AI: Powering the Future

AI-driven Application Modeling and Logic

Leveraging Machine Learning analysis from over 12 million anonymized application logics, MxAssist's Logic Bot employs deep learning to offer contextually driven, real-time suggestions.

AI-driven - Best Practices Assessment

MxAssist's Best Practice Bot functions as a compliance tool, scrutinizing your application for adherence to recognized anti-patterns and development best practices.

AI-Powered - Validation Automation

MxAssist's Data Validation Bot simplifies the process of data validation by utilizing pre-configured expressions, replacing the manual and repetitive setup tasks.

Presenting the Mendix ML Kit for Low-Code Machine Learning Deployment

Low-code application platforms (LCAPs) represent a burgeoning market, expected to grow by approximately 30% over the next few years. Concurrently, the past decade has witnessed a surge in Artificial Intelligence (AI), fueled by the escalating computational capabilities and the abundance of big data.

While LCAPs are transforming the approach to building applications, AI, and Machine Learning (ML) are reshaping the types of applications that can be created. These dual trends are converging as LCAPs mature and gain adoption among enterprises seeking to construct intricate, mission-critical applications. Consequently, the next iteration of LCAPs should facilitate the development of what's termed as 'AI-Enhanced Business Apps’.

However, generative AI cannot operate in isolation.

Generative AI has the potential to enhance the capabilities of knowledge workers. For instance, a legal firm constantly generating diverse legal documents might explore the use of generative AI to tailor documents specifically to each case.

While the saved time in these scenarios is valuable, it merely streamlines routine tasks. There exists additional untapped value within AI.

In a recent article authored by Somnath Singh titled "Bill Gates: People Don’t Realize What’s Coming," Singh suggests that this emerging wave of AI, propelled by generative tools such as ChatGPT, will soon revolutionize how enterprises address business challenges.

Singh introduces the concept to readers of "a world where the distinction between technical and non-technical work fades away." This prediction holds truth—individuals may require fewer technical skills in a specific area to fulfill their work objectives.

Nonetheless, generative AI cannot function independently.

Composite AI Greater Combined Strength

When we pause and reflect, it's essential to recognize that artificial intelligence encompasses a wide spectrum. Like other emerging technologies, the hype surrounding any specific type of AI should be approached with caution. It's crucial to discern the specific type of AI being discussed. This assessment aids in determining whether a particular AI technology or method can effectively address a specific challenge or uncover opportunities for your organization.

Resolving challenges through machine intelligence typically involves various facets in practicality. Regardless of the richness and inspiration that a particular AI technique, such as generative AI, might offer, it primarily addresses only one aspect of enhancing or automating tasks through AI. To effectively tackle real-world issues using artificial intelligence, a combination of AI techniques is often necessary, rather than relying solely on one approach. Gartner refers to this as "Composite AI."

Composite AI amalgamates multiple AI and advanced analytics capabilities to yield improved and more reliable outcomes. For instance, when enhancing or automating human decision-making or engaging with virtual agents, a knowledge graph often complements generative AI. As the name suggests, it serves as a valuable method to encapsulate human experience and judgment digitally, leading to enhanced results and instilling user confidence in system-generated responses.

Consider this instance of composite AI:An application enables customers of an insurance company to capture a photo of their car post-accident. Through this image, an automatic damage assessment is generated. Subsequently, a report is produced based on this assessment.

If the car is deemed beyond repair, the system initiates a call to the nearest towing company on behalf of the car owner.

In this example, the insurance organization employs several AI frameworks to automate manual tasks and aid in human decision-making. Image recognition captures and evaluates the damage, machine learning analyzes the extent of damage by comparing the vehicle with a standard model, while generative AI produces a comprehensive report. This composite AI system then determines if the car is totaled and acts by contacting a towing company for pickup.

Embrace the Excitement

"Technology, with all its implications, signifies continuous change."

AI consistently propels itself forward, constantly altering the landscape it has recently transformed. Recognizing its impacts and the potential benefits it offers is the key to consistently propelling your organization forward. For me, the excitement surrounding AI doesn't lie in its ability to replicate, but rather in what we can accomplish with it and the intricate challenges we can address.

As the buzz surrounding AI escalates once more, it might revolve around AI capable of applying human judgment while reducing the need for extensive technical expertise to execute tasks. Consequently, careers will shift, job responsibilities will evolve, and industries will undergo significant changes.

Yet, that's the essence of technology, isn't it?

It falls on you to discern which AI technologies, beyond just one heavily promoted, will aid in sustaining your organization's relevance."