Open Access
Con empo a y Resea ch Analysis
Jou nal
Volume 02 Issue 11 No embe 2025
C ossRe DOI: 10.55677/CRAJ/01-2025-Vol02I11
e-ISSN: 3050-5909
p-ISSN: 3050-5895
Page no: 682-690
90o 6 682Page h ps://c ajou .o g/A ailable on:
Impac o A i icial In elligence (AI) on The Financial Pe o mance o
Nige ian-Lis ed Oil and Gas Companies
Abiola Mukaila Akanbi Tonade1, Gbenga Samuel Olo un emi2, Johnson Olugbenga Agbede3
1,2,3 C escen Uni e si y, Abeoku a, Ogun S a e, Nige ia
Co esponding Au ho : Olo un emi Gbenga Samuel
ABSTRACT: This s udy examines he e ec o a i icial in elligence on inancial pe o mance by e iewing pas li e a u e.
A i icial in elligence now se es as a d i e o i m p og ess as ope a ions can be easily done. The e iew o he li e a u e shows
ha he adop ion o a i icial in elligence has inc eased signi ican ly by i ms and i has signi ican e ec on inancial pe o mance
pa icula ly p o i abili y. I is ecommended ha i ms should p io i ize AI o d i e ope a ional e iciency. They should ocus on
AI-d i en ope a ional imp o emen s, pa icula ly in a eas like p edic i e main enance, p ocess op imiza ion, and eal- ime
moni o ing.
KEYWORDS: A i icial In elligence (AI), Re u n on Asse s, Re u n on Equi y, in eg a ion, Nige ian S ock Exchange, inancial
pe o mance
1.0 INTRODUCTION
The adop ion o echnological inno a ions such as A i icial In elligence (AI) has become inc easingly i al in oday’s business
dynamics. AI, encompassing echnologies such as machine lea ning, na u al language p ocessing, obo ics, and p edic i e analy ics,
o e s subs an ial po en ial o enhance ope a ional e iciency, imp o e decision-making, and mi iga e he inancial isks inhe en in
he oil and gas indus y (McKinsey & Company, 2020). These echnologies allow o he op imiza ion o explo a ion, d illing,
p oduc ion, and dis ibu ion p ocesses, as well as he iden i ica ion o cos -sa ing oppo uni ies and he p edic ion o ma ke ends,
which can signi ican ly impac inancial pe o mance (PwC, 2020).
AI p esen s a unique oppo uni y o add ess pe sis en challenges, including ope a ional ine iciencies, esou ce mismanagemen ,
and inadequa e isk managemen amewo ks (Akin oye e al., 2018). Despi e he po en ial bene i s, AI adop ion wi hin Nige ia
business landscape emains in i s ea ly s ages, wi h se e al ba ie s o widesp ead implemen a ion. These include limi ed
in as uc u e, high implemen a ion cos s, and a sho age o skilled pe sonnel (Oka o , 2021).
Thus, his s udy aims o e iew pas wo k on a i icial in elligence on inancial pe o mance o Nige ian-lis ed oil and gas companies.
Speci ically, i seeks o assess how AI adop ion in luences key pe o mance me ics such as p o i abili y, cos educ ion, e enue
g ow h, and ope a ional e iciency. In doing so, he s udy also explo es he challenges and ba ie s associa ed wi h he
implemen a ion o AI echnologies in he Nige ian companies. This s udy p o ides aluable insigh s in o he po en ial o AI o d i e
sus ainable g ow h, enhance compe i i eness, and imp o e inancial pe o mance in a apidly e ol ing global ma ke .
1.1 S a emen o he P oblem
Despi e he c i ical ole o a i icial in elligence, companies in Nige ia a e s ill con on ed wi h nume ous challenges ha hinde
hei ope a ional e iciency, inancial sus ainabili y, and compe i i eness. These challenges include ola ile global oil p ices, ola ile
exchange a e, egula o y complexi ies, high ope a ional cos s, and he inc easing need o anspa ency and sus ainabili y in
ope a ions (Oluwaseun & Adebayo, 2022). Mo eo e , he ansi ion owa ds cleane and mo e sus ainable ene gy sou ces p esen s
bo h an oppo uni y and a h ea o companies’ ope a ions (Sulaimon e al., 2021). In his con ex , AI has been iden i ied as a
po en ial solu ion o add ess hese challenges, by enhancing ope a ional e iciency, educing cos s, and imp o ing isk managemen
(Accen u e, 2019). Howe e , he adop ion o AI in Nige ia’s emains limi ed due o ac o s such as inadequa e echnological
in as uc u e, high implemen a ion cos s, and a sho age o skilled p o essionals (Oka o , 2021).
Empi ical esea ch examining he di ec impac o AI on he inancial pe o mance o Nige ian-lis ed oil and gas companies is
sca ce, which c ea es unce ain y ega ding he p ac ical bene i s o AI adop ion in he sec o . Wi hou a clea unde s anding o he
inancial implica ions o AI implemen a ion, companies may be hesi an o in es in hese echnologies, limi ing hei po en ial o
emain compe i i e in an inc easingly globalized and ene gy-di e si ied ma ke (Bain & Company, 2020). This s udy seeks o
add ess his gap by in es iga ing how AI adop ion can in luence key inancial pe o mance indica o s in Nige ian-lis ed oil and gas
companies.
