
would also gene a e 97 million new jobs in AI ields, om big da a and machine lea ning o
in o ma ion secu i y and digi al ma ke ing.
AI echnologies ha e ad anced apidly in he las ew yea s (Chang e al., 2023); cu en ly,
gene al-pu pose in as uc u es a e a ailable (Aghion e al., 2018; Pa ick, 2018), and
be ween 2015 and 2021 he numbe o AI pa en s inc eased 30 imes (Ganglmai e al., 2021).
A he same ime, Fizsbein e al. (2020) highligh ha AI has led o apid p oduc i i y gains.
Technological changes h ough AI a e oppo uni ies o in es men and economic g ow h.
(Fu man and Seamans, 2019).
Am i ea u Economic Recommends
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Some au ho s belie e ha he ques ion whe he AI can ans o m economies and boos
economic g ow h emains open. B ynjol sson, Rock and Sy e son (2021) and Raj and
Seamans (2017) poin ou ha he e is cu en ly a lack o ele an da a on AI co e age a i m
le el, and Alde ucci e al. (2020) belie e ha i is necessa y o ocus on companies ha in en
AI. The AI- ained labou sho age is ano he cons ain o he inclusion o AI in i ms
(Co ela ionOne, 2019).
The de elopmen o new p oduc s wi h he help o AI elimina es leng hy expe imen a ion
(B aguinsky e al., 2021), as AI algo i hms gene a e accele a ed lea ning and educe he
unce ain y o homologa ion. I u ns ou ha AI is de ini ely s imula ing economic g ow h,
al eady becoming a echnology o gene al use (Gold a b, Taska and Teodo idis, 2023).
Bughin e al. (2018) es ima e ha , in indus y, 90% o companies’ in es men s in AI a e
in e nal and only 10% come om acquisi ions. The pe cen age o companies in es ing in AI
in jus one yea is 29.5%, compa ed o 70.6% o obo ics (Humlum, 2019). Globally,
consis en wi h indings made by Caliendo e al. (2020), in p ac ice, p oduc quan i ies
inc ease o such an ex en ha lowe p ices a e cha ged.
Some au ho s conside AI a p edic i e echnology (Ag awal, Gans and Gold a b, 2019),
while o o he s AI is a gene al-pu pose echnology (Gold a b, Taska and Teodo idis, 2023).
I is no able, howe e , ha AI echnologies ha e scale e ec s ha a ou la ge i ms, which
accumula e la ge amoun s o da a as a by-p oduc o hei economic ac i i y (Fa boodi and
Veldkamp, 2022). The bene i s o AI la gely depend on who owns big da a – he key inpu
o AI echnologies, acco ding o Fedyk and Hodson (2023) and Babina e al. (2024).
Howe e , he e is no sys ema ic da a on he companies’ AI in es men s. Acco ding o some
au ho s, hey amoun o app ox. 140 billion USD/yea globally. We can conclude ha
economis s, in gene al, do no ha e a s ong po en ial o p edic he u u e. I is es ima ed ha
in 12 de eloped economies, AI could double annual global economic g ow h a es by 2035.
2. Resea ch me hodology
Me hodologically, ou a icle p esen s a no el app oach o demons a e he ex en o which
AI con ibu es o economic g ow h, and mo e b oadly, ou me hod sugges s ha new
echnologies such as AI ha e clea and dis inc ad an ages in he speed o posi i e change in
he na ional economy. The esea ch in his a icle is based on he o iginal ma hema ical
o malisa ion o modelling, sys ema isa ion, and in e p e a ion ela ionships in he ield.
In gene al, o he unde s anding and ope a ionalisa ion o AI in he Romanian economy, we
p opose o go h ough a p ocedu al algo i hm o ob ain uni a ia e classi ica ions (𝐶0). In ac ,
he o malisa ion s a s om he mul i a ia e classi ica ion (𝐶𝑚) when o his concep ion
he e a e a numbe o classes (𝑁𝑐) o knowledge ha en e he so-called in elligen c ea ion.
