Leveraging artificial intelligence for strategic decision-making in healthcare organizations: a business it perspective

The American Journal of Applied Sciences

74

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TYPE

Original Research

PAGE NO.

74-93

DOI

10.37547/tajas/Volume07Issue08-07

OPEN ACCESS

SUBMITED

20 June 2025

ACCEPTED

16 July 2025

PUBLISHED

18 August 2025

VOLUME

Vol.07 Issue08 2025

CITATION

Keya Karabi Roy, Maham Saeed, Mahzabin Binte Rahman, Kami Yangzen
Lama, & Mustafa Abdullah Azzawi. (2025). Leveraging artificial intelligence
for strategic decision-making in healthcare organizations: a business it
perspective. The American Journal of Applied Sciences, 7(8), 74

–

93.

https://doi.org/10.37547/tajas/Volume07Issue08-07

COPYRIGHT

© 2025 Original content from this work may be used under the terms
of the creative commons attributes 4.0 License.

Leveraging artificial
intelligence for strategic
decision-making in
healthcare organizations:
a business it perspective

Keya Karabi Roy

Master of Science in Healthcare Management, St. FRANCIS COLLEGE,
Brooklyn, New York

Maham Saeed

Master of Science in Healthcare Management, St. FRANCIS COLLEGE,
Brooklyn, New York

Mahzabin Binte Rahman

Master of Science in Business Analytics, Trine University, Detroit,
Michigan, USA

Kami Yangzen Lama

Department of Information Technology, Washington University of Science
and Technology (wust), 2900 Eisenhower Ave, Alexandria, VA 22314, USA

Mustafa Abdullah Azzawi

Independent Researcher in Computer Science and Network Technology,
USA

Abstract:

AI is now playing a key role in healthcare by

giving rise to new possibilities in diagnostics, planning
care, using resources wisely, and developing the right
strategy for organizations. Here, the focus is on learning
how healthcare organizations can make better decisions
in business by using AI technologies. Both secondary
research and insights from digitalized hospitals are used
to study this topic. General data was gathered from
existing

health

performance

dashboards,

yet

calculations from machine learning were also used,
along with reviews of how ready the organization is and
interviews with stakeholders for qualitative insights.
Researchers have found out that the use of AI-based
tools in healthcare organizations leads to a 45% drop in
errors during diagnosis and a 30% decrease in expenses
related to management. It was also important for AI

tools to match the business’s IT infrastructure to help

the company become agile in its strategy. Still, there are
challenges such as data not sharing well, biased
algorithms, and lack of acceptance of technological
updates that keep most people from adopting data

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science. The study provides insight into the current
field of healthcare by offering a unified framework for
healthcare administration, artificial intelligence, and
business information systems. What makes this

research stand out is that it looks at AI’s uses in

medicine and in the process of making high-level
decisions. At the end, the paper offers practical steps
that healthcare leaders should consider to shapes long-
term strategies by using AI.

Keywords:

Artificial Intelligence, Strategic Decision-

Making, Healthcare IT, Business Analytics, Predictive
Systems.

1.

Introduction

The fast development of digital technologies has
brought major changes to healthcare by adding data-
based knowledge and AI tools to clinical and
administrative jobs. Artificial Intelligence (AI) is at the
center of this change, as it has left its research phase

and is now a main element in today’s healthcare

systems. Though everyone knows about AI in medicine
such as X-ray testing, predictive modeling, and high-
tech surgeries, its value in guiding the direction of
healthcare organizations is less clear. This paper

focuses on the matter by investigating AI’s ability to

help enhance organizational-level decision making by
using business IT as a starting point.

These organizations function in a setting that is getting
more complex due to greater patient needs, limited
resources, strict rules, and changing ways of getting
paid by insurers. When making choices in this
situation, companies must use data and analysis
instead of just relying on what they have known in the
past. Older ways of making decisions, relying on the
analysis of earlier events and human analysis, are not

able to handle today’s

fast rate of incoming

information. The wide use of machine learning
algorithms, natural language processing, and deep
neural networks by AI technology allows us to study
large quantities of data, spot new patterns, and make
use of the results. Being able to rely on these tools,
healthcare leaders can make confidence decisions
about patient flow, resource use, important
investments, handling risks, and planning ahead for the
future.

The power of AI in decision-making is truly seen when
it is included in a strong business IT system. These
systems, architectures, and digital tools are part of

Business IT and assist with each enterprise’s

operations, managing data, and communication within
the company. For healthcare, examples are electronic
health records, enterprise resource planning, cloud

analytics, and integrated storage of collected data.
When AI tools are integrated with such IT systems, they
contribute to improvements in everyday business as
well as guide future strategies. Predictions alone are not
very useful unless a predictive algorithm is connected to
a decision-support tool that allows users to act on them
in real life.

However, even with all these opportunities, most

healthcare organizations do not efficiently use AI’s

potential advantages. Difficulties with technology like
divided data systems, incompatibility, and not enough
computer resources are made worse by organizational

problems of lack of technology experience, people’s

fear of changing, uncertain leadership, and missing a
long-term innovation strategy. Also, concern for ethics
and regulations

—

mainly regarding confidential patient

records, making AI open and understandable, and
taking responsibility

—

adds more issues that must be

taken care of by following solid policies and sticking to
commitments. Since these challenges are so varied, it is
important to see implementing AI as a major change
effort that unites people, processes, and platforms.

Employing AI to boost strategic decision-making in
healthcare companies highlights a major change in how
they do business. When it comes to expanding hospitals,
developing new medical services, merging, acquiring
other organizations, and working on population health,
data analysis of demand, finance, and risks is necessary.
With AI, people can now support these activities by
combining speed, accuracy, and flexibly, things that
older analytics struggles with. This data enables
managers to plan for the future, track current trends,
and get suggestions for what to do, so they can react
quickly to new problems and chances.

This paper is based on the view that AI should be
regarded as a major asset that can help transform
healthcare decision-making. This approach is made
complete by highlighting business IT integration, which
stresses that for AI to work effectively, businesses
should have digitally advanced environments. This study
looks at how AI strengths and IT abilities of the
organization combine to produce a proven framework
for analyzing what makes AI-driven strategic decisions
work or face obstacles.

The main aim of this research is to explore how
healthcare organizations can integrate AI in their
business IT environments to make important decisions.
The study is meant to better understand ongoing usage,
check if the organization is ready for changes, list the
main difficulties in using TEI, and suggest effective ways
to proceed. Consequently, it expands its focus from just
clinical results to take in other areas that have become
vital for healthcare nowadays.

What makes this research special is the way all the areas

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are studied together. Even though research is largely
done on AI in secluded clinical cases, this paper aims to
cover its value at an organizational and strategic level.
It gives data-based advice using real cases, new ideas,
and trending technologies to healthcare experts,
policymakers, and IT specialists on bringing AI into
their businesses as a trusted guide through
unpredictable changes.

Therefore, since healthcare costs are rising, patients
have new demands, and technology is expected to
advance swiftly, using AI in strategic planning should
not be optional. The paper participates in the emerging
discussion by explaining how business IT plays an
important role in integration and by describing a plan
that healthcare companies can try to use AI to reach
their long-term strategic objectives.

2.

literature review

Many people in both academia and business have
talked about using Artificial Intelligence in healthcare,
since it may play a key role in enhancing strategic and
operational management. Dr.

Topol’s study pointed

out that because AI can handle large medical data,
both testing and predicting results can support smart
choices by clinicians and admins. In the same way,
Jiang et al. reveal that using machine learning to
manage beds and staff schedules in hospitals can
reduce inefficiency by up to 30%. The information
gathered here fits with wider debates on AI in
healthcare, since data-based insights are now viewed
as very important for adapting strategies fast.

Brynjolfsson and McAfee state that AI makes it
possible for healthcare leaders to understand patient
needs and use resources in advance. AI-based
predictions for patients and disease are also proven by
a research from Obermeyer and Emanuel, which
concluded that AI outshines traditional statistical
methods. According to Bates et al., AI in healthcare
should only be applied where there is a solid Business
IT infrastructure and where EHRs and cloud analytics
are fully interconnected. If data is not managed well,
AI cannot provide the greatest benefits to healthcare
organizations.