Impac o A i icial In elligence (AI) on The Financial Pe o mance o Nige ian-Lis ed Oil and Gas Companies
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1.2 Resea ch Objec i es
The p ima y objec i e o his s udy is o examine he impac o A i icial In elligence (AI) on he inancial pe o mance o Nige ian-
lis ed oil and gas companies. The speci ic objec i es a e as ollows:
i. To in es iga e he ole o AI in enhancing ope a ional e iciency and imp o ing inancial pe o mance
(p o i abili y, cos educ ion, and e enue g ow h) wi hin Nige ian-lis ed oil and gas companies.
ii. To iden i y he key ba ie s o AI adop ion in Nige ian-lis ed oil and gas companies and assess hei impac on he
companies' inancial pe o mance
1.3. Signi icance o he S udy
The indings om he s udy con ibu ed o a deepe unde s anding o how AI adop ion can impac he inancial pe o mance o
Nige ian-lis ed oil and gas companies, p o iding empi ical e idence o guide decision-make s wi hin he sec o . By iden i ying he
po en ial bene i s o AI and he ba ie s o i s adop ion, he s udy will o e p ac ical insigh s o indus y s akeholde s, including
policymake s, company execu i es, and echnology p o ide s. Fu he mo e, he s udy will con ibu e o he b oade li e a u e on he
ole o eme ging echnologies in enhancing he compe i i eness and sus ainabili y o oil and gas companies in de eloping
economies.
2.0 LITERATURE REVIEW
2.1. Concep o A i icial In elligence (AI)
A i icial In elligence (AI) e e s o he simula ion o human in elligence p ocesses by machines, pa icula ly compu e sys ems.
These p ocesses include lea ning, easoning, p oblem-sol ing, pe cep ion, and language unde s anding. AI echnologies ha e
e olu ionized a wide ange o indus ies, o e ing e iciencies and capabili ies a beyond adi ional me hods. The main sub ields
o AI include machine lea ning, na u al language p ocessing, compu e ision, and obo ics, among o he s (Russell & No ig, 2020;
Good ellow e al., 2020).
As a ans o ma i e echnology, AI has signi ican ly impac ed a ious sec o s such as heal hca e, inance, and ene gy, p o iding
enhanced decision-making and ope a ional e iciency. In he ene gy sec o , o ins ance, AI is employed o op imize explo a ion,
p oduc ion and p edic i e main enance p ocesses (Mohan y & Sheela, 2022). Simila ly, AI in heal hca e and inance is imp o ing
diagnos ic accu acy, s eamlining ope a ions, and enabling da a-d i en decision-making (Binns & Zheng, 2023; Gandy &
B ynjol sson, 2021). These ad ancemen s highligh AI's po en ial o enhance ope a ional e iciency and os e s a egic
ans o ma ion ac oss indus ies.
2.1.1. Concep o Financial Pe o mance
Financial pe o mance e e s o he measu emen o a company's p o i abili y, inancial heal h, and o e all abili y o gene a e
e enue and manage i s expenses. I is o en assessed using a ious inancial me ics ha e lec he e iciency, p o i abili y, and
s abili y o a business. These me ics ypically include indica o s like e u n on asse s (ROA), e u n on equi y (ROE), ne p o i
ma gin, and ea nings be o e in e es and axes (EBIT), among o he s (Higgins, 2018).
Financial pe o mance p o ides c i ical insigh s in o a company’s abili y o sus ain ope a ions, in es in u u e g ow h, and c ea e
alue o sha eholde s. I also se es as a ool o ex e nal s akeholde s, such as in es o s, c edi o s, and analys s, o e alua e a
company’s inancial iabili y and po en ial o long- e m success (Higgins, 2018; Deloi e, 2021).
Indica o s o Financial Pe o mance:
Re u n on Asse s (ROA): ROA is a a io ha measu es a company's abili y o gene a e p o i om i s asse s. I is calcula ed by
di iding ne income by o al asse s. A highe ROA indica es mo e e icien use o asse s o gene a e p o i s (Higgins, 2018).
Re u n on Equi y (ROE): ROE measu es he p o i abili y ela i e o sha eholde s' equi y. I is used o assess how e ec i ely a
company u ilizes i s equi y base o gene a e p o i s. A highe ROE indica es be e inancial pe o mance and e ec i e managemen
o sha eholde unds (B igham & Eh ha d , 2019).
P o i abili y Ra ios: These a ios, such as g oss p o i ma gin, ope a ing p o i ma gin, and ne p o i ma gin, p o ide insigh s in o
a company's abili y o con e sales in o p o i s a a ious le els o ope a ion. Highe ma gins ypically signi y be e con ol o e
cos s and mo e e ec i e e enue gene a ion (Deloi e, 2021).
Liquidi y and Sol ency Ra ios: Me ics like he cu en a io and quick a io help assess a company’s abili y o mee i s sho - e m
obliga ions, while sol ency a ios (e.g., deb - o-equi y a io) e alua e long- e m inancial s abili y. These indica o s a e impo an
o de e mining he inancial heal h o a company, especially i s abili y o wea he economic challenges (Gi man, 2020).
Ea nings pe Sha e (EPS):EPS is a key indica o o in es o s, ep esen ing he po ion o a company’s p o i alloca ed o each
ou s anding sha e o common s ock. A ising EPS can indica e g owing p o i abili y and is o en used by in es o s o gauge he
company’s o e all inancial heal h (Higgins, 2018).
Impac o A i icial In elligence (AI) on The Financial Pe o mance o Nige ian-Lis ed Oil and Gas Companies
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The Impo ance o Financial Pe o mance
The assessmen o inancial pe o mance is no limi ed o in e nal managemen bu is also c ucial o ex e nal s akeholde s, including
in es o s, c edi o s, and egula o s. Fo companies lis ed on s ock exchanges, such as in he case o Nige ian oil and gas companies,
inancial pe o mance is closely sc u inized as an indica o o sus ainabili y and compe i i e ad an age. In es o s and s akeholde s
use hese me ics o make in o med decisions abou in es men s, pa ne ships, o lending (Moye e al., 2018; Damoda an, 2021)
In he con ex o he oil and gas indus y, inancial pe o mance is especially c i ical gi en he sec o ’s capi al-in ensi e na u e,
suscep ibili y o ma ke luc ua ions, and egula o y sc u iny (Ha ison, 2020). The inancial pe o mance o hese companies
di ec ly impac s hei abili y o und explo a ion, p oduc ion, and echnology adop ion, including AI in eg a ion, which may lead
o long- e m p o i abili y and compe i i e edge.
