Voici comment vous pouvez rationaliser votre charge de travail grâce à l’intelligence artificielle dans l’innovation technologique.

Streamlining workloads with AI in technological innovation begins with integrating Business Intelligence and data analysis to derive actionable insights from vast datasets. By automating workflows, AI reduces manual tasks, enables faster and more efficient operations. For instance, predictive maintenance leverages machine learning models to anticipate equipment failures, minimizing downtime and optimizing resource utilization. AI also enhances creativity by automating routine tasks, allowing us to focus on innovative solutions. Unsupervised learning models enables continuous learning and adaptation to new data, promoting an environment of ongoing innovation. These strategies help us stay competitive in technology-driven industries.

AI solutions do not lend well to a generalist approach. You have to put on a specialist hat and become a student. Understanding what’s going on “under the hood” helps determine if a challenge can and should be solved with AI, identify the right tech candidates for a solution, and set the right expectations/milestones/etc. Be inquisitive. Understand more than just definitions of key concepts like RAG and DLLs, then learn by doing. Go to a leading AI software company (like LangChain) and work through several coding use cases (summarize a pdf, play with prompts). The experience can be edifying. It’s satisfying to run your own code, even if copied line by line. A small commitment to this approach will transform your ability to streamline AI.

Just as Search Machines (such as Yahoo or Google) transformed the way we look up information via the internet about 20 years ago, generative AI tools (such as ChatGPT) disrupt the way we do everyday tasks today. And we are still at the very beginning of this trend. The use of genAI will accelerate heavily over the next years. As for the search engine revolution back then same is true for genAI: use it or loose it!

AI can only help you solve problems, if you understand what problems you are trying to solve. Take the time to understand your workflows and business processes, then you can start to think about how you can augment, enhance or optimise them with machine learning, automation or artificial intelligence. Too many people are are thinking about which AI tool to buy, rather than which business problems they would like to solve!

Before diving into AI integration, it's essential to recognize that AI is not a one-size-fits-all solution. Each AI system or model is designed with specific goals in mind and is best suited for particular types of tasks. Gaining a solid grasp of AI fundamentals allows you to critically evaluate which AI tools and techniques align with your specific needs and objectives. This knowledge enables you to tailor AI applications effectively to your workflow, ensuring they complement and enhance your existing processes rather than complicate them. By understanding AI's core principles and limitations, you can make informed decisions about where and how to implement these technologies for maximum impact.

We are experiencing exponential data growth at an unprecedented speed, with 95% of it being unstructured and much generated in real-time. AI is critical in faster decision-making and better business outcomes through automation, scalability, improved predictions, and content generation. Traditionally, delivering trusted data for analytics has involved highly time-intensive, manual processes. AI can help overcome key challenges: classifying and organizing vast amounts of data, assessing and improving data quality, and ensuring regulatory compliance. AI can automate the entire data journey - from raw to integrated, cleansed, and governed datasets - improving efficiency and providing a competitive edge by extracting more insights from metadata.

In large corporations with diverse divisions, having a data-driven approach to innovation is essential. This means grounding all innovation projects in a robust data backbone, such as an Innovation Platform, which prioritizes systematic management and collaboration. By ensuring that ideas are effectively captured, evaluated, and implemented, and by leveraging customization, automation, and integration, organizations can align their innovation efforts with strategic goals. AI and Generative AI further enhance this process by optimizing the innovation portfolio, ensuring efficient resource allocation for maximum return on investment and providing a cohesive view that accelerates collaboration and decision-making.

Using AI to clean and analyze data can be the difference between not looking at your data at all and charting a path to data-driven decision-making. Add streaming data, and AI becomes an invaluable tool. Data visualization and analysis should go hand in hand. The human eye is a marvel and often detects patterns and outliers better than computers. But the data needs to be appropriately presented (correct visual(s), scaled, transformed). AI can double as data presenter and pattern/outlier analyzer. In this way, humans can team up with AI in a discovery process--dynamically drilling down and exploring new areas. Particularly with big data, you often don’t know what to look for. It’s the discovery process that unlocks insights and innovation.

If companies want to keep their competitive edge on today’s business world, having a data-driven approach to analysis is the only way. There are 2 pre-requisite to this approach: 1- building a data strategy where data specialists will clean, organize and use the data to create AI and machine learning solutions at scale, allowing faster and better creation of insights for business decisions 2- but all this is nothing if the rest of the company is not ready for this shift. Educating the non specialists and transforming the ways of working is crucial for a successful development. The machine/AI still needs a human to make the most of the analysis and take the necessary action/decisions

AI is can easily summarize large volume data, so now knowledge not equal to remembering lot of things. Also get quick insights from large volume of data now can accelerate decision making in work flow.

Anything that is repetitive and monotonous can be replaced by AI. - Start automating tasks that are repetitive, monotonous, time-consuming, and non-value-adding. - For example, noting down and writing minutes of meetings (MoM) is a non-value-adding task but important from a compliance and documentation perspective. List such tasks and begin automating them using AI.

La IA es el mejor aliada para ayudar en el flujo de trabajo, ayuda a realizar las tareas repetitivas que seguramente aportan valor, pero le restan tiempo al profesional para seguir creando valor en la mejora de sus procesos.