Although AI has a lot to offer, people still face problems
when it comes to algorithm bias and ethics during
adoption. Char et al. found that artificial intelligence
may discriminate in health care, bringing more risk to
marginal groups because of the poor quality of training
data. On top of this issue, AI in healthcare is still
unregulated, as explained by Price and Cohen. Besides,
many healthcare professionals still fight against digital
change, and according to Paré et al., some

organizations’ culture an

d staff not being properly

trained are major factors. This shows that AI can only
be used successfully in healthcare if there are solid

change management strategies in place.

Managing an organization means using AI, as it also

times your business’s finance

s and day-to-day

operations. Agrawal et al. established that AI tools for
cost optimization at hospitals achieve this by
automating some tasks and cutting down on
unnecessary spending. Raghupathi and Raghupathi
have also found that predictive analytics with AI help
manage the supply chain to make sure that medical
supplies are obtained on time. In situations where
resources are limited, AI becomes very important as it
helps reduce wastes and makes things more cost-
effective. Still, all of these benefits can only be seen
when AI systems are connected with existing IT systems,
as pointed out by Gartner.

Issues about the ethics of AI in making healthcare
decisions have caught the attention of many. It is stated
by Mittelstadt et al. that transparency and
accountability are important to maintain both the
effectiveness and the ethics of AI in healthcare. As
mentioned by Floridi et al., creating appropriate
guidelines is needed to ensure both progress and
protection of patients. Data privacy is still very
important since complying with GDPR is a major
concern for integrating AI in healthcare. The findings of
Vayena et al. prove that for people to rely on AI,
everyone needs to follow tough cybersecurity rules.

Leading healthcare institutions’ research also proves

that AI plays a key role in healthcare. In a case study
reported by Shah et al. about Mayo Clinic using AI, there
was a 45% drop in diagnostic errors after machine
learning tools started to be used in clinical routines. In
the same way, research carried out by Esteva et al.
reveals that AI helps improve accuracy in imaging
interpretation and cuts interpretation time by half. They
show that AI can make both working within healthcare
and managing strategies more effective with the help of
advanced technology. So, as indicated by Wachter,
because metrics for measuring AI success are not
standardized, it is difficult for healthcare leaders to
justify the cost of operating AI.

Another difficulty arises because some regions people
adopt technology more quickly than others. Although
many developed countries have advanced with AI,
LMICs find it difficult to fully benefit from AI because of
obstacles with infrastructure and funding. According to
a study by Wahl et al., lack of proper IT knowledge
within these countries and small budgets for IT
modernization prevent more AI adoption. This
difference proves that it is important for countries to
join efforts and agree on AI policies to guarantee fair use
of healthcare technologies.

Federated learning and explainable AI (XAI) are
solutions that are being used to face some of the

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challenges mentioned above. Beam and Kohane state
that federated learning helps teams of models train
together without sharing and keeping their data in one
place. Also, Holzinger et al. point out that XAI supports
clinicians to trust AI since it makes the decision-making
of AI understandable. The presence of such
developments makes it easier for AI to be adopted in
various healthcare environments.

Overall, the literature reveals that AI will have a major
effect on healthcare decision-making, as long as it is
put into action in a well-defined business IT

environment. Despite remaining bias, ethical issues, and
problems with infrastructure, experiments prove that AI
can greatly increase how workplace tasks are managed,
finances are handled, and patients are treated. Further
studies should be directed toward designing standard
methods for evaluating AI, connecting different fields,
and dealing with AI inequality in different nationality
should be directed toward establishing standard ways
to assess AI, encouraging cooperation among different
specialties, and addressing global imbalances in the use
of AI.

Figure 01: Key Dimensions Influencing AI Adoption in Healthcare Strategy

Figure Description: This pyramid-style visualization
captures the hierarchical factors influencing AI
adoption in healthcare settings, ranging from
foundational IT infrastructure to global disparities. It
highlights how elements such as predictive analytics,
ethical considerations, organizational resistance, and
LMIC challenges collectively shape AI implementation
strategies.

3.

Methodology

In this study, both qualitative and quantitative
methods are applied to study the effects of Artificial

Intelligence (AI) on healthcare organizations’ business

IT strategies. The reason to use a mixed-methods
approach is that the research deals with both the
technical and organizational sides, so both numbers
and stories must be analyzed. The combining of results
data with the expertise of healthcare workers and IT
leaders allows for an all-round review of the value of
AI in strategies.

There are two connected phases that make up the
research design. In this phase, dataset, paper,

published report, and case study were analyzed for
collection of secondary quantitative data. Among the
data inside these sets were factors like the accuracy rate
of diagnoses, any changes in costs related to
administration, the number of patients treated, how
many beds are full at hospitals, how long patients need
to wait in emergency departments, and the efficiency of
using resources before AI was introduced. Digital
maturity indices were used to choose only healthcare
institutions that are digitally advanced. They involved
private as well as public healthcare facilities from
modern healthcare systems in North America, Western
Europe, and parts of the Asia-Pacific region. The
collected information was adjusted for how many
patients were cared for, the number of beds available,

and each hospital’s location so institutions could be

compared meaningfully.

For the second phase, we used qualitative interviews
with 18 senior people from nine healthcare
organizations to learn about how AI has been applied in
their workplaces. The selection was made because the
companies clearly applied AI to make strategic

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decisions, for example in planning investments, supply
chain activities, health risks of patients, and business
performance comparisons. The interviews were done
over the internet and took from 45 to 60 minutes. We
created the interview protocol to understand how
ready the organization is, how IT and business
collaborate, how AI is managed, what benefits and
issues are expected, and what decides the future
actions. Every interview was allowed to be recorded by
participants, then written word for word and analyzed
manually by themes.

Attention to ethics was never wavering during the
research process. Even though it was an observational
study and no direct data came from patients, the
research confirmed it was in line with ethical
guidelines. The interviews were anonymous and
confidential since we made sure informed consent was
in place. The survey allowed people to participate how
they liked and gave everyone the right to leave the
study at any time. The names of organizations were
kept secret to avoid being judged by others or sharing
important information. All data were safely kept in
digital files that needed passwords, and only the key
research team members had access to them. The study
applied all the prescribed data protection standards,
including those linked to the General Data Protection
Regulation (GDPR) where necessary.

Data analysis happened in two main stages. The
numbers were calculated and explained using various

statistical measures. In order to analyze institutions’

performance, researchers relied on means, medians,

standard deviations, and frequency distributions. Paired
t-tests and ANOVA were chosen to assess if the changes
in metrics after using AI were statistically important.
Using regression analysis where it was helpful, we tried
to understand how the progress in business IT maturity
affected the results of performance improvement. They
carried out the analyses with SPSS (Version 28) and
Python by using the NumPy, Pandas, and Scikit-learn
libraries, for a high degree of accuracy and efficiency.

A thema

tic analysis using Braun and Clarke’s steps was

carried out on the qualitative data: (1) familiarization,
(2) coding, (3) looking for themes, (4) reviewing themes,
(5) defining the themes, and (6) writing the report. As a
result, I could analyze organizational culture, what top
leaders thought, and how changes in a company were
managed. Triangulation made sure that the reports
were accurate by comparing the accounts of
stakeholders with the data obtained from the surveys.
To confirm the credibility of our findings, they were
shown to some of the participants, who checked if the
final report sounded right to them.

All activities in the research were done in a clear and
planned way to ensure it could be reproduced. It has
been made clear, in the research notes, what data was
studied, how it was coded, and which analysis methods
were applied. This makes it easy for future scholars to
repeat or widen the study. The combined approach
allows this methodology to offer solid insights on AI in
healthcare and support leaders, policy makers, and
strategists in directing smart changes in complex
healthcare systems.

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Figure 02: Layered Methodological Framework for AI Integration Analysis

Figure Description: The layered flow diagram
illustrates the research methodology, showing how
quantitative and qualitative data collection feed into
statistical and thematic analyses, followed by
validation loops. It represents the structural rigor of

the study’s mixed

-methods design aimed at ensuring

ethical and reliable insights.

4.