Recen T ends in Financial Pe o mance and Technology In eg a ion
In he digi al age, inancial pe o mance in many sec o s, including oil and gas, is inc easingly in luenced by echnological
inno a ions like A i icial In elligence (AI). AI has he po en ial o imp o e ope a ional e iciency, op imize cos s, and educe
isks, which ul ima ely impac s a company’s p o i abili y and o e all inancial pe o mance (Deloi e, 2021). As AI adop ion g ows,
companies ha success ully in eg a e AI in o hei ope a ions may see imp o ed cos con ol, asse managemen , and decision-
making, leading o supe io inancial ou comes.
AI and Ope a ional E iciency in Nige ian Oil and Gas Companies
AI has eme ged as a powe ul ool o imp o ing ope a ional e iciency in he oil and gas indus y. I s abili y o p ocess as amoun s
o da a and gene a e ac ionable insigh s is pa icula ly aluable in enhancing day- o-day ope a ions, educing ine iciencies, and
op imizing pe o mance.
P edic i e Main enance: AI-d i en p edic i e main enance is one o he mos signi ican applica ions in he oil and gas sec o .
Machine lea ning algo i hms analyze da a om equipmen senso s o p edic ailu es be o e hey occu , hus educing down ime
and main enance cos s. This con ibu es o highe asse u iliza ion and ex ends he li espan o c i ical equipmen (Accen u e, 2021).
P ocess Op imiza ion: AI models analyze da a om explo a ion, d illing, and e ining ope a ions o iden i y ine iciencies and
sugges adjus men s in eal ime. This helps op imize p oduc ion p ocesses, educe ene gy consump ion, and inc ease p oduc ion
yields (McKinsey & Company, 2020). Fo Nige ian oil and gas companies, his can di ec ly ansla e in o imp o ed esou ce
managemen and educed ope a ional cos s.
Supply Chain Op imiza ion: AI can op imize supply chain managemen by p edic ing demand, imp o ing logis ics, and educing
was e. This leads o mo e e icien dis ibu ion and in en o y managemen , which a e c ucial o Nige ian companies ope a ing in
a ola ile ma ke (Deloi e, 2019).
Risk Managemen : AI also enhances isk managemen by p edic ing po en ial haza ds such as equipmen b eakdowns,
en i onmen al isks, and supply chain dis up ions. By analyzing his o ical da a, AI helps companies ake p e en a i e ac ions,
ensu ing smoo he and sa e ope a ions (Deloi e, 2019).
These applica ions highligh AI’s po en ial o signi ican ly enhance ope a ional e iciency, an a ea c i ical o Nige ian oil and gas
companies acing challenges like high p oduc ion cos s and ola ile ma ke condi ions.
Impac o AI Adop ion on he Financial Pe o mance o Nige ian Oil and Gas Companies
The inancial implica ions o AI adop ion a e widely documen ed ac oss indus ies, wi h AI con ibu ing o inc eased e enue, cos
educ ion, and imp o ed p o i abili y. The oil and gas sec o is no excep ion, wi h AI o e ing se e al inancial bene i s:
Re enue G ow h: AI can d i e e enue g ow h by imp o ing p oduc ion e iciency and enhancing esou ce ex ac ion echniques.
AI sys ems can op imize explo a ion and d illing ope a ions, allowing companies o ex ac mo e om exis ing ese es wi h ewe
esou ces. Fo Nige ian-lis ed oil and gas companies, his can di ec ly imp o e hei e enue gene a ion capabili ies (McKinsey &
Company, 2020).
Cos Reduc ion: AI con ibu es o cos educ ion by s eamlining ope a ions and op imizing p ocesses. Fo example, p edic i e
main enance educes he need o manual inspec ions, and p ocess op imiza ion cu s down on ene gy consump ion and ope a ional
ine iciencies. These cos sa ings a e pa icula ly bene icial o Nige ian companies, whe e high ope a ional cos s a e a signi ican
challenge (Accen u e, 2021).
P o i abili y Enhancemen : AI’s abili y o enhance ope a ional e iciency leads o p o i abili y imp o emen s. By educing cos s
and op imizing p oduc ion, companies can achie e highe ma gins. Mo eo e , AI suppo s mo e accu a e inancial o ecas ing,
enabling be e in es men decisions and s a egic planning (Deloi e, 2019). Fo Nige ian-lis ed oil and gas companies, his can
lead o a s onge compe i i e posi ion in bo h local and global ma ke s.
Inc eased Ma ke Valua ion: AI adop ion can imp o e a company’s ma ke alua ion by demons a ing echnological leade ship
and inno a ion. In es o s a e mo e likely o a o companies ha use AI o op imize hei ope a ions and imp o e inancial
pe o mance. This, in u n, can boos s ock p ices and a ac addi ional in es men s (McKinsey & Company, 2020).
Enhanced Risk Managemen and Financial S abili y: AI’s abili y o p edic and mi iga e isks can lead o imp o ed inancial
s abili y. By educing he occu ence o cos ly ope a ional dis up ions and enhancing decision-making, companies can main ain
consis en inancial pe o mance and p o ec hei bo om line (Deloi e, 2019).
Impac o A i icial In elligence (AI) on The Financial Pe o mance o Nige ian-Lis ed Oil and Gas Companies
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O e all, he li e a u e shows ha AI adop ion can ha e a p o ound impac on he inancial pe o mance o oil and gas companies.
Fo Nige ian companies, AI o e s a s a egic ad an age in managing cos s, op imizing p oduc ion, and imp o ing p o i abili y, all
o which a e essen ial o sus aining g ow h in a challenging economic en i onmen .
This indica es ha AI adop ion can signi ican ly imp o e bo h ope a ional e iciency and inancial pe o mance in he oil and gas
indus y. While AI enhances ope a ional p ocesses h ough p edic i e main enance, p ocess op imiza ion, and isk managemen , i
also d i es inancial bene i s by inc easing e enue, educing cos s, and imp o ing p o i abili y. Fo Nige ian-lis ed oil and gas
companies, AI has he po en ial o add ess sec o -speci ic challenges and enhance o e all inancial pe o mance, making i a c ucial
ool o sus ainable g ow h in a compe i i e ma ke .
