Bridging Human and AI Decision-Making with LLMs: The RAGADA
Tapio Pitk
and Leena Pitk
Department of Computer Science and Engineering, Aalto University, Finland
Department of Industrial Engineering and Management, Aalto University, Finland
RAG: Retrieval Augmented Generation, RAGADA: Retrieval Augmented Generation Algorithmic Decision
Alignment, LLM Large Language Models, IR Information Retrieval, Multi-Agent Systems (MAS).
The Retrieval Augmented Generation Algorithmic Decision Alignment (RAGADA) architecture is an ad-
vancement in AI-augmented decision-making for corporate environments. This paper discusses RAGADAs
innovative architecture that merges RAG and Multi-Agent System (MAS) with sophisticated business algo-
rithms and dynamic interfaces, enhancing natural language interaction between AI systems and users. This fu-
sion extends AI’s reach, facilitating adaptable decision-making tools for leaders, in line with evolving business
strategies and ethical standards. Experimental validation of RAGADA within the banking sector, involving di-
verse stakeholder groups ranging from customers to business and ethical managers, confirms its effectiveness.
The system adeptly translates natural language inquiries into actionable insights, thereby improving the user
experience and decision-making transparency. This validation underscores RAGADAs potential to transform
stakeholder engagement and demonstrates a leap in utilizing AI for strategic and ethical business management.
The recent enhancements of Large Language Mod-
els (LLMs) like ChatGPT has marked a significant
shift in artificial intelligence (AI), establishing a
new paradigm in human-machine interaction. These
LLMs have transitioned from academic innovations
to a ’killer application in AI’ with wide variety of use
cases across various industries, offering intuitive and
adaptable interfaces for diverse applications. Their
advancement in natural language processing has rev-
olutionized machine comprehension, enabling com-
plex dialogue and task execution.
LLMs are evolving beyond sophisticated chatbots
to offer a platform that transforms interaction with
machines, democratizing AI access for users with var-
ied technical expertise. A pressing research question
is aligning these algorithms with human values and
objectives (Christian, 2020), highlighting the need for
a multidisciplinary approach in AI (Wilson, 1999). AI
now merges data science and mathematics with ethics
and leadership, ensuring a holistic development and
deployment of technologies like LLMs.
This paper presents RAGADA, an innovative soft-
ware architecture integrating Retrieval Augmented
Generation with dynamic, user-friendly interfaces for
both customers and executives. RAGADA aims to
revolutionize AI systems’ user interaction, focusing
on natural language processing to enhance customer
experience and allow executives to transparently ad-
just algorithmic decisions, thereby improving user
satisfaction and strategic agility in corporate environ-
2.1 LLMs and RAG
The advent of the Transformer model (Vaswani et al.,
2017) catalyzed significant advancements in Natu-
ral Language Processing (NLP) and Large Language
Models (LLM), as seen in models like BERT (Devlin
et al., 2018), GPT-3 (Brown et al., 2020), and GPT-
4. These LLMs have demonstrated remarkable text
generation abilities, applicable in various domains
including automated decision-making (Brown et al.,
Pitkäranta, T. and Pitkäranta, L.
Bridging Human and AI Decision-Making with LLMs: The RAGADA Approach.
DOI: 10.5220/0012705000003690
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 812-819
ISBN: 978-989-758-692-7; ISSN: 2184-4992
Proceedings Copyright © 2024 by SCITEPRESS Science and Technology Publications, Lda.
The integration of LLMs with Retrieval Aug-
mented Generation (RAG) represents a key devel-
opment in enhancing NLP and AI. RAG effec-
tively combines pre-trained language models with ex-
ternal knowledge retrieval, significantly improving
LLMs’ adaptability and accuracy (Karpukhin et al.,
2020). It mitigates the constraints of LLMs’ static
knowledge bases by dynamically incorporating exter-
nal data, thus boosting the models’ contextual rele-
vance (Lewis et al., 2020).
Research underscores the utility of LLMs in com-
plex decision-making (Petroni et al., 2019), and
RAG’s ability to update these models with current
information is especially valuable in dynamic corpo-
rate scenarios (Guu et al., 2020), (Borgeaud et al.,
2022). The LLM-RAG synergy augments automated
decision-making in businesses by addressing chal-
lenges in managing real-time, context-specific data.