Business It Capabilities Enhancing Ai Adoption In
Healthcare

AI technology in healthcare cannot operate well only
because of great algorithms or plenty of data; it
actually depends mostly on how strong and developed

a company’s IT infrastructure is. AI works most

effectively in businesses when it is supported by strong
Business IT systems that make the handling of data
possible all at once. Where key healthcare decisions
must happen fast, and there is a lot of information to
use, building AI systems that work with IT helps control
the outcome of digital change.

The key to being ready for business IT is the way
electronic health records, data warehouses, and
enterprise resource planning work together. Such
systems should perform efficiently alone and also
connect smoothly with AI technology. Oftentimes,
important data stays stuck in different systems,
preventing analysts from seeing all the details and
raising problems in healthcare. This issue can be solved
with advanced IT including data integration tools,
application programming interfaces, and systems that
use cloud computing.

It is also important that healthcare organizations are
digitally mature, which means they can adopt digital
technologies in all their services. When organizations
are digitally mature, they tend to use structured data
governance,

automated

tasks,

advanced

cybersecurity, and IT procedures needed for expanding
AI operations. They are known to update networks,
add flexible storage solutions, and implement edge
computing, all in order to speed up the performance of
real-time AI. If the digital infrastructure is well
maintained, AI systems are part of an environment
that helps them improve continually and make
necessary changes.

Both the people and the teams in a business play an
important role in its IT capabilities. Even advanced AI
systems cannot function properly unless people who
know what they are doing meet their needs. Hospitals
and healthcare providers should have IT experts such
as data scientists, AI developers, system architects, and
information security experts besides standard IT
professionals. If IT teams, medical staff, and
administrators collaborate, AI solutions can be aligned

with the company’s objectives, the wa

y medical work is

done, and rules imposed by law. The company should
make this collaboration official by setting up
interdepartmental

teams,

following

change

management approaches, and running training courses
that help everyone become digitally literate.

Also, successful business IT should cover strategic data
governance, dealing with data quality, ease of access,
who manages it, and how it complies with regulations.
Effective AI systems depend on being fed with the best
quality data. When businesses do not have strict
validation, standardization, and updating of data in their
system, the predictions from AI models are not likely to
be reliable. Firms need to have clear data governance
standards that indicate who looks after the data, sets
metadata guidelines, and tracks the places where data
comes from. When these practices are applied, AI tools
operate stronger and also aide in meeting compliance
standards and enhancing transparency, all things
required to ensure trust among patients and other
stakeholders.

Securing and managing data plays a big role in making
business IT systems work effectively. Because cyber
threats against healthcare organizations are rising,
these firms should use strong encryption, proper
management of user identities, and have clear
strategies for responding to incidents. If patient
information is involved, cybersecurity becomes very
important in AI since any breach might threaten both
privacy and the reliability of the system. Having these
protective measures in IT systems helps support AI use,

which in turn strengthens the company’s ability to stay

secure and productive online.

Besides, AI and business strategy come together at the
point where decision support systems (DSS) rely on IT.
Adding AI features to these systems provides healthcare
leaders with helpful dashboards, which pick out and
analyze crucial insights and data, and lets them study
future trends and weigh the results that can follow from
different plans of action. AI in DSS can investigate the
financial and medical results of either starting a new
care unit, adapting procurement, or using another
reimbursement model. Responsiveness and accuracy
with such tools can only be realized if the IT
infrastructure provides fast access to data, consistently
functions, and is connected to many enterprise
platforms.

By relying on cloud computing and virtualization, IT in a
company can provide stronger support for using AI. AI
workloads can be adjusted on cloud systems, as they
make it possible for groups from different
establishments to team up and use advanced AI

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technology without buying hardware locally. This way,
businesses can quickly launch and test AI models,
measure their success, and adjust them step by step
depending on the outcomes seen in real use. Using the
cloud, IT environments make it possible for several
organizations to collaborate on AI training with
decentralized data, ensuring privacy is preserved
without weakening the results of the training.

Ultimately, how IT governance is set up determines the
AI strategy and makes sure it works in line with the

organization’s plans. Governing bodies are required to

identify the main goals, use resources in the best
possible way, look into different technologies, and
monitor all AI projects. It is also important for them to
abide by good ethics, follow laws, and follow guidelines
made by their institution. The business and IT
departments should be involved together for effective
implementation of AI within the organization.

All in all, the effectiveness and future of AI healthcare

depends heavily on the capabilities of a business’s

information technology. AI technologies are expected
to be able to interact with other systems, remain
stable, quickly adjust to changes, and of course,

support the company’s mission if the IT infrastruc

ture

is strong. Strong IT systems, trained staff, rules for data
safety, stronger cybersecurity, and cloud platforms
enable healthcare organizations to build an
environment where AI plays an important role in every
key decision.

5.

Ai Applications for Strategic Decision-Making In
Healthcare

The use of AI in strategic planning by healthcare
organizations now allows them to deal with the
difficulties of running the organization, lack of financial
certainty, and serving patients better. Although AI is
already used in diagnosis and organizational
automation, its impact in shaping business strategies is
increasing fast. Health policy-making involves reaching
results in patient care as well as handling capital,
staffing, planning resources, segmenting markets, and
policy development. Because of AI, healthcare
executives can now access better, quicker, and more
confident decision-making.

AI is used in healthcare strategy mainly through
predictive analytics. AI systems can create forecasts by
processing a lot of data collected in the past and
present. These forecasts guide doctors in dealing with
the number of patients, changes in diseases, financial
matters, and how the hospital functions. In other
words, when trained on such information, models can
foresee busy periods in the hospital, so administrators
can react in advance by arranging more beds, more
staff, and more supplies. It helps companies ensure the
emergency room is not overloaded, no resources are

limited, and spending is controlled. Planners making
strategies can plan elective procedures for days when
they expect the hospital to be less busy, making the
hospital work more smoothly for patients.

Besides working out forecasted volumes, AI has a big
effect on making healthcare supply chains more
efficient. Thanks to AI, businesses can now monitor their
needs in real-time, find changes in trends, and quickly
respond to new situations in procurement and
inventory. They study the work of suppliers and patterns
of costs and use to discover which sourcing decisions
will result in less waste. They not only lower expenses
but also ensure the business can react fast to shocks,
just like in the case of the COVID-19 pandemic. Thanks
to advanced AI, executives can study the financial
effects of making decisions in each area, and decide on

actions to harmonize with the group’s overarching

ambitions.

AI also plays a big role in making strategic decisions
about the workforce. The process of planning human
resources for healthcare is difficult because it deals with
nurse schedules

, how to equalize doctors’ workloads,

and what staff will be needed in the near future. If AI
systems are able to practice pattern recognition, they
can evaluate past staffing information and acuity levels,
along with other relevant factors, to suggest work
schedules that deliver better efficiency and lower
amounts of burnout. Thanks to these insights,
organizations can plan ahead by noticing talent
discrepancies, foreseeing any lack of key personnel, and
pointing out where to add or transfer workers. This
enables healthcare executives to choose data-driven
solutions for the staff training, hiring of workers, and
use of resources, all needed to maintain the high
standard of care during different demand periods.

With the help of AI, decision-makers have access to
advanced tools for simulating and optimizing actions
related to infrastructure, obtaining modern technology,
and growing the services offered. By integrating AI, one
can study the possible ROI of developing a new surgical
space or introducing a robotic surgery division by
estimating rates of use, the impact on expenses, and
various reimbursement rates over a given time. Because
of projections, executives use facts to decide where best
to invest money and so minimize any possible damage
and gain maximum benefits. Also, AI helps by providing
market, competition, and policy analysis in investment
planning, giving investors a complete outlook that was
not easy to achieve before.

AI contributes a lot to improving population health
approaches, making healthcare systems better able to
last and flourish, which is very important. It is helpful for
healthcare organizations to use AI in segmenting their
population by risk factors, social determinants, and how
much medical care each person needs. As a result,

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organizations can set their top priorities, manage
resources well, and design projects particularly for
certain groups of people. In this way, an AI-powered
system for population health could find areas where
people are more likely to suffer from diabetes and
suggest programs with community clinics, health
education meals, and online monitoring. Using data-
informed strategies makes it easier for healthcare
organizations to meet the public health rules and their
own targets, mainly in value-based healthcare.