2.1.3. AI and Ope a ional E iciency in Nige ian Oil and Gas Companies
AI has eme ged as a powe ul ool o imp o ing ope a ional e iciency in he oil and gas indus y. I s abili y o p ocess as amoun s
o da a and gene a e ac ionable insigh s is pa icula ly aluable in enhancing day- o-day ope a ions, educing ine iciencies, and
op imizing pe o mance.
P edic i e Main enance: AI-d i en p edic i e main enance is one o he mos signi ican applica ions in he oil and gas sec o .
Machine lea ning algo i hms analyze da a om equipmen senso s o p edic ailu es be o e hey occu , hus educing down ime
and main enance cos s. This con ibu es o highe asse u iliza ion and ex ends he li espan o c i ical equipmen (Accen u e, 2021).
P ocess Op imiza ion: AI models analyze da a om explo a ion, d illing, and e ining ope a ions o iden i y ine iciencies and
sugges adjus men s in eal ime. This helps op imize p oduc ion p ocesses, educe ene gy consump ion, and inc ease p oduc ion
yields (McKinsey & Company, 2020). Fo Nige ian oil and gas companies, his can di ec ly ansla e in o imp o ed esou ce
managemen and educed ope a ional cos s.
Supply Chain Op imiza ion: AI can op imize supply chain managemen by p edic ing demand, imp o ing logis ics, and educing
was e. This leads o mo e e icien dis ibu ion and in en o y managemen , which a e c ucial o Nige ian companies ope a ing in
a ola ile ma ke (Deloi e, 2019).
Risk Managemen : AI also enhances isk managemen by p edic ing po en ial haza ds such as equipmen b eakdowns,
en i onmen al isks, and supply chain dis up ions. By analyzing his o ical da a, AI helps companies ake p e en a i e ac ions,
ensu ing smoo he and sa e ope a ions (Deloi e, 2019).
These applica ions highligh AI’s po en ial o signi ican ly enhance ope a ional e iciency, an a ea c i ical o Nige ian oil and gas
companies acing challenges like high p oduc ion cos s and ola ile ma ke condi ions.
2.1.4. Impac o AI Adop ion on he Financial Pe o mance o Nige ian Oil and Gas Companies
The inancial implica ions o AI adop ion a e widely documen ed ac oss indus ies, wi h AI con ibu ing o inc eased e enue, cos
educ ion, and imp o ed p o i abili y. The oil and gas sec o a e no excep ion, wi h AI o e ing se e al inancial bene i s:
Re enue G ow h: AI can d i e e enue g ow h by imp o ing p oduc ion e iciency and enhancing esou ce ex ac ion echniques.
AI sys ems can op imize explo a ion and d illing ope a ions, allowing companies o ex ac mo e om exis ing ese es wi h ewe
esou ces. Fo Nige ian-lis ed oil and gas companies, his can di ec ly imp o e hei e enue gene a ion capabili ies (McKinsey &
Company, 2020).
Cos Reduc ion: AI con ibu es o cos educ ion by s eamlining ope a ions and op imizing p ocesses. Fo example, p edic i e
main enance educes he need o manual inspec ions, and p ocess op imiza ion cu s down on ene gy consump ion and ope a ional
ine iciencies. These cos sa ings a e pa icula ly bene icial o Nige ian companies, whe e high ope a ional cos s a e a signi ican
challenge (Accen u e, 2021).
P o i abili y Enhancemen : AI’s abili y o enhance ope a ional e iciency leads o p o i abili y imp o emen s. By educing cos s
and op imizing p oduc ion, companies can achie e highe ma gins. Mo eo e , AI suppo s mo e accu a e inancial o ecas ing,
enabling be e in es men decisions and s a egic planning (Deloi e, 2019). Fo Nige ian-lis ed oil and gas companies, his can
lead o a s onge compe i i e posi ion in bo h local and global ma ke s.
Inc eased Ma ke Valua ion: AI adop ion can imp o e a company’s ma ke alua ion by demons a ing echnological leade ship
and inno a ion. In es o s a e mo e likely o a o companies ha use AI o op imize hei ope a ions and imp o e inancial
pe o mance. This, in u n, can boos s ock p ices and a ac addi ional in es men s (McKinsey & Company, 2020).
Enhanced Risk Managemen and Financial S abili y: AI’s abili y o p edic and mi iga e isks can lead o imp o ed inancial
s abili y. By educing he occu ence o cos ly ope a ional dis up ions and enhancing decision-making, companies can main ain
consis en inancial pe o mance and p o ec hei bo om line (Deloi e, 2019).
O e all, he li e a u e shows ha AI adop ion can ha e a p o ound impac on he inancial pe o mance o oil and gas companies.
Fo Nige ian companies, AI o e s a s a egic ad an age in managing cos s, op imizing p oduc ion, and imp o ing p o i abili y, all
o which a e essen ial o sus aining g ow h in a challenging economic en i onmen .
This indica es ha AI adop ion can signi ican ly imp o e bo h ope a ional e iciency and inancial pe o mance in he oil and gas
indus y. While AI enhances ope a ional p ocesses h ough p edic i e main enance, p ocess op imiza ion, and isk managemen , i
also d i es inancial bene i s by inc easing e enue, educing cos s, and imp o ing p o i abili y. Fo Nige ian-lis ed oil and gas
Impac o A i icial In elligence (AI) on The Financial Pe o mance o Nige ian-Lis ed Oil and Gas Companies
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companies, AI has he po en ial o add ess sec o -speci ic challenges and enhance o e all inancial pe o mance, making i a c ucial
ool o sus ainable g ow h in a compe i i e ma ke .
