Mas e Deg ee P og am in
In o ma ion Sys ems and Technologies Managemen
C i ical success ac o s in BPM implemen a ion: C ea ing AI
suppo ed decision engine o he business.
Vic o Lipo
Mas e Thesis
p esen ed as pa ial equi emen o ob aining he Mas e Deg ee in In o ma ion Managemen
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
MGI
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
C i ical success ac o s in BPM implemen a ion: C ea ing AI suppo ed decision engine o
he business.
by
Vic o Lipo
Mas e Thesis p esen ed as he pa ial equi emen o ob aining a Mas e 's deg ee in
In o ma ion Managemen , specializa ion in In o ma ion Sys ems and Technologies
Managemen
Supe ised by
Ped o Manuel Maia Mal a, PhD
NOVA In o ma ion Managemen School
July, 2024
i
STATEMENT OF INTEGRITY
I he eby decla e ha ing conduc ed his academic wo k wi h in eg i y. I con i m ha I ha e no
used plagia ism o any o m o undue use o in o ma ion o alsi ica ion o esul s along he
p ocess leading o i s elabo a ion. I u he decla e ha I ha e ully acknowledged he Rules
o Conduc and Code o Hono om he NOVA In o ma ion Managemen School.
[Lisboa, 13/07/2024]
ii
ABSTRACT
In oday's apidly e ol ing business en i onmen , achie ing ope a ional e iciency,
anspa ency, and ma ke esponsi eness is c ucial o success. Business P ocess Managemen
(BPM) is a c i ical ool in his pu sui , encompassing he design, con igu a ion, enac men , and
analysis o business p ocesses o d i e con inuous imp o emen . Recen ad ancemen s in
Gene a i e P e- ained T ans o me (GPT) models o e new oppo uni ies o enhance BPM
h ough imp o ed decision-making and p og ess- acking capabili ies. Howe e , he e is a
signi ican gap in he li e a u e conce ning he sys ema ic de elopmen o AI-powe ed
decision engines o suppo BPM implemen a ion.
This hesis add esses his gap by pa ially employing Design Science Resea ch Me hodology
(DSRM) wi h some limi a ions desc ibed in he documen o de elop a decision engine a i ac
ailo ed o BPM adop ion. The esea ch u ilizes concep o C i ical Success Fac o s (CSFs)
essen ial o BPM, inco po a ing AI o au oma e and op imize decision-making p ocesses. The
me hodology in ol es a ho ough li e a u e e iew, he concep ualiza ion and design o a
decision engine model, and he de elopmen o a p o o ype. This p o o ype is e alua ed
h ough expe e iews, p o iding c i ical insigh s in o i s e ec i eness and p ac ical
applicabili y.
The indings indica e ha he in eg a ion o AI, pa icula ly GPT models, can signi ican ly
enhance BPM by p o iding dynamic insigh s and au oma ing complex decision-making
p ocesses. The de eloped decision engine p o o ype o e s a s uc u ed app oach o BPM
adop ion, ailo ed o di e en o ganiza ional s ages and suppo ed by a comp ehensi e lis o
CSFs. This esea ch con ibu es o bo h academic knowledge and p ac ical applica ions,
o e ing a ounda ional amewo k o o ganiza ions o enhance hei BPM e o s and
ad ance he dialogue on da a-d i en decision-making in business p ocess implemen a ion.
KEYWORDS
Business P ocess Managemen ; A i icial In elligence; Decision Engine; C i ical Success Fac o s
Sus ainable De elopmen Goals (SDG):
iii
TABLE OF CONTENTS
S a emen o In eg i y ...................................................................................................... i
Abs ac ............................................................................................................................ ii
Lis o Figu es ....................................................................................................................
Lis o Tables .................................................................................................................... i
Lis o Abb e ia ions and Ac onyms ............................................................................... ii
1. In oduc ion ................................................................................................................ 1
2. Li e a u e e iew ........................................................................................................ 2
2.1. Key Concep s and De ini ions o AI- ela ed hings ............................................. 2
2.1.1. A i icial In elligence (AI) .............................................................................. 2
2.1.2. La ge Language Models (LLMs) ..................................................................... 2
2.1.3. GPT (Gene a i e P e- ained T ans o me ) .................................................. 3
2.1.4. Business P ocess Managemen (BPM) .......................................................... 3
2.1.5. C i ical Success Fac o s (CSFs) ...................................................................... 3
2.1.6. Decision Engine ............................................................................................. 3
2.1.7. Sen imen Analysis ........................................................................................ 3
2.2. De ailed Explana ions o AI Componen s ............................................................ 3
2.2.1. Machine Lea ning (ML) ................................................................................. 3
2.2.2. Na u al Language P ocessing (NLP) .............................................................. 4
2.2.3. Deep Lea ning ............................................................................................... 4
2.3. Iden i ica ion o cu en s a e o bpm adop ion o legal en i y ........................... 4
2.4. Role o AI in BPM ................................................................................................. 6
3. Me hodology .............................................................................................................. 9
3.1. P oblem Iden i ica ion ......................................................................................... 9
3.2. Objec i es o a Solu ion ....................................................................................... 9
3.3. Design and De elopmen ..................................................................................... 9
3.4. Summa y o An icipa ed Ou comes ................................................................... 10
4. Empi ical S udy ......................................................................................................... 12
4.1. App oach o decision engine p o o ype ............................................................ 12
4.2. Decision engine p o o ype ................................................................................. 13
4.3. Simula ed Example o p ac ical applica ion. ...................................................... 16
5. Resul s and discussion .............................................................................................. 19
5.1. Exposi ion o Empi ical Resul s .......................................................................... 19

i
5.2. Valida ion o Empi ical Resul s .......................................................................... 19
5.2.1. CEO ............................................................................................................. 20
5.2.2. CFO .............................................................................................................. 20
5.2.3. Manage ...................................................................................................... 21
5.2.4. Summa y o he eedback: .......................................................................... 21
5.3. Discussion .......................................................................................................... 22
5.4. DE P o o ype Implemen a ion Adjus men s Based on Expe Sugges ions ....... 22
6. Conclusions and u u e wo ks ................................................................................... 24
6.1. Syn hesis o he Wo k Done wi h Highligh o he Recommenda ions Made ... 24
6.1.1. Key Recommenda ions: .............................................................................. 24
6.2. Limi a ions o he Wo k Done ............................................................................ 25
6.3. Fu he Wo k Possible o Do .............................................................................. 25
Bibliog aphical Re e enczoes ......................................................................................... 26
Appendix A - E hics Commi ee Repo ......................................................................... 28
Appendix B – Decision engine p o o ype ...................................................................... 29
Appendix C - Expe Ques ionnai e ............................................................................... 30
LIST OF FIGURES
Figu e 2.4.1 - BPM li ecycle en iched wi h AI ............................................................................ 7
Figu e 4.1.1 - DE p o o ype logic ............................................................................................. 12
Figu e 4.2.1 - BPM Adop ion S age P og ession ...................................................................... 15
Figu e 4.2.2 - CSF Sco ing App oach ........................................................................................ 15
i
LIST OF TABLES
Table 2.3.1 - Top 10 ac o s a ec ing BMP implemen a ion (Cas o e al., 2019) .................... 5
Table 2.3.2 - CSFs o di e en s ages o BPM adop ion (Buh e al., 2015) ............................... 6
Table 4.2.1 - BPM Adop ion S ages ......................................................................................... 13
Table 4.2.2 - C i ical Success Fac o s (CSFs) o S age 1 .......................................................... 14
Table 4.3.1 - BPM adop ion simula ion ................................................................................... 16
Table 5.4.1 - DE adjus men s based on eedback .................................................................... 22
ii
LIST OF ABBREVIATIONS AND ACRONYMS
AI
A i icial In elligence
BPM
Business P ocess Managemen
CSF
C i ical Success Fac o s
DSRM
Design Science Resea ch Me hodology
GPT
Gene a i e P e- ained T ans o me
LLM
La ge Language Models
NLP
Na u al Language P ocessing
ROI
Re u n on In es men
SDG
Sus ainable De elopmen Goals
DE
Decision Engine
7
domains and asks (Teubne e al., 2023) and GPT-like models, ha e signi ican ly enhanced
he abili y o analyze and iden i y bo h explici and implici p ocesses wi hin an
o ganiza ion. I means ha you can apply GPT-like models o s uc u ed and uns uc u ed
da a o dig he p ocesses o de elop solu ion o he p oblem.
