Applied artificial intelligence is an innovative technology that provides practical solutions to real-world problems in a variety of fields, from transportation to healthcare to communications.
Applied AI can not only optimize processes and improve efficiency but also transform the way people interact with their environment, from inventory management to critical business decisions.
In this article, we'll explore the benefits and challenges of applied AI, some use cases in supply chain optimization, and trends in 2024. We'll also explain how Acid Labs can help you implement applied artificial intelligence in your business.
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What is applied AI?
Applied AI is the practical application of artificial intelligence techniques, algorithms, and models to solve specific problems. In other words, it involves taking AI out of the lab and applying it to real-world tasks in a variety of industries.
The essence of applied AI lies in its dedication to solving concrete problems rather than abstract theories. It involves developing practical and usable solutions in everyday environments, spanning industries such as healthcare, finance, transportation, and manufacturing, among others.
What are the benefits of applied AI?
Applied AI offers several significant benefits, including:
- Improve the efficiency, productivity, and quality of processes, products, and services, reducing costs and human errors.
- Improve strategic decision-making at a business level from a more objective point of view, based on the analysis of large volumes of data.
- Enhance creativity, innovation, and the development of new solutions to current and future problems.
- Contribute to sustainability, conservation, and environmental protection by optimizing the use of natural resources and reducing environmental impact.
What are the challenges of applied AI?
Despite its positive impact, applied AI presents challenges that need to be addressed, such as:
- Identify appropriate use cases where AI can add tangible value. This involves not only identifying opportunities for AI integration, but also validating the potential benefits in terms of cost savings, revenue generation, or performance improvement. It is necessary to align AI initiatives with the strategic goals of each company or organization and ensure a clear return on investment (ROI).
- Technical implementation difficulties. Applied AI requires an adequate technological infrastructure that guarantees the systems’ availability, scalability, and security. It also requires qualified and trained personnel to design, develop, implement, and maintain applied AI solutions and integrate them with existing technologies. The quality and maintenance of data, which is the essential input for the operation of applied AI, must also be ensured.
- Adopt a data-driven culture. Applied AI requires a paradigm shift in how organizations think and act, basing decisions and actions on data and evidence rather than intuition or experience. This includes fostering a data culture that encourages its use, analysis, and learning, as well as innovation and experimentation. It also means establishing data governance that defines policies, roles, and responsibilities for data management.
Use cases for AI in supply chain optimization
The supply chain is an ideal area for applying artificial intelligence because of its complexity, dynamics, and strategic relevance. According to a McKinsey study, applying AI-based forecasting to supply chain management can reduce errors by 20-50%, and translate into a reduction in lost sales and product unavailability of up to 65%.
Here are some examples of use cases where Acid Labs has applied AI to the supply chain, as well as the industries where its use is most prevalent:
1. Inventory Optimization
Using artificial intelligence to predict optimal inventory levels can significantly reduce the costs associated with overstocking and out-of-stocks. This approach is particularly beneficial for industries such as e-commerce, pharmaceuticals, and automotive, among others.
For example, AI can analyze historical sales data, consumption trends, and seasonal factors to determine the amount of inventory needed to meet demand.
2. Intelligent Warehouse Management
La IA también se puede utilizar para optimizar la disposición y recuperación de productos en los almacenes, lo que mejora la eficiencia y reduce los tiempos de respuesta.
Por ejemplo, la IA puede utilizar sensores y cámaras para rastrear la ubicación de los productos en el almacén y generar rutas óptimas para los empleados. Este enfoque beneficia a industrias como la logística, el retail y la manufactura.
3. Routing and Transportation Logistics
La IA calcula las rutas más eficientes para la entrega de productos, lo que permite ahorrar tiempo y dinero, además de mejorar la experiencia del cliente. Por ejemplo, la IA puede considerar factores como el tráfico, las condiciones meteorológicas y los horarios de entrega al calcular las rutas.
Esta aplicación resulta beneficiosa en diversos sectores, incluyendo transporte y logística, comercio electrónico y distribución.