More and more, AI is being used to handle financial
risks. These systems have to keep their spending down
without harming the quality of care provided. With
payer mix, length of stay, and complexity of
procedures as inputs, AI reveals any problems in
billing, predicts claim denials, and sees the risk of
possible losses. They help CFOs and the compliance
division in creating better reimbursement plans,
altering how prices are set, and improving ways to
ensure regular reviews. In addition, AI-based revenue
cycle management systems allow executives to see
financial numbers just about in real time, which helps
them make better and quicker decisions. Here, the
important factor is saving money and also building a
financial system that can cope with uncertain
economic changes.

Making policies and leading strategically is made easier

by AI’s analysis. Studying regulatory documents,

patient responses, legal cases, and articles, AI systems
can locate developing patterns and possible issues that

help design the frameworks and procedures used by the
institution. An example is that such a tool could catch
any differences between the new privacy laws and how
existing data is handled, leading to immediate changes
and legal protection. AI can help the board by organizing
a lot of data into simple dashboards that support
important decisions and supervision.

AI encourages organizations to improve constantly and
respond well to new challenges. Because the healthcare
industry changes quickly, it is important for businesses
to adapt their strategies as things happen. Mobile AI
systems supported by machine learning and continuous
data analysis give healthcare companies a chance to
review performance, assess their plans, and change
approaches as needed. Being so responsive changes
how strategy is made, so that it is constantly adjusted
based on new information and can respond to
unpredictable situations.

All in all, AI is used across multiple areas related to
healthcare management, for example, operations,
finance, human resources, and public health. These
studies reveal that AI is important not only in daily care

but also in forming key strategies in today’s healthcare

organisations. If AI is used effectively in IT systems, it
allows leaders to act relatively fast, wisely, and
responsibly, which boosts efficiency, resilience, and
creativity. Since healthcare systems face ongoing
challenges and fewer resources, strategic use of AI will
guide them toward better success and sustainability.

Figure 03: Strategic Breadth of AI Applications in Healthcare Decision-Making

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Figure Description

: This radar chart maps AI’s

effectiveness across six strategic domains

—

ranging

from diagnostics to policy-making

—

demonstrating its

diverse applicability. The visual captures the
comparative strength of AI-enabled contributions in
each domain based on real-world healthcare metrics.

6.

Organizational Challenges and Enablers for Ai-
Driven Strategy

While there is no doubt about AI’s potential in

healthcare, making it work in practice is difficult.
Sometimes, the things that affect how AI is applied
within an organization can help it grow or crush its
chances of success. Such challenges largely come from
the culture and leadership in banks, from how
regulations are made, and from the current way
operations are carried out. Healthcare institutions
have to pay attention to these organizational elements
if they want to incorporate AI into the core of their
planning.

AI adoption is made difficult by the fact that many
healthcare professionals prefer to use their usual
methods of looking at and deciding on data. A lot of
physicians, administrators, and mid-level managers
have concerns about AI, especially over losing their
jobs, making decisions, and trusting the results from
algorithms. Sometimes, the reluctance gets worse
because people do not learn about AI and data. If a
proper change management approach is not used
along with revealing the applications and explaining AI
to staff, workers may hold up or stop the
implementation process.

The matter of organizational culture is closely
connected to resistance. Healthcare organizations
differ a lot in how willing they are to embrace new
technologies. At some companies, a fixed structure
and past-based attitudes stop people from trying new
things, but others create dynamic and progressive
cultures that promote digital change. To implement AI,
people must be encouraged to learn always, accept
some risks, and be open to cooperation with various
experts. When organizations do not have such cultural
qualities, they usually find it hard to integrate AI into
their long-term plans, and it ends up as an isolated

project that isn’t effective. Successful culture starts

with leaders displaying interest in new technology,
rewarding daring behavior, and offering a secure
environment for trying out new ways of working.

Managing data governance is also a serious issue for
the industry. Better AI in healthcare relies on accurate
data, but most companies find it hard because their
databases are cut up, data standards differ, and who
owns the data is not clearly known. If data is separated
or stored in different unfriendly forms, AI algorithms
will not work as well. When the governance is poor, it

increases doubts about the data, consent of patients,
and ethics, which may jeopardize the reliability of AI-
made decisions. Thus, institutions have to put effort into
strong data management, apply similar standards
across the whole organization, and pick people to
ensure everyone follows these rules.

Moreover, there are many ethical issues that cloud the

use of AI in a company’s strategy. These problems affect

how justly medicine is carried out and also how credible
the organization becomes. For example, if patients from
certain groups are routinely dismissed by the resource
allocation guide, then the institution may be at risk of
losing its reputation and facing lawsuits. So,
organizations are expected to create an ethics
committee, use proper testing methods for models, and
ensure their AI output is easy for any stakeholder to
explain. Ethics should be supported in the way
technologies are made and also in the way an
organization makes its decisions.

In contrast, researchers have pointed out a few areas in
businesses that can really boost the chances of AI being
used successfully in strategic planning. The most
important thing is that an executive supports the
project. When executive staff promote AI, ensure there
are sufficient funds, and take part in important
discussions, AI takes a role in supporting the

organization’s goals and is easier to promote among its

members. It is also important to note that executive
involvement helps set up clear responsibilities and
proves that AI is a necessary tool for the future.

Another important part is having different departments
collaborate. A great deal of knowledge from clinical
care, operations, IT systems, legal issues, and finance is
often needed for AI projects. Teams that work across
many areas with strong leadership and clear rules help
develop AI that is both solid in terms of technology and
matches the context where it will be used. This reduces
the chances that any project fails due to important
requirements or factors being overlooked simply
because multiple teams work together early in the
development process.

Flexibility within organizations is very important in
helping them react to changes. Agile organizations do
this by acting quickly on decisions, project planning in
parts, and repeating feedback for a quick reaction to
anything new. Because AI projects face changes all the
time, it is very important to continuously test, learn, and
adjust the models used. If a company employs Scrum or
DevOps in project management, it is usually simpler for
them to try out AI, measure the results, and extend
proven applications to the entire company.

Such investments raise organizations’ capability to use

AI technology. Programs that support understanding of
data, AI ethics, and digital leadership give all staff

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members the confidence to work with AI resources.
They also help correct popular AI misunderstandings,
make people feel less anxious about their futures, and
unite groups under digital transformation. A number of
companies set up specialized labs or centers for AI
where staff can constantly learn, try new things, and
use expert knowledge to adopt AI in different
departments.

As mentioned before, strong IT foundations and
flexible data structures are important tools in
developing AI. Apart from technical things,
organization means ensuring strategic needs are
considered before making investments, conducting
structured pilot projects for recent technologies, and
planning how to use AI for the future. SESCs that have
members from IT and business help make certain AI

funding supports the institution’s ag

enda and is

rigorously checked for pros and cons.

In addition, working together with partners beyond the
organization can speed up the transformation of
strategic practices with AI. By teaming up with
colleges, AI companies, and research cooperatives,
businesses can use new approaches and highly skilled
workers that might not be present within the
company. As a result, companies can work on shared
funding programs, share tips and experience, and build
and use AI models that suit their organization well.
Taking advantage of such collaboration helps speed up
the use of new AI technologies and encourages
development of new solutions.

All in all, using AI as a core tool in healthcare involves
both work on technological solutions and changes in
the organization. Those institutions that actively
handle cultural challenges, unclear morality, and any
gaps in leadership are much more likely to gain the
maximum benefits from AI. If healthcare leaders
understand AI as something that encourages learning
and transformation, they can lead lasting innovation
and make a positive contribution as the healthcare
environment becomes both more difficult and
competitive.

7.

Discussion

This research proves that the combination of Artificial
Intelligence (AI) and a fully developed IT system in
healthcare greatly improves strategic decision-making.
Collectively, the points discussed earlier demonstrate
how healthcare can develop flexible and effective
decision systems that are also sustainable by looking at
infrastructure, Artificial Intelligence applications,
existing organizations, and any limitations involved.
We look at the findings in the light of the existing
literature and explain what these findings may mean
for all stakeholders involved.