In he esea ch done by (Weinzie l e al., 2024), he e a e h ee equen examples o using
ML:
• p o iding decision suppo h ough p edic ions
• disco e ing accu a e p ocess models
• imp o ing esou ce alloca ion.
Keeping his in mind, esea che s ha e de eloped se e al BPM-speci ic solu ions ha
ex ac in o ma ion om ex ual documen s using Na u al Language P ocessing
echniques. (G ohs e al., 2023)
In his esea ch, G ohs e al., 2023 illus a ed how LLM can be u ilized o p ac ical BPM
asks which equi ed ex ual documen s as inpu . Cha GPT4 was u ilized o his ask. Fo
all h ee asks, GPT4 pe o ms simila ly o o be e han he benchma k, i.e., speci ic
applica ions o he espec i e ask (G ohs e al., 2023).
The e a e also inc easing numbe o se ices o Con e be ween P ocess Models and
Na u al Language Tex u ilizing g aphical modeling languages. I allows s akeholde s o
collec i ely desc ibe execu able business p ocesses on a concep ual le el. (F ey ag e al.,
n.d.).
Comp ehensi e li e a u e e iew being done on he opic o “Machine lea ning in business
p ocess managemen ” by (Weinzie l e al., 2024). Resul ed in en iched BPM li ecycle BPM
li ecycle o Dumas e al. (2018). Which gi es highe le el p ac ical iew pe spec i e on he
a eas, whe e AI can be applied.
Figu e 2.4.1 - BPM li ecycle en iched wi h AI

8
F om he pe spec i e o ou esea ch, i helps o unde s and be e he ongoing p ocesses
in he o ganiza ion and ack he p ocesses which a e adop ed o BPM.
Machine Lea ning (ML) has been pa icula ly use ul in imp o ing ce ain pa s o Business
P ocess Managemen (BPM). These imp o emen s mos ly happen in a eas whe e he e is
a lo o da a, such as disco e ing new p ocesses, analyzing hem, and keeping an eye on
hem o e ime. In hese a eas, da a abou business ac i i ies is usually a ailable in
de ailed eco ds, which ML can easily use (Sa ke , 2021; Weinzie l e al., 2024).
Howe e , esea che s sugges ha we could make e en mo e p og ess by using
echniques om ields like na u al language p ocessing (NLP) and compu e ision. NLP
helps compu e s unde s and and wo k wi h human language, while compu e ision helps
hem unde s and images and ideos. By combining hese echniques wi h ML, we could
c ea e new and be e ways o manage business p ocesses ha go beyond jus using da a
logs. This could lead o inno a i e solu ions ha a e mo e e ec i e and e sa ile.
Weinzie l e al., 2024 also p opose o conside he en e p ise p ocess ne wo k ins ead o
isola ed business p ocesses. In doing ha , ML applica ions ecei e inpu da a om
mul iple da a sou ces, including con ol- low in o ma ion om di e en business
p ocesses and p ocess con ex in o ma ion ela ed o he business p ocesses.
Le e aging LLMs and GPT models acili a es comp ehensi e p ocess iden i ica ion,
dynamic oadmap de elopmen , eal- ime implemen a ion moni o ing, and he acking
o quali a i e me ics. These capabili ies add ess signi ican gaps in adi ional BPM
app oaches, pa ing he way o mo e e ec i e and e icien sys ems. By con inuously
in eg a ing AI-d i en insigh s, o ganiza ions can ensu e hei BPM ini ia i es a e no only
success ul bu also adap able o u u e challenges and oppo uni ies (G ohs e al., 2023;
Weinzie l e al., 2024).
In addi ion o adi ional pe o mance me ics, acking quali a i e me ics such as
sen imen and in ol emen (how people eel and how in ol ed hey a e in hei wo k) is
i al o comp ehensi e BPM e alua ion. AI echnologies, including sen imen analysis and
engagemen acking ools, can analyze quali a i e da a o p o ide deepe insigh s in o
employee mo ale, cus ome sa is ac ion, and o e all o ganiza ional engagemen . These
insigh s p o ide a deepe unde s anding o he wo kplace a mosphe e and cus ome
expe iences, allowing o ganiza ions o pinpoin a eas ha need imp o emen and ake
speci ic ac ions o enhance o e all BPM esul s.
9
3. METHODOLOGY
This sec ion ou lines he me hodology adop ed o his esea ch, aiming o iden i y and u ilize
C i ical Success Fac o s (CSFs) wi hin BPM o de elop a decision engine a i ac ha suppo s
BPM implemen a ion.