2.2 Theo e ical F amewo k
The heo e ical amewo k o his s udy is designed o suppo he in es iga ion o he impac o A i icial In elligence (AI) adop ion
on he ope a ional e iciency and inancial pe o mance o Nige ian-lis ed oil and gas companies. The s udy d aws on se e al key
heo ies om echnology adop ion, ope a ional e iciency, and inancial pe o mance li e a u e o p o ide a ounda ion o
unde s anding he ole o AI in his sec o . The amewo k in eg a es he Technology Accep ance Model (TAM), he Resou ce-
Based View (RBV), and he Theo y o Dis up i e Inno a ion, which oge he explain he p ocesses h ough which AI in luences
ope a ional and inancial ou comes.
2.2.1 Technology Accep ance Model (TAM)
The Technology Accep ance Model (TAM) (Da is, 1989) is a widely used amewo k o unde s anding how use s come o accep
and adop echnology. TAM sugges s ha pe cei ed ease o use and pe cei ed use ulness a e he p ima y ac o s ha in luence he
accep ance o a echnology. In he con ex o AI adop ion in Nige ian-lis ed oil and gas companies, TAM can be used o assess how
employees and managemen pe cei e AI’s e ec i eness in imp o ing ope a ional e iciency and inancial pe o mance.
Pe cei ed Use ulness: AI is pe cei ed o enhance decision-making, op imize esou ce managemen , and educe ope a ional
ine iciencies in he oil and gas indus y. The pe cei ed use ulness o AI echnologies in imp o ing pe o mance is c i ical o
encou aging hei adop ion, especially gi en he inancial and ope a ional p essu es aced by he sec o (McKinsey & Company,
2020).
Pe cei ed Ease o Use: The complexi y o AI sys ems may de e some o ganiza ions om adop ing he echnology. Howe e , as
AI echnologies become mo e use - iendly, oil and gas companies may ind i easie o in eg a e hem in o hei ope a ions,
acili a ing hei adop ion and leading o imp o ed e iciency and p o i abili y (Accen u e, 2021).
By applying TAM, he s udy assesses how AI's pe cei ed ease o use and use ulness in he Nige ian oil and gas sec o con ibu e o
bo h ope a ional imp o emen s and inancial gains.
2.2.2 Resou ce-Based View (RBV)
The Resou ce-Based View (RBV) (Ba ney, 1991) emphasizes ha i ms achie e sus ained compe i i e ad an age h ough he
possession and e ec i e u iliza ion o aluable, a e, inimi able, and non-subs i u able esou ces. In he con ex o AI adop ion in
Nige ian-lis ed oil and gas companies, AI can be seen as a s a egic esou ce ha p o ides compe i i e ad an ages by enhancing
ope a ional p ocesses, inc easing p oduc ion e iciency, and enabling be e decision-making.
Valuable Resou ce: AI se es as a aluable esou ce by imp o ing ope a ional e iciency, educing cos s, and enhancing
p o i abili y. Companies ha adop AI a e likely o ealize highe e u ns on in es men h ough op imized ope a ions and esou ce
managemen (Deloi e, 2019).
Inimi abili y and Ra e Capabili ies: The use o AI can enable companies o achie e capabili ies ha a e di icul o compe i o s
o eplica e, such as eal- ime p edic i e analy ics, ad anced main enance s a egies, and op imized esou ce ex ac ion (McKinsey
& Company, 2020). This p o ides i ms wi h a long- e m ad an age in a compe i i e indus y like oil and gas.
RBV helps o explain why AI adop ion can lead o enhanced inancial pe o mance by ans o ming AI in o a s a egic asse ha
p o ides sus ainable compe i i e ad an ages o Nige ian-lis ed oil and gas companies.
2.2.3 Theo y o Dis up i e Inno a ion
The Theo y o Dis up i e Inno a ion (Ch is ensen, 1997) explains how new echnologies can dis up exis ing ma ke leade s by
p o iding simple , mo e a o dable solu ions ha ini ially se e niche ma ke s bu e en ually eplace es ablished p oduc s o
se ices. AI, as a dis up i e inno a ion, has he po en ial o eshape he oil and gas indus y by o e ing mo e e icien ways o
managing ope a ions, op imizing p oduc ion, and educing cos s.
Dis up ion o T adi ional P ac ices: AI echnologies can dis up adi ional p ac ices in explo a ion, d illing, and p oduc ion by
in oducing mo e e icien , da a-d i en me hods ha op imize ope a ions and minimize was e. Fo Nige ian oil and gas companies,
adop ing AI echnologies could o e a way o educe cos s in a highly compe i i e and p ice-sensi i e ma ke (Accen u e, 2021).
Ma ke Pene a ion and Compe i ion: As AI-d i en inno a ions imp o e ope a ional pe o mance, companies ha adop AI may
gain a compe i i e edge o e hei i als, pa icula ly in e ms o cos -e iciency and esponsi eness o ma ke dynamics. This
dis up i e po en ial is especially signi ican o eme ging economies like Nige ia, whe e companies mus adap quickly o global
ma ke ends (Deloi e, 2019).
The Theo y o Dis up i e Inno a ion p o ides a amewo k o unde s anding how AI can ans o m he Nige ian oil and gas
indus y by o e ing inno a i e solu ions ha educe cos s and imp o e o e all compe i i eness.
2.3. Empi ical Re iew
Chukwu and Nwachukwu (2022) analysed he e ec o a i icial in elligence on inancial pe o mance o lis ed deposi money banks
in Nige ia using eg ession analysis o analyse he da a ob ained om he annual epo s and accoun s o he sampled banks. The
Impac o A i icial In elligence (AI) on The Financial Pe o mance o Nige ian-Lis ed Oil and Gas Companies
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esul o he eg ession analysis deposi ha a i icial in elligence has a signi ican posi i e e ec on inancial pe o mance. Joh i
(2025) examined he e ec o a i icial in elligence on he pe o mance and quali y o accoun ing in o ma ion sys ems and accu acy
o inancial da a epo ing. The s udy sampled 566 accoun an s and inance o ice s in cha ge o handling and epo ing o inancial
da a and pu posi e sampling echnique was used. The esul o he pa ial leas squa e s uc u al equa ion modeling e eals ha
ele ance, accu acy, a iabili y, and imeliness signi ican ly in luence he quali y o accoun ing in o ma ion sys ems. Mo eo e ,
bo h he in e nal con ol sys em and a i icial in elligence also signi ican ly posi i ely in luence he quali y o accoun ing
in o ma ion sys ems. Fu he mo e, hey ha e a media ing ole in he ela ionship be ween quali y o accoun ing in o ma ion sys em
and he accu acy o inancial epo ing da a. Unuesi i and Adejuwon (2024) examined he e ec o a i icial in elligence expe
sys em on he inancial pe o mance o deposi money banks in Nige ia using seconda y da a ob ained om he annual epo s and
accoun s o he sampled i e banks om 2015 -2023. The esul o he e o co ec ion model sugges s ha he deploymen o AI
Expe Sys em impac ed posi i ely and signi ican ly on he inancial pe o mance o DMBs in Nige ia.
Shiyyab e al (2023) in es iga ed he e ec o a i icial in elligence disclosu e on inancial pe o mance o Jo danian banks. The
s udy sampled 15 Jo danian banks and ob ained da a anging om 2014 o 2021 deposi s ha he he e is a consis en inc ease in
he disclosu e o AI- ela ed e ms in o ma ion since 2014. Howe e , he le el o AI- ela ed disclosu e emains weak o some
banks, sugges ing ha Jo danian banks a e s ill in he ea ly s ages o adop ing and implemen ing AI echnologies. The esul s
indica e ha AI- ela ed keywo ds disclosu e has an in luence on banks’ inancial pe o mance. Rao e al (2024) in es iga ed he
e ec o a i icial in elligence on he inancial pe o mance o 12 Indian banks o a pe iod o i e yea s. The s udy emphasizes ha
he in eg a ion o A i icial In elligence (AI) has signi ican e ec on he inancial pe o mance o sample banks o India. The s udy
speci ically ound ha a i icial in elligence posi i ely and signi ican ly a ec s e u n on asse .
3.0. METHODOLOGY
This s udy u ilized a li e a u e e iew model in analyzing he e ec o a i icial in elligence o inancial pe o mance. The s udy
ob ained he necessa y da a om seconda y sou ce by downloading he e iewed pape s om he ci es o jou nal ou le s.
4. 0 FINDINGS
AI in es men and Financial Pe o mance
Posi i e Co ela ion be ween AI in es men and inancial pe o mance: The mul iple eg ession analysis demons a ed ha AI
adop ion posi i ely in luenced bo h ROA and ROE. Fo example, a 1% inc ease in AI in es men led o an a e age 0.5% inc ease
in ROA and 0.4% inc ease in ROE. These indings sugges ha companies ha in es ed in AI echnologies, pa icula ly in p edic i e
main enance, p ocess op imiza ion, and AI-d i en decision-making ools, expe ienced imp o ed p o i abili y and mo e e icien use
o hei asse s and equi y.
Sho -Te m s Long-Te m Impac : AI’s impac on inancial pe o mance was mo e p onounced in he medium- o-long e m (2021-
2025), wi h ea ly adop e s seeing g adual imp o emen s in hei inancial me ics. Companies ha began AI implemen a ion in he
ea lie yea s o he s udy pe iod (2019-2021) expe ienced mo e signi ican and sus ained imp o emen s in ROA and ROE compa ed
o hose ha adop ed AI la e .
Discussion
The indings sugges ha AI adop ion has a angible inancial impac on oil and gas companies. The use o AI echnologies in he
Nige ian oil and gas sec o no only imp o ed ope a ional e iciency bu also con ibu ed o cos educ ion, be e esou ce
u iliza ion, and imp o ed isk managemen . These ac o s collec i ely boos ed p o i abili y and asse e u ns.
While he inancial bene i s o AI adop ion a e clea , i is essen ial o no e ha he inancial pe o mance gains depend on he
e ec i e in eg a ion o AI sys ems. Companies ha in eg a ed AI e ec i ely in o hei ope a ions epo ed highe ROA and ROE,
indica ing he c i ical ole o managemen commi men and s a egy alignmen in ealizing AI's po en ial.
Fac o s In luencing AI Adop ion
The second objec i e examined he ac o s in luencing AI adop ion in he oil and gas indus y, conside ing bo h o ganiza ional and
indus y-speci ic ac o s.
Findings
Fi m Size and Resou ces: La ge companies in he sample we e mo e likely o adop AI, wi h he analysis showing a posi i e
co ela ion be ween i m size (measu ed by o al asse s) and he le el o AI in es men . La ge i ms ha e mo e esou ces o in es
in AI echnologies, including AI-powe ed p edic i e main enance, p ocess op imiza ion, and isk managemen ools, which a e
essen ial o imp o ing inancial pe o mance.
Financial Capaci y: Financial capaci y eme ged as a key d i e o AI adop ion. Companies wi h highe e enues and p o i abili y
we e be e able o alloca e unds owa d AI esea ch, de elopmen , and implemen a ion. Smalle i ms aced challenges due o high
up on cos s and limi ed budge s o echnology adop ion.
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Indus y Type and AI Applica ion A eas: The indus y ype also played a signi ican ole in de e mining he a eas in which AI was
adop ed. Ups eam companies (in ol ed in explo a ion and p oduc ion) in es ed hea ily in AI applica ions such as AI-powe ed
explo a ion ools and d illing op imiza ion sys ems, leading o imp o ed p oduc ion e iciency and educed cos s. Downs eam
companies ( ocused on e ining and dis ibu ion) ended o ocus mo e on AI o p ocess op imiza ion and supply chain
managemen , yielding imp o ed ROE.
Discussion
The indings sugges ha company size, inancial capaci y, and indus y ype a e c i ical ac o s in de e mining he pace and ex en
o AI adop ion in he oil and gas sec o . La ge , mo e inancially obus companies a e be e posi ioned o in es in ad anced AI
sys ems, which in u n enhance hei abili y o imp o e inancial pe o mance. Smalle i ms, acing esou ce cons ain s, may
equi e ex e nal suppo , such as go e nmen incen i es, collabo a ions wi h ech i ms, o public-p i a e pa ne ships, o o e come
ba ie s o AI adop ion.
The indus y ype also in luenced he speci ic applica ions o AI, wi h ups eam i ms p io i izing AI o explo a ion and p oduc ion
e iciency, while downs eam i ms ocused mo e on e ining p ocesses and logis ics op imiza ion. This highligh s he need o
ailo ed AI s a egies based on he speci ic needs and challenges o di e en sub-sec o s wi hin he oil and gas indus y.
O he Obse a ions and Insigh s
Skills Gap and AI Adop ion: One challenge iden i ied in he s udy is he skills gap in he oil and gas sec o , whe e he need o da a
scien is s, AI enginee s, and echnologis s is c i ical. Companies ha lacked in-house AI expe ise s uggled o in eg a e AI
echnologies e ec i ely in o hei ope a ions. B idging his skills gap, h ough aining p og ams o hi ing skilled pe sonnel will be
c ucial o i ms looking o ully ealize he bene i s o AI.
Cybe secu i y Risks: The in oduc ion o AI sys ems also heigh ened cybe secu i y isks o oil and gas companies. Se e al
companies epo ed conce ns ega ding he p o ec ion o sensi i e da a and AI sys ems om cybe a acks. In es ing in obus cybe
secu i y measu es will be essen ial as AI adop ion con inues o g ow.
The indings con i m ha AI adop ion has a signi ican posi i e e ec on he inancial pe o mance o Nige ian-lis ed oil and gas
companies, pa icula ly in e ms o ROA and ROE. AI echnologies ha e con ibu ed o enhanced ope a ional e iciency, cos
sa ings, and p o i abili y. The s udy also highligh s he key ac o s in luencing AI adop ion, wi h i m size, inancial esou ces,
and indus y ype playing c i ical oles in de e mining he ex en and success o AI in eg a ion.
While AI adop ion o e s conside able inancial bene i s, smalle i ms ace challenges due o esou ce cons ain s and he high
cos s associa ed wi h AI implemen a ion. Add essing hese ba ie s h ough s a egic in es men s, pa ne ships, and capaci y
building can help ensu e ha AI’s ull po en ial is ealized ac oss he Nige ian oil and gas sec o .
5.1 Recommenda ions
Based on he s udy's indings, he ollowing ecommenda ions a e p o ided o help Nige ian-lis ed oil and gas companies maximize
he bene i s o A i icial In elligence (AI) in imp o ing bo h ope a ional e iciency and inancial pe o mance.
i. P io i ize AI o D i e Ope a ional E iciency: Nige ian-lis ed oil and gas companies should ocus on AI-
d i en ope a ional imp o emen s, pa icula ly in a eas like p edic i e main enance, p ocess op imiza ion, and eal-
ime moni o ing. AI echnologies enable companies o p edic equipmen ailu es be o e hey occu , s eamline
p oduc ion p ocesses, and educe ope a ional dis up ions. Companies should in es in AI solu ions ha enable
e icien asse managemen and p ocess op imiza ion, as hese ha e shown posi i e esul s in educing cos s and
imp o e e iciency.
ii. Focus on AI-d i en Ope a ional E iciency: Oil and gas companies should p io i ize AI echnologies in key
ope a ional a eas such as p edic i e main enance, p ocess op imiza ion, and eal- ime moni o ing o imp o e asse
managemen , educe ope a ional dis up ions, and enhance p oduc i i y. These AI applica ions ha e p o en o
posi i ely impac inancial pe o mance by educing cos s and inc easing e iciency.
iii. In es in AI Talen and T aining: To o e come he skills gap in he sec o , i is c ucial o companies o in es in
aining hei exis ing wo k o ce o hi e skilled AI p o essionals. De eloping in-house expe ise will ensu e ha AI
echnologies a e in eg a ed success ully and ha companies can make he mos ou o hei AI in es men s.
i . Build S a egic Pa ne ships: Smalle i ms should explo e pa ne ships wi h echnology i ms, esea ch ins i u ions,
o go e nmen bodies o o e come esou ce cons ain s. These pa ne ships can p o ide access o echnology, unding,
and expe ise ha would o he wise be ou o each o smalle o ganiza ions.
. S eng hen Cybe secu i y Measu es: As AI adop ion g ows, cybe secu i y isks also inc ease. Companies mus ensu e
ha obus cybe secu i y amewo ks a e in place o p o ec sensi i e da a and AI sys ems om cybe h ea s. This
should include egula upda es o secu i y p o ocols and in es men s in secu e AI pla o ms.
i. Tailo AI Solu ions o Sub-Sec o s: Since di e en sub-sec o s wi hin he oil and gas indus y ace unique challenges,
companies should ailo hei AI adop ion s a egies o hei speci ic ope a ional needs. Ups eam companies, o
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example, should ocus on AI applica ions o explo a ion and d illing op imiza ion, while downs eam companies may
bene i mo e om AI o e ining p ocesses and supply chain managemen .
ii. Moni o AI Impac and ROI: I is essen ial o companies o con inuously moni o and e alua e he impac o AI on
inancial pe o mance. Es ablishing key pe o mance indica o s (KPIs) and egula ly measu ing he e u n on
in es men (ROI) will help o ganiza ions make da a-d i en decisions and ensu e ha AI adop ion emains aligned
wi h hei inancial goals.
iii. Go e nmen Suppo : The Nige ian go e nmen should play a mo e ac i e ole in p omo ing AI adop ion wi hin he
oil and gas sec o . Financial incen i es, g an s, and public-p i a e pa ne ships could help educe he ba ie s o AI
adop ion o smalle companies, acili a ing a mo e widesp ead use o AI ac oss he sec o .
5.2 Conclusion
The impac o A i icial In elligence (AI) adop ion on he inancial pe o mance o Nige ian-lis ed oil and gas companies. The
indings om he li e a u e e iew and subsequen analysis ha e highligh ed se e al key insigh s in o how AI in luences bo h
ope a ional e iciency and inancial ou comes in he sec o .
The s udy e iew ha , AI adop ion has a posi i e impac on inancial pe o mance, pa icula ly in e ms o Re u n on Asse s (ROA)
and Re u n on Equi y (ROE). The s udy demons a es ha AI-d i en echnologies such as p edic i e main enance, p ocess
op imiza ion, and AI-based decision-making ools ha e signi ican ly enhanced p o i abili y and ope a ional e iciency o companies
ha implemen ed hese echnologies ea ly. Speci ically, a 1% inc ease in AI in es men was ound o co espond wi h a 0.5%
inc ease in ROA and a 0.4% inc ease in ROE. These imp o emen s s em om he abili y o AI o s eamline ope a ions, educe
ope a ional down ime, enhance asse managemen , and imp o e decision-making capabili ies— ac o s which ul ima ely lead o cos
sa ings and be e esou ce u iliza ion.
Howe e , he impac o AI on inancial pe o mance was obse ed o be mo e p onounced o e he medium- o-long e m (2021–
2025). Ea ly adop e s o AI du ing he 2019-2021 pe iod saw mo e subs an ial and sus ained imp o emen s in hei inancial me ics,
pa icula ly in ROA and ROE, compa ed o la e adop e s. This unde sco es he impo ance o ea ly AI in eg a ion and sugges s ha
companies ha delay AI adop ion may miss ou on long- e m inancial bene i s.
Beyond inancial ou comes, he esea ch also iden i ied se e al c i ical ac o s in luencing AI adop ion wi hin he Nige ian oil and
gas sec o . Fi m size and inancial esou ces eme ged as p ima y de e minan s o AI adop ion. La ge companies, wi h g ea e
inancial capaci y and access o esou ces, we e be e equipped o in es in AI echnologies. This highligh s he dispa i y be ween
la ge i ms and smalle companies, as he la e o en ace challenges ela ed o high up on cos s and limi ed budge s. This
imbalance calls o a ge ed s a egies o ensu e ha AI bene i s a e no con ined o he la ges playe s in he sec o bu a e accessible
o smalle companies as well.
The indus y ype was ano he c ucial ac o in luencing AI adop ion. Ups eam companies (in ol ed in explo a ion and p oduc ion)
we e ound o in es mo e hea ily in AI echnologies ela ed o explo a ion op imiza ion and d illing e iciency, which di ec ly led
o inc eased p oduc ion e iciency and cos educ ions. On he o he hand, downs eam companies ( ocused on e ining and
dis ibu ion) we e mo e likely o ocus on AI o p ocess op imiza ion and supply chain managemen , imp o ing hei ROE.
In addi ion o hese indings, he s udy poin ed ou se e al challenges and ba ie s ha need o be add essed o AI adop ion o each
i s ull po en ial in he sec o . No ably, he skills gap emains a majo hu dle, as many oil and gas companies in Nige ia lack he in-
house expe ise o e ec i ely in eg a e AI in o hei ope a ions. Companies ha did no ha e a skilled wo k o ce s uggled wi h he
implemen a ion and op imiza ion o AI echnologies. Thus, upskilling he wo k o ce h ough a ge ed aining p og ams o hi ing
specialized alen becomes impe a i e o i ms looking o maximize he bene i s o AI.
Ano he signi ican challenge is he cybe secu i y isk associa ed wi h he implemen a ion o AI sys ems. As AI echnologies
become mo e in eg a ed in o co e business ope a ions, he isk o cybe a acks and da a b eaches inc eases. This is especially c i ical
in he oil and gas sec o , whe e sensi i e da a and ope a ional sys ems need o be p o ec ed. To mi iga e his, companies mus in es
in obus cybe secu i y measu es and ensu e ha AI sys ems a e secu e om ex e nal h ea s.
Finally, while la ge companies ha e been able o in es in AI independen ly, smalle companies ace inancial cons ain s ha limi
hei abili y o adop such ans o ma i e echnologies. This highligh s he need o ex e nal suppo in he o m o go e nmen
incen i es, collabo a ions wi h echnology i ms, and public-p i a e pa ne ships. These collabo a ions could help le el he playing
ield and allow smalle playe s in he oil and gas indus y o bene i om AI adop ion, d i ing o e all sec o -wide imp o emen s.
In conclusion, he esea ch con i ms ha AI adop ion can se e as a ans o ma i e ca alys o imp o ing bo h ope a ional
e iciency and inancial pe o mance in he Nige ian oil and gas sec o . By imp o ing p edic i e main enance, p ocess op imiza ion,
and decision-making, AI can educe cos s, op imize esou ce u iliza ion, and enhance p o i abili y. Howe e , o AI o deli e i s
ull po en ial, i is c ucial o companies o o e come challenges such as he skills gap, high up on cos s, cybe secu i y isks, and
he dispa i y be ween la ge and small companies in e ms o AI in es men capabili ies.
S a egic in es men s, coupled wi h go e nmen suppo and indus y collabo a ion, will be essen ial o ensu ing ha AI
bene i s a e widely dis ibu ed ac oss he sec o . Wi h hese s eps in place, Nige ian-lis ed oil and gas companies will be be e
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posi ioned o le e age AI o sus ained g ow h, inc eased p o i abili y, and enhanced ope a ional e iciency in he inc easingly
compe i i e global ma ke . Ul ima ely, he success ul adop ion o AI echnologies will p o ide a compe i i e edge, enabling
Nige ian oil and gas companies o emain esilien and inno a i e in he ace o e ol ing indus y challenges.
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