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.

  • Facilitates modeling and crafting application logics (referred to as microflows).
  • Aids developers in creating microflows up to 30% faster.
  • Delivers real-time recommendations for the most suitable next activity in your microflow with a 95% accuracy rate.
  • Grasps context by scrutinizing a microflow upon the introduction of a new activity or decision midway.
  • Automates development by pre-filling parameters for an action.
  • Efficiently streamlines development by pre-filling parameters for an action.

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.

  • Conducts automatic evaluations of app models in alignment with Mendix's best practices.
  • Identifies anti-patterns and provides developers with guided steps for issue resolution.
  • Comes equipped with a comprehensive best practice guide containing resolution instructions.
  • Enhances application quality, performance, and security.
  • Automatically refines the app model by implementing 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.

  • Automates the creation of distinct page elements that often necessitate data validation.
  • Instantly generates a validation microflow by a simple right-click on a supported element.
  • Enables packaging and reuse of validations across application models, ensuring consistency and minimizing maintenance efforts.

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’.



Applications of Intelligent Technology

AI-Enhanced Business Apps leverage data patterns to predict outcomes instead of relying on explicit programming. This often leads to automating manual tasks or adopting a more intelligent approach to business processes, thereby boosting workplace efficiency, cutting costs and risks, and improving customer satisfaction. Here are various examples of AI and ML use cases:

Analysis and Classification of Sentiments

  • Determine customer feedback sentiment (positive versus negative)
  • Categorize customer feedback or inquiries into specific support or business categories.

Detection of Objects

  • Identify defective products within a factory's production line!
  • Recognize the type of defect in factory product line items.
  • Classify medical images.
  • Count objects within a factory setting.

Anomaly Identification

  • Identifying suspicious bank transactions
  • Unusual correlations within business metrics (e.g., increased product sales but decreased revenue due to pricing errors)
  • Detecting anomalies within inventory

Recommendation Systems

  • Suggesting optimal offers for insurance agents according to customer needs
  • Recommending alternate products or services to online shoppers based on past purchases.

Predictive Analysis

  • Predicting cash flow using historical and seasonal trends
  • Anticipating necessary inventory based on sales patterns.
  • Forecasting demand for a hotel chain considering internal and external influences.
  • Adjusting pricing dynamically based on demand forecasts.

What Lies Ahead for Generative AI in the Corporate World?

Beyond the realm of analysts, there's a resurgence of artificial intelligence (AI) hype in the broader landscape, particularly centered on ChatGPT and generative AI. I won't venture to predict AI's future in a decade. What I can affirm is that AI's adoption in the corporate sphere will persistently expand and transform—whether involving ChatGPT or not. It's crucial to leverage AI to maximize its advantages for years ahead.


Can generative AI dominate the AI landscape?

Previously, challenges in designing AI and applying conventional machine learning hindered its enterprise implementation. The process involved significant effort in obtaining data, preparing it, constructing, training, and finally deploying the model. Even if successful, it required continuous energy and resources for model maintenance, managing various forms of "drift" that degrade accuracy and efficiency over time.

As computational capabilities surge and datasets expand, AI integration within enterprises is on the rise. According to Forrester analyst Diego lo Guidice, the utilization of AI, machine learning, or deep learning has increased from 67% in 2021 to 73% in 2023.

I would contend that AI adoption is currently approaching near-universality, albeit the definition of "adoption" varies. "Traditional" artificial intelligence already drives essential functions such as recommendations spanning diverse use cases, automated document processing, algorithmic trading, supply chain planning, and cybersecurity.

The primary focus of machine learning currently revolves around simplifying routine tasks, excelling in easily automatable assignments, and outpacing human speed in those tasks. These AI applications are swiftly becoming commonplace in everyday work environments, undoubtedly offering value in terms of speed, time efficiency, meeting compliance standards, and more. However, these advantages have become basic requirements for enterprises.

This new era of machine intelligence, characterized by generative AI—regardless of its true level of "intelligence"—has undeniably sparked extensive exploration and investment, and for valid reasons. Tools like ChatGPT, Bard, or Dall-E showcase remarkable technical prowess.

Generative AI holds the potential to unlock substantial value and carries vast implications across all work domains. It's tempting to assume that due to its immense data processing capabilities and extraordinary abilities, generative AI might overshadow other forms of machine intelligence. It's easy to imagine that with some clever "prompt engineering" and adept "fine-tuning," it could generate solutions for any problem. Sounds plausible, doesn't it?

ChatGPT has the potential to expedite human tasks. I recently came across a story about a software tester who juggles multiple responsibilities, overseeing IT operations for a small New England farm. When the farm's management tasked the tester with finding a specific IoT solution for their hydration system, what typically would have taken several days was accomplished in a matter of hours. Leveraging ChatGPT, the tester efficiently compiled a list of nearly 60 requirements by blending their expertise and specialized experience in agricultural IT with generative AI, significantly accelerating their work.


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.

Causation and Effectiveness


This brings me to an intriguing facet of composite AI: Causal AI.

Causal AI transcends generative AI or machine learning predictions and automation. Unlike these technologies, which lack conceptual understanding and discernment, Causal AI can scrutinize input data and, if the model is appropriately trained, make decisions akin to human reasoning. Through Causal AI, it becomes possible to encapsulate employees' judgment within specific systems or workflows. This isn't merely about task automation; it's about enhancing decision-making.

With Causal AI, professionals can elevate their skills and be equipped to make quicker, better decisions. Over time, there's potential to entrust such models with specific decision types. By leveraging the right dataset and confiding in it for certain decision categories, organizations can augment their entire workforce with enhanced decision-making capabilities, fostering speed and efficiency.

Though it's a few years down the road, it represents the next evolution in AI. Consider the potential value this technology can unlock. Causal AI extends beyond time-saving and financial gains. It encompasses predictive forecasting coupled with recommendations for tailored sales strategies targeting specific customer segments. For example, Causal AI can scrutinize stock market data, empowering financial institutions to make informed investment decisions by assessing the relationships among stock prices, economic indicators, governmental policies, and news headlines.

While machine learning aids in task automation within a supply chain, Causal AI goes further—it doesn't just replicate human tasks; it mirrors human decision-making and identifies bottlenecks and inefficiencies in processes. Beyond identification, it also fosters improvements in performance.

Causal AI serves as another avenue to achieve superior outcomes.

Implementations like Causal AI, within the broader scope of composite AI, will effectively blur the lines between technical and non-technical work. Consequently, the investment made in developing AI-enabled applications and smart apps will yield exponential growth in returns.


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."

Why MXTechies?

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Increase developer productivity and capacity.

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Extend automation without limitation.

Integrate data and logic from any data source, system, or service.

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