Generative AI Accelerates Application Modernization
Key Points
- Modern applications are deeply embedded in daily life, and their heterogeneity and inter‑dependencies across an organization make upgrades risky without comprehensive, enterprise‑wide planning.
- Application modernization means updating legacy systems with modern capabilities to generate new business value, driven by goals such as leveraging innovation, boosting productivity, or meeting compliance requirements.
- The modernization journey typically follows two stages: an advisory phase that inventories and assesses each application’s fate (retire, SaaS, containerize, refactor, or rewrite) and a planning phase that sequences migrations while ensuring coexistence with legacy assets.
- Generative AI can dramatically accelerate both phases by automatically summarizing poorly documented “spaghetti” code, identifying change‑needed components, and generating documentation, cutting advisory cycles from weeks to days.
- By using AI to uncover embedded business logic early in the planning stage, organizations can create more accurate migration roadmaps, reduce reliance on scarce specialist developers, and achieve faster, lower‑risk modernization.
Sections
- Challenges of AI‑Driven Application Modernization - The speaker explains how pervasive, heterogeneous software systems require organization‑wide, AI‑guided planning to upgrade legacy applications without disrupting complex interdependencies.
- AI-Driven Legacy Code Transformation - The passage describes how AI can speed up planning, reverse‑engineer, generate, and automatically convert legacy code to modern platforms, citing IBM’s real‑world success.
- Embracing Continuous AI Transformation - The speaker highlights the rapid rise of generative AI models for application modernization, urging organizations to begin AI‑driven transformation now to gain a competitive edge.
Full Transcript
# Generative AI Accelerates Application Modernization **Source:** [https://www.youtube.com/watch?v=AINoTKMZSGI](https://www.youtube.com/watch?v=AINoTKMZSGI) **Duration:** 00:07:43 ## Summary - Modern applications are deeply embedded in daily life, and their heterogeneity and inter‑dependencies across an organization make upgrades risky without comprehensive, enterprise‑wide planning. - Application modernization means updating legacy systems with modern capabilities to generate new business value, driven by goals such as leveraging innovation, boosting productivity, or meeting compliance requirements. - The modernization journey typically follows two stages: an advisory phase that inventories and assesses each application’s fate (retire, SaaS, containerize, refactor, or rewrite) and a planning phase that sequences migrations while ensuring coexistence with legacy assets. - Generative AI can dramatically accelerate both phases by automatically summarizing poorly documented “spaghetti” code, identifying change‑needed components, and generating documentation, cutting advisory cycles from weeks to days. - By using AI to uncover embedded business logic early in the planning stage, organizations can create more accurate migration roadmaps, reduce reliance on scarce specialist developers, and achieve faster, lower‑risk modernization. ## Sections - [00:00:00](https://www.youtube.com/watch?v=AINoTKMZSGI&t=0s) **Challenges of AI‑Driven Application Modernization** - The speaker explains how pervasive, heterogeneous software systems require organization‑wide, AI‑guided planning to upgrade legacy applications without disrupting complex interdependencies. - [00:03:03](https://www.youtube.com/watch?v=AINoTKMZSGI&t=183s) **AI-Driven Legacy Code Transformation** - The passage describes how AI can speed up planning, reverse‑engineer, generate, and automatically convert legacy code to modern platforms, citing IBM’s real‑world success. - [00:06:14](https://www.youtube.com/watch?v=AINoTKMZSGI&t=374s) **Embracing Continuous AI Transformation** - The speaker highlights the rapid rise of generative AI models for application modernization, urging organizations to begin AI‑driven transformation now to gain a competitive edge. ## Full Transcript
There is software all around us.
From the gadgets we use, to how we communicate,
it's embedded everywhere. And because applications
are so integrated in our everyday lives,
we need to make sure that they are maintained
and upgraded constantly.
The challenge when it comes to modernizing applications
lies in heterogeneity and complexity.
An organization, as you can imagine,
is built on multiple systems
and each system is built on many applications.
These applications, in turn, can rely on external services,
like APIs, to perform specific functions.
Each piece plays its part.
Upgrading any one component could potentially
disrupt this delicate balance.
So, to modernize successfully, the planning must span
the entire organization.
That's where I come in.
Welcome to AI Academy.
My name is Maja Vukovic.
I'm an IBM Fellow and Researcher specializing
in strategies for AI driven application modernization.
So, what is application modernization?
Simply put, application modernization involves
taking an outdated computing system
and updating it with modern, well-aligned capabilities
and features to create new business value.
Businesses are constantly reinventing their ways of working
for multiple reasons.
To take advantage of the latest innovation,
to enhance productivity, or sometimes just to avoid
non-compliance risks. And every time business operations
are refined, the applications powering them
need to be modernized as well.
