Artificial intelligence has reached all aspects of our lives, expanding human capacity with insights and predictive power that we could never achieve on our own.
The cloud has made enormous computing power available, and now we can explore insights and information from a massive amount of data with the help of algorithms. With this in mind, Microsoft is working to democratize the use of artificial intelligence (AI), making it possible for everyone, even non-technical users, to build AI models. Democratizing AI also means all organizations can access this type of technology at lower costs, so they can create more solutions. Therefore, with the idea of inclusion and democratization, low-code AI technology was born.
In fact, this is an approach by the Seven Giants (Microsoft, Google, Meta, Amazon, Baidu, Alibaba, and Tencent) that treats AI as a commodity, with the main goal of transforming the power of machine learning into a standardized service that can be acquired and accessed through cloud platforms.
In this article, we will focus on AI Builder which is a Microsoft Power Platform low-code AI capability. With it, your business can use intelligence to automate processes and glean insights from your data in Power Apps and Power Automate.
Why Should You Integrate AI Into Your Business Processes?
Artificial intelligence in the business context takes advantage of the fact that companies have automatically labeled huge amounts of data for decades. AI can mine these databases for hidden correlations that often escape the human eye.
The main examples of well-structured corporate data sets include price histories, credit card usage, and mortgage defaults. Businesses could even detect fraud, make smarter negotiations, and discover inefficiencies in supply chains.
More ways low-code AI can bring efficiency to the workplace is through task automation and error reduction, along with providing real-time insights and enhancing the customer experience. Leveraging these capabilities can give your business a competitive advantage with improved processes that rely less on traditional coding and development strategies.
When talking specifically about Microsoft AI Builder, incorporating it into your business processes is highly recommended, mainly because of two key points:
- Simple creation process and user-friendly development: AI models can be developed and managed by anyone with step-by-step guidance and simplified management and governance.
- Extendable capabilities: Developers can quickly expand their low-code apps with the aid of built-in connectors to Azure APIs.
What is Power Platform AI Builder?
When talking about AI Builder, it is important to understand that we are talking about an AI capability, or in other words, a subset of AI.
An AI capability refers to the ability of a system, device, or software to perform intelligent tasks that would normally require human intelligence, such as recognizing speech, making predictions, understanding natural language, and learning from data. They are enabled by algorithms, models, and frameworks that can be trained to perform specific tasks.
Thus, AI Builder is a Power Platform AI capability which can perform tasks such as prediction, form processing, object detection, category classification, and entity extraction, in addition to applied productivity prebuilt AI scenarios like key phrase extraction, language detection, sentiment analysis, text recognition (OCR), business card reader, entity extraction, receipt processing (preview), and category classification (preview).
Power Platform AI Builder Main Features
AI Builder offers a range of model types tailored for various business scenarios. Here are some examples:
- Custom Object Detection Model: If you need to detect your products in images, AI Builder enables you to refine and create a custom model specifically for this task. You have the flexibility to build, train, and publish the model according to your requirements.
- Prebuilt Receipt Scanning Model: To streamline your expense reporting process, AI Builder provides a prebuilt model that automates the scanning and processing of business receipts. With this model, you can quickly enhance productivity by eliminating manual effort.
- Custom Prediction Model: If you aim to develop a targeted marketing campaign based on patterns in your historical data, AI Builder offers a custom prediction model. This model can be tailored to your business, utilizing your own historical data to provide valuable insights.
Take a look at some common business scenarios suggested by Microsoft, in which AI can help:
Benefits of Using the Power Platform AI Builder
Among its many benefits, the highlights are Power Platform AI Builder’s user-friendly interface and a low-code environment for building models. It is a wizard-style interface that substantially simplifies the development process. There are two types of models that you can create:
- Prebuilt Models: AI Builder offers a variety of prebuilt templates for common business use cases, such as object detection, form processing, and prediction models. These templates provide a head start by eliminating the need to start from scratch and accelerate the deployment of AI solutions.
- Custom Models: AI Builder allows businesses to build custom AI models tailored to their specific needs. This level of customization enables organizations to address unique business scenarios and extract relevant insights from their own data.
These functionalities collectively contribute to enhancing business operations, driving innovation, and enabling organizations to harness the power of artificial intelligence to every user.
In addition, AI Builder seamlessly integrates with other components of the Microsoft Power Platform, including Power Apps, Power Automate (previously known as Microsoft Flow), and Power BI. These integrations allow businesses to leverage AI capabilities within their existing Power Platform solutions, enhancing productivity and efficiency.
Power Platform AI Builder Integrations
The integration of AI Builder is native to the Power Platform ecosystem. It provides organizations with a powerful suite of tools to create, deploy, and manage AI-driven solutions. These integrations offer users the flexibility to seamlessly incorporate AI capabilities into their current workflows, applications, and data analysis processes, eliminating the need for complex development or specialized skills.
Power Platform AI Builder and Power Apps
MS AI Builder provides a selection of models that are ready to use, and in some cases, adapted to your organization. These models can greatly improve the Power Apps user experience with interactive controls.
For deeper research, read: Use AI Builder models in Power Apps
Power Platform AI Builder and Power Automate
Power Automate provides AI Builder actions that facilitate the utilization of different model types within workflows. By incorporating AI Builder actions into your flows, you can accomplish the following:
- Perform Model Inference: You can leverage the outputs of upstream actions, such as receiving email attachments, dropping SharePoint files, or creating records in a Microsoft Dataverse table, to perform model inference using AI models to analyze the data and extract valuable insights.
