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In e na ional Jou nal o Ad ance and Applied Resea ch
www.ijaa .co.in
ISSN – 2347-7075
Impac Fac o – 8.141
Pee Re iewed
Bi-Mon hly
Vol. 6 No. 38
Sep embe - Oc obe - 2025
A i icial In elligence and S a is ical Models in Business and Managemen : A
Comp ehensi e Re iew
Yogi a M. Sadani
Assis an P o esso ,Depa men o S a is ics
D . D. Y. Pa il. A s, Comme ce and Science college, Aku di, Pune-44
Co esponding Au ho –Yogi a M. Sadani
DOI - 10.5281/zenodo.17294622
Abs ac :
The apid de elopmen o a i icial in elligence (AI) and ad anced s a is ical modeling has
ans o med business and managemen esea ch, eshaping p ac ices in inance, human esou ce
managemen , ope a ions, isk assessmen , and s a egic planning. This e iew syn hesizes insigh s
om eigh een ounda ional and con empo a y s udies spanning business analy ics, AI-d i en
decision-making, and s a is ical app oaches o o ganiza ional pe o mance. F om ea ly s a is ical
app oaches such as Al man’s (1968) landma k s udy applied disc iminan analysis o bank up cy
p edic ion, se ing an ea ly ounda ion o s a is ical app oaches in inance and Ba ney’s (1991)
esou ce-based iew, o con empo a y AI-d i en applica ions in alen analy ics, s a egic planning,
aud de ec ion, and digi al ans o ma ion, he e iew demons a es how s a is ical igo and AI
capabili ies con e ge o imp o e decision-making and i m pe o mance. D awing on me hodologies
such as disc iminan analysis, s uc u al equa ion modeling, deep lea ning, and sys ema ic e iews,
he pape highligh s he e olu ion om s a is ical anspa ency o AI adap abili y. We conclude ha
combining in e p e abili y wi h p edic i e accu acy o e s he s onges pa h o sus ainable
compe i i e ad an age.
Keywo ds: A i icial In elligence, S a is ical Models, Business Analy ics, Decision-Making, Fi m
Pe o mance, Talen Analy ics, Risk Assessmen , Digi al T ans o ma ion.
In oduc ion:
A i icial In elligence (AI) has
ansi ioned om being a echnological
cu iosi y o a main s eam enable o
compe i i e ad an age in business and
managemen . While classical s a is ical
echniques emphasized anspa ency and
me hodological igo , AI-based app oaches
ha e been alued o hei lexibili y and
s ong p edic i e capabili ies. This s udy
sys ema ically e iews seminal and ecen
wo ks, posi ioning hem wi hin a amewo k
o decision-making, pe o mance ou comes,
and sus ained compe i i e ad an age.
The in eg a ion o a i icial
in elligence (AI) and s a is ical modeling has
ede ined how businesses app oach decision-
making, pe o mance e alua ion, and
compe i i e ad an age. Ea ly con ibu ions,
such as Al man’s (1968) seminal wo k on
bank up cy p edic ion using disc iminan
analysis, pa ed he way o s a is ical igo in
business esea ch. Wi h ad ancemen s in
compu a ional powe , esea che s such as
K aus, Feue iegel, and Oz ekin (2018)
demons a ed he po en ial o deep lea ning in
ope a ions, while Gómez-Caicedo e al. (2022)
illus a ed AI’s g owing ole in business
analy ics. This pape e iews bo h
ounda ional and con empo a y esea ch o
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Yogi a M. Sadani
2
e alua e he complemen a i ies and con as s
be ween s a is ical and AI-d i en app oaches.
(F om ea ly s a is ical models → AI-d i en
app oaches)
Figu e1: Time line o AI & S a is ical Models
in Business
Li e a u e Re iew:
1. S a is ical Founda ions in Business
Resea ch:
Al man (1968) pionee ed s a is ical
applica ions in inance by using disc iminan
analysis o p edic bank up cy, a amewo k
la e ex ended by Pe ei a, Bas o, and Fe ei a-
da-Sil a (2014), who compa ed s a is ical and
AI models in ailu e p edic ion. Bol on e al.
(2002) e iewed aud de ec ion, highligh ing
he e ec i eness o s a is ical app oaches
be o e AI echniques gained p ominence.
These s udies es ablish he ounda ion o
in e p e abili y and anspa ency in s a is ical
models.