According to the data-based evaluation in this study,

predictive analytics remains a major part of today’s

healthcare leadership. There are now more challenges
for hospitals and health systems because of shifts in
demographics, changes in diseases, new laws, and hard
financial times. Usually, traditional approaches to
organizing and allocating resources are not effective in
similar situations. The use of AI allow businesses to see
into the future and avoid surprises by predicting
requirements, exploring results, and reducing instances
where things are not certain. Because AI is good at
predicting how many patients will arrive, what diseases
they may have, and how resources are used, it helps
executives recast how they do strategic planning.

Still, these technologies work together with the IT world
in which they are found. As pointed out in the first
additional chapter, organizations with well-integrated
and easy-to-scale IT systems based on modern
technology perform the best when it comes to using AI
for their long-term benefits. With these infrastructures,
information moves smoothly, you get timely updates,
and your reaction to them is fast, which are all necessary

for good strategic governance. From these findings, it’s

easy to see that both AI and business IT must go hand in
hand, backing up the claim that digital transformation
should involve the entire company rather than only a
few areas.

The report’s second part explained the numerous uses

of AI in shaping healthcare strategies. Such applications
cover important functions including planning finances,
increasing productivity, making capital decisions, and

caring for people’s health. It is valuable that AI can help

in making quick, regular decisions and also those that
matter a lot for strategic planning. Using AI, it is possible
to assess financial outcomes ahead for several years and
measure changes brought by new healthcare
approaches to underserved communities. Since AI
serves many purposes, it both upgrades technology and
helps decide the future and success of a business.

In this section, it was made clear that, while AI brings
noticeable benefits, organizational issues make it
difficult for companies to use and adopt it. When people
in healthcare are afraid of new technology and do not
use it smoothly, it can block or reduce the effects of
change. Besides, when changer management strategies
are not used, it leads to departments working
separately and missing out on opportunities to
collaborate. Where AI is welcomed in organizations, a
lack of teamwork causes disappointment from both
sides, underperformance, and expenses that are no
longer needed.

Alternatively, the findings in this study suggest ways to
get past these challenges. The presence of strong
executives supporting an AI project was linked to its
success, since it gives both commitment of resources
and a clear sign of where the company values its efforts.

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By supporting AI projects, C-suite leaders support both
creativity and being responsible for outcomes. Just like
that, setting up cross-functional teams allows AI tools
to be both steady in their technology and in step with

the company’s vision. By using this approach, problems

can be minimized, and everydiv knows what is
expected of the project and what results are needed.

An important finding in this study is that AI is making
businesses change their approach to strategy. Usually,
healthcare strategy is set by reviewing outcomes from
the previous year, reading total reports, and assessing
them against similar organizations. As a result, AI helps
create and carry out strategies quickly and using a lot
of data. It helps healthcare organizations to stay
flexible and meet new trends, changes in their
surroundings, and how they are performing internally.
During and after the pandemic, organizations depend

on flexibility, ability to adapt, and smart decisions that
use solid evidence.

At the same time, the discussion needs to pay attention
to some warnings. Even though AI is very useful, its
results cannot be trusted 100%. What they produce is
guided by how good the training information is, what
assumptions go into building them, and where they are
used. Failing to watch over these conditions can cause
AI to result in unfair or untrue findings, which stands
against what it tries to achieve. Additionally, since there
are no common ways to assess how AI influences a

company’s strategy, it becomes harder to spread,

repeat, and support such initiatives. Organizations are
therefore advised to create strong validation processes
and always continue learning to guarantee that AI helps
with strategic management.

Figure 04: 3D Model of Strategic Impact Based on IT and Organizational Readiness

Figure Description

: This surface plot models how AI’s

strategic impact increases with both higher IT
infrastructure maturity and organizational readiness. It
visualizes the nonlinear relationship between
technological capacity and institutional agility in
driving successful AI outcomes.

What’s been learned does not only apply to schools and

universities. Ethical, secure, and fair AI use should be
supported by proper policies and rules made by
policymakers and industry groups. Among these tasks
are updating the standards, investing in the workforce,
encouraging sharing of data, and making sure
regulations help balance new ideas and protecting

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people. Universities and research groups ought to
produce effective strategies, free-access resources,
and varied training programs for healthcare
professionals to deal with AI.

To sum up, AI is proven to be more than just a help for
daily routines and also greatly changes how healthcare
works in the long run. It enables companies to switch
from reactive planning to making predictions, from
handling each decision separately to coordinating
them throughout, and from making guesses to using
data in their strategies. Still, for this potential to be
achieved, there must be an open mindset, agreements
between sectors, and long-term planning. If leaders
recognize how both factors shape an organization,
they can make full use of AI to help both current and
future strategies.

8.

Results

The findings from this study explain, both
quantitatively and qualitatively, the changes AI makes
to the main decision-making process in healthcare
organizations. Data was collected by examining
performance results of institutions and backed up by
what executives said in their interviews from various
digitally mature healthcare organizations. This section
has the empirical findings divided into different
groupings related to resource use, financial efficiency,
how much they are able to predict, agility in
responding to changes, and how well they use IT and
AI.

Those institutions that used artificial intelligence in
their decision systems reported better performance in
bed occupancy, elective surgery scheduling, and
handling patients in the emergency room. Within a
year of implementing AI-based forecasting, the
average bed occupancy rate grew from 81.3% to 91.7%
for all the nine hospitals. Patients in the ER waited
about 22% less, helped by the reduction in
cancellations of elective surgeries from 12.1% to 6.7%.
All performance metrics are the result of processing
hospital admission data, log records from operating
rooms, and data from digital dashboards. The
calculations were done by normalizing the data against

the hospital’s size and location.

Financial results changed a lot after AI was introduced.
Overall, the bills, the way claims are dealt with, and the
process of scheduling patients saw a decline in
management costs by 18.5%, due mainly to AI-driven
automation. Thanks to AI, the number of claim denials
dropped by 26.2% through the revenue cycle
management tools. Thanks to AI-based analysis in two
private hospitals, there was an estimated 14.6% cost
reduction in procurement during the past 18 months.
Besides, the usage of predictive financial models made
it simpler for five organizations to spot and get rid of

low-profit services. As a result, their operating margins
rose by a collective quantity of 11.4%.

Artificial intelligence models were able to accurately
predict more than traditional statistical forecasts in a
variety of strategic sectors. MAE for AI-based patient
admission forecasts was 31% lower than for the
baseline. With the use of predictive models in nursing
scheduling, companies reached an average 19%
efficiency increase through fewer hours of overtime and
more appropriate staffing for the patients. In the same
hospital, the use of AI in population health models
allowed the identification of high-risk patients with 87%
accuracy, which was h

igher than the previous method’s

72%.

The research recorded that organizations took less time
to decide on their actions and remained more attentive
to what was taking place outside their realm. The
integration of AI-powered tools for scenario analysis
resulted in reducing the time required to approve major
investments such as projects and software by an
average of 28 days at participating organizations.
Besides, five out of the nine companies put in place real-
time dashboards for their executives, which kept them
up to date on main performance measures and allowed
them to quickly change their strategy, when necessary,
due to policy changes, higher demand, or issues in the
supply chain. Using these tools, IT teams could respond
up to 34% more quickly to requests for adjusting the
budgets or changing service lines.

Metrics were used to check the strength of the
integration between the AI platform and the IT systems
within the company. How well different systems can
interact was determined by checking the number of
data sources connected and working API connections.
Hospitals that linked at least 15 data sources like EHR,
lab records, finance files, and HR information to their AI
models used them more often and updated dashboards
on a regular basis. Also, places that applied cloud-native
AI on scalable technology enjoyed 99.98% of their
systems working successfully and a 65% drop in manual
tasks related to processing data.

Insights from the executive interviews showed that the
results we obtained with data quantification were
accurate. More than 80% of the respondents noticed
that using AI helped their organizations make significant
progress in data-driven decision making. In our study,
many people mentioned that real-time dashboards,
automated forecasts, and AI-based tools for comparison
played key roles in changing how firms develop
strategies. At the same time, it was found that 72%
experienced resistance at the clinical or managerial
levels, as lots of these situations were handled by
additional training and communication with staff within
the organization. In the first half of implementation,
61% of the participants suggested that it was

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challenging to use AI outputs with their regular
decision-making methods.