- Process Model Inference Results: With the completed model inference, you can process the results using downstream actions. For example, you can send the results via email, store them in Dataverse records, or communicate them through messages in Teams.
To learn more, read: Use AI Builder in Power Automate
Power Platform AI Builder and Dynamics 365
Dynamics 365 applications span sales, marketing, human resources, finance, supply chain management, project service automation, field service, commerce, and more. One of the major advantages of Dynamics 365 is its integration with Microsoft’s Power Platform, allowing you to build custom and automated experiences around your data and processes using Power BI, Power Apps, and Power Automate, all with low code.
You can use AI Builder to apply machine learning to improve your business insights and processes inside D365 modules.
To gain insights on D365 modules, read: What is Microsoft Dynamics 365? | Tero
How to Make the Most of AI Builder
To set a solid foundation for your AI Builder project and take full advantage of it, you must understand the problem you are trying to solve or the business process you want to enhance using AI capabilities, so defining a clear and specific objective is crucial.
You don’t do AI/machine learning for the sake of doing it. You do it for a reason connected to what you’re already doing. - Ines Montani, CEO of Explosion.
Project management and governance are also very important to establish practices and governance frameworks to ensure timely progress, effective collaboration, and adherence to project goals and timelines. It is important that the leadership is aware of the AI project lifecycle. You can find useful information about this topic in the AI Transformation Playbook.
In this era of AI, Data Privacy and Compliance requirements must be defined to ensure that you handle sensitive data securely and adhere to applicable regulations, such as data protection laws or industry-specific guidelines.
Engage experts who possess a deep understanding of the problem or the business process you are addressing. Their expertise can contribute valuable insights during model development and evaluation. At Tero, we pay special attention to this point in all our development works, as we believe that aligning technical and business knowledge from the beginning is key to moving forward, setting realistic expectations, and avoiding future frustrations.
Additionally, with AI Builder, everyone is empowered to build models with the right approach. The Microsoft AI Builder documentation has several guides and tips through the development process. To make the most out of this learning process, ensure you plan your employees’ training time and foster an environment where team members feel comfortable expressing concerns or challenges without fear of retribution and also feel free to share achievements with the technology.
Lastly, gain insights into the success of your AI initiatives, identify areas for improvement, and make data-driven decisions to maximize the benefits of AI implementation. Measuring the success of AI initiatives can be done using Key Performance Indicators (KPIs) that align with the specific goals and objectives of your organization. Some important KPIs for AI Builder projects include:
- Mean Time to Repair (MTTR) which refers to the time it takes to resolve an issue, while the First Contact Resolution Rate (FCRR) signifies the percentage of problems resolved by level 1 IT support without escalation. Furthermore, the quantity of tickets received by an IT team on a monthly basis serves as a concrete metric.
- Secondary metrics, such as customer satisfaction, net promoter scores, and total cost of ownership, are derived from more direct metrics and are considered indirect indicators. However, it is crucial to emphasize that these indirect metrics should be built upon and grounded in direct, observable metrics.
AI-related KPIs assist companies in quantifying the success of their AI initiatives by showcasing tangible returns on investment (ROI). The ROI can be quantified in terms of time saved, cost reduction, or labor optimization. It is advisable to rely on the most directly observable and measurable metrics as the primary indicators, and subsequently convert them to other relevant metrics, if required.
With the availability of enormous computing power in the cloud and the ability to leverage algorithms, Microsoft is actively working towards democratizing the use of AI. This inclusive approach is embodied in the development of low-code AI technology, such as AI Builder, which empowers businesses to automate processes and extract valuable insights from data within the Power Platform ecosystem.
To secure the success of AI Builder projects, organizations should prioritize several key steps.
Firstly, it is crucial to gain a clear understanding of the specific problem or business process that the AI capabilities aim to address. This foundation allows for a focused approach towards achieving the desired outcomes.
Secondly, establishing robust project management and governance practices that align with the AI project lifecycle is essential. By following established frameworks and guidelines, organizations can effectively manage resources, timelines, and deliverables, ensuring smooth progress throughout the project. Engaging experts who possess deep domain knowledge is another critical factor. Their valuable insights and expertise contribute to the development and evaluation of AI models.
Investing in employee training and fostering a culture of open communication are equally important. Furthermore, creating an environment where concerns, challenges, and achievements can be openly shared fosters collaboration and accelerates project success in an agile manner.
Lastly, actively developing and implementing AI-related Key Performance Indicators enables organizations to track progress, measure performance, and identify areas for improvement. These KPIs, grounded in data-driven insights, guide decision-making processes, and help optimize the benefits derived from AI implementation.
By following these steps, organizations can enhance the likelihood of achieving successful outcomes with their AI Builder projects, driving innovation, efficiency, and value across their operations. They can create competencies that quickly respond to change, emerging markets, and major technological discontinuities, with this state-of-the-art technology.
If you need help with your AI Builder implementation, do not hesitate to contact us!
In writing this article, valuable assistance from ChatGPT was received, a language model developed by OpenAI. ChatGPT provided helpful information, suggestions, and responses that contributed to the overall content and structure of this article.
Nadella, Satya (2017). Hit Refresh
Lee, Kai-Fu (2019). AI Superpowers: China, Silicon Valley and the New World Order