Edición
13, julio-diciembre 2025
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.
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.
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.
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