The ini ial idea was o apply Design Science Resea ch Me hodology (DSRM) as a amewo k
o his s udy (Hasan & Sha i zadeh, 2020; Hunzike & Blankenagel, 2024). Howe e , due o
he lack o access o a eal-wo ld company, i was no easible o accomplish s ages o DSRM
such as Demons a ion and E alua ion ully. Ne e heless, some pa s o DSRM we e applied,
such as P oblem Iden i ica ion, Objec i es o a Solu ion, Design and De elopmen . De ailed
desc ip ion p o ided below:
3.1. PROBLEM IDENTIFICATION
Objec i e: The p ima y goal a his s age is o iden i y he cu en challenges and gaps in
BPM implemen a ion. This in ol es a deep di e in o exis ing li e a u e and esea ch o
pinpoin a eas whe e BPM p ac ices a e lacking, pa icula ly ocusing on he sys ema ic
de elopmen o AI-suppo ed decision engines. The objec i e is o es ablish a clea
unde s anding o he p oblems aced by o ganiza ions in adop ing and op imizing BPM
sys ems.
App oach: A comp ehensi e li e a u e e iew was conduc ed o ga he insigh s in o he
cu en s a e o BPM adop ion, common challenges, and he po en ial ole o AI in
enhancing BPM. This in ol ed analyzing a ious schola ly a icles, indus y epo s, and
case s udies o iden i y ecu ing hemes and issues. The li e a u e e iew also included
an examina ion o exis ing AI echnologies and hei applica ions in BPM, p o iding a
ounda ional unde s anding o how AI can be le e aged o add ess iden i ied challenges.
A his s age we iden i ied impo ance o CSFs o BPM adop ion as well as b eakdown o
BPM adop ion o di e en S ages (s a es o o ganiza ion).
3.2. OBJECTIVES OF A SOLUTION
Objec i e: The nex s ep is o de ine he goals and equi emen s o he AI-suppo ed
decision engine. This in ol es se ing clea , measu able objec i es ha he decision engine
aims o achie e. The objec i e is o ensu e ha he p oposed solu ion add esses he
iden i ied challenges and gaps in BPM implemen a ion e ec i ely.
App oach: Insigh s om he li e a u e e iew and expe in e iews we e u ilized o
ou line he objec i es. The objec i es we e e ined h ough i e a i e eedback, ensu ing
hey a e aligned wi h he needs o o ganiza ions and he capabili ies o AI echnologies.
This app oach ensu ed ha he solu ion is bo h heo e ically sound and p ac ically
ele an .
3.3. DESIGN AND DEVELOPMENT
Objec i e: The p ima y goal he e is o de elop he decision engine a i ac based on he
iden i ied CSFs and he de ined objec i es. This in ol es a de ailed design and
10
de elopmen p ocess, ensu ing ha he decision engine is obus , scalable, and capable
o add essing he iden i ied challenges in BPM implemen a ion.
S age 1: Iden i ica ion o Cu en S a e o BPM Adop ion
App oach: The cu en BPM adop ion s age o he o ganiza ion was assessed using a
s uc u ed amewo k. This amewo k was de i ed om he esea ch o Buh e al.
(2015), which ca ego izes BPM adop ion in o a ious s ages and iden i ies associa ed
CSFs o each s age.
S age 2: Iden i y CSFs o Respec i e S a e
App oach: The lis o c i ical success ac o s (CSFs) was condensed om Buh e al.
(2015) esea ch o ocus on he mos ele an ac o s o each BPM adop ion s age. This
in ol ed p io i izing CSFs ha ha e he highes impac on BPM success and a e mos
applicable o he o ganiza ion's cu en s a e. This p ocess included:
• Re iewing he de ailed desc ip ions and jus i ica ions o each CSF.
• E alua ing hei applicabili y based on he o ganiza ion's speci ic con ex and
needs.
• Engaging wi h subjec ma e expe s o alida e he selec ion and p io i iza ion o
CSFs.
S age 3: De elop a Sco ing App oach o Respec i e S a e
App oach: The sco ing app oach was de eloped wi h he suppo o GPT-4 h ough a
se ies o p omp s and p ac ical expe ience. This p ocess in ol ed:
• De ining clea c i e ia o each CSF based on li e a u e and expe inpu .
• De eloping a a ing scale (e.g., 1 o 5) o each c i e ion o quan i y he le el o
achie emen o pe o mance.
• Tes ing he sco ing app oach h ough simula ed scena ios and expe alida ion o
ensu e i s eliabili y and alidi y.
P omp s example:
• Please sugges lis o c i e ia which can be used o sco ing {p oposed sco ing
app oach}.
• Wha kind o scales can be applied o {measu emen c i e ia}.
Responses we e c i ically analyzed and adop ed o he opic.
S age 4: C i ical E alua ion
App oach: In e iews wi h expe s ac oss di e en le els o he co po a e hie a chy
we e conduc ed o e alua e he p o o ype om a ious pe spec i es. This included:
• Conduc ing s uc u ed in e iews wi h CEO le el, CFO le el and line manage s o
ga he di e se insigh s.
• Analyzing he eedback o iden i y common hemes, s eng hs, and a eas o
imp o emen in he decision engine.
3.4. SUMMARY OF ANTICIPATED OUTCOMES
The an icipa ed ou comes o his esea ch include:
11
• A lis o C i ical Success Fac o s (CSFs) o BPM implemen a ion, ailo ed o di e en
s ages o BPM adop ion.
• A heo e ical model o an AI-suppo ed decision engine ha inco po a es hese
CSFs, p o iding a s uc u ed app oach o enhance BPM p ac ices.
• C i ical insigh s om expe s ac oss di e en le els o expe ise in BPM.
This esea ch aspi es o con ibu e o bo h academic knowledge and p ac ical applica ions
in BPM. By p o iding a ounda ional amewo k o AI-enhanced BPM implemen a ion, i
aims o suppo o ganiza ions in achie ing highe le els o ope a ional e iciency,
anspa ency, and ma ke esponsi eness.
12
4. EMPIRICAL STUDY
4.1. APPROACH TO DECISION ENGINE PROTOTYPE
De elopmen o Decision Engine (DE) can add ess complexi y and add anspa ency o he
p ocess o BPM adop ion. As pe de ini ion, decision engines a e sophis ica ed so wa e
sys ems designed o au oma e he decision-making p ocesses wi hin an o ganiza ion.
I ope a es by analyzing inpu da a h ough a se o p ede ined ules, algo i hms, o
machine lea ning models o make decisions o ecommenda ions wi hou human
in e en ion (pu ely based on a ailable da a). These engines a e pa icula ly aluable in
scena ios whe e as , consis en , and accu a e decisions a e c ucial, such as in c edi
sco ing, aud de ec ion, cus ome se ice, and many o he ope a ional p ocesses.
Fo his pape , we decided s ep aside om gene alized ac o s and align decision engine
wi h di e en s ages o BPM adop ion. To do his, we de eloped he p o o ype in he
ollowing logic:
Figu e 4.1.1 - DE p o o ype logic
The idea behind is ha be o e we s a , we should unde s and whe e we a e now. When
we unde s and whe e we a e, we can decide wha o do nex and how o do i .