Imagine a system with a lot of unstructured code--
tangled, messy and difficult to read.
To make sense of this spaghetti code,
you either need the subject matter expertise
or you need to apply complex techniques
that can help you extract it.
But because of the shortage of developers
with the right skills, it's easier said than done.
This is where generative AI can make a critical difference
to accelerate your application modernization journey.
Once you make the decision to modernize,
the process will kick off in two phases.
The first is the advisory phase.
Here you take the portfolio of applications
written in different languages on different platforms
and understand which among them need to be retired,
transformed to software-as-a-service (SaaS),
containerized, refactored, or rewritten.
This is to understand the ROI
and figure out if a transformation is needed at all.
Next is the planning phase, where you lay out
the right order to migrate the applications,
understanding how they will coexist
with legacy applications and test them.
The advantages of generative AI start to take effect
as soon as you begin the modernization process.
In the advisory phase, for instance,
you can use generative AI to summarize the legacy code,
which very often is poorly documented,
identify pieces of it that need to be changed
and generate documentation for it.
As a result, in some scenarios, AI can help shorten
the advisory cycle from weeks to days.
As you begin the planning phase,
you may first want to understand the business logic
that's embedded in the existing code.
With reverse engineering and AI, you can quickly summarize
the code and analyze legacy applications to decide
what to retain and remove.
Once you have figured out what to do with the code,
the code generation capabilities of AI let you create
millions of lines of code very quickly,
saving valuable time and labor.
With just the natural language prompt,
developers get a head start, or in some cases,
even fully generated code where developers simply validate.
It's also likely that you will have some legacy applications
that are not compatible with the new platform,
a cloud platform, for instance,
and you need to retain these applications.
In these cases, you can use code conversion to rewrite
or rather translate the existing languages to newer ones.
With AI, there is the opportunity to automate
the conversion and enable it for many languages.
During the conversion process, developers can now use AI
to identify and eliminate similar code snippets
and replace them with common services.
And beyond theory and strategy, we are seeing success
in real world scenarios.
IBM has been working with a large IT organization
on their transformation.
They were looking for their legacy code
to be automatically converted to the new target language.
And with AI existed code conversion, we were able
to automatically translate about 85% of this custom code.
And not only that, this translated code also aligned
with their existing DevOps practices,
so massive time and cost savings.
In another instance, a large banking client was looking
to modernize and refactor their applications
built over the past decades.
They wanted to make applications more agile in the cloud
while avoiding significant investments in redevelopment.
We used AI to recommend the components
and generate the stubs for these new components
and identify any debt code,
any unused code in their applications.
With AI, we were able to accelerate the process
of refactoring and reduce the application transformation
time from months to weeks.
Think about how this was done in the past.
Transformation planning, to give an example,
was done through extensive rules and code in spreadsheets.
When you have hundreds of servers
and applications to migrate, imagine the complexity
of managing it all through these sheets.
Every time you hit a bump, you had to revisit
the spreadsheets to make changes.
And to further complicate matters,
each customer has their own unique requirements,
which means you had to manually craft these journeys.
AI helps to radically streamline the workload migration
to the new architecture.
It also enables teams to observe workload performance
in the new environment
so they can optimize and refine the process.
With AI, you gain the capability to adapt on the fly.
One of the biggest advantages of AI assisted transformation
is the ability to learn and adapt faster.
There's a saying that has always held true in tech,
"What's new today is legacy tomorrow."
The point is that modernization isn't one and done effort.
It's an ongoing journey.
So, having the best processes and practices in place
is often more important than one specific
modernization outcome.
I love this stuff.
The challenge of getting better and faster every day!
We are in what feels like a golden moment
for transformation.
There's so much innovation happening
that my Slack channels are always lighting up.
A new model comes up practically every minute
across various domains.
And the large language model trained for coding
that we rely on for application modernization
is also seeing a lot of action.
And with the pace of innovation in AI models,
this is just the beginning.
So, what does the future hold?
With the developments in AI coming in fast and furious,
the future will be full of endless opportunities.
Imagine generative models teaming up
and collaborating on tasks,
or systems starting to self-heal and self-evolve.
Remember the periodic upgrades to applications
I talked about earlier?
What if the applications could seamlessly transform
and get better on their own?
Compared to traditional transformation,
this stuff sounds like science fiction,
but it's going to get real very quickly.
The possibilities are limitless, and my advice to everyone
is to start planning now.
Whether that's on your own or with a partner,
kick off your AI transformation practice today
because it's going to be a huge competitive
differentiator going forward.
Thank you for watching.
Please join us again for future episodes of AI Academy
as we explore some of the most important topics
in AI for business.