2.2 AI Safety and Management
The integration of large language models in decision-
making processes necessitates careful consideration
of ethical aspects (Christian, 2020). Studies have crit-
ically examined the risks associated with these mod-
els, emphasizing ethical dilemmas, potential biases,
and environmental concerns due to their scale and
complexity (Bender et al., 2021). The call for a re-
sponsible and deliberate approach in the development
and application of these models is driven by their ex-
tensive societal implications (Bender et al., 2021).
This discussion underscores the need for balancing
technological progress with ethical responsibility in
AI advancements (Christian, 2020).
Applying AI and ML for decision making ratio-
nalizes decision making criteria and needs objectives
in digital format. Balanced Scorecard is one frame-
work for performance measurement that integrates fi-
nancial and non-financial metrics (Kaplan, 1992).
In our study, we provide details on an experi-
mental implementation concerning decision-making
within the banking sector. The implementation in the
banking sector, particularly from an organizational
capability perspective, is further elaborated in (Dash
et al., 2021).
2.3 Metadata Management and Data
Effective metadata management is crucial for or-
ganizations handling extensive data assets (Olesen-
Bagneux, 2023). It consolidates data storage, search,
and management, thereby facilitating efficient ac-
cess and comprehension of organizational data. By
employing machine learning and AI, these systems
enhance data governance and quality through auto-
mated classification and improved dataset accessibil-
ity. Such tools play a key role in democratizing data
within organizations, ensuring regulatory compliance
and preserving data integrity with transparent audit
trails. As data complexities grow, the strategic imple-
mentation of data catalogs becomes increasingly im-
portant for maximizing data resource utilization and
meeting regulatory standards.
A wide array of both open-source and com-
mercial data catalog software is available. Ex-
amples of open-source software include Open-
Metadata:, Apache Atlas:, Datahub:,
Metacat:, Amund-
sen:, and CKAN:
2.4 Multi-Agent Systems
Multi-agent systems (MAS) are collections of au-
tonomous, interacting agents that work together to
solve complex problems that are beyond the capabil-
ities of individual agents acting alone (Wooldridge,
2009). Research in MAS covers a wide range of top-
ics including coordination mechanisms, communica-
tion protocols, and collective decision-making strate-
gies. Notable advancements in the field have demon-
strated that MAS can effectively handle dynamic and
uncertain environments, which are common in real-
world scenarios such as traffic management, robotic
teams, and distributed control systems (Stone and
Veloso, 2000).
One of the fundamental challenges in MAS is the
design of cooperation strategies that enable agents to
work together harmonously. This requires sophis-
ticated negotiation protocols and conflict resolution
techniques, which are crucial for maintaining coher-
ent behavior among agents (Rosenschein and Zlotkin,
2.5 Agent Interaction Protocols and
Chat Templates
Software agents adhere to interaction protocols,
notably those established by FIPA standards ( ), focusing
on communication-centric design for context-aware
responses. Similarities are evident in the way LLM-
based chatbots manage conversational flow. They
blend preset and adaptive responses, evolving with
Bridging Human and AI Decision-Making with LLMs: The RAGADA Approach
user preferences and external data integration, which
results in enhanced accuracy and improved customer
interaction experiences.
Figure 1: RAGADA High Level Architecture: interactions
between human input and software components.
This section describes Innovative Software Architec-
ture for Retrieval Augmented Generation Algorithmic
Decision Alignment (RAGADA).
3.1 Architecture: Interaction Model
The Figure 1 describes the high-level components of
the RAGADA architecture. In this section we discuss
the main roles and interactions of the components in
this architecture.
In the RAGADA architecture, the AI Agent Con-
troller plays a pivotal role in orchestrating interactions
between users and the decision-making logic. It be-
gins by assimilating the initial text provided by the
user and then identifies the specific chat template or
interaction protocol the user is engaging with. The
AI Agent Controller carefully selects the most suit-
able interaction agent, based on the user’s context and
intent, and ensures that the user has the necessary
permissions for it. This careful selection is crucial
for ensuring that the system’s responses are coher-
ent and consistent, adhering to a uniform interaction
In the RAGADA architecture, the Interaction
Agent serves as the central conversational AI inter-
face that facilitates the dialogue between the human
user and the system. This agent is responsible for in-
terpreting user input in natural language and generat-
ing appropriate responses that are contextually rele-
vant to the user’s requests or inquiries. By leverag-
ing a selection template and a repository of available
algorithms, the Interaction Agent dynamically con-
structs replies that not only provide information but
also guide the user through the system’s functional-
ities. The Interaction Agent can handle a variety of
inputs, transforming user queries into structured data
that can be processed by the Business Logic: Deci-
sion Algorithms. Moreover, it ensures that user in-
teractions are intuitive and efficient, enhancing user
engagement and satisfaction with the system.