13
4.2. DECISION ENGINE PROTOTYPE
F om ou pe spec i e decision engine p o o ype should be adop able and p ac ical. I
should guide he end use h ough he di e en s ages; he e o e, i should help answe ing
wo ini ial ques ions:
• Whe e he o ganiza ion is now?
• Wha should i do nex ?
To answe o he i s ques ion he p o o ype includes desc ip ion o BPM adop ion s ages
as pe Buh e al., 2015. Iden i ica ion, and Mo ing o wa d ac ions we e de i ed wi h
suppo o Cha GPT4 using p omp s like:
• based on you expe ience, how can o ganiza ion be iden i ied a {S age}
• please sugges possible ac ions o he o ganiza ion o ad ance u he
and summa ized o he able below:
Table 4.2.1 - BPM Adop ion S ages
S age
Desc ip ion
Iden i ica ion
Mo ing Fo wa d
Ac ions
Awa eness and
Unde s anding
o BPM
O ganiza ions
ecognize he
impo ance o BPM.
P ocess
imp o emen is ad
hoc, eac i e, and
uns uc u ed.
BPM is a new
concep o he e is
limi ed
unde s anding o
p ocess managemen
p inciples
o ganiza ion-wide.
In oduce o mal
BPM aining and
es ablish a sense o
u gency o p ocess
imp o emen .
Desi e o Adop
BPM
O ganiza ions a e
mo i a ed o adop
BPM and a e
beginning o plan
hei ini ia i es.
Managemen shows
in e es in BPM, and
ini ial esou ces a e
alloca ed.
Secu e op
managemen
suppo , appoin a
p ojec champion,
and de ine business
d i e s.
BPM P ojec s
O ganiza ions
execu e speci ic BPM
p ojec s wi h de ined
objec i es and plans.
Clea objec i es,
plans, and ex e nal
consul an guidance
a e in place.
Communica e
objec i es, seek
p o essional
guidance, and
mo i a e eam
membe s.
BPM P og am
BPM is in eg a ed
in o he
o ganiza ion’s
b oade s a egy and
p og ams.
Con inuous op
managemen
suppo , p o essional
guidance, and
e ec i e
communica ion a e
e iden .
Ensu e ongoing
suppo , main ain
consul an
pa ne ships, and
iden i y KPIs.
P oduc iza ion
o BPM
BPM p ac ices a e
s anda dized and
Key pe o mance
indica o s (KPIs) a e
Focus on aining,
secu ing op
14
p oduc ized wi hin
he o ganiza ion.
de ined, employees
a e educa ed and
mo i a ed, and BPM
is embedded in
decision-making.
managemen
endo semen , and
e ining p ocesses
based on KPIs.
Fo example:
S age 1: Awa eness and Unde s anding o BPM
• Desc ip ion: O ganiza ions a his s age ha e begun o ecognize he impo ance o
BPM. P ocess imp o emen is ypically ad hoc, eac i e, and uns uc u ed. The
o ganiza ion ecognizes BPM as a po en ial alue-add and begins o unde s and i s
p inciples and bene i s.
• Iden i ica ion: I BPM is a new concep o i he e is limi ed unde s anding o p ocess
managemen p inciples o ganiza ion-wide, i 's likely a his s age.
• Mo ing Fo wa d: In oduce o mal BPM aining and es ablish a sense o u gency o
p ocess imp o emen .
I he desc ip ion ma ches he ype o o ganiza ion, he o ganiza ion can become
awa e o ocus poin s and sugges ions ele an o he s age. In ou example:
• Focus: Educa ion on BPM impo ance and i s impac on pe o mance.
• Educa ion and Communica ion: Conduc wo kshops and aining sessions o
educa e s akeholde s abou he bene i s and p inciples o BPM.
• Visible Execu i e Suppo : Secu e and showcase execu i e endo semen o highligh
he impo ance o BPM wi hin he o ganiza ion.
• Case S udies and Success S o ies: Sha e case s udies o success ul BPM
implemen a ions o illus a e po en ial gains and encou age buy-in.
The same applies o po en ial p oblems. As a esul , he o ganiza ion ge s a lis o
CSFs o conside . In ou example, o he i s s age o BPM adop ion, CSFs include
empowe men o employees, cus ome ocus, and openness o changes (Buh e al.,
2015).
Table 4.2.2 - C i ical Success Fac o s (CSFs) o S age 1
CSF
Focus Poin s
Ac ions
Empowe men o
Employees
T aining and wo kshops o
enhance unde s anding o
BPM.
Conduc mon hly BPM aining
sessions and in e ac i e
wo kshops.
15
Cus ome Focus
Implemen eedback loops o
align p ocesses wi h cus ome
needs.
In oduce su eys and eedback
o ms pos -cus ome
in e ac ions.
Openness o
Changes
Encou age a cul u e o
adap abili y and con inuous
imp o emen .
De elop and communica e a
s uc u ed change managemen
plan, implemen a ecogni ion
p og am o success ul changes.
By knowing CSFs, he o ganiza ion can de elop sco ing app oaches, measu emen c i e ia,
scales, e c.
Below is a lowcha ep esen ing he p og ession h ough di e en s ages o BPM
adop ion, highligh ing key ac ions and ocus poin s a each s age.
Figu e 4.2.1 - BPM Adop ion S age P og ession
A ada cha showing he sco ing o di e en CSFs helps isualize he s eng hs and a eas
o imp o emen o each ac o .
Figu e 4.2.2 - CSF Sco ing App oach
Including hese ables and cha s enhances he cla i y and p ac icali y o he p o o ype,
making i easie o o ganiza ions o na iga e h ough he s ages o BPM adop ion.
16
4.3. SIMULATED EXAMPLE OF PRACTICAL APPLICATION.
Wi h he suppo o Cha GPT4 we ha e done se ies o p ac ical simula ions on how i can
be applied o he eal-wo ld scena ios.
Company P o ile:
Name: Tech Inno a o s Inc.
Indus y: Technology Solu ions
Cu en BPM Adop ion S age: Awa eness and Unde s anding o BPM
The company decides o s a BPM adop ion smoo hly by ocusing on wo key p ocesses:
he Cus ome Suppo P ocess and he P oduc De elopmen P ocess.
P omp s used:
• Please gene a e lis o possible asks o {s ep} o {ac ion / ask} o {s age}
• Wha possible esul s/ ou comes can be o {s ep} o {ac ion / ask} o {s age}
Table 4.3.1 - BPM adop ion simula ion
S ep
Ac ion/Task
Expec ed Resul
1. Ini ial Assessmen
Su eys
Conduc in e nal su eys o
ga he insigh s om employees.
