Category: digital-transformation

Exploring AI’s Impact on Jobs: Task-Based Analysis
AI Integration in Systems Engineering, produced using DALL-E, 2024-05-31

In the landscape of rapid technological advancement, the intersection of Artificial Intelligence (AI) and job roles is a topic of intense discussion. As Salman Khan insightfully points out in his new book “Brave New Words,” people aren’t replaced by AI but by individuals who leverage AI more productively. This perspective, while intriguing, calls for a deeper exploration of how AI impacts various job roles in today’s market.

Understanding AI’s Impact on Systems Engineering

One fascinating approach to gauge AI’s influence on jobs is proposed by There’s an AI for That. The website offers an ‘Impact Index’ for various job roles, including Software Engineer, Systems Engineer, Mechanical Engineer, and thousands of others. This index is calculated based on the number of AI applications that support tasks related to these roles. While specific and limited, this approach provides a more concrete perspective than speculative opinions like ‘I believe AI will never …’ or ‘Everybody will loose their jobs’ that are vague beliefs at best and sometimes even cause unnecessary alarm.

Breaking Down Job Roles into Tasks

To truly understand AI’s impact on jobs, it’s essential to break them down into their constituent tasks. For each task, we must examine whether AI can already support it effectively, whether AI needs refinement or regulation, or whether AI is currently incapable of supporting the task.

Sal Khan’s book serves as a real eye-opener in this context illuminating what can be achieved in one year if you go all in. E. g., the one-on-one AI tutor called ‘Khanmigo’ initially gone live along GPT-4 shall not give the final answers right away, but instead guide the student step by step (a so-called Socratic tutor). It can summarize the learning process and give valuable feedback to the teacher incl. recommendations. It can even identify weak spots the teacher might wish to focus on. And it assists the teacher in creating lesson plans saving a lot of time and freeing resources.

Drawing inspiration from this work, one could investigate systems engineering in a similar vein and develop an ‘MBSEamigo’ analogous to ‘Khanmigo’. The core idea here is to redefine roles based on tasks. In situations where role definitions vary, the strategy would be to identify common denominators and refine roles down to the tasks.

For instance, consider the role of a systems engineer. This job can be broken down into approximately 1,000 tasks. Each task can then be assessed for AI impact. Here’s a glimpse into some typical tasks and their AI impact:
- Running Coach: AI Impact: 100% | AIs: 12
- Codebase Q&A: AI Impact: 95% | AIs: 9
- Problem Solving: AI Impact: 90% | AIs: 15
- Diagrams: AI Impact: 50% | AIs: 12
- Data Protection: AI Impact: 50% | AIs: 4
- Conversations with Clients: AI Impact: 50% | AIs: 1
- Audits: AI Impact: 5% | AIs: 1
- 3D Images: AI Impact: 5% | AIs: 22
A Closer Look at Key Tasks

Running Coach (AI Impact: 100%)

In systems engineering, optimizing processes and workflows is crucial. AI tools can act as running coaches, continuously analyzing and improving these processes with precision and speed. For example, AI can automate routine checks and suggest improvements, ensuring systems run smoothly and efficiently.

Codebase Q&A (AI Impact: 95%)

AI-driven tools like code analyzers and automated testing frameworks significantly enhance codebase management. They can identify bugs, suggest fixes, and predict potential issues before they become critical, thereby reducing downtime and increasing productivity.

Problem Solving (AI Impact: 90%)

AI excels in problem-solving by offering data-driven insights and predictive analytics. For systems engineers, this means quicker diagnosis of issues and more effective solutions. AI can simulate various scenarios to find the most optimal solutions, saving time and resources.

Applying Foundational Engineering Principles

Incorporating AI into systems engineering must align with foundational engineering principles. This means ensuring that AI tools are used to enhance precision, reliability, and efficiency. Systems engineers should focus on maintaining robustness and accuracy in their projects while leveraging AI to handle repetitive and data-intensive tasks.

