AI is now essential for software architects. While it may seem that AI has recently become important, it has been around since the 1950s. In this episode, we discuss how AI affects modern software architecture and why understanding AI is crucial for our careers. We explore the new challenges and opportunities AI brings, such as the need for structured data and the importance of ethical considerations. As we navigate this complex landscape, it is clear that knowledge of AI will shape the future of software design and architecture.

Takeaways:

1. AI has been an integral part of software since the 1950s, not just a recent trend.
2. As software architects, we need to understand AI's applications and implications in our work.
3. AI creates new architectural challenges and opportunities that we must navigate effectively.
4. Data management is critical for AI systems, requiring careful structuring and ethical sourcing.
5. AI introduces unpredictability in software behavior, requiring us to plan for uncertainty.
6. Ethics in AI is now a significant concern for architects, affecting our design decisions.

Links referenced in this episode:

1. softwarearchitectureinsights.com

Hello and welcome to Software Architecture Insights your go to resource for empowering software architects and aspiring professionals with the knowledge and tools they require to navigate the complex landscape of modern software design.

It seems like AI has become suddenly important to software architects in nearly all indust.

It's the latest and greatest technology in a world of fast moving technologies.

Actually, AI is not suddenly important.

But the silver bullet promise of AI has remained more science fiction than fact.

But in recent years, AI has gone from futuristic concept to everyday reality.

AI is no longer just a science fiction concept, nor is it a research topic in a university computer science department.

Instead, AI is everywhere.

It's on your phone, it's in your car, it's in your home, and it's making its way into nearly every application you use.

As a software architect, knowledge about AI is not only important, it's essential for your career.

You may or may not be heavily involved in AI now, but increasingly you will be.

Now, most of us won't be training new AI models or devising new AI systems ourselves.

There are experts who do that.

And if you're interested in being one of those experts and in AI model development, that's great.

But all of us, whether we are AI experts or not, will be consumers of AI models and all flavors of AI tools.

You'll be using AI tools to help you write and create plans and documents.

You and your team will be using AI tools to develop your applications.

Whether it's AI assisted design, customers, code development or testing, you will use AI to assist you in making design and architectural decisions.

And increasingly, you will be integrating AI components directly into your applications you are architecting.

AI is everywhere in nearly every industry.

In healthcare, AI is used to scan medical images.

It's used to examine health surveys and doctor's notes in order to assist in diagnosing diseases and complications.

In finance, AI is used extensively for fraud detection.

It's also a major component in automated customer service systems.

It's even used to predict market responses and futures in order to make timely investments.

In retail, AI drives recommendation engines on the front end and inventory management and supply chain optimization on the back end.

In software itself, AI is changing how we write code, how we test code and how we deploy it, and even how we design and architect entire systems.

The Application you are architecting may not specifically be labeled AI powered, but chances are somewhere in a stack from infrastructure to end customer experience, AI is involved in creating, operating and supporting your application.

Today, as a software architect, you'll be involved in aspects of AI other than being a consumer at one end or being an AI model developer at the other end of the spectrum.

You might be involved in understanding the ethical implications of AI usage.

You might be involved in understanding the current limits of AI and how those limits compare to to the expectation hype that surrounds it.

You might be involved in figuring out creative ways to get around limitations and issues with current generation AI systems.

And you might be involved in following AI trends and advancements to understand how these enhancements can impact the applications you are building and architecting.

In all of these cases, one thing is clear.

You need to understand what AI can do, what it can't do, and how it changes the software architecture landscape.

So why does all of this matter to you as a software architect?

Well, because AI introduces brand new architectural challenges.

And along with those challenges, it creates even more opportunities to properly utilize AI systems.

Other components of your system become even more important than they were before.

Data Pipelines Become Critical AI is hungry for data.

Tons and tons of data.

We tend to just throw whatever data we have available to our AI systems.

But more and more we need to provide data in a structured and format reasonably optimized for AI's consumption.

This includes labeling data appropriately, providing traceability of the data, and ethically sourcing the data.

That affects how you design systems for data ingestion, storage and governance.

AI model lifecycle management is now part of the broader system life cycle.

Which AI models and what version of those models you use can have wide ranging impact on your application's performance?

Deploying, monitoring, retraining and versioning AI models becomes a task as important as data management, application version pipelining or infrastructure revision management.

AI is an integral part of the DevOps tool chains and system boundaries are shifting.

Traditional software is rule based and relatively predictable.

If you tell an application to do something, it typically will do whatever it was designed to do over and over and over again.

Modern AI systems introduce probabilistic behavior.

Ask an AI system twice to do the same thing and you will likely get two different results.

You need to architect for uncertainty, explainability and human oversight.

And finally, ethics become an architectural concern.

Bias, misuse and hallucinations are very real problems in AI systems.

They introduce architectural risk into your applications that were not present before.

As an architect, your role is to bring structure to complexity.

AI adds a new dimension of complexity, but AI also brings a powerful new set of tools to your application.

As a software architect, you do not have to become an AI expert, but you do need you must become familiar with AI and understand its value, its potential, its problems, its risks, and its real and virtual costs.

Thank you for joining us on Software Architecture Insights.

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