2. Eme gence o AI in Business Analy ics:
AI’s abili y o p ocess la ge-scale,
complex da a is exempli ied in Gómez-
Caicedo, Gai án-Angulo, and Bacca-Acos a
(2022), who de ails i s ole in business
analy ics. K aus e al. (2018) highligh ed how
deep lea ning models could be applied o sol e
complex p oblems in ope a ions esea ch,
o e ing imp o emen s o e con en ional
analy ics, while Da enpo (2018) ames AI
as he nex e olu iona y s ep a e adi ional
analy ics. Gup a (2021) complemen s his by
p esen ing p ac ical applica ions o business
analy ics using hyb id s a is ical-AI
app oaches.
3. Talen Managemen and HR Analy ics:
Sha ma and Bha naga (2017)
emphasize alen analy ics as a s a egic ool
o managing wo k o ce ou comes, while Qin
e al. (2023) p o ide a comp ehensi e AI
su ey on alen analy ics, highligh ing
s a is ical and AI syne gies in wo k o ce
op imiza ion. Amabile (2020) adds a unique
pe spec i e by linking AI wi h c ea i i y,
sugges ing AI-human collabo a ion as a
ca alys o inno a i e ou comes.
4. AI in Decision-Making and S a egic
Planning:
Chen, Espe ança, and Wang (2022)
empi ically examine AI-enabled decision-
making using PLS-SEM, showing i s
media ing e ec on i m pe o mance.
Simila ly, Fayaz, Amin, and Iqbal (2024)
assess AI’s ole in s a egic planning, s essing
i s ans o ma i e e ec on manage ial
decisions. Cui (2025) p o ides e idence om
Chinese en e p ises, con i ming AI’s ole in
digi al ans o ma ion and pe o mance
enhancemen . Liu e al. (2022) ex end his by
e iewing sys ema ic con ibu ions o AI-
enabled digi al s a egies.
5. AI in Finance and Risk Assessmen :
Bahnsen e al. (2020) apply AI in
inancial isk assessmen , demons a ing
p edic i e powe in dynamic en i onmen s.
Boone e al. (2018) demons a ed how
uncon en ional da a such as Google T ends
can be inco po a ed in o o ecas ing,
enhancing adi ional s a is ical me hods wi h
eal- ime in o ma ion. Ba ney (1991), while
no AI-speci ic, in oduces he esou ce-based
iew (RBV), aming i m esou ces
(including AI capabili y) as d i e s o
compe i i e ad an age.
Theo e ical F amewo k:
By le e aging da a analy ics, alen
op imiza ion, and isk e alua ion, AI sys ems
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Yogi a M. Sadani
3
suppo decision-making p ocesses ha
con ibu e o be e e iciency, s onge
pe o mance, and sus ainable compe i i e
ad an age.
Figu e 2: Concep ual F amewo k: AI → Decision-Making → Fi m Pe o mance
Compa a i e Analysis:
1. Compa a i e Analysis o Me hods:
Fea u e
S a is ical Models (Al man,
Bol on, Pe ei a)
AI Models (K aus, Qin, Cui,
e c.)
In e p e abili y
High
(clea coe icien s, a ios)
Medium-Low (black box
issue)
P edic i e Accu acy
Mode a e
High
(deep lea ning, big da a)
Da a Requi emen
Smalle da ase s
La ge da a se s needed
Applica ion
Domains
Finance,
Bank up cy p edic ion
Finance, HR, Ope a ions,
S a egy
Flexibili y
Rigid assump ions
Adap i e, scalable
2. Compa a i e s udy o Founda ional and Con empo a y Con ibu ions Ac oss Resea ch
Domains
S udy
Domain
Me hod/Model
S a is ical Concep
Con ibu ion
Al man (1968)
Finance
Disc iminan
Analysis
Ra ios, Z-sco e
Bank up cy p edic ion
Pe ei a e al.
(2014)
Finance
AI s. S a is ics
Compa a i e
modeling
Business ailu e
p edic ion
Bol on e al.
(2002)
Finance
S a is ical Re iew
F aud de ec ion
me hods
Ea ly s a is ical aud
models
Boone e al.
(2018)
Ma ke ing
Google T ends
Co ela ion,
o ecas ing
Sales p edic ion
K aus e al.
(2018)
Ope a ions
Deep Lea ning
Op imiza ion
AI in ope a ions
esea ch
Chen e al.
(2022)
Managemen
PLS-SEM
S uc u al modeling
AI decision-making
pa hways
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Yogi a M. Sadani
4
Liu e al.