To do this:
- Verification and Validation: Regularly test AI tools to ensure they produce reliable and accurate results.
- System Integration: Seamlessly integrate AI into existing systems without disrupting core functionalities.
- Continuous Improvement: Use AI for ongoing analysis and optimization of engineering processes.
- Documentation and Transparency: Keep thorough documentation of AI’s role and decisions in the engineering process for transparency and traceability.
The Future of Systems Engineering with AI

The journey of AI in systems engineering is just beginning. By continually refining AI tools and expanding their capabilities, we can create more efficient, innovative, and secure systems.

As highlighted in Engineering.com, AI has the potential to revolutionize engineering by automating complex tasks, predicting system behaviors, and providing advanced analytics. Currently, using tools requires understanding how to manipulate and connect various elements, which involves tedious tasks like moving the mouse around. This repetitive work, similar to past challenges in the CAD world, is expected to be minimized in the future.

SElive also identifies potential AI use cases. This integration not only boosts productivity but also fosters innovation by enabling engineers to focus on more strategic and creative aspects of system development. One notable example is the Technology Summarizer, which employs AI algorithms to analyze and condense vast amounts of technical documentation. This tool helps engineers quickly grasp essential information, stay updated with the latest advancements, and make informed decisions without being bogged down by extensive reading.

The upcoming book “AI Assisted MBSE with SysML by Doug Rosenberg, Tim Weilkiens (mbse4u.com))” explores the integration of Artificial Intelligence in Model-Based Systems Engineering (MBSE) using the Systems Modeling Language (SysML). It highlights how AI can automate and enhance various MBSE tasks and provides methodologies along a comprehensive, step-by-step design of a Scanning Electron Microscope.

Final Thoughts

The integration of AI in the job market isn’t about replacement but about redefining roles and tasks so AI can be leveraged for increased productivity. The key lies in understanding AI’s impact on individual tasks and adapting job roles accordingly. As AI continues to evolve, our approach to integrating it into our workflows must also adapt, ensuring we stay ahead in this dynamic field.

I am convinced that we will see a net job effect again as we have been seeing with automation. To the younger generations: experts are still needed. Do not let social media make you panic! Anybody can get anything out of ChatGPT, but only the expert speaks the sophisticated language of a discipline achieving much better results that also need to be curated by the very same expert.
2024-05-31
Some Thoughts on MBE for Digital Threads

This discussion emphasizes the importance of data-based engineering in the context of model-based (system) engineering, particularly in developing digital threads. Key principles proposed include prioritizing the system over data, integration over data islands, and data over visualization and documents.

Schematic Tool Grid

5-minute read:
On MBE for Digital Threads

Summary

The “data” in context refers to that used in information systems contributing to the digital thread, more specifically, data in tools located within an engineering architecture framework along vertical and horizontal dimensions. Several tools are involved in this framework, each serving different functions and dimensions. For instance, MagicDraw refines budget-level vehicle functions into product use cases, while Codebeamer provides product requirements that help shape these use cases.

The integration of these tools creates a grid that needs to be extended to account for other aspects such as the publication of model data and the development of test cases. While bundling combination cells into fewer tools like PLM can simplify this process externally, similar integration still occurs internally. Ultimately, this integrated tool grid should ideally reflect the productive systems, reinforcing the necessity of an integrated approach.

2024-04-07
Domain-Specific Languages explained for Stakeholders

Imagine you’re in a bustling professional kitchen. The heat, the noise, the coordinated chaos. This is a lot like software development, and you’re about to see why.

Get your introduction to the concept and benefits of Domain-Specific Languages (DSLs). It’s quite a topic, but this culinary journey will make it, well, digestible.

https://labs.openai.com/s/3mptF5d1dlDf7HmyqcgvCn1M

5-minute read:
Domain-Specific Languages explained for Stakeholders (mem.ai)

Summary

Amuse-Bouche (Software Development through Cooking): Welcome to the kitchen, where every role, from the head chef to the dishwasher, has a counterpart in a software development team.

Appetizer (Recipes do change with Time): Just as orders in a kitchen can change based on customer feedback or ingredient availability, tasks in software development are also adjusted in response to feedback and constraints.