The middle level components in this structure are
RAG Transform and the Language Model (LLM),
key components of the classic Retrieval-Augmented
Generation (RAG) model that elevates response qual-
ity by weaving in external data. The RAG Trans-
form is pivotal, acting as the analytic engine where
user inputs undergo initial processing and enhance-
ment. It ingests raw user queries and augments them
with context derived from the Catalogues—a reposi-
tory replete with domain-specific data. This context-
enrichment empowers the RAG Transform to inter-
pret the queries with precision, significantly refining
the system’s output in terms of relevance and speci-
Next to the RAG Transform resides the Language
Model (LLM), presumably a Large Language Model
akin to GPT. The LLM employs the enriched inputs
from the RAG Transform to create nuanced and con-
textually informed responses. It taps into a vast re-
serve of learned patterns to formulate replies that ex-
hibit a high degree of linguistic finesse, akin to human
The system’s decision-making prowess is further
enhanced by integrating Business Logic with Deci-
sion Algorithms, which operate in concert with the
RAG components. They leverage both the founda-
tional data and the context processed by the AI to
implement domain-specific directives and heuristics.
These algorithms are indispensable in scenarios that
necessitate not just information retrieval but also au-
tonomous decision-making, such as in financial ser-
vices or healthcare.
In essence, RAGADAs architecture is a har-
monious fusion of traditional RAG elements with
cutting-edge AI decision-making mechanisms. It
combines the RAG Transform’s contextual acuity, the
LLM’s generative capabilities, and the AI Agent Con-
troller’s ability to select the right interaction agent,
all underpinned by robust Business Logic. This con-
fluence results in a system adept at addressing com-
plex user inquiries with remarkable precision and ef-
ficiency, positioning RAGADA as a formidable tool
across a multitude of automated, intelligent interac-
tion domains.
A very important component is the catalogue,
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
which consists of multiple sub-components and will
be discussed in the subsequent section. The catalogue
serves both as an input for the RAG Transform and as
a modeling instrument to bridge the concepts.
3.2 Architecture: Catalogue Layer
Figure 2: RAGADA Architecture: Metadata catalogue re-
lationships and high level description.
The depicted Figure 2 illustrates an interconnected
framework of metadata catalogues that serve as the
backbone for a data-driven decision-making system.
This systematic arrangement ensures that every step
in the algorithmic process is underpinned by a struc-
tured repository of information, spanning from raw
data to core business values.
Central to this framework is the ’Data Catalog’,
which meticulously details the datasets available for
use. This catalogue is instrumental in providing the
foundational data inputs to the ’Algorithm Catalog’,
which, in turn, specifies the algorithms that will pro-
cess the input data. The Algorithm Catalog is a com-
prehensive directory that not only lists the algorithms
but also delineates their data requirements, expected
outputs, and the relevant business objectives they are
designed to achieve. It ensures a seamless flow of
information by creating explicit links to the Data Cat-
alog and the Decision Catalog.
The ’Decision Catalog’ is a critical component
that collates the various decisions or recommenda-
tions generated by the algorithms. It acts as a refer-
ence point for the end outputs of the decision-making
process, encapsulating the inferences drawn from the
data through the algorithms. This catalogue is directly
informed by the outputs of the Algorithm Catalog, en-
suring that decisions are traceable back to their algo-
rithmic origins.
Moreover, the ’Business Objectives Catalog’
aligns the algorithmic decisions with the strategic
goals of the organization. It provides a clear linkage
between the algorithms’ functions and the overarch-
ing objectives they are meant to serve, guaranteeing
that algorithmic outputs are not only data-driven but
also strategically focused.
