How Avaya Uses AI to Modernize Contact Center Workflows

One of the most powerful, practical uses of AI in contact center modernization is helping enterprises understand what they already have. Legacy workflows can contain years of routing logic, prompts, integrations, variables, exception paths, and custom code. I’ve seen how much time can go into simply reconstructing that logic before modernization can begin. 

At Avaya, we use AI to analyze existing customer configurations, system artifacts, knowledge assets, and source materials, including  Avaya Aura / CC Elite and Avaya Experience Portal (AEP). The AI engine documents existing business logic, automatically generates reviewable Avaya Infinity workflows, and validates the output.

Some customers have spent decades building contact center logic around how their operations actually work. AI-assisted modernization can reduce months of manual discovery and workflow reconstruction to hours, while helping teams carry proven logic forward.

Here’s what happens behind the scenes.

We Start with the Application Our Customers Already Run 

AI-assisted contact center modernization starts with the source, not a blank workflow designer.

A standard source project can include the main flow XML, project variables, properties files, XML configurations, grammar files, and Java backing classes. The process can also work from WAR and JAR source packages when the original OD project files are no longer available.

Those files can contain years of routing decisions, variables, prompts, custom logic, integrations, dependencies, and exception handling. Starting with the source gives the AI a more direct view into the workflow logic our customers already rely on. 

An existing Avaya Orchestration Designer application provides source material for AI-assisted workflow modernization. 

1.  AI turