Edición
13, julio-diciembre 2025
ARTIFICIAL INTELLIGENCE IN BUSINESS
DECISION- MAKING: A FOCUS ON SIMULATORS
1
César Gerardo
Arroyo Matarrita, [email protected]
Received: April 2025
Accepted: June 2025
Abstract. Artificial Intelligence (AI) is revolutionizing business
decision-making by enabling
the analysis of massive datasets, accurate forecasting, and optimized
strategies. This study examines how AI-powered
simulators function as critical tools for scenario
modeling, risk evaluation, and uncertainty mitigation in
strategic, operational, and financial decision-making. The analysis explores
benefits such as enhanced speed, objectivity, and adaptability, alongside ethical, technical,
and organizational challenges. Real-world case studies demonstrate that
synergy between human judgment and AI strengthens business management.
Responsible and phased implementation is essential for ensuring success.
Keywords.
Artificial Intelligence, Decision-Making,
Simulators, Business Strategy, Data Analysis, Business Management, Automation.
INTELIGENCIA ARTIFICIAL EN LA TOMA DE DECISIONES EMPRESARIALES: UN ENFOQUE
EN LOS SIMULADORES
1 César Gerardo Arroyo Matarrita, [email protected] Licenciatura en Administración de
empresas Universidad San Marcos.
Código ORCID:
https://orcid.org/0009-0006-5681-2683
DOI: https://doi.org/10.64183/g60g6r47
Recibido: Abril
2025
Aceptado: Junio
2025
Resumen. La Inteligencia Artificial (IA) está revolucionando la toma de decisiones empresariales al permitir el análisis de conjuntos de datos masivos, la elaboración de pronósticos precisos y la optimización de estrategias. Este estudio examina cómo los simuladores basados en IA funcionan como herramientas cruciales para el modelado de escenarios, la evaluación de riesgos y la mitigación de la incertidumbre en la toma de decisiones estratégicas, operativas y financieras. El análisis explora beneficios como mayor velocidad, objetividad y adaptabilidad, junto con desafíos éticos, técnicos y organizativos. Estudios de casos reales demuestran que la sinergia entre el juicio humano y la IA fortalece la gestión empresarial. Una implementación responsable y gradual es esencial para garantizar el éxito.
Palabras clave: Inteligencia Artificial, Toma de Decisiones, Simuladores, Estrategia Empresarial,
Análisis de Datos, Gestión Empresarial, Automatización.
In today’s dynamic
business landscape, Artificial Intelligence (AI) is redefining how
organizations make decisions. The increasing
complexity of markets and the abundance
of data have accelerated the adoption of AI systems in business administration. A pivotal
component in this transformation is the use of
simulators, which allow companies to model scenarios and predict outcomes
before making critical decisions. While AI already plays a vital role in many
organizations, its application in high-level strategic decisions remains
emergent—only around 7% of firms currently use AI for major strategic choices.
Nevertheless, the trend is unmistakable: business leaders increasingly
recognize that effective AI usage will be a key driver of future
competitiveness. This paper explores the core concepts of AI in
decision-making, the application of simulators in business management,
associated benefits and challenges, and real-world case examples, all supported
by recent academic sources.
Artificial Intelligence
in business decision- making refers to the use of algorithms and computational
models to support or automate decision processes. This encompasses machine
learning techniques, deep learning, rule-based
systems, and other approaches that analyze vast datasets, identify
patterns, and recommend optimal actions. A key
concept is AI-driven decision
support systems, which integrate
internal and external
data to present managers with
valuable analyses and forecasts. These systems range from predictive models for demand forecasting to optimization algorithms for resource allocation.
AI could manage both
structured (routine, well-defined) and unstructured (novel, strategic)
decisions. Traditionally, strategic decisions have relied on executive
intuition and experience. However, advances in AI challenge this paradigm by
demonstrating that certain models can emulate or even match aspects
of human reasoning
in strategic contexts. For instance, large
language models have
generated and evaluated business strategies at a level comparable to human
entrepreneurs and investors in controlled settings, indicating that AI can
contribute meaningfully to strategic formulation by offering speed and diverse
perspectives.
Another crucial concept
is computational simulation applied to decision-making. Simulation involves
creating a digital model of a business
system (a market,
a company, a production line,
etc.) and experimenting with different conditions or strategies to observe
potential outcomes. AI-powered simulators combine intelligent algorithms with
simulation models, enabling not only behavioral projections based on historical data but
also adaptive learning from each iteration. Integrated AI in simulations allows real-time parameter adjustment and
optimal solution finding in complex scenarios
that are difficult to analyze
manually.
In summary, key
foundational concepts include AI as a data-driven enabler, intelligent
decision support systems, and simulated environments to test and refine
strategic options before real-world implementation. These pillars form the
theoretical basis for understanding how smart technologies are applied in
modern business administration.
Applications
of Simulators in Business Administration
Simulators have become essential
tools in
business administration, increasingly enhanced by AI for improved
effectiveness. A business simulator is software that recreates business
dynamics in a controlled environment where
users make decisions and observe outcomes without real-world
risk. While traditionally used in education and executive training, simulators
now support strategic and operational planning within firms.
In educational and
managerial training contexts, business simulation games (BSGs) have proven
effective pedagogical strategies. These allow students
and future managers to run simulated organizations in
realistic and interactive environments, facing
market challenges, competing,
and learning from the results of their decisions. Participants can set prices, invest in marketing, manage inventory or human resources, and observe the financial
impact of their choices. Recent studies highlight that BSGs enhance
participants’ decision-making skills, analytical thinking, and responsiveness.
The integration of AI adds another dimension: through cognitive computing, AI
evaluates student decisions, provides adaptive feedback, and generates dynamic scenarios. A qualitative study using the AI-enhanced Business
Global simulator by Company Game revealed both strengths and challenges,
suggesting that AI enriches learning experiences by delivering more objective
evaluations and continuous improvement opportunities.
In strategic business
planning, AI-assisted simulation is used to model markets and competitor
behavior. Before launching a product or entering a new market, companies can
simulate different scenarios: What if we adjust prices? How will competitors
react to an increase in market share? AI
enhances the realism and utility of these simulations by rapidly analyzing vast
historical datasets and calibrating predictive models. Recent studies suggest
that AI enables virtual strategy experiments by testing multiple actions in
simulated markets and estimating competitor or consumer responses. This
capacity to “rehearse” strategies reduces uncertainty and risk, mitigating adverse outcomes before
real-world implementation.
Operationally,
AI-integrated simulators optimize production, logistics, and resource
management. Digital twins—virtual replicas of physical systems—are an emerging
concept. These twins, fed by real-time data and AI algorithms, simulate
operational adjustments to enhance efficiency. Managers can forecast
bottlenecks, test production line configurations, or
inventory strategies, and make informed decisions. A 2023 study on sustainable
production lines confirmed the value
of AI-enabled simulation: the combination of simulation models and AI
significantly improved energy and material resource management from both
economic and environmental perspectives.
In finance and risk
management, AI-powered simulators
aid investment decisions and financial planning. Classical techniques like
Monte Carlo simulations are enhanced
by AI through the inclusion of machine learning for probability
distributions and financial variable correlations. Financial institutions have
developed simulators that evaluate millions of asset portfolio combinations and recommend optimal strategies based on risk profiles.
Similarly, in risk management, simulating economic crises with models trained on past downturns
prepares firms with robust contingency plans.
In sum, simulators in
business administration are applied in leadership skill
development, strategic
experimentation, operational optimization, and financial planning. The
integration of AI broadens their reach and accuracy, providing decision-makers with a
virtual lab to explore complex decisions safely before real-world
implementation.
Adopting AI in
decision-making processes provides numerous benefits that enhance business management. One of the most
cited advantages is the ability to process large
volumes of information quickly and accurately. AI systems can analyze data in
seconds that would take months to process manually, extracting hidden insights
from massive databases. This speed results in better-informed decisions: AI
identifies patterns and trends that human analysts might miss. For instance, machine learning
models can uncover correlations between market variables and sales performance,
informing more effective marketing decisions.
In strategic planning,
AI systems like language models generate multiple strategic alternatives and
suggest the most promising, expediting theoretical analysis in business
strategy. This enables
companies to consider
a broader spectrum of options before choosing a course of action.
Another significant
benefit is improved decision effectiveness and accuracy. AI reduces
human error and cognitive bias. Routine or numerical decisions (e.g., inventory restocking, fraud detection) can be automated for consistency and
reduced mistakes. Even in complex decisions,
AI acts as an objective “second opinion.” Recent studies indicate that
AI tools lead to less biased and more comprehensive decisions, as algorithms impartially evaluate data without being influenced
by prejudice or fatigue.
AI also incorporates
real-time information into decisions. For example, dynamic pricing algorithms
adjust service or product rates based on demand
and competition, optimizing revenue. E-commerce firms like Amazon use intelligent algorithms to
compare internal and external data
and autonomously update prices within predefined limits, maximizing
competitiveness and profit.
AI-enhanced simulators offer a
safe environment for learning and experimentation. Organizations can test
strategies or operational changes without incurring real costs or jeopardizing
business stability. Executives might simulate entering a new market using an
AI-supported global market model that integrates economic and cultural data. Simulation
results reveal potential obstacles or advantages, informing more confident
final decisions.
Additionally, AI enables
faster decision- making. In volatile settings, real-time analysis is critical.
Smart systems monitor
key indicators and alert managers at the right moment. For example,
AI-driven financial systems instantly recommend investment strategy changes in
response to market shifts,
avoiding losses or seizing favorable conditions. In supply chain management, AI
reconfigures distribution routes in response
to disruptions (e.g., natural disasters, regulations) by simulating
logistics scenarios and selecting the optimal option.
AI also democratizes
access to advanced analytics. With user-friendly interfaces and smart business
intelligence tools, complex analyses become accessible beyond data experts.
This empowers mid-level managers to make data-driven decisions without full analyst
reliance, accelerating decision- making across organizational levels.
Finally, AI enhances organizational resilience. By detecting complex patterns, it
anticipates future issues and suggests preventive actions. For instance, AI systems identifying early signs of
customer dissatisfaction on social media may recommend service improvements
before reputational damage occurs. In manufacturing, predictive maintenance
algorithms determine optimal machine downtime before costly failures, blending
component lifespan simulation with sensor data analysis.
In summary, AI in
business decision-making improves information accuracy and speed, reduces biases,
facilitates risk-free strategy
testing, dynamically adapts to environmental changes, and expands access
to analytical tools. When properly leveraged, these benefits contribute to sustainable competitive advantage.
Despite its advantages,
integrating AI into business decision-making presents
substantial challenges that must be addressed for effective
and ethical implementation. A key issue is data bias and quality.
AI learns from historical
data, and if these datasets contain biases (e.g.,
past human prejudices or unbalanced representations), the system may perpetuate
or amplify them. A known case involved hiring algorithms that unintentionally
discriminated due to biased training data. Business decisions risk favoring
certain strategies or markets based on spurious data correlations. Continuous
human oversight and curated data are
essential to ensure fair and rational AI recommendations.
Another critical
challenge is the lack of transparency and explainability in many AI models,
especially deep learning. Decision- makers may hesitate to follow AI advice if its reasoning is opaque. In sensitive
areas such as finance or personnel decisions, understandable explanations are
vital for trust. The so-called “black box” problem complicates accountability:
if an automated decision yields adverse
results, who is responsible—the
machine or the manager? User trust in intelligent systems hinges on transparency; recent research emphasizes that clear explanations and model interpretability are key to AI
acceptance. Explainable AI (XAI) approaches aim to mitigate this, though balancing model accuracy and
comprehensibility remains a research priority.
Organizational resistance to change is also common.
AI integration is not just a
technological shift but a cultural one. Managers and staff must trust automated
recommendations, which may provoke concerns about control
or job security. Some executives, accustomed to intuitive
decision-making, may resist algorithmic suggestions that contradict their
instincts. Overcoming this requires training and value demonstration: when
users see AI improving their
effectiveness, adoption increases. Companies should foster a human-machine
collaboration mindset, where AI augments rather than replaces human
capabilities. Studies indicate that augmented decision- making, in which humans retain final control,
is the preferred model.
Technical and resource
challenges exist as well. Enterprise-wide AI deployment requires robust infrastructure—from
powerful computing capabilities to integrated data platforms. Small and mid-sized
firms may lack these
resources. Moreover, skilled personnel in data science,
AI engineering, and digital change management are necessary.
Talent shortages can stall adoption or lead to flawed implementations. Research underscores the importance of proper infrastructure
and skilled teams for successful AI initiatives. Without these, even the best
tools may fail to deliver results or cause decision confusion.
Ethical and legal issues
cannot be ignored. Using AI in decision-making raises
questions about data privacy (especially personal client or employee
data), responsibility for automated outcomes, and regulatory
compliance. For instance, if an AI simulator suggests a profitable but ethically questionable
strategy (e.g., mass layoffs), executives must weigh social impact.
Emerging regulations—such as the EU’s AI Act—may mandate transparency, risk
assessments, and algorithmic audits. Firms must align AI usage with ethical principles and legal
frameworks, potentially through data ethics committees or independent algorithm
reviews.
Another concern is dependency and reliability. Overreliance on AI without
maintaining internal analytical capabilities can leave firms vulnerable. System failures,
cyberattacks, or environmental shifts not reflected in historical
data may disrupt AI
decision-making. Hence, human
oversight (“human-in-the-loop”) remains vital, especially for critical decisions, until systems prove superior
reliability in specific contexts.
Conceptually, determining when and where to delegate
decisions to AI is complex.
Not all decisions should be
automated. Identifying which decisions can be fully handled by AI (e.g., minor inventory
adjustments) and which require
human scrutiny (e.g., corporate strategy) is itself a strategic task. Effective human-AI
interaction will be a core leadership skill, combining human
creativity and intuition with machine logic and speed.
In conclusion, AI challenges in business decision-making include data
bias, transparency, cultural resistance, infrastructure and talent needs, ethical and legal considerations, technological
dependency, and appropriate autonomy levels. Addressing these is essential for
responsible AI integration that preserves trust and decision quality.
Theory and prior
analysis manifest in practical examples where AI and simulators directly impact
business decisions:
Strategy generation in
startups: A recent study explored AI tools in startup strategy. Researchers
used advanced language models to generate business strategies based on problem
descriptions, comparing results with human experts. The AI-generated strategies
were rated on par with those by entrepreneurs
and investors. The AI also evaluated and scored business plans using investor-like criteria, showing its potential in
early-stage strategic decision-making by offering viable ideas and objective
assessments.
Financial automation in
banking (Ant Financial): In finance, Ant Financial (an Alibaba affiliate)
exemplifies AI-delegated decisions. Their system approves loans to small
businesses and consumers in seconds using machine learning to assess credit
risk. Processing multiple variables (income, payment history, alternative data),
the system automates credit
decisions without human input in most cases. This speeds microcredit delivery
and reduces bias, evaluating applicants objectively by data rather than human intuition.
Content curation and
personalization (Netflix): In entertainment, Netflix uses AI to decide what content to recommend or
produce. Reinforcement learning algorithms tailor content recommendations,
optimizing engagement and retention. Each user interaction feeds a preference simulator that decides
the next show or movie. On a strategic level, Netflix uses AI simulations to forecast demand for
content types, guiding multimillion-dollar production investments.
Corporate training with
business simulators: Multinationals uses AI simulators for leadership
training. A major auto manufacturer
developed a global market simulator to train executives in emerging market
entry. The AI- powered simulator adjusts macroeconomic and
competitive conditions based on user decisions, offering performance reports
with insights and improvement suggestions.
Supply chain
optimization with digital twins: A global
logistics firm implemented a digital twin of its distribution network, linking
real- time warehouse, vehicle, and order data to an intelligent simulator.
During demand spikes, the company simulated responses—inventory relocation, route changes, temporary
transport—evaluating service and cost impacts. The AI recommended the optimal
option, enabling agile and evidence-based decision-making.
Sustainable and efficient production: Cardoso et al. (2023) studied
AI-supported simulation in sustainable production lines. Simulation
models with intelligent optimization let managers test equipment configurations and workflows to cut energy and waste. Results confirmed that
such systems improve environmental and economic outcomes. For example,
adjusting maintenance schedules saved energy during peak hours without hurting
productivity.
These examples confirm
that AI in decision- making is real across sectors. Whether startups
or global firms, organizations using these technologies achieve tangible
performance gains. Each case also highlights the need for high-quality data,
clear criteria, and human oversight. Best practices include starting with small-scale pilots and combining
expert knowledge with
automated suggestions.
Conclusion Artificial Intelligence, enhanced
by simulators, is transforming business decision-making in profound and
multidimensional ways. This paper reviewed key concepts, applications, benefits,
challenges, and real-world examples, demonstrating AI’s potential as a powerful
ally for executives. Core concepts included AI’s capacity to analyze
large datasets and generate
decision alternatives, while simulators provided controlled environments to
test business strategies and policies.
Applications span
executive education, strategic planning, operational optimization through
digital twins, and financial forecasting. Benefits include faster, more
informed, and potentially less biased decisions; real-time data integration; complex scenario evaluation; and broader access to advanced
analysis. Cutting-edge firms report market responsiveness and resource efficiency
thanks to AI support in decision-making.
However, challenges are
equally significant. Data bias, transparency, cultural resistance,
infrastructure needs, and ethical concerns must be addressed to achieve
responsible AI adoption. Human oversight remains crucial to validate AI recommendations and ensure accountability.
Case studies reaffirm
the applicability of the AI-simulation tandem—from startups
to banks and supply chains,
showing measurable decision
improvements. Success lies in gradual integration, matching AI to high- value
domains and adapting processes and training accordingly.
Ultimately, AI,
empowered by simulation, is poised to become
an indispensable component of modern business
administration. When implemented diligently, it democratizes advanced analytics, improves accuracy and speed, and fosters strategic
innovation. Companies that balance
algorithmic precision with human insight—and proactively confront
technical and ethical issues—will be best positioned to thrive in this
decision-making revolution. Future research should explore the evolving human-technology dynamic to
develop sustainable and effective frameworks for AI integration across
organizational decision levels.
Cardoso, M., Enrique,
A., Pinto Ferreira,
L., & Peláez- Lourido, G. C. (2023).
The
use of simulation and artificial intelligence as a decision support
tool for sustainable production lines. Procedia Manufacturing, 64, 1041–1048. https://doi.org/10.4028/p-Cv6rt1
Csaszar, F. A., et al. (2024).
Artificial intelligence and strategic
decision-making: Evidence from entrepreneurs and investors. Strategy Science. https://doi.org/10.1287/stsc.2024.0190
Meissner, P., & Narita, Y.
(2023, October 6). How artificial intelligence will transform decision- making. World
Economic Forum. https://dialnet.unirioja.es/servlet/articulo?codigo=9666377
Mejía Vera, S. E., Nava Ore, J. E., & Cedeño Cedeño, R. J. (2025). The role of artificial intelligence in managerial decision-making. Revista InveCom, 5(4), 39–53. https://doi.org/10.5281/zenodo.14816449
Vargas, F. D. M., & Jiménez, J. A. P. (2019). Business
simulators as a teaching-learning strategy in entrepreneurial training
incorporating AI in decision-making. Revista Eficiencia, 1(1). https://editorial.com.co/ascolfa/index.php/eficiencia/article/view/37
Velez, A., & Alonso, R. K. (2023).
Business simulation games for the development of decision making: A systematic review.
Education Sciences, 13(2), 168.
https://doi.org/10.3390/educs