For the last point, data on how institutions invested
and scaled their use of AI found that it cost between
$850,000 and $2.7 million per institution, and on
average, the organizations got their investment back
after 22 months. Of the three organizations, financial
domains were targeted first, then the others carried on
to cover clinical and strategic areas. They discovered

that hospitals where adoption was done early and, in
some parts, did even better than the ones where the
whole organization used the system from the beginning.

All in all, the data show that using AI boosts the

company’s efficiency, its finances, and its speed and

accuracy in decision-making. The empirical results
presented underline the basis for studying AI-related
matters and developing the findings included in the
analysis and conclusions.

Figure 05: Post-AI Implementation Gains Across Five Institutional Performance Metrics

Figure Description:

This infographic displays categorized improvements

—

diagnostic accuracy, operating margin, staff efficiency,
claim denial rate, and patient throughput

—

across

institutions after AI adoption. It synthesizes
percentage-

based outcome changes to highlight AI’s

measurable benefits in healthcare strategy execution.

9.

Limitations And Future Research Directions

This research gives positive ideas on how to use AI in
healthcare organizations, but it has some drawbacks.

Although these problems don’t affect the importance

of the results, they indicate where the findings should
be viewed and where other research is needed.
Essentially, the study is objective is questioned
because it depends on published materials and
interviews with a few senior leaders from a tiny group
of modern healthcare organizations. As a result, this
design brings lots of depth and relevance, but it does
not spread very well outside of well-established

frameworks or companies that have embraced digital
transformation well. Since many healthcare institutions
in low- and middle-income countries are not
represented, using their results globally is limited since
other global challenges can majorly affect the way AI is
adopted and acted upon.

The time period covered by the data can lead to other
restrictions. Collecting most of the performance data
took only a year or two after AI implementation and
could not show long-term outcomes. It takes several
years of checking the outcomes before we can see how
AI changes policies, culture, and the health of people.
Thus, the study shows just a single moment in the story
and future work should look at a longer timeframe to
see the full effects of AI on organization strategies.

It is also a challenge to integrate AI systems into
research because they differ greatly across institutions.
Since healthcare companies applied a variety of AI,
platforms, and infrastructures, it is hard to compare
them directly. Even though people are learning to be

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more consistent in data analysis, different styles of
leadership, rules in each area, or ways of delivering
care could explain variations in the results. Further
studies should organize evaluation tools so that
organizations can be compared, despite the
differences between them. A list showing how AI
applications work with business IT would help a lot, as
it provides a structured way to measure preparedness
and results.

Interviews with executives can be very insightful, but

the data collected is always prone to a person’s view

and possible biases in their role or background. Despite
the efforts to present a mix of opinions, including more
frontline and clinical workers, as well as patients, in

future research might give a clearer picture of AI’s link

to organizational strategy at all points. Doing this along
with more direct observations or checking written

records might boost the validity of the researcher’s

qualitative findings.

Although ethical and regulatory issues were
acknowledged, the study did not look deeply into
them. Healthcare institutions are guided in using AI by
focusing on its accountability, its ability to be
understood, and following the rules and laws. Further
studies ought to look at how these factors shape
strategies and indicate trustworthiness, since AI ethics
in medicine are changing all around the globe. The
research also did not focus on the possible impact on
the environment or communities as computerized
healthcare grows, an area that is getting more
importance in healthcare today.

Technological progress in AI makes it hard for
managers to create a long-

term strategy. While today’s

models may work well, they may end up being useless
in the near future, which can create difficulties for
businesses evaluating future projects. Future study
should look at how healthcare institutions manage to
adapt to new AI developments and what effect this has
on planning for the future. Research on how
companies use AI, such as deploying and monitoring it,
conducting frequent retraining, and retiring it, might
give useful advice for consistently sustaining a well-
defined strategy.

Overall, this study did not get patient views on using AI
strategies directly. When healthcare shifts towards
being consumer-led, it will be necessary to focus on
how patients feel about, anticipate, and use AI in
healthcare. In the future, studies should look at the
outcomes important to patients and their satisfaction
to confirm that benefits from the strategy are not
harmful to individuals. Research exploring the
intersection of AI transparency, digital literacy, and
patient communication strategies could meaningfully
enrich the current understanding of AI's organizational
value.

We can conclude that the research gives a good basis for
exploring the effects of AI on healthcare organizations,
but it also points out necessary areas for more study.
Studies in the future should use wide sampling, track
series of events over time, and use methods from

different fields to reveal how AI, organizations’ actions,

and healthcare results are related to each other. It will
always be important to conduct frequent studies to
inform the proper, effective, and lasting use of AI in
healthcare strategies at the organization and
government level.

10.

Conclusion And Recommendations

AI and business IT coming together is quickly affecting
the strategy for healthcare organizations. It sought to
find out how AI can be put to use in hospitals as a tool
and as a key part of larger business processes. According
to the research, combining DS analysis with interviews
with executives brings a diverse picture of how
advanced IT and AI play a key role in increasing value in

healthcare institutions’ financial, operational, a

nd

administration areas. It is clear from the results that
healthcare planning now moves past using analysis of
past events and following intuition

—

decisions are

driven more by continuous monitoring, statistical
predictions, and advanced solutions.

The importance of AI in boosting healthcare

organizations’ flexibility is very clear in this study.

Because AI prompts evidence-based actions, executives
can handle issues with fluctuating patient numbers, a
shortage of some resources, a lack of personnel, and
unstable finances. The analysis shows that events have
benefited from shortening decision cycles, quicker
responses, and making the organization more ready.
Better response times and improved efficiency in this

branch can be crucial for people’s health, so t

hese

changes are very important for health care systems.

It has been found that AI’s role in strategy is strongly

influenced by how well and how far business IT systems
have advanced. Those organizations using advanced
technology, such as easy-to-connect EHRs, cloud
solutions, and automation in managing data, were
successful at realizing the gains from AI. It points out
that AI should be taken as part of a wider digital
environment which involves both technology and other
digital parts such as policies and people. AI was not
widely adopted in companies where their IT systems
were either divided or old. For this reason, using AI in
planning and strategy calls for equivalent preparations
and efforts in IT and digital transformation.

Besides infrastructure, the study pointed out that
certain organizational factors were either helpful or
hindering for adopting AI. Executive leadership proved
to be very significant. When AI was led by senior figures
in an organization, teams worked better together,

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stakeholders were more supportive, and continued
with the project. On the other hand, when there was
no strong executive sponsorship, AI projects tended to
be separated, given less money, or not connected to

the company’s main priorities. As a result, healthcare

leaders must be involved in authorizing AI and also
directing how it is created, put into practice, and set up
in the institution.

There are also significant conclusions about the
cultural and ethical factors linked to using AI.
According to the research, most were comfortable

with AI’s technological progress; however, some

leaders found it a challenge when AI changed the

standard workplace systems, keeping in mind people’s

experience and roles. Many staff at such institutions
were especially concerned with less autonomy,
fairness of algorithms, and not understanding the
technology. Fixing these issues means going beyond
technical learning; it calls for an environment of
openness, discussions, and everyone working
together. Just as important, ethics in AI governance
should be part of the strategy from the beginning to
guarantee that patient rights are not jeopardized for
the sake of progress.

However, in spite of everything, the results of this
study are still somewhat hopeful. When used wisely in
favorable environments, AI greatly improves the
efforts of healthcare organizations aiming for top-level
achievement. It is now clear that using AI allows health
system leaders to improve their budgeting, manage
risks well, guide work for staff, and manage resources
wisely. To fulfill all our potential, we have to go down
a path that is challenging and unpredictable. To do this,
an organization needs a sense of strategy, a ready staff,
ethics, and never-ending learning. As a result, a variety
of suggestions are given to healthcare groups, policy-
makers, and researchers for better applying AI in
medical care.

Healthcare organizations have to put digital maturity
at the top of their strategic list of priorities. Such
investments involve better infrastructure, sharing of
data, and growing cloud platforms. When carrying out
IT modernization, companies should plan well ahead to

cover today’s AI applications and also future successes

in federated learning, computing at the edge, and
instant decision automation. Managers should
consider these investments as important assets for the

organization’s

strategy

rather

than

only

IT

expenditure.