Clea unde s anding o cu en
BPM knowledge and
engagemen .
In e iews
Conduc in e iews wi h key
s akeholde s.
Iden i y pain poin s and
oppo uni ies o
imp o emen .
Documen Re iew
Analyze exis ing p ocess
documen a ion, aining eco ds,
and pe o mance me ics.
Iden i y gaps and a eas
needing enhancemen .
Ou come
The assessmen e ealed ha while he e was an
acknowledgmen o he need o BPM, he p ocess imp o emen
ini ia i es we e ad hoc, eac i e, and uns uc u ed. This placed
Tech Inno a o s in he "Awa eness and Unde s anding o BPM"
s age.
2. De ine CSFs
Empowe men o
Employees
Enhance employees'
unde s anding o BPM and
encou age con ibu ions.
Imp o ed employee
engagemen and pa icipa ion
in BPM ini ia i es.
Cus ome Focus
Implemen mechanisms o
ga he and ac on cus ome
eedback.
Be e alignmen o cus ome
needs wi h suppo p ocesses.
23
cus ome
eedback, and
os e
adap abili y.
and inc eased
lexibili y.
clea inancial
me ics.
inancial cla i y,
and adap abili y.
De elop
Sco ing
App oach
De elop c i e ia
and a ing scales
o each CSF
based on
li e a u e and
expe inpu .
S uc u ed
e alua ion o BPM
e ec i eness.
Manage : Ensu e
sco ing is simple
and ac ionable.
P ac ical and easy-
o-apply sco ing
sys em o
e alua ing BPM
e ec i eness.
Implemen
Decision
Engine
Conduc BPM
aining sessions,
in oduce
eedback
mechanisms, and
communica e
change
managemen
plans.
Inc eased
unde s anding o
BPM, enhanced
cus ome eedback
in eg a ion, and
highe adap abili y
o change.
CEO: Balance AI
insigh s wi h
human
in e ac ion; CFO:
Ensu e cos -
bene i analysis;
Manage :
Suppo ca ee
de elopmen .
Imp o ed BPM
unde s anding,
be e eedback
in eg a ion, cos -
e ec i e decisions,
and enhanced
employee g ow h.
E alua ion
and Resul s
Measu e
imp o emen s in
employee
engagemen ,
cus ome
sa is ac ion, and
adap abili y.
Demons a ed
imp o emen s in
BPM p ac ices and
s akeholde
engagemen .
CFO: Include
inancial KPIs;
Manage :
Regula eedback
sessions.
Comp ehensi e
e alua ion showing
enhanced BPM
p ac ices, inancial
pe o mance, and
s akeholde
engagemen .
This esea ch con ibu es o bo h academic discou se and p ac ical applica ions in BPM. By
de eloping and alida ing an AI-suppo ed decision engine p o o ype, i o e s a ounda ional
amewo k o o ganiza ions o enhance hei BPM e o s.
The amewo k can be used in any ype o o ganiza ion and on di e en s ages o
de elopmen , bu p ac ical applica ion should be suppo ed by u he esea ches.
The in eg a ion o AI echnologies and human-cen ic design p inciples ensu es ha BPM
ini ia i es a e e ec i e, adap able, and aligned wi h o ganiza ional goals. Fu u e esea ch
could ocus on e ining he decision engine based on b oade indus y eedback and explo ing
i s applica ion in di e en o ganiza ional se ings.

24
6. CONCLUSIONS AND FUTURE WORKS
In his inal chap e , we syn hesize he wo k done in his esea ch, highligh he
ecommenda ions made, discuss he limi a ions encoun e ed, and sugges possible di ec ions
o u he esea ch.
6.1. SYNTHESIS OF THE WORK DONE WITH HIGHLIGHT TO THE RECOMMENDATIONS MADE
This hesis explo ed he in eg a ion o AI-suppo ed decision engines in o Business P ocess
Managemen (BPM) o enhance BPM adop ion and implemen a ion. The p ima y
con ibu ions o his esea ch a e:
Li e a u e Re iew: A comp ehensi e li e a u e e iew iden i ied C i ical Success Fac o s
(CSFs) c ucial o BPM implemen a ion and examined he ole o AI, pa icula ly La ge
Language Models (LLMs) like GPT, in BPM.
De elopmen o a Decision Engine P o o ype: Using some o he componen s o Design
Science Resea ch Me hodology, we de eloped a decision engine p o o ype ailo ed o
di e en s ages o BPM adop ion. The engine was designed o guide o ganiza ions h ough
awa eness, adop ion, p ojec execu ion, and p oduc iza ion s ages, p o iding s age-
speci ic ecommenda ions.
Empi ical S udy: The p o o ype was es ed h ough a simula ed case s udy o Tech
Inno a o s Inc., demons a ing i s p ac ical applica ion and e ec i eness in imp o ing
BPM p ac ices.
Expe Valida ion: In e iews wi h indus y expe s alida ed he app oach and p o ided
c i ical eedback o imp o emen . The ecommenda ions om expe s included
managing expec a ions, inco po a ing inancial me ics, ensu ing clea communica ion,
and balancing AI-d i en insigh s wi h human judgmen .
6.1.1. Key Recommenda ions:
Realis ic Communica ion: Clea ly communica e he bene i s and limi a ions o BPM o
a oid o e hyping and o build a solid ounda ion.
Leade ship Commi men : Ensu e s ong and empa he ic leade ship o d i e BPM
ini ia i es.
Financial Me ics: In eg a e inancial pe o mance me ics o demons a e ROI and
secu e execu i e suppo .
Human In e ac ion: Balance AI insigh s wi h human-cen ic ea u es o build us and
acili a e engagemen .
25
T aining and Educa ion: Conduc p ac ical aining and wo kshops o enhance
employee unde s anding.
6.2. LIMITATIONS OF THE WORK DONE
Se e al limi a ions we e encoun e ed du ing his esea ch:
Lack o Real-Wo ld Company Access: The inabili y o access a eal-wo ld company limi ed
he scope o empi ical alida ion. The simula ed case s udy p o ided aluable insigh s bu
lacked he complexi y o eal-wo ld implemen a ion.
Scope o Expe In e iews: While expe eedback was c ucial, he sample size was small.
A b oade ange o indus y pe spec i es could p o ide mo e comp ehensi e alida ion.
P o o ype Tes ing: The decision engine p o o ype was es ed in a con olled en i onmen .
Real-wo ld es ing migh e eal addi ional challenges and a eas o e inemen .