Main Course (Managing Complexity for a growing Kitchen): As our kitchen grows, we need tools and techniques to manage the complexity. Imagine a Kitchen Modeling Language (KitchenML) that helps us coordinate all the moving parts.

Dessert: Finally, let’s reflect on our journey. We’ve seen how a kitchen and a software development team can be similar, but also how they differ. One is physical and sequential, the other virtual and iterative.

2023-09-24
Tool Styles for Architecture

#architecture #clarity #velocity #direction #enterprise #modeling

When doing enterprise architecture as well as systems engineering (or system architecture in detail) the question arises if there can be one meta model like Archimate and one tool that does it all.

That means, supporting the strategic portfolio level (comparable to city planning) as well as the development-oriented system level (architecture for one building at a time).

Roles vs Tool Styles

A very important requirement if talking about ‘enterprise’ is easy access to the captured landscape and its building blocks if for business products, applications, data, or technology as well as blueprints, planned architectures, and governance. This access must be provided for a range of users comprised by many people in the enterprise very probably having various roles and skills.

Since all this architectural information shall not only be consumed but also improved and maintained in a distributed fashion, it becomes clear that a tool focusing on diagram-first modeling style cannot be the answer no matter if based on UML, SysML, Archimate, or any other formal modeling language. The reason is that most of the users are not able to model and only a fraction can be taught due to the costs. Mostly, the focus lies on roles already having a certain skill set you can easily build upon, i.e., mostly roles matching the word ‘architect’. Prominent examples of such expert tools are MagicDraw (Cameo) by No Magic (now Dassault Systèmes), ARIS Architect by Software AG, Adonis by BOC, Innovator by MID, Enterprise Architect by Sparx, and others. But, as we will see, any of these can be part of your overall story.

Since the average user needs a tool that makes live easier and not harder, most EA tool vendors have focused on an approach that is data-first ERP style – typically providing web-based access to collected portfolio information (products, applications, business objects, technologies, and such). That information is presented as profiles or sheets like for each application which can also be updated via data forms. Tools like Alfabet (planningIT) by Software AG, LeanIX by LeanIX, LUY by iteratec, ADOIT by BOC, and others follow this path. From the captured data, they automatically produce dynamic graphical or tabular reports. Some of these tools also support Archimate ranging from basic import over addition to their own metamodels to own metamodels based on Archimate.

But why do EA tools always provide more than Archimate provides? This is because many important aspects in daily life are missing in Archimate like roles and permissions, multi-tenancy, life cycle information (planned, active, deactivated; generally state per date interval), portfolio planning capabilities (as-is, plan, to-be; the later with alternatives), tool integration features (requirements, publication, test management), and a lot more.

Scaled Architecture

On the other hand, the last decade has shown, that focusing only on the strategic portfolio level ignoring the reality on the ground easily leads to the ivory-tower syndrome producing badly accepted plans to change the IT landscape.

In order to avoid this, it is important to couple portfolio data with refined software and hardware architectures. The portfolio acts as the common structure and its content had better reflect the software and hardware inside (compare with reporting). And that’s where the above mentioned architects come into play again. They can bridge this gap by drilling down deeper to system architecture level and even further.

In that case, diagram-first modeling style tools for experts are more appropriate. As mentioned above, these are typically based on UML, SysML, Archimate, another modeling language, or even a combination of those. Modeling tools supporting Archimate as a dialect can make integration with enterprise architecture tools that are also supporting Archimate a little easier.

Conclusion

Being able to address both worlds is an important issue and not an easy task. Common meta models may help, but are not a must. More important is the ability to map high-level enterprise architecture blocks to medium-level system architecture content and that in turn to low-level system design content which can also partly be reverse engineered directly from running systems.

There are also tools that address both tool styles like ADOIT or upcoming versions of Bpanda that might be helpful, too. Let’s call it hybrid data-diagram style. Again, it is not a must, especially if different tools are already set in the organization and shall be integrated. The options are ranging from built-in integration features like export/import capabilities to separate integration tools like Smartfacts which provides a portal merging data from different tools via OSLC or classic synchronization.

2021-05-08