Implemente IA para automatizar tarefas repetitivas e rotineiras. Desde a gestão de projetos até o controle de versões, a automação pode liberar tempo para que você e sua equipe se concentrem em atividades de maior valor, como a inovação e a resolução de problemas complexos.

One time at work, we implemented AI for workflow automation, and the results were transformative. Automating routine tasks with AI can reduce operational costs by up to 30%, according to Deloitte. Whether it's automating report generation or customer service responses, AI takes the grunt work off your plate, allowing you to focus on innovation. The key here is to identify repetitive tasks that drain time and energy and let AI handle them. It's like having an extra set of hands that never gets tired. #WorkflowAutomation #AIAutomation #Efficiency #BusinessTransformation

IA é excelente para automatizar tarefas repetitivas e rotineiras, como agendamentos, filtragem de e-mails e entrada de dados, liberando tempo para atividades mais estratégicas.

This eliminates the reactive aspect of workflow and convert them into proactive work flow strategy, which can reduce downtime

In my experience, predictive maintenance powered by AI can be a game-changer, especially in industries reliant on machinery or technology. With AI, you can predict equipment failures before they happen, potentially saving millions in downtime costs. A study by PwC found that AI-driven predictive maintenance can reduce maintenance costs by 20% and unplanned outages by 50%. This isn't just about fixing problems—it's about preventing them from happening in the first place. Investing in predictive maintenance is an investment in the longevity and reliability of your operations. #PredictiveMaintenance #AIPredictiveAnalytics #OperationalEfficiency #CostSavings

Aproveite a IA para prever falhas e necessidades de manutenção antes que ocorram. A manutenção preditiva usa dados para antecipar problemas, permitindo intervenções preventivas. Isso minimiza o tempo de inatividade e otimiza o uso dos recursos, aumentando a eficiência operacional.

La formation continue est essentielle pour maximiser l'adoption et l'efficacité de l'intelligence artificielle (IA) au sein des entreprises. En démocratisant la connaissance de l'IA à tous les niveaux, en offrant des formations pratiques, et en maintenant les compétences à jour avec les dernières avancées, les organisations peuvent s'assurer que leurs équipes sont compétentes et prêtes à tirer parti des technologies IA. Cela favorise non seulement l'innovation, mais aussi la compétitivité dans un environnement technologique en constante évolution.

Utilizando IA, é possível prever falhas de equipamentos, economizando tempo e recursos, além de aumentar a eficiência operacional.

Generative AI can significantly enhance processes like design thinking by using digital personas to quickly and accurately understand customer problems. By following the five key steps—Empathize with users, Define the core issues, Ideate potential solutions, Prototype your ideas, and Test them quickly—Gen AI allows you and your team to iterate more efficiently. This leads to finding the best combination of ideas, resulting in optimal outcomes for innovation challenges that satisfy all stakeholders involved.

Use IA como uma ferramenta para estimular a criatividade. Ferramentas baseadas em IA podem gerar novas ideias, protótipos e soluções, complementando o processo criativo humano. Isso pode levar a inovações mais rápidas e robustas, ampliando as capacidades da sua equipe.

If creative thinking is thinking outside the box, then call the right friend or co-worker for the task. Asking AI to generate outside-the-box ideas is typically a fruitless exercise. Creativity generally requires a hefty amount of intuition, nuance, and background information—areas where AI has little capability. However, AI is at its best as a turbo iterator—speeding up conceptual roundtrips as ideas are conceived, developed, tested, and either tossed out or kept. It will instantly validate assumptions and give the idea form. AI is particularly good in this role because it doesn’t need to be accurate. Asking, “Should I start a submarine cruise line?” requires a list of considerations, pros and cons, etc., not a precise response.

Como cuando Einsten trabajaba en la oficina de patentes, no hay mejor forma de expandir la creatividad del ser humano, que conociendo de nuevos temas y distintos enfoques. La IA te permite todo eso, te permite descubrir un tema de otro sector, y conversar con un profesional de otro enfoque.

Using large language models (LLMs) as an additional input is crucial for enhancing your innovation process. Exploring various open-source models can deepen your understanding of their capabilities, but always keep security concerns in mind and ensure your data is protected. Carefully choose whether to run models in the cloud or on your premises based on your security needs. As more models run on edge devices like mobiles and computers, they will learn from our behaviors, gaining more context and becoming increasingly efficient. This marks just the beginning of a powerful collaboration between humans and AI.

Incorpore o aprendizado contínuo em IA para manter-se atualizado com as últimas inovações e melhorias tecnológicas. Com a evolução constante da IA, é essencial que você e sua equipe continuem a aprender e a se adaptar, garantindo que as ferramentas de IA sejam utilizadas de maneira eficaz e eficiente.

Modelos de aprendizado não supervisionado permitem que a IA continue se adaptando a novos dados, promovendo um ciclo contínuo de inovação

It is important to stay up-to-date with AI advancements to ensure a competitive advantage. I see the continued use of these tools as a way to not only enhance my own knowledge, but also to help AI become more personalized and efficient for their needs. This learning cycle, both mine and AI's, is something I see as key to increasing productivity and promoting AI.