6.3. FURTHER WORK POSSIBLE TO DO
Fu u e esea ch could add ess he limi a ions and expand on he indings o his hesis:
Real-Wo ld Implemen a ion: Conduc case s udies wi h eal-wo ld companies o alida e
and e ine he decision engine p o o ype in di e se o ganiza ional se ings.
B oade Expe Feedback: Engage wi h a la ge and mo e di e se g oup o indus y expe s
o ob ain comp ehensi e alida ion and insigh s.
Ad anced AI In eg a ion: Explo e he in eg a ion o mo e ad anced AI echniques, such
as ein o cemen lea ning and hyb id AI models, o enhance he decision engine's
capabili ies.
Longi udinal S udies: Conduc longi udinal s udies o assess he long- e m impac o AI-
suppo ed decision engines on BPM adop ion and pe o mance.
Cus omiza ion o Di e en Indus ies: De elop indus y-speci ic e sions o he decision
engine o add ess unique challenges and equi emen s in sec o s such as heal hca e,
inance, and manu ac u ing.
By add essing he limi a ions and pu suing u he esea ch, we can con inue o ad ance
he ield and suppo o ganiza ions in achie ing g ea e ope a ional e iciency and ma ke
esponsi eness.
26
BIBLIOGRAPHICAL REFERENCZOES
Buh, B., Ko ačič, A., & Indiha Š embe ge , M. (2015). C i ical success ac o s o di e en
s ages o business p ocess managemen adop ion – a case s udy. Economic Resea ch-
Ekonomska Is aži anja, 28(1), 243–258.
h ps://doi.o g/10.1080/1331677X.2015.1041776
Cas o, B. K. D. A., D esch, A., & Vei , D. R. (2019). Key c i ical success ac o s o BPM
implemen a ion: A heo e ical and p ac ical iew. Business P ocess Managemen
Jou nal, 26(1), 239–256. h ps://doi.o g/10.1108/BPMJ-09-2018-0272
Dumas, M., La Rosa, M., Mendling, J., & Reije s, H. A. (2018). Fundamen als o Business P ocess
Managemen . Sp inge Be lin Heidelbe g. h ps://doi.o g/10.1007/978-3-662-56509-
4
F ey ag, T., Kanzle , B., Lege , N., & Semling, D. (n.d.). NLP as a Se ice: An API o Con e
be ween P ocess Models and Na u al Language Tex .
G ohs, M., Abb, L., Elsayed, N., & Rehse, J.-R. (2023). La ge Language Models can accomplish
Business P ocess Managemen Tasks (a Xi :2307.09923). a Xi .
h p://a xi .o g/abs/2307.09923
Han, J., Pei, J., & Tong, H. (2022). Da a mining: Concep s and echniques. Mo gan kau mann.
Hasan, R., & Sha i zadeh, R. (2020). Design Science Resea ch Me hodology As a Solu ion-
O ien ed Me hodology.
Hunzike , S., & Blankenagel, M. (2024). Design Science Resea ch Design (pp. 97–116).
h ps://doi.o g/10.1007/978-3-658-42739-9_6
Khu ana, D., Koli, A., Kha e , K., & Singh, S. (2023). Na u al language p ocessing: S a e o he
a , cu en ends and challenges. Mul imedia Tools and Applica ions, 82(3), 3713–
3744. h ps://doi.o g/10.1007/s11042-022-13428-4
Liu, B. (2020). In oduc ion. In Sen imen Analysis: Mining Opinions, Sen imen s, and Emo ions
(pp. 1–17). Camb idge Uni e si y P ess.
Sa ke , I. H. (2021). Machine Lea ning: Algo i hms, Real-Wo ld Applica ions and Resea ch
Di ec ions. SN Compu e Science, 2(3), 160. h ps://doi.o g/10.1007/s42979-021-
00592-x
Teubne , T., Fla h, C. M., Weinha d , C., Van De Aals , W., & Hinz, O. (2023). Welcome o he
E a o Cha GPT e al.: The P ospec s o La ge Language Models. Business & In o ma ion
Sys ems Enginee ing, 65(2), 95–101. h ps://doi.o g/10.1007/s12599-023-00795-x
an de Aa, H., Ca mona, J., Leopold, H., Mendling, J., & Pad ó, L. (2018, Augus ). Challenges
and Oppo uni ies o Applying Na u al Language P ocessing in Business P ocess
Managemen .
Van De Aals , W. M. P. (2013). Business P ocess Managemen : A Comp ehensi e Su ey. ISRN
So wa e Enginee ing, 2013, 1–37. h ps://doi.o g/10.1155/2013/507984
Weinzie l, S., Zilke , S., Dunze , S., & Ma zne , M. (2024). Machine lea ning in business p ocess
managemen : A sys ema ic li e a u e e iew. Expe Sys ems wi h Applica ions, 253,
124181. h ps://doi.o g/10.1016/j.eswa.2024.124181
27
Weske, M. (2012). In oduc ion. In M. Weske (Ed.), Business P ocess Managemen : Concep s,
Languages, A chi ec u es (pp. 3–23). Sp inge Be lin Heidelbe g.
h ps://doi.o g/10.1007/978-3-642-28616-2_1
Copeland, B. (2024). a i icial in elligence. Encyclopedia B i annica.
h ps://www.b i annica.com/ echnology/a i icial-in elligence
McDonough, M. (2024). la ge language model. Encyclopedia B i annica.
Cha GPT4. (2024). Cha GPT. h ps://cha gp .com/
Amazon Web Se ices. (2024). Wha is GPT? Amazon. h ps://aws.amazon.com/wha -is/gp /
28
APPENDIX A - ETHICS COMMITTEE REPORT

29
APPENDIX B – DECISION ENGINE PROTOTYPE
S age o BPM adop ion
Focus poin s and
sugges ions ele an o
he s age
Po en ial p oblems a he
espec i e s age
CSFs o add ess and me hods o
use
P oposed Sco ing app oach Measu men c i e ia Scale Da a sou ces Example o insigh and ecommenda ions Decision logic
Empowe men o Employees: O e
aining and wo kshops o imp o e
employees' unde s anding o BPM and i s
bene i s, and c ea e a sugges ion sys em
ha enables hem o con ibu e ideas.
Emplo yee Empowe men : Sco ing could
be based on employee aining ini ia i es,
he p esence o a sugges ions p og am,
and he le el o au onomy employees
ha e in hei oles.
Numbe o BPM- ela ed aining sessions
held.
Employee pa icipa ion a e in BPM
aining and wo kshops.
Scale: 1 (No
in ol emen ) o 5
(Full in ol emen
and empowe men
in BPM ac i i ies).
In e nal sugges ion
sys ems, HR eco ds.