(2022)
Managemen
Sys ema ic
Re iew
Thema ic coding
Digi al ans o ma ion
Qin e al.
(2023)
HR
AI Su ey
Compa a i e
analysis
Talen analy ics
Amabile (2020)
C ea i i y
Concep ual
Su p ise, no el y
AI-human c ea i i y
Ba ney (1991)
S a egy
RBV
F amewo k
Resou ce heo y
Sus ained ad an age
3. S a is ical Concep s Ac oss S udies:
S a is ical Concep
Applica ion in AI Resea ch
Examples om S udies
Reg ession (linea /logi /p obi )
Modeling ela ionships be ween
business a iables
Pe ei a e al., Cui
S uc u al Equa ion Modeling
(SEM/PLS-SEM)
Tes ing media ion, mode a ion,
causal pa hways
Cui, PLS-SEM s udy
Classi ica ion Me ics (ROC,
AUC, Gini, Con usion Ma ix)
Valida ing AI p edic i e accu acy
Business ailu e, aud
de ec ion
E o Analysis (MSE, RMSE)
Measu ing p edic i e pe o mance
K aus e al., ope a ions
o ecas ing
Hypo hesis Tes ing ( - es , chi-
squa e)
Su ey da a alida ion, adop ion
s udies
Fayaz e al.
Su i al Analysis
Employee u no e p edic ion
Qin e al.
Cos -sensi i e Modeling
Economic impac o
misclassi ica ion
F aud de ec ion pape s
No e: Sou ce: Adap ed omPe ei ae al.(2014),K ause al.(2018),Liue al.(2022),ando he s.
Findings and Thema ic Mapping:
The body o li e a u e sugges s ou
cen al a eas— inance, HR, s a egic planning,
and ope a ions—whe e AI has ei he
supplemen ed o ou pe o med adi ional
s a is ical echniques:-
Finance & Risk (Al man, Bol on,
Bahnsen)
Human Resou ces (Sha ma &
Bha naga , Qin, Chak abo y)
S a egic Planning (Fayaz, Liu, Cui)
Ope a ions & C ea i i y (K aus,
Da enpo , Amabile)
Figu e 3: Thema ic Map o AI Applica ions in
Business
Figu e 4: Applica ion A eas o AI in Business
IJAAR Vol. 6 No. 38 ISSN – 2347-7075
Yogi a M. Sadani
5
Discussion:
The e iewed li e a u e e eals a shi
om s a is ical in e p e abili y owa d AI
adap abili y. While s a is ical me hods emain
aluable o anspa ency and heo y-building,
AI o e s supe io p edic i e capaci y in
complex, dynamic en i onmen s. In HR, AI-
d i en alen analy ics highligh i s po en ial
o op imizing wo k o ce ou comes. In
s a egic domains, AI enables da a-d i en
decision-making, aligning wi h RBV
pe spec i es. Howe e , in e p e abili y and
e hical challenges emain cen al issues
equi ing u he explo a ion. Findings e eal a
ecu ing balance be ween he cla i y o e ed
by s a is ical models and he supe io
p edic i e powe o AI sys ems. S a is ical
models emain aluable in domains equi ing
anspa ency, while AI excels in la ge-scale,
dynamic en i onmen s. The con e gence o
he wo sugges s a u u e o hyb id models
combining explain abili y and p edic i e
powe .
Conclusion and Fu u e Resea ch Agenda:
This e iew es ablishes ha AI and
s a is ical me hods a e no subs i u es bu
complemen a y app oaches. S a is ical models
o e cla i y, while AI ensu es adap abili y and
p edic i e s eng h. Fu u e esea ch should
ocus on hyb id amewo ks, explainable AI,
and c oss-domain applica ions o balance
in e p e abili y wi h inno a ion. In eg a ing
hese me hods ac oss inance, HR, ope a ions,
and s a egy will be essen ial o sus aining
compe i i e ad an age in he digi al e a. This
e iew emphasizes he complemen a y oles o
AI and s a is ics in business and managemen
esea ch. While s a is ics p o ide obus ness
and in e p e abili y, AI o e s adap abili y and
p edic i e s eng h. Upcoming s udies could
ocus on:
De eloping hyb id app oaches ha
me ge he in e p e abili y o s a is ical
in e ence wi h he p edic i e s eng hs
o AI.
In es iga e e hical and go e nance
issues in AI-d i en decisions.
Ex end AI applica ions beyond
inance and HR in o sus ainabili y,
c ea i i y, and o ganiza ional
inno a ion.
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