4. Automate Purchasing and Procurement
La IA ofrece la posibilidad de automatizar el proceso de compra, identificando de forma precisa a los proveedores más idóneos y negociando precios óptimos.
Esta capacidad es relevante para sectores como la construcción, la salud y los servicios financieros. La IA también reduce el riesgo de fraude, corrupción y errores humanos en este ámbito.
5. Supply Chain Risk Management
La IA identifica y evalúa riesgos potenciales en la cadena de suministro, permitiendo a las empresas tomar medidas proactivas para mitigarlos.
Este enfoque es especialmente relevante en sectores como finanzas, salud y energía. La IA puede ayudar a prevenir y resolver problemas como la escasez de suministros, las interrupciones en la producción, las fluctuaciones de la demanda, los desastres naturales, los ataques cibernéticos, entre otros.
Four applied AI trends in 2024
En 2024 se anticipa un continuo crecimiento y una rápida evolución de la Inteligencia Artificial Aplicada.
Algunas de las tendencias más destacadas que se aproximan son:
1. Democratization of AI
Este cambio de paradigma busca hacer la inteligencia artificial accesible a más usuarios, desde empresas de todos los tamaños hasta individuos sin grandes conocimientos de programación.
Al ampliar el acceso a la IA, se permite el desarrollo de aplicaciones transformadoras, democratizando los beneficios de la innovación y fomentando un entorno más inclusivo.
2. Multimodal AI
Los modelos de IA multimodales permiten a la IA comprender información a través de diversas modalidades como texto, imágenes, audio y video.
Esto se traduce en sistemas que procesan información de manera más similar a la cognición humana, siendo prometedores para campos como la salud y las finanzas, donde una IA contextualizada puede ofrecer insights más profundos y apoyar decisiones más informadas.
Moving beyond the limitations of unimodal systems, this evolution toward richer insights promises to create more natural and engaging interactions between humans and AI.
3. Augmented Working
AI is having a major impact on the work landscape by automating tasks, optimizing processes, and bringing new insights. Rather than simply replacing, AI complements and augments human work, enhancing people's capabilities and performance.
This trend is known as augmented working, where AI is used to improve the work experience and outcomes. For example, by providing personalized information, training, and coaching, assisting decision-making, facilitating collaboration, or stimulating creativity.
These advances require a redefinition of traditional roles, highlighting the importance of training programs to effectively harness the potential of AI. The key lies in the synergy between AI's analytical capabilities and human creativity, along with emotional intelligence, thus reshaping the work environment.
4. Responsible AI
While AI promises to improve the quality of life, its advancement poses crucial challenges in areas such as privacy, security, fairness, transparency, accountability, and ethics. Society demands AI that is aligned with human values and social norms, respecting individual rights and freedoms.
En este contexto, surge la tendencia de la IA responsable, un conjunto de principios, prácticas y herramientas destinadas a asegurar que la IA sea ética, justa, transparente y responsable.
Este enfoque aborda las implicancias sociales y jurídicas de la IA, empleando técnicas de auditoría, explicabilidad, verificación y evaluación de impacto. El objetivo es garantizar que la IA cumpla con estándares de calidad, confianza y legalidad, respondiendo así a las expectativas crecientes de la sociedad.
Want to implement applied AI? Rely on a strategic partner
Applied AI can transform the way you use data to drive business success. However, its implementation requires deep technical knowledge, an understanding of your business problems, and efficient execution aligned with your goals.
If you want to implement applied artificial intelligence in your organization, you need a strategic partner like Acid Labs to guide you through every step of the process.
What sets Acid Labs apart is our unique combination of deep technical expertise and a consultative approach. We not only implement solutions but also get to the heart of your business challenges.
Through in-depth analysis of your environment and open, collaborative communication with your team, we focus on understanding your business problem to deliver customized solutions that optimize operational efficiency, reduce costs, improve decision-making, and give you the ability to predict market trends and behaviors.
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