Ac ionable Insigh : Employees a e no sugges ing p ocess
imp o emen s, indica ing a lack o empowe men .
Recommenda ion:
De elop and implemen an idea managemen sys em o
cap u e employee sugges ions.
Hold egula wo kshops and aining sessions o educa e
employees on BPM p inciples.
Encou age manage s o os e a cul u e ha alues inpu
om all eam membe s.
I he a e age sco e o employee
empowe men is below 3, he
decision engine ecommends
p io i izing employee engagemen
ini ia i es.
Cus ome Focus: Implemen a eedback
loop wi h cus ome s o unde s and hei
needs and expec a ions, and in eg a e his
eedback in o p ocess imp o emen plans.
Cus ome Focus: Measu e he ex en o
which cus ome eedback in luences
p ocess changes and he alignmen o
p ocesses o cus ome needs.
Exis ence and u iliza ion o cus ome
eedback mechanisms in p ocesses.
Numbe o p ocess imp o emen s d i en
by cus ome eedback.
1 (No in eg a ed) o
5 (Fully in eg a ed
and d i ing BPM
ini ia i es).
Cus ome se ice
eedback, p ocess
documen a ion.
I cus ome ocus is a ed high bu
change openness is low, sugges
s a egies o mo e esponsi e
p ocess imp o emen s based on
cus ome eedback.
Openness o Changes: Encou age a
cul u e ha alues adap abili y h ough
egula communica ion abou he bene i s
o change and ecogni ion o eams ha
emb ace and lead success ul change
ini ia i es.
Openness o Change: Assess he
o ganiza ion's his o y and eadiness o
change, he exis ence o change
managemen s a egies, and lexibili y in
cu en p ocesses.
Exis ence and u iliza ion o cus ome
eedback mechanisms in p ocesses.
Numbe o p ocess imp o emen s d i en
by cus ome eedback.
1 (Resis an o any
change) o 5
(Ac i ely emb aces
change).
Chang e log s, p ojec
managemen ools.
In ol emen and Full Suppo o Top
Managemen : Ensu e ha op
managemen no only endo ses bu is also
ac i ely in ol ed in BPM ini ia i es by
leading p ojec kick-o s and egula ly
e iewing p og ess.
Top Managemen In o l emen : Sco e
based on isible suppo om leade ship,
alloca ion o esou ces o BPM, and
communica ion abou he impo ance o
BPM om op managemen .
F equency and consis ency o
communica ion om op managemen
ega ding BPM.
In es men in BPM ini ia i es (budge ,
esou ces).
1 (No suppo ) o 5
(Highly isible and
commi ed suppo ).
Mee ing minu es,
execu i e
communica ions.
Ac ionable Insigh : Top managemen in ol emen in
BPM ini ia i es is minimal.
Recommenda ion:
Schedule egula BPM b ie ings o op managemen o
epo on p og ess and alue.
Appoin a senio execu i e as a BPM sponso o inc ease
isibili y and commi men .
C ea e a BPM s ee ing commi ee including op
managemen o o e see ini ia i es.
I op managemen suppo sco es
less han 3, igge a
ecommenda ion o execu i e
wo kshops on BPM alue.
I a p ojec champion is no
iden i ied o sco es low,
ecommend es ablishing a clea
champion ole wi h de ined
esponsibili ies and au ho i y.
P ojec Champion: Appoin a espec ed
leade wi hin he o ganiza ion o ac as a
BPM champion who will ad oca e o and
d i e BPM e o s.
P ojec Champion P esence: E alua e
he appoin men o p ojec champions o
BPM ini ia i es and he au ho i y and
esou ces gi en o hem.
Iden i ica ion and ac i i y le el o BPM
champions o p ocess owne s.
Impac assessmen o p ojec champions
on BPM p ojec s (p og ess, success
s o ies).
1 (No champion) o
5 (S ong, e ec i e
champion
p omo ing BPM).
P ojec
documen a ion,
s akeholde su eys.
Business D i e s (a Sense o U gency):
Iden i y and communica e he business
impe a i es ha necessi a e BPM, such as
ma ke p essu e o e iciency gains, o
c ea e a sense o u gency o change.
Sense o U gency/Business D i e s:
De e mine whe he he e's a clea
unde s anding ac oss he o ganiza ion o
he need o BPM, d i en by compe i i e
p essu e, e iciency goals, o o he
business impe a i es.
Documen a ion and communica ion o
business impe a i es d i ing BPM
adop ion.
Response ime o c i ical p ocess- ela ed
issues o ma ke changes.
1 (No d i e s
iden i ied) o 5
(Clea d i e s
iden i ied and
communica ed wi h
u gency).
S a egic plans,
in e nal
communica ions.
Clea ly De ined Objec i es and Plan:
De elop a o mal p ojec cha e o each
BPM p ojec ha ou lines i s objec i es,
scope, expec ed bene i s, and imeline.
P ojec Communica ion: Sco e he
cla i y and equency o communica ion
abou BPM p ojec goals, p og ess, and
ou comes.
Cla i y and dissemina ion o p ojec goals
and miles ones ac oss he o ganiza ion.
Numbe o documen ed and
communica ed BPM p ojec plans.
1 (Ve y unclea ) o
5 (Ex emely clea
and well unde s ood
by all s akeholde s).
P ojec cha e s,
communica ion
eco ds.
Ac ionable Insigh : P ojec objec i es and plans a e no
well-communica ed ac oss he o ganiza ion.
Recommenda ion:
U ilize a cen alized communica ion pla o m o
dissemina e BPM p ojec upda es.
Include clea communica ion plans in all BPM p ojec
cha e s.
Es ablish egula all-hands mee ings o discuss BPM
p ojec s and in i e eedback.
When sco es o p ojec
communica ion and cla i y a e
low, he logic emphasizes
imp o ing p ojec cha e s and
communica ion plans.
I mo i a ion o change is high
bu guidance om consul an s is
low, he logic sugges s a e iew o
consul an selec ion c i e ia and
engagemen e ec i eness.
P o essional Guidance o Ex e nal
Consul an s: Engage wi h consul an s
who ha e a p o en ack eco d in BPM
o p o ide expe ise and an ex e nal
pe spec i e.
Ex e nal Consul an s' Guidance: Ra e
he ex en and e ec i eness o ex e nal
consul ancy use, including he ans e o
knowledge and p ac ices o in e nal eams.
Consul an engagemen sco e based on
p ojec ou comes and eedback.
ROI om con sul an -led ini ia i es e sus
in e nally led.
1 (No in ol ed/no
con ibu ion) o 5
(Signi ican
in ol emen /con ib
u ion).
Consul an
pe o mance
e alua ions, p ojec
ou come analyses.