Lastly, the ’Values Catalog’ upholds the ethical
and value-based considerations that should guide ev-
ery decision. This catalogue ensures that the data in-
puts, algorithmic processes, and business objectives
are all in alignment with the organization’s core val-
ues. It acts as a moral compass, steering the decision-
making process towards outcomes that are not only
effective but also ethically sound.
Together, these catalogues create a robust and
ethically-grounded architecture that integrates diverse
sets of information into a coherent system, designed
to facilitate informed and value-aligned decision-
making in a corporate environment.
We are not aware of any standard conversational AI
RAG benchmark dataset suitable for evaluating RA-
GADA architecture. Our research evaluates the RA-
GADA model, specifically within the banking sector,
known for its complex decision-making processes.
We also compare the performance to fine tuned GPT-4
and custom GPT-4 implementations.
Experimental Implementation. The experimental
validation of RAGADA within the banking sector
involved a series of user stories designed to assess
the system’s decision-making capabilities. We con-
structed a comprehensive algorithm catalogue from
banking sector use cases portion of which is illus-
trated in Figure 3.
The experimental phase focused on the detailed
exploration of the getLoanDecision algorithm, a piv-
otal component of our algorithmic catalogue, as de-
picted in Figure 3. This choice was made to thor-
oughly illustrate the customer interaction process
from multiple stakeholder perspectives. The ‘get-
LoanInterestRate‘ algorithm stands out due to its
complexity in balancing diverse inputs, such as credit
scores and market rates, and its direct impact on
customer satisfaction and financial product perfor-
mance. Our implementation simulated various real-
world scenarios to evaluate the algorithm’s robustness
and adaptability, scrutinizing its decision-making pro-
cess in dynamically adjusting interest rates in re-
sponse to fluctuating economic indicators and cus-
tomer credit profiles.
4.1 Focus on One Algorithm
Focusing on a single decision algorithm, we explored
the model’s capabilities in a controlled yet intricate
environment, representing broader banking decision
frameworks. This approach facilitated an in-depth
Bridging Human and AI Decision-Making with LLMs: The RAGADA Approach
Figure 3: Example algorithm catalogue.
analysis and provided insights into stakeholder inter-
actions within real-world banking scenarios.
In this paper, we will explain how we integrated
the ’getLoanDecision’ algorithm into the Algorithm
Catalog, along with the necessary additional catalog
definitions as defined in Figure 2, and how stakehold-
ers interact with the algorithm. Although we created
several other similar algorithms for the catalog (get-
CreditCardDecision, getBankAccountDecision, get-
MortgageLoanDecision, etc.), we will not focus on
those in this paper.
We assume that the bank employs role-based au-
thentication and authorization for users, and we will
omit these aspects from this experiment.
The study’s next section examines user stories
from bank employees, customers, and management,
revealing how each group interacts with and perceives
the algorithm. This analysis aims to showcase the
algorithm’s functionality and its impact on diverse
stakeholders, underscoring RAGADAs potential to
transform decision-making in complex organizational
4.2 User Stories
User stories used in this implementation:
As a bank user, I would like to apply for a short-
term loan.
As a bank user, I would like to complain to the
ethical value alignment algorithm that I am being
asked ethically questionable information while
applying for a loan.
As a bank compliance department manager, I
would like to remove ethically questionable data
points from loan decision making.
As a bank loan business department manager, I
would like to change the business target for short-
term loan decisions from minimizing credit prob-
lems to acquiring new customers.
Figure 4: Example Algorithm: getLoanDecision(Input):
4.3 Example Algorithm:
getLoanDecision(Input): Decision
In the modern corporate environment, particularly
within large organizations, the plethora of decision-
making processes constitutes a complex landscape,
often involving hundreds or thousands of decisions
ripe for algorithmic transformation. Our research
delved into this realm, focusing on the experimen-
tal application of algorithms in a practical setting.
We developed ’getLoanDecision’, an algorithm tai-
lored to streamline the loan approval process. This
algorithm processes various customer profile inputs
to generate loan decisions.
For a realistic implementation, we
utilized a Kaggle dataset specifically
designed for bank loan scenarios (
predication/data ). This dataset, encompassing a
wide range of customer scenarios typical in the
financial sector, served as the training ground for
our algorithm, ensuring it reflects real-world banking
We trained a decision tree logic from the data, as
illustrated in Figure 4, along with some exploratory
analysis. We opted for decision trees because their
logic can be relatively easily understood by humans,
which aids in analyzing ethical compliance and un-
derstanding how the algorithm aims to achieve the
business targets.
