How do you use AI and business intelligence in your field?

Start by defining clear objectives and the challenges you aim to address with AI and business intelligence. Understand the specific problems you want to solve or opportunities you want to explore. Tip: Start with specific, measurable, achievable, relevant, and time-bound (SMART) goals. Understand the pain points of your organization or industry, and align AI and business intelligence initiatives accordingly.

AI solutions are as various as the problems they solve. As a small business operator, large language models such as Bard and ChatGPT have helped erase tasks such as creating marketing content calendars, writing scripts for informational videos, writing LinkedIn articles to help establish subject matter expertise, creating our website's FAQ page, and analyzing the data of customer segments to help personalize the website experience and increase conversion. Completing the "Custom Instructions" fields on ChatGPT helped personalize the responses with a focus on my business' value proposition and service differentiation. Employing a consistent test-and-learn methodology to prompt engineering has also led to improved response results.

Start by just asking the AI what you should do. Just give GPT-4 enough context and it will be able to solve the problem for you. Obliviously you generally need experience in the field where you are trying to solve a problem (eg Marketing) But you can just get the AI to build the process and steps to solve it. The experience comes in to essentially just QA what’s happening and making sure that it gets executed properly.

In the realm of manufacturing, the integration of AI and BI heralds a new era of innovation. The initial stride begins with a meticulous assessment of objectives and bottlenecks. Manufacturers must discern the production hurdles they aim to surmount, the efficiencies they aspire to attain, and the quality enhancements they seek to realize. It’s a journey into a data-driven future where every piece of information holds the key to progress. Manufacturing entity may aspire to enhance production line efficiency, minimize resource wastage, and elevate product quality. AI algorithms can monitor machinery in real-time, predicting maintenance needs. Business intelligence systems can scrutinize historical data to unveil patterns.

Identifying clear goals and challenges is the cornerstone of any AI endeavor, especially in wound care. By setting specific objectives, like improved patient outcomes or more efficient treatment plans, organizations can harness AI's power to address precise issues. However, the challenges, such as data quality, privacy concerns, or integration complexities, can't be overlooked. A deep understanding of these challenges allows for crafting AI-driven solutions that are both innovative and grounded in the realities of advanced wound care, ensuring that the technology serves as an effective tool rather than just a novel addition.

Choosing the right tools, platforms and technologies might be difficult part overall. We see a fast pace of technological changes. Especially in the field of AI. Secondly this changes often unimaginable and hard to predict. Therefore you need to choose an approach of a flexible, open and adaptable blueprint to fulfill your current needs but also to be open for future unknown and unpredictable needs.

Select AI and business intelligence tools and platforms that align with your goals. Consider factors like scalability, ease of use, and compatibility with your existing systems. Tip: Evaluate tools and platforms not only for their current capabilities but also for their potential to scale as your needs grow. Consider open-source solutions, which can offer cost-effective flexibility.

The right tool can enhance data collection, accelerate analysis, and provide actionable insights to healthcare professionals. In wound care, where real-time and accurate data can significantly influence patient outcomes, leveraging platforms that seamlessly integrate with existing systems and harness the power of AI can be a game-changer. A thoughtful selection not only ensures the accuracy and reliability of AI-driven insights but also enhances user adoption, ensuring that technological advances truly benefit the patients and practitioners.

The most important part in choosing the right tools and platforms is not the tool but your team and their expertise of using that tool. Exactly like buying a expensive camera cannot make someone a better photographer. But focus on people + skill can make this decision easier.

Sometimes the tools you start building with are not the ones that eventually work in production. Be ready for change. Technology keeps improving. There may be an entirely new cloud integration that a new tool solves for your team that you never used before or when you made the MVP. That's okay. AI is built on complex Data engineering pipelines that do the heavy lifting of moving data from one place to another. The pipelines may use newer tooling, but their objectives would remain the same. Product managers should remain calm and comfortable with changing tools.