People Who A e Willing and Mo i a ed
o Change: Selec p ojec eam membe s
who a e open o change and ha e
demons a ed adap abili y; in ol e hem
in decision-making o keep hem
mo i a ed.
Change Willingness: Measu e he
o ganiza ion's eadiness o change
speci ic o BPM p ojec s, including s a
engagemen and pa icipa ion.
P e and pos - aining assessmen sco es on
BPM eadiness.
Employee u no e a es in depa men s
unde going BPM p ojec s.
1 (High esis ance)
o 5 (Eage
willingness and
mo i a ion o
change).
Engagemen su eys,
pa icipa ion eco ds
in BPM ini ia i es.
Miscommunica ion: I no
e ec i ely communica ed, BPM
can be seen as a buzzwo d a he
han a aluable me hodology,
leading o skep icism.
Insu icien Execu i e Suppo :
Wi h ou s ong leade ship
endo semen , BPM e o s may
no be aken se iously o
p io i ized.
O e whelm wi h In o ma ion:
An abundance o case s udies and
in o ma ion can lead o
con usion abou whe e o s a o
un ealis ic expec a ions.
Cul u al Resis ance: E en wi h
awa eness, he e can be inhe en
esis ance o change wi hin he
o ganiza ion's cul u e ha is ha d
o o e come.
Misalignmen o Expec a ions:
S akeholde s may ha e di e en
expec a ions om BPM, leading
o con lic s o disillusionmen i
no managed well.
Selec ion o Inco ec P ojec s:
Choosing he w ong p ojec s o
ini ial BPM e o s can esul in a
lack o isible success and
diminished c edibili y.
P ojec Managemen Issues:
Wi h ou s ong p o jec
managemen , BPM p ojec s may
ace delays, budge o e uns, o
scope c eep.
Inadequa e Measu emen :
Poo ly de ined KPIs o a lack o
measu emen ools can lead o
ine ec i e moni o ing and he
inabili y o demons a e alue.
Desi e o Adop BPM
Desc ip ion: The o ganiza ion
acknowledges he need o BPM and
is mo i a ed o s a adop ing BPM
p ac ices. The e migh be some
in o mal p ocess imp o emen s, bu
no o ganized e o ye .
The o ganiza ion decides o commi
o BPM p ac ices and s a s o build
in e nal desi e o change and p ocess
imp o emen .
Iden i ica ion: I he e is en husiasm
abou BPM and discussions abou i s
po en ial bene i s a e occu ing, bu
no s uc u ed app oach has been
aken, his indica es s age 2.
Mo ing Fo wa d: De elop a clea
ision and s a egy o BPM and s a
engaging s akeholde s o suppo
BPM ini ia i es.
Focus: Building in e nal buy-in
and p epa ing he o ganiza ion
o change.
Cul u e and Change
Managemen : Fos e a cul u e
ecep i e o change by in ol ing
employees in he BPM discussion
ea ly on. Add ess esis ance
p oac i ely.
S akeholde Engagemen :
Iden i y and engage key
s akeholde s o c ea e a coali ion
ha desi es BPM adop ion,
ensu ing hei needs and
conce ns a e add essed.
BPM P ojec s
Desc ip ion: The o ganiza ion has
ini ia ed speci ic BPM p ojec s,
ocusing on imp o ing pa icula
p ocesses. These a e o en pilo
p ojec s ha se e as p oo o
concep .Speci ic BPM p ojec s a e
ini ia ed, o en as pilo s o ini ial
e o s o apply BPM p inciples o
pa icula business p ocesses.
Iden i ica ion: I he e a e one o
mo e BPM p ojec s unde way wi h
dedica ed esou ces and goals, he
o ganiza ion is in his s age.
Mo ing Fo wa d: Analyze he
ou comes o hese p ojec s o e ine
he BPM app oach and p epa e o
b oade implemen a ion.
Focus: Gaining expe ience in
BPM and demons a ing ea ly
wins.
Selec Pilo P ojec s: Choose
ini ial BPM p ojec s ca e ully,
a ge ing high-impac a eas
whe e quick wins a e achie able.
P ojec Managemen Bes
P ac ices: U ilize s uc u ed
me hodologies and obus p ojec
managemen p ac ices o ensu e
he success o BPM p ojec s.
Moni o ing and Measu emen :
Implemen KPIs o measu e he
pe o mance o BPM p ojec s,
using he da a o d i e decision-
making and con inuous
imp o emen .
Awa eness and Unde s anding o
BPM
Desc ip ion: O ganiza ions a his
s age ha e begun o ecognize he
impo ance o BPM. P ocess
imp o emen is ypically ad hoc,
eac i e, and uns uc u ed.
O ganiza ion ecognizes BPM as a
po en ial alue-add and begins o
unde s and i s p inciples and bene i s.
Iden i ica ion: I BPM is a new
concep o i he e is limi ed
unde s anding o p ocess
managemen p inciples o ganiza ion-
wide, i 's likely a his s age.
Mo ing Fo wa d: In oduce o mal
BPM aining and es ablish a sense o
u gency o p ocess imp o emen .
Focus: Educa ion on BPM
impo ance and i s impac on
pe o mance.
Educa ion a nd Co mmunica ion:
Conduc wo kshops and aining
sessions o educa e s akeholde s
abou he bene i s and p inciples
o BPM.
Visible Execu i e Suppo :
Secu e and showcase execu i e
endo semen o highligh he
impo ance o BPM wi hin he
o ganiza ion.
Case S udies and Success
S o ies: Sha e case s udies o
success ul BPM implemen a ions
o illus a e po en ial gains and
encou age buy-in.
S age #1
S age #2
S age #3
30
APPENDIX C - EXPERT QUESTIONNAIRE
1. In you expe ience, wha a e he key elemen s o ocus on du ing he BPM adop ion?
2. How would you desc ibe he mos e ec i e me hods o in oduce BPM concep s o
he o ganiza ion?
3. Wha ole do you see o leade ship in BPM adop ion?
4. Do you expec any challenges du ing implemen a ion o BPM? How can hey be
mi iga ed?
5. Wha a e he mos c i ical success ac o s o BPM adop ion ha you belie e should
be p io i ized?
6. I you can apply a decision engine model o BPM, wha ea u es do you hink would
help you he mos ? Wha o a oid and how o s uc u e be e ?
7. How do you iew he po en ial ole o AI in BPM, especially in iden i ying and
edesigning business p ocesses?
8. F om you pe spec i e, wha ypes o da a sou ces can be used o he decision
engine? Would ex e nal inpu s like cus ome eedback add alue?
9. Should a BPM ool balance da a-d i en insigh s wi h human judgmen ? O be pu ely
echnical?
31