4.4 RAGADA Experimental
In our research, we combined advanced technological
tools and programming languages to operationalize
our algorithm and user stories. We used Apache Atlas
for data management and cataloging, a tool crucial
for maintaining data integrity and compliance in the
banking sector. For processing at the Retrieval Aug-
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Figure 5: Comparison between: Propted LLM, Custom
mented Generation (RAG) level, Python Langchain
was employed, known for its effectiveness in handling
complex language models. This choice allowed for
nuanced interpretation of language patterns, enhanc-
ing user-algorithm interaction. Additionally, Python
Scikit-learn was utilized for training the algorithm,
selected for its comprehensive data analysis tools and
user-friendly interface. Its capability to manage large
datasets and diverse algorithms ensured our model’s
accuracy and adaptability, aligning with our objective
to develop an efficient, user-friendly, and adaptable
4.5 RAGADA Comparison
The performance of RAGADA, integrated with GPT-
4, was compared against other AI configurations, in-
cluding those OpenAI’s GPT models. The results,
as summarized in Figure 5, illustrate RAGADAs su-
perior performance, achieving successful outcomes
across various scenarios. These ranged from cus-
tomer interactions, such as loan applications and
complaints about ethical considerations, to internal
managerial decisions affecting the system’s business
logic. The success of RAGADA in these user sto-
ries confirms its potential to revolutionize decision-
making processes by leveraging advanced natural lan-
guage understanding and contextually aware AI inter-
4.6 Role Playing Users Stories with the
In developing our system, we focused on natural
language dialogue for intuitive interaction, ensur-
ing user-friendliness across various technical skills.
Comprehensive testing confirmed the interface’s ef-
fectiveness and adaptability, demonstrating the sys-
tem’s ability to dynamically respond to diverse user
inputs and offer tailored solutions in complex scenar-
ios. Next we will go through a couple of previously
described user stories as chat prompts with our RA-
4.7 Discussion: Customer Applying for
a Loan
Client: I would like to apply for a short term
RAGADA: May I confirm that you are inter-
ested in applying for a short-term loan and
wish to proceed with this application? The
interest rate for this loan would be 12 month
EURIBOR + 0.5 percent as bank margin. I
can make an automatic decision once I receive
the necessary input data from you. First, I will
verify the information we have on file from the
CRM system to ensure it is current.
Client: Sounds good!
RAGADA: Certainly, based on our CRM sys-
tem, here are the details we have on record for
your loan application: 1.) Gender: Male 2.)
Married: Yes 3.) Education: Bachelor’s De-
gree 4.) SelfEmployed: No 5.) ApplicantIn-
come: $4,500 per month 6.) CreditHistory:
Yes (indicating a positive credit history) 7.)
PropertyArea: Zip code 10007 (New York)
Please confirm if these details are accurate and
Client: These are correct!
RAGADA: Excellent, thank you for confirm-
ing. Now, let’s proceed with collecting the re-
maining information needed for your loan ap-
plication. Could you please provide the fol-
lowing details? 1.) Dependents: How many
individuals rely on your income for support?
2.) CoapplicantIncome: If applicable, what
is the monthly or annual income of the co-
applicant? 3.) LoanAmount: How much are
you looking to borrow? 4.) LoanAmount-
Term: Over how many months or years do you
wish to repay this loan?
Once you provide this information, we’ll be
able to complete your loan application pro-
Client: Here is the information: 1.) De-
pendents: I have one boy who is seven
years old 2.) CoapplicantIncome: my wife
makes $50,000 per year 3.) LoanAmount: I
need $27,500 for new Tesla Model 3 car 4.)
LoanAmountTerm: We were thinking three
RAGADA: Thank you for providing the nec-
essary details. I have now updated your loan
application with the following information:
1.) Dependents: 1 (a seven-year-old boy)
Bridging Human and AI Decision-Making with LLMs: The RAGADA Approach
2.) CoapplicantIncome: $50,000 per year (as
contributed by your wife) 3.) LoanAmount:
$27,500 (for the purchase of a new Tesla
Model 3 car) 4.) LoanAmountTerm: 3 years
Could you confirm this data is correct? Af-
ter that I will now call the automatic decision
logic with this information.