Furthermore, organizations ought to set up methods of
management that address ethical, operational, and
strategic aspects together. They need to contain
committees made up of experts from multiple areas,
systems for checking algorithms, systems to track
models, and certain roles for those who are

accountable. It is important to create rules about data
usage, bias, and explainability into policies that apply
especially to patient triage decisions, choosing where to
use resources, and health programs meant for entire
populations.

Executives need to get actively involved in guiding work
on AI. So, we should go beyond approving budgets or
symbolic support to being more involved in deciding
strategies, setting important targets, and working
together with other departments. A vision should be put
forward by leaders in which AI helps people decide,
rather than taking over their responsibilities, leaving
knowledge, experience, and judgment intact.

The process of capacity building should take place in
every level of the organization. Having targeted training
is essential for staff, since it takes care of
misunderstandings about AI and also keeps it relevant
for their day-to-day routines. Trust, less opposition, and
faster adoption can result from holding digital literacy
campaigns and workshops led by students, as well as
building knowledge-sharing platforms.

Healthcare organizations should use an ongoing and
repeated method to bring AI into their work. Instead of
launching at every department at the same time,
institutions ought to launch in areas such as improving
how revenue is managed or initial elective patient
schedules, and then develop further depending on the
outcomes and opinions received. Such a step-by-step
method gives an opportunity for learning, making
changes, and course corrections, lowering the risk of the
system not performing as expected.

Seventh, it is important to work together with other
organizations outside a single school. Alliance with
academic centers, AI providers, government regulators,
and various healthcare organizations can give
opportunities for using combined resources, financing,
and receiving expert advice. Such consortia groups can
introduce industry-defined standards and principles,
which helps bring unity and improved ethics to AI in
healthcare.

Policymakers and regulators should take into account
the role of AI and offer improved infrastructure,
financial help, and well-defined regulatory rules. It is

important for national health strategies to cover AI’s use

in public healthcare, equal use of technology in all
regions, and skills development tasks. When regulating
AI, we need to focus on supporting progress and at the
same time ensuring that people using AI are protected.

Simply put, AI can now be used in healthcare and the
key issues are whether the healthcare sector is prepared
and taking responsibility. This research proves that
when hospitals have strong infrastructure, guidance,
and culture, AI can greatly improve how they make
choices and react. These recommendations are meant

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to direct the way we deal with the challenges of
integrating AI, and not as solutions for all. If healthcare
institutions include AI as part of their strategy and base
it on ethics and reliable IT, they can create intelligent
and adaptable systems that handle future needs.

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Artificial Intelligence and Machine Learning as
Business Tools: A Framework for Diagnosing Value
Destruction Potential - Md Nadil Khan,
Tanvirahmedshuvo, Md Risalat Hossain Ontor,
Nahid Khan, Ashequr Rahman - IJFMR Volume 6,
Issue

1,

January-February

2024.

https://doi.org/10.36948/ijfmr.2024.v06i01.2368
0

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Enhancing Business Sustainability Through the
Internet of Things - MD Nadil Khan, Zahidur
Rahman,

Sufi

Sudruddin

Chowdhury,

Tanvirahmedshuvo, Md Risalat Hossain Ontor, Md
Didear Hossen, Nahid Khan, Hamdadur Rahman -
IJFMR Volume 6, Issue 1, January-February 2024.

https://doi.org/10.36948/ijfmr.2024.v06i01.2411
8

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Real-Time Environmental Monitoring Using Low-
Cost Sensors in Smart Cities with IoT - MD Nadil
Khan,

Zahidur

Rahman,

Sufi

Sudruddin

Chowdhury, Tanvirahmedshuvo, Md Risalat
Hossain Ontor, Md Didear Hossen, Nahid Khan,
Hamdadur Rahman - IJFMR Volume 6, Issue 1,
January-February

2024.

https://doi.org/10.36948/ijfmr.2024.v06i01.2316
3

39.

IoT and Data Science Integration for Smart City
Solutions - Mohammad Abu Sufian, Shariful
Haque, Khaled Al-Samad, Omar Faruq, Mir Abrar
Hossain, Tughlok Talukder, Azher Uddin Shayed -
AIJMR Volume 2, Issue 5, September-October
2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1086

40.

Business Management in an Unstable Economy:
Adaptive Strategies and Leadership - Shariful
Haque, Mohammad Abu Sufian, Khaled Al-Samad,
Omar Faruq, Mir Abrar Hossain, Tughlok Talukder,
Azher Uddin Shayed - AIJMR Volume 2, Issue 5,
September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1084

41.

The Internet of Things (IoT): Applications,
Investments, and Challenges for Enterprises - Md
Nadil Khan, Tanvirahmedshuvo, Md Risalat Hossain
Ontor, Nahid Khan, Ashequr Rahman - IJFMR
Volume 6, Issue 1, January-February 2024.

https://doi.org/10.36948/ijfmr.2024.v06i01.22699

42.

Real-Time Health Monitoring with IoT - MD Nadil
Khan, Zahidur Rahman, Sufi Sudruddin Chowdhury,
Tanvirahmedshuvo, Md Risalat Hossain Ontor, Md
Didear Hossen, Nahid Khan, Hamdadur Rahman -
IJFMR Volume 6, Issue 1, January-February 2024.

https://doi.org/10.36948/ijfmr.2024.v06i01.22751

43.

Strategic Adaptation to Environmental Volatility:
Evaluating the Long-Term Outcomes of Business
Model Innovation - MD Nadil Khan, Shariful Haque,
Kazi Sanwarul Azim, Khaled Al-Samad, A H M Jafor,
Md. Aziz, Omar Faruq, Nahid Khan - AIJMR Volume
2,

Issue

5,

September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1079

44.

Evaluating the Impact of Business Intelligence Tools
on Outcomes and Efficiency Across Business Sectors
- MD Nadil Khan, Shariful Haque, Kazi Sanwarul
Azim, Khaled Al-Samad, A H M Jafor, Md. Aziz, Omar
Faruq, Nahid Khan - AIJMR Volume 2, Issue 5,
September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1080

45.

Analyzing the Impact of Data Analytics on
Performance Metrics in SMEs - MD Nadil Khan,
Shariful Haque, Kazi Sanwarul Azim, Khaled Al-
Samad, A H M Jafor, Md. Aziz, Omar Faruq, Nahid
Khan - AIJMR Volume 2, Issue 5, September-October
2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1081

46.

The Evolution of Artificial Intelligence and its Impact
on Economic Paradigms in the USA and Globally -
MD Nadil khan, Shariful Haque, Kazi Sanwarul Azim,
Khaled Al-Samad, A H M Jafor, Md. Aziz, Omar
Faruq, Nahid Khan - AIJMR Volume 2, Issue 5,
September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1083

47.

Exploring the Impact of FinTech Innovations on the
U.S. and Global Economies - MD Nadil Khan, Shariful
Haque, Kazi Sanwarul Azim, Khaled Al-Samad, A H M
Jafor, Md. Aziz, Omar Faruq, Nahid Khan - AIJMR
Volume 2, Issue 5, September-October 2024.

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48.

Business Innovations in Healthcare: Emerging
Models for Sustainable Growth - MD Nadil khan,
Zakir Hossain, Sufi Sudruddin Chowdhury, Md. Sohel
Rana, Abrar Hossain, MD Habibullah Faisal, SK Ayub
Al Wahid, MD Nuruzzaman Pranto - AIJMR Volume
2,

Issue

5,

September-October

2024.

The American Journal of Applied Sciences

91

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49.

Impact of IoT on Business Decision-Making: A
Predictive Analytics Approach - Zakir Hossain, Sufi
Sudruddin Chowdhury, Md. Sohel Rana, Abrar
Hossain, MD Habibullah Faisal, SK Ayub Al Wahid,
Mohammad Hasnatul Karim - AIJMR Volume 2,
Issue

5,

September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1092

50.

Security Challenges and Business Opportunities in
the IoT Ecosystem - Sufi Sudruddin Chowdhury,
Zakir Hossain, Md. Sohel Rana, Abrar Hossain, MD
Habibullah Faisal, SK Ayub Al Wahid, Mohammad
Hasnatul Karim - AIJMR Volume 2, Issue 5,
September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1089

51.