A i s s ep is he sepa a ion o classes/sub-classes (∆𝑠), ollowed by ans o ma ions o / o
linea isa ion (∆𝑙) o no ice he p ocessing in ques ion in a mo e simpli ied manne . In he
end, i is a ma e o me ici y/ul ame ici y ob ained by simpli ying calcula ions o
de e mina ions ela ed o he iden i ica ion and analysis o linea disc iminan s. The
ans o ma ions o linea isa ions (i) can eco d ela i e/absolu e ampli udes, highligh ing
he dep h o e inemen s o elimina e eminiscences/ edundancies, wi h e e ence o educing
spaces/dis ances be ween da a. In he li e a u e in he ield, conce ns a e highligh ed o he
sea ch o global classi ica ion models o o spa iali y/spa ialisa ion, espec i ely, he
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948 Am i ea u Economic
localisa ion o classi ica ions. The mos impo an p ope y o AI, in ou opinion, in he
p ocess o sea ching o classi ica ions becomes p ocedu al adap abili y, an aspec ha does
no in oduce he obliga ion o es ablish immu able me hods, p ocedu es, echniques, e c. in
he ield.
As such, i can be w i en:
{(𝐶𝑚) (𝑠)
→
{𝐶}
∀[𝑀𝑎𝑥(∆𝑠)∗𝑀𝑖𝑛(𝑁𝑐)] (∆𝑙)
→
(𝐶𝑜)
(𝐶𝑜)∈[{𝐶}𝑓(𝑠)⊂(𝐶𝑚)]≈[{𝐶}𝑓(𝑠)∩(𝐶𝑚)] (1)
A he same ime:
{𝐶}:(𝑅,𝑅)−[𝛷{𝐶}+𝛷(𝜀𝑖)+𝛷(𝛥𝐶𝑚)]=(𝐶𝑜)𝑓(𝛥𝑙) (2)
{(𝛥𝑠)∗(𝑁𝑐)}
→
𝑀𝑎𝑥𝑀𝑖𝑛𝑓(𝛥𝑙) (3)
On he o he hand:
{∑ (𝜀𝑖)
𝑘𝑖=1 =𝑀𝑎𝑥𝛷(𝜀𝑖)
∑∆(𝐶𝑚)𝑖
𝑘𝑖=1 =𝑀𝑎𝑥𝛷(∆𝐶𝑚) (4)
and
[{𝐶}:(𝑅,𝑅)]−{±∑ (𝜀𝑖)
𝑘𝑖=1 ±∑∆(𝐶𝑚)𝑖
𝑘𝑖=1 }=(𝐶𝑜)|(∆𝑙) (5)
Making n obse a ions o he ype (𝑎𝑖,𝑏𝑖,𝑐𝑖,…,𝑧𝑖)∈ℝ in he eal space (Romanian
companies ha ha e AI ac i i ies), each a iable p o ides a sub-image (SI) in he se {Mo}⊃
[(SI)ai;(SI)bi;…;(SI)zi], ha become co esponden s, membe s o an incipien non-
eminiscen and non- edundan classi ica ion. The placemen o sub-images in a iance-
co a iance ma ices depic s he dimensional and quali a i e disp opo ions o obse a ions
made on AI in he economic p oduc i e ield, in e e yday li e. Elimina ing o educing
disc epancies means educing he i ual weigh o e en s ound wi h AI in
supe ised/unsupe ised da abase simula ions in he eal economy/new economy. In he
con ex , he alsely app op ia e and non-co a ia ional analog se {Mo} appea s, which is non-
dis anced om he se o ini ially iden i ied/delimi ed sub-images. On his basis, AI
p ocessings ha a e conside ed un ue o en appea .
The e o e:
{𝑀𝑜}∧{𝑀𝑜} (6)
Also:
{[𝑚𝑎𝑥𝛷(𝜀𝑖)]∧[𝑚𝑎𝑥𝛷(𝜀𝑖)]
[𝑚𝑎𝑥𝛷(∆𝐶𝑚)]∧[𝑚𝑎𝑥𝛷(∆𝐶𝑚)] (7)
[{∑ (𝜀𝑖)
𝑘𝑖=1 }∗{∑ (∆𝐶𝑚)
𝑘𝑖=1 }]
→
(𝐶𝑜)−(𝛷{𝑀𝑜})(𝐶𝑚)={𝑀𝑜}(𝐶𝑜) (8)
On he inal con igu a ion {Mo}(Co), ela ed o AI, in as uc u es/machines show lea ning
e o s, equally on he pa o humans, unde s anding and pe cei ing o assuming he na u e
Am i ea u Economic Recommends
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Vol. 26 • No. 67 • Augus 2024 949
and ealism o he sub-images alida ed o be con ained in a consolida ed non- eminiscen
and non- edundan classi ica ion o knowledge.