Client: Yes, information is correct.
RAGADA: We are pleased to inform you that
your loan application has been successfully
processed and approved through our advanced
automatic decision-making algorithm. As a
result of this approval, the requested funds
have been deposited directly into your bank
account. Should you have any questions or re-
quire further assistance, please do not hesitate
to contact us.
Client: Thank you! This was excellent cus-
tomer service!!!
4.8 Discussion: Customer Complaining
About Potential Ethical Issues
Client: I was recently granted a short term
loan. Thank you for that. However, what
bothers me is that it seems you also used my
address and Zip code in the decision making.
Could you confirm this.
RAGADA: Yes, I can confirm that our
Bank Automatic Algorithm Decision Cata-
logue mentions that Zip Code is used as an
input for short term loan decision making.
Client: I think there are ethical problems us-
ing ZIP code in the automatic decision mak-
ing. Zip code might contain racial signals and
discriminating people who apply for a loan
based on their race.
RAGADA: Thank you for the comment. In
our bank algorithms are not allowed to change
input datasets to decision making automati-
cally. Would you like me to post this ethical
concern to our ethical committee to process?
Client: Yes. Can you also ask your committee
to let me know what they decided?
RAGADA: Of course! I have now created a
ticket for the ethical committee and added you
into the distribution list of the outcome.
Client: Thank you!
4.9 Discussion: Compliance Manager
Aligning Decision Making with the
Compliance Manager: The short-term loan
algorithm currently uses the customer’s Zip
code in the decision-making process. This
data point cannot be used due to potential ethi-
cal issues. Please remove it from the decision-
making process.
RAGADA: I have added a filter to the ’get-
LoanDecision’ algorithm that excludes the
Zip code from the input parameters. This
change has been documented as an audit trail
in the Algorithm Catalog. Currently, I am not
authorized to retrain the model automatically.
If you have concerns about the impact of this
change, I can create a ticket for our data sci-
ence department to analyze the effect.
Compliance Manager: Thank you. Please
proceed with that and include me in the ticket
so I can be informed about the outcome.
4.10 Discussion: Business Department
Manager Changing Business
Objectives for the Algorithm
Business Department Manager: I have
changed the business objectives of the short-
term loan algorithm from minimizing credit
losses to including a weighted customer ac-
quisition objective. Could you retrain the
RAGADA: Currently, I am not authorized to
retrain the model automatically. However, I
can create a ticket for our data science depart-
ment to analyze the effect.
Business Department Manager: Thank you.
Please proceed with that and include me in the
ticket so I can be informed about the outcome.
Our research on the RAGADA model marks a sig-
nificant advancement in bridging gap between hu-
man stakeholders communicating with natural lan-
guage with automatic algorithmic decision-making.
ICEIS 2024 - 26th International Conference on Enterprise Information Systems
Focusing on the banking ’getLoanDecision’ algo-
rithm, we’ve unveiled the complexities of algorith-
mic decisions and different stakeholders in finance.
This model handles diverse scenarios, from loan ap-
plications to ethical dilemmas, exemplifying its abil-
ity to streamline customer service and operational ef-
ficiency. However, it also raises ethical issues, like
the use of ZIP codes in decision-making, highlighting
the importance of ethics in algorithm development.
Addressing such concerns proactively, including re-
ferring them to an ethical committee, demonstrates
RAGADAs potential in managing sensitive matters,
crucial for trust and transparency in banking. Over-
all, RAGADAs nuanced decision-making approach
offers promising prospects for evolving financial ser-
This research introduces and validates the RAGADA
model, a novel AI-based software architecture en-
hancing corporate decision-making. Successfully im-
plemented in the banking sector, RAGADA leverages
natural language interfaces to facilitate complex deci-
sions, demonstrating significant advancements in AI
and corporate governance. It has shown potential in
improving decision accuracy, user satisfaction, and
strategic adaptability. Future exploration includes ex-
tending RAGADAs applicability to various sectors
and enhancing its capabilities for more complex sce-
narios. Emphasizing ethical AI and user-centric de-
sign, the model presents a substantial progression in
AI-integrated corporate decision-making, offering a
blueprint for future advancements in AI-augmented
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