The second step embarks on a journey to collect and prepare this invaluable resource. Manufacturers must recognize that the quality and quantity of data underpin the triumph of AI and BI endeavors. It’s akin to mining for precious gems; each data point must be scrutinized for relevance, accuracy, completeness, and consistency. Manufacturers employ a multifaceted approach to gather data, ranging from surveys and web scraping to APIs and sensors. These sources collectively paint a comprehensive picture of operations. For instance, sensors on machinery detect real-time performance metrics, while surveys from floor operators offer qualitative insights.

Data is the foundation of AI and business intelligence. Collect relevant data from various sources, ensuring it's clean, structured, and well-prepared for analysis. Address data quality issues and establish data governance practices. Tip: Ensure data quality from the beginning. Implement data cleansing and validation processes. Document the data's lineage and maintain a robust data catalog for easy reference.

In the realm of AI, the adage "garbage in, garbage out" holds true. Data collection and preparation is the foundation upon which effective AI-driven solutions are built, especially in specialized fields like wound care. By ensuring that the data is comprehensive, accurate, and representative of varied wound conditions, we lay the groundwork for AI algorithms to perform optimally. Moreover, preparing data—cleaning it, normalizing it, and removing any biases—is essential to ensure the resultant AI models are unbiased and reliable. In advanced wound care, this meticulous approach to data paves the way for insights that can truly revolutionize patient outcomes.

It is eminent that you also consider AI Biases, which lead to systematic errors. Moreover, it impacts the ethics of inclusive AI. In my line of emergency communication work situational disability must be eliminated and we cannot count on AI that does not consider the marginalized.

The most common approach at this stage involves meticulously identifying the sources and the diverse data types at our disposal, scrutinizing how each data point can directly or indirectly impact the decision-making process of our model. Invariably, developers are tasked with the crucial process of extracting pertinent insights from the raw "data" - a procedure aptly referred to as Feature extraction. This method seamlessly progresses from gathering unprocessed data to sieving out the most germane "information" essential for model training, ultimately affording us the ability to derive inference grounded in a wealth of "knowledge."

AI models are just algorithms written in a very specific way. The more they are fed data and information, the better they become. And that’s where testing and correcting your finding by feeding the information back to your AI model becomes crucial. To create context and drive maximum value, I keep different models/engagements for different fields and tasks.

Machine learning, deep learning, and neural networks serve as the building blocks of intelligent solutions. These tools empower factories to predict equipment failures, optimize production schedules, and even enhance product design. Within this creative space, different approaches and frameworks come into play. The supervised learning guides processes with historical data, while unsupervised learning reveals hidden patterns in manufacturing operations. Reinforcement learning enables systems to adapt and improve through continuous interaction. The process of building and testing model solutions is akin to forging the sharpest tool in the workshop. It’s the key to staying competitive in an ever-evolving industry.

Building and testing models is a transformative phase in the AI journey, especially in areas as sensitive as wound care. Here, algorithms are trained on the meticulously prepared data, but the real value surfaces during testing. In advanced wound care, this means evaluating the AI solutions against real-world wound scenarios, understanding their predictions, and adjusting for any discrepancies. Continuous testing ensures that the models adapt to the ever-evolving nature of healthcare practices. The end goal is to foster solutions that seamlessly complement clinical judgment, leading to enhanced patient care and more efficient treatment strategies.

Develop AI models or business intelligence solutions tailored to your objectives. Experiment with different algorithms and techniques to find the most effective ones. Rigorously test your models to ensure accuracy and reliability. Tip: Start with a minimum viable product (MVP) to quickly validate your approach and gather feedback. Regularly benchmark and compare different algorithms to optimize performance.

Indeed, model building is very important. Consider few examples, 🔹In marketing, for recommendation systems, collaborative filtering with ML can personalize product suggestions. Training these models on user behavior data & evaluating using accuracy ensures better customer experiences. 🔹In healthcare, AI models predict disease outbreaks using ML. They analyze historical data for patterns & employ accuracy metrics to validate results. Accurate predictions enable proactive public health responses, like vaccination campaigns, crucial for disease control. 🔹In finance, risk assessment relies on ML models. For eg., using decision trees to evaluate credit risk. Metrics like precision help ensure accurate predictions, reducing financial losses.