In his amewo k, we eso ed o esea ch h ough in e iewing 72 Romanian companies
wi h AI as a ield o ac i i y, and he answe s we e sys ema ised in acco dance wi h ela ions
(6), (7), and (8), in ables wi h powe alloca ions (coe icien s o impo ance). In his way,
he in ol emen o AI in he issue o he domes ic economy is deduced, emphasising i s
possible p opo ional g ow h wi h compu e isa ion, digi isa ion and he gene al inc ease o
knowledge.
3. Resul s and discussion
As pa o he esea ch, esponses we e ecei ed om a numbe o 72 companies ha ag ee
o he exis ence o 63 “ac i i ies” ype a iables in he ield o AI. (Table no. 1)
Table no. 1. The a iables de ec ed ( ecei ed as esponses) om he en i ies ha ha e
he o malisa ion and applica ion o AI as ield o ac i i y
No.
c .
Va iables
Ac
Yes
Yes/
No
No
No
M
As
AI o ex end/enhance knowledge
1.
sys ems and so wa e on quali y pla o ms;
2
0.70
1.00
1.00
2
0.35
.041
2.
cus omised web applica ions;
1
0.82
-
0.00
-
0.82
.043
3.
cus omised so wa e and digi al inno a ion;
1
0.60
-
0.00
-
0.60
.008
4.
he a chi ec u e o he digi al ans o ma ion app oach;
1
0.72
-
0.00
-
0.72
.042
5.
so wa e and ha dwa e o unde s anding he de ails;
1
0.79
-
0.00
-
0.79
.043
6.
web and mobile applica ion so wa e, De Ops, machine
lea ning;
1
0.80
-
0.00
-
0.80
.043
7.
AI solu ions o deep unde s anding o con en ;
1
0.56
-
0.00
1
0.56
.009
8.
AI o na u al language p ocessing (NLP), Tex Analy ics
and Speech o Tex ;
1
0.53
-
0.00
1
0.53
.009
9.
so wa e lib a ies o neu omo phic compu e ision;
1
0.60
-
0.00
-
0.60
.008
10.
da a in elligence and AI.
1
0.48
-
0.00
1
0.48
.009
AI o echnologies
1.
con inuous p esence a he end o he bes echnologies;
1
0.78
-
0.00
-
0.78
.038
2.
wo k in a complex en i onmen wi h upda ed echnologies;
1
0.83
-
0.00
-
0.83
.044
3.
echnical alen in he ield;
1
0.81
-
0.00
-
0.81
.043
4.
he abili y o in eg a e ad anced echnologies;
2
0.79
1.00
1.00
1
0.39
.043
5.
simpli ica ion o complex echnical p oblems in a imely
manne ;
1
0.84
-
0.00
-
0.84
.044
6.
nea sho ing and o sho ing echnologies;
1
0.69
-
0.00
-
0.69
.008
7.
deep- ech AI pla o m wi h ICT sys em, in as uc u e, code,
algo i hms, sc ip s and echnical p ocesses;
1
0.69
-
0.00
-
0.69
.008
8.
AI empla e gene a ion echnology;
1
0.59
-
0.00
1
0.59
.007
9.
in eg a ion o AI echnologies;
2
0.78
1.00
1.00
-
0.36
.043
10.
essen ial and ans o ma i e machine in elligence o
en e p ises;
1
0.70
-
0.00
1
0.70
.041
11.
Eye-T acking and B ain-Compu ing In e ace echnologies,
gene a i e and pa ame ic me hods in Game Engine
en i onmen s;
1
0.69
-
0.00
-
0.69
.008
12.
so wa e obo s using UipA h, Rink echnologies.
1
0.42
-
0.00
1
0.42
.006
AI o deep/ad anced se ices
1.
echnological se ices in he de elopmen o cus omised
so wa e and IT ou sou cing;
2
0.84
1.00
1.00
1
0.41
.044
AE
Pa icipa ion o A i icial In elligence in Economic G ow h in Romania
950 Am i ea u Economic
No.
c .