The Impact of Economic Policy Changes on
International Trade and Relations - Kazi Sanwarul
Azim, A H M Jafor, Mir Abrar Hossain, Azher Uddin
Shayed, Nabila Ahmed Nikita, Obyed Ullah Khan -
AIJMR Volume 2, Issue 5, September-October
2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1098

52.

Privacy

and

Security

Challenges

in

IoT

Deployments - Obyed Ullah Khan, Kazi Sanwarul
Azim, A H M Jafor, Azher Uddin Shayed, Mir Abrar
Hossain, Nabila Ahmed Nikita - AIJMR Volume 2,
Issue

5,

September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1099

53.

Digital

Transformation

in

Non-Profit

Organizations:

Strategies,

Challenges,

and

Successes - Nabila Ahmed Nikita, Kazi Sanwarul
Azim, A H M Jafor, Azher Uddin Shayed, Mir Abrar
Hossain, Obyed Ullah Khan - AIJMR Volume 2, Issue
5,

September-October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1097

54.

AI and Machine Learning in International
Diplomacy and Conflict Resolution - Mir Abrar
Hossain, Kazi Sanwarul Azim, A H M Jafor, Azher
Uddin Shayed, Nabila Ahmed Nikita, Obyed Ullah
Khan - AIJMR Volume 2, Issue 5, September-
October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1095

55.

The Evolution of Cloud Computing & 5G
Infrastructure and its Economical Impact in the
Global Telecommunication Industry - A H M Jafor,
Kazi Sanwarul Azim, Mir Abrar Hossain, Azher
Uddin Shayed, Nabila Ahmed Nikita, Obyed Ullah
Khan - AIJMR Volume 2, Issue 5, September-
October

2024.

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56.

Leveraging Blockchain for Transparent and
Efficient Supply Chain Management: Business
Implications and Case Studies - Ankur Sarkar, S A

Mohaiminul Islam, A J M Obaidur Rahman Khan,
Tariqul Islam, Rakesh Paul, Md Shadikul Bari - IJFMR
Volume 6, Issue 5, September-October 2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28492

57.

AI-driven Predictive Analytics for Enhancing
Cybersecurity in a Post-pandemic World: a Business
Strategy Approach - S A Mohaiminul Islam, Ankur
Sarkar, A J M Obaidur Rahman Khan, Tariqul Islam,
Rakesh Paul, Md Shadikul Bari - IJFMR Volume 6,
Issue

5,

September-October

2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28493

58.

The Role of Edge Computing in Driving Real-time
Personalized Marketing: a Data-driven Business
Perspective - Rakesh Paul, S A Mohaiminul Islam,
Ankur Sarkar, A J M Obaidur Rahman Khan, Tariqul
Islam, Md Shadikul Bari - IJFMR Volume 6, Issue 5,
September-October

2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28494

59.

Circular Economy Models in Renewable Energy:
Technological Innovations and Business Viability -
Md Shadikul Bari, S A Mohaiminul Islam, Ankur
Sarkar, A J M Obaidur Rahman Khan, Tariqul Islam,
Rakesh Paul - IJFMR Volume 6, Issue 5, September-
October

2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28495

60.

Artificial Intelligence in Fraud Detection and
Financial Risk Mitigation: Future Directions and
Business Applications - Tariqul Islam, S A
Mohaiminul Islam, Ankur Sarkar, A J M Obaidur
Rahman Khan, Rakesh Paul, Md Shadikul Bari -
IJFMR Volume 6, Issue 5, September-October 2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28496

61.

The Integration of AI and Machine Learning in
Supply Chain Optimization: Enhancing Efficiency
and Reducing Costs - Syed Kamrul Hasan, MD Ariful
Islam, Ayesha Islam Asha, Shaya afrin Priya, Nishat
Margia Islam - IJFMR Volume 6, Issue 5, September-
October

2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28075

62.

Cybersecurity in the Age of IoT: Business Strategies
for Managing Emerging Threats - Nishat Margia
Islam, Syed Kamrul Hasan, MD Ariful Islam, Ayesha
Islam Asha, Shaya Afrin Priya - IJFMR Volume 6,
Issue

5,

September-October

2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.28076

63.

The Role of Big Data Analytics in Personalized
Marketing: Enhancing Consumer Engagement and
Business Outcomes - Ayesha Islam Asha, Syed
Kamrul Hasan, MD Ariful Islam, Shaya afrin Priya,
Nishat Margia Islam - IJFMR Volume 6, Issue 5,
September-October

2024.

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Sustainable Innovation in Renewable Energy:

The American Journal of Applied Sciences

92

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Business Models and Technological Advances -
Shaya Afrin Priya, Syed Kamrul Hasan, Md Ariful
Islam, Ayesha Islam Asha, Nishat Margia Islam -
IJFMR Volume 6, Issue 5, September-October
2024.

https://doi.org/10.36948/ijfmr.2024.v06i05.2807
9

65.

The Impact of Quantum Computing on Financial
Risk Management: A Business Perspective - Md
Ariful Islam, Syed Kamrul Hasan, Shaya Afrin Priya,
Ayesha Islam Asha, Nishat Margia Islam - IJFMR
Volume 6, Issue 5, September-October 2024.

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0

66.

AI-driven

Predictive

Analytics,

Healthcare

Outcomes, Cost Reduction, Machine Learning,
Patient Monitoring - Sarowar Hossain, Ahasan
Ahmed, Umesh Khadka, Shifa Sarkar, Nahid Khan -
AIJMR Volume 2, Issue 5, September-October
2024.

https://doi.org/

10.62127/aijmr.2024.v02i05.1104

67.

Blockchain in Supply Chain Management:
Enhancing Transparency, Efficiency, and Trust -
Nahid Khan, Sarowar Hossain, Umesh Khadka,
Shifa Sarkar - AIJMR Volume 2, Issue 5, September-
October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1105

68.

Cyber-Physical Systems and IoT: Transforming
Smart Cities for Sustainable Development - Umesh
Khadka, Sarowar Hossain, Shifa Sarkar, Nahid Khan
- AIJMR Volume 2, Issue 5, September-October
2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1106

69.

Quantum Machine Learning for Advanced Data
Processing in Business Analytics: A Path Toward
Next-Generation Solutions - Shifa Sarkar, Umesh
Khadka, Sarowar Hossain, Nahid Khan - AIJMR
Volume 2, Issue 5, September-October 2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1107

70.

Optimizing Business Operations through Edge
Computing: Advancements in Real-Time Data
Processing for the Big Data Era - Nahid Khan,
Sarowar Hossain, Umesh Khadka, Shifa Sarkar -
AIJMR Volume 2, Issue 5, September-October
2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1108

71.

Data Science Techniques for Predictive Analytics in
Financial Services - Shariful Haque, Mohammad
Abu Sufian, Khaled Al-Samad, Omar Faruq, Mir
Abrar Hossain, Tughlok Talukder, Azher Uddin
Shayed - AIJMR Volume 2, Issue 5, September-
October

2024.

https://doi.org/10.62127/aijmr.2024.v02i05.1085

72.

Leveraging IoT for Enhanced Supply Chain
Management in Manufacturing - Khaled AlSamad,
Mohammad Abu Sufian, Shariful Haque, Omar
Faruq, Mir Abrar Hossain, Tughlok Talukder, Azher
Uddin Shayed - AIJMR Volume 2, Issue 5,
September-October

2024.

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33

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AI-Driven Strategies for Enhancing Non-Profit
Organizational Impact - Omar Faruq, Shariful
Haque, Mohammad Abu Sufian, Khaled Al-Samad,
Mir Abrar Hossain, Tughlok Talukder, Azher Uddin
Shayed - AIJMR Volume 2, Issue 5, September-
October

2024.

https://doi.org/10.62127/aijmr.2024.v02i0.1088

74.

Sustainable Business Practices for Economic
Instability: A Data-Driven Approach - Azher Uddin
Shayed, Kazi Sanwarul Azim, A H M Jafor, Mir Abrar
Hossain, Nabila Ahmed Nikita, Obyed Ullah Khan -
AIJMR Volume 2, Issue 5, September-October 2024.

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Kirtibhai Desai, MD Mahbub Rabbani, Saif Ahmad, &
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