Implement your AI and business intelligence solutions into your workflow. Continuously monitor their performance and impact on your business. Be prepared to fine-tune or adapt your models as needed. Tip: Implement a robust model deployment process, including version control. Continuously monitor your models for drift and performance degradation, using automated alerting systems when possible.

To realize AI/BI's full value, robust deployment and monitoring is critical. Integrate validated models into existing systems via APIs and containers. Build real-time dashboards to track key performance metrics post-deployment. Set up alerts for monitoring and prompt issue detection. Collect user feedback to identify areas for improvement. Update models frequently as new data arrives to maintain predictive power. Leverage MLOps and automation to streamline retraining and redeployment. Stay on top of advancements in AI/BI and incrementally upgrade solutions. Sustained success requires diligent measurement of impact and continuously keeping models aligned with evolving business needs through rapid iteration.

Deploying & monitoring AI & BI results is very important. For instance, 🔹In education, implementing adaptive learning models requires integrating them into the Learning Management System (LMS) via APIs. Regular monitoring using dashboards ensures student progress aligns with educational objectives. Feedback from student performance data helps refine the models, ensuring a personalized learning experience. AI & BI offer substantial value in education. 🔹In healthcare, deploying predictive models for patient diagnosis involves integrating them with Electronic Health Records (EHR) systems. Regular monitoring through dashboards ensures accuracy. Updating models based on new medical data & feedback improves patient care (through AI & BI).

Our work doesn't end once the product built using AI models is deployed to production! What if the data changes? What if the customer changes? What if the model fails for unseen data? There has to be continuous monitoring for every model in production. Ensure that there will be alerts designed in your environments looking for risks at all times.

Deploying and monitoring AI models are vital for their success: Deployment: Choose a suitable environment (cloud, on-premises) and ensure scalability and security. Create user-friendly APIs for integration. Monitoring: Define performance metrics, continuously track data input, and employ anomaly detection. Implement robust logging and alert systems, monitor resource usage, gather user feedback, and ensure compliance. Efficient deployment guarantees user accessibility, while continuous monitoring guarantees optimal performance. These practices safeguard against issues, adapt to changing conditions, and improve user experiences. By following these steps, AI models can effectively serve their intended purposes and deliver long-term value.

In the field of software engineering, AI and business intelligence (BI) play critical roles in various ways. AI is employed for enhancing software performance and user experience through features like predictive analytics, anomaly detection, and recommendation. BI tools are utilized to gather and analyze data generated by software applications, helping businesses make informed decisions. AI-driven automation streamlines software development processes, reducing manual workloads. AI-powered chatbots and virtual assistants can improve customer support within software products. Machine learning models are employed for software security, identifying and mitigating potential vulnerabilities.

I think overall it’s about value creation for business enabled by AI/BI technologies and data as foundation. It can range from unlocking new insights, automating processes, finding new revenue streams, to drive innovation and business change, ...

Drawing from my experience, I would recommend avoiding analysis paralysis and instead opting for practicality by taking an action-oriented approach. Start by collaborating closely with your team to pinpoint a “no regret” use case that offers both high-value potential and manageable risk. While it might be tempting to compile an extensive catalog of potential use cases, it’s far more advantageous to focus on translating your chosen use case into a tangible success story. This single achievement can carry more weight and demonstrate the value of AI to your organization. It will not only enhance team morale but also generate a sense of enthusiasm and forward momentum which will be instrumental in propelling your AI initiatives forward.

Text Analysis: AI-powered NLP techniques process vast amounts of text data, extracting insights and sentiments to inform business decisions and market trends. Chatbots and Virtual Assistants: Chatbots and virtual assistants powered by AI. Content Generation: AI generates content, such as news articles and product descriptions, reducing manual content creation efforts and maintaining quality. Translation and Localization: NLP models assist in translation and localization, enabling businesses to reach global markets more effectively. Predictive Analytics: AI and BI combine to provide predictive insights, helping businesses forecast demand, optimize operations, and make data-driven decisions.

Think about what you want to automate? Why you want to automate it and the consequences and impact that the automation will have. Thinking with this three prong will help you consider responsible technology usage.