Va iables
Ac
Yes
Yes/
No
No
No
M
As
2.
i ual assis an s o managing epe i i e and ime-consuming
asks;
1
0.73
-
0.00
-
0.73
.041
3.
clean and e o - ee code se ice;
1
0.79
-
0.00
-
0.79
.043
4.
excellen esponse ime and quali y wo kmanship;
1
0.84
-
0.00
-
0.84
.044
5.
independen con e sa ional AI pla o m wi h in e connec ed
i ual assis an s;
1
0.69
-
0.00
-
0.69
.008
6.
Ressi pla o m o gene a i e AI assis an s;
1
0.59
-
0.00
1
0.59
.007
7.
au oma ic ex ac ion, clean design;
1
0.61
-
0.00
-
0.61
.007
8.
capabili ies solu ions based on AI/ machine lea ning;
1
0.68
-
0.00
-
0.68
.008
9.
au oma ion scena ios in en i onmen s wi h common solu ions;
1
0.83
-
0.00
-
0.83
.044
10.
new applica ions and suppo ing hei main enance;
1
0.83
-
0.00
-
0.83
.044
11.
Wise Agen suppo au oma ion pla o m (websi e cha ,
Facebook, e-mail, phone call), CRM in eg a ion o
con e sa ion escala ion (Wise oice Dialog Builde );
1
0.68
-
0.00
-
0.68
.008
12.
ad anced solu ions o isual, oice na u al language sea ch,
cloud sea ch que y p ocessing and classi ica ion;
1
0.49
-
0.00
1
0.49
.006
13.
w i eGPT as web ex ension;
1
0.78
-
0.00
-
0.78
.043
14.
AI cha bo o be in eg a ed on websi es.
1
0.71
-
0.00
-
0.71
.040
AI o managemen
1.
educing complexi y h ough human-cen e ed inno a ion;
1
0.80
-
0.00
-
0.80
.043
2.
pla o m o o ganising he imeline o in o ma ion low;
1
0.81
-
0.00
-
0.81
.043
3.
on- ime deli e y and pe sis en isk managemen ;
2
0.83
1.00
1.00
-
0.42
.044
4.
anspa ency abou wha can and canno be done h ough AI;
1
0.83
-
0.00
-
0.83
.044
5.
eam o ien ed owa ds op imal esul s;
1
0.80
-
0.00
-
0.80
.043
6.
o ganisa ional cul u e wi h cus omised so wa e solu ions,
dedica ion and selec i i y;
1
0.75
-
0.00
-
0.75
.045
7.
mo e o he communi y, no jus o i s own employees;
1
0.59
-
0.00
-
0.59
.007
8.
inno a i e, lexible and ha dwo king;
1
0.76
-
0.00
-
0.76
.047
9.
op communica ion, explici AI da a upda es;
3
0.80
2.00
2.00
-
0.27
.043
10.
wo k e hic, quali y and eal- ime deli e y;
1
0.79
-
0.00
-
0.79
.043
11.
signi ican skills, high commi men and company cul u e;
1
0.78
-
0.00
-
0.78
.043
12.
he a en ion and p ide he eam has in he ield o AI;
1
0.69
-
0.00
-
0.69
.008
13.
managing he speed o change;
1
0.68
-
0.00
-
0.68
.008
14.
quick iden i ica ion o e o s and ixes.
1
0.79
-
0.00
-
0.79
.043
AI o de elopmen
1.
posi i e app oach o AI de elopmen p ojec s;
2
0.82
1.00
1.00
-
0.41
.043
2.
essen ial pa ne s in he de elopmen o pla o ms;
1
0.83
-
0.00
-
0.83
.044
3.
cha bo , mobile, web and E he eum Blockchain Sma con ac ;
1
0.77
-
0.00
-
0.77
.043
4.
he abili y o deli e high-quali y AI knowledge p oduc s;
1
0.78
-
0.00
-
0.78
.043
5.
complexes ha in eg a e AI, big da a, web o desk op solu ions.
1
0.69
-
0.00
-
0.69
.008
AI o economics / business
1.
ede ining he ables o business ope a ions h ough AI solu ions;
1
0.82
-
0.00
-
0.82
.043
2.
AI pla o m o iden i ying paymen e o s;
2
0.83
1.00
1.00
-
0.42
.044
3.
al e na i e eali ies h ough VR, AR and AI echnologies using
ad anced compu ing me hods;
1
0.72
-
0.00
-
0.72
.042
4.
codeless lows, AI models wi h cus om da ase s;
1
0.78
-
0.00
-
0.78
.043
5.
la ge language models, Big Da a Analy ics, wi h he powe o AI;
1
0.67
-
0.00
1
0.67
.008
6.
eliable and dis ibu ed nano- asks o AI augmen a ion;
1
0.58
-
0.00
1
0.58
.007
7.
Hawking wi h in elligen AI solu ions;
1
0.69
-
0.00
1
0.69
.008
8.
pla o m o pe sonalised, a ge ed and au oma ed expe iences
using da a and AI.
1
0.81
-
0.00
-
0,81
.043
No es: Ac = IA ac i i ies/company; [0.00 – 1.00] = weigh ing coe icien s (impo ance).
I is s a ed ha he companies did no ully answe all he ques ions in he in e iews;
howe e , he eceip o hose incen i es is conside ed ep esen a i e and wi h he po en ial
Am i ea u Economic Recommends
AE
Vol. 26 • No. 67 • Augus 2024 951
o ex apola ion a he mac oeconomic le el, demons a ing he meaning, he ecommended
di ec ion o ollow, namely he in ensi e use o AI in he cu en domes ic economy o
gene a e i s eal g ow h in he u u e s ages. The ac i i ies ( ecei ed h ough he w i en
in e iews) we e sys ema ised acco ding o he numbe o en i ies (Ac), hei con i ma ion in
he ields o ac i i y o he AI en e p ises (Yes wi h a weigh o 10, Yes/No/ wi h a weigh
o 5, No wi h a weigh o 1), he numbe o obse a ions (N0), mean (M) and calcula ion o
s anda d de ia ions (N0).
The main goal pu sued in his a icle e e s o he elucida ion o he pa icipa ion o AI in he
economic g ow h o Romania in he coming yea s, how AI con ibu es o he eme gence and
unc ioning o he new domes ic economy, and h ough i s na ional con ibu ion o pa icipa e in
he EU’s economic secu i y. In summa y, a e ecei ing he answe s and assigning weigh s
(impo ance coe icien s in he su ey), a e examining he limi s o a ia ion o he a e age and
s anda d de ia ions, he nume ical able o he si ua ions is ou lined below. (Table no. 2)
Table no. 2. Syn hesis o measu ed incen i e alues a AI companies
To al no. o AI a iables/ac i i ies
63
To al no. o AI companies esea ched/in e iewed
72
To al no. o obse a ions (u e ances) eco ded abou he AI
81
No. o AI companies wi h mul iple ields o ac i i y
8
Va iable con i ma ion weigh AI ac i i ies (in eg a ed a e age)
[0.42-0.84]
Sha e o un ela ed op ions on AI ac i i y a iables (in eg a ed mean)
9 ac i i y
[0.00-2.00]
Rejec a iables / AI o non-AI ac i i ies
15
S anda d de ia ions
[0.08-0.47]
The main in e p e a ions e e o he ollowing:
in he esea ched companies, he e is a numbe o AI ac i i ies (63) which a e p ac ical,
applied opics/ hemes o signi ican esolu ion, a ac ha can a ac he a en ion o o eign
in es o s elying on he ac uali y o commi men s and challenges de e mined by Romanian
en i ies in he ield;
almos all he esea ched companies ha e dis inc , singula ields o ac i i y in AI; he e is
no dissipa ion on mul iple alignmen s, which shows inc eased specialisa ion in AI on
dis inc ly add essed subjec solu ions (ou o he o al o 72 companies, only 8 ha e double
o iple ields o ac i i y);
he in eg a ed a e age o he weigh s o con i med AI ac i i ies (success ul, wi h
achie emen s) is con ained in he in e al [0.42 - 0.84], which shows ha mo e han 80%
o he en i ies ha e business capabili y in he ield;
he in eg a ed a e age o he weigh s o un ela ed ac i i ies (unsuccess ul, wi h ailu es) is
included in he ange ha emphasises ha only 9 ac i i ies (in p opo ion o only 13.30%
o he en i ies) do no ha e business capabili y on some AI alignmen s; in he answe s, 15
a iables/ ypes o ac i i ies we e ejec ed as no being add essed by he s udied en i ies;
he s a is ical s anda d de ia ions a e included in he in e al [0.08 - 0.47], alues ha show
he educed dis ance (sca e ing) om he posi i e solu ions gi en by he
a iables/ac i i ies; lacking la ge dis ancing, hey a e close o he pu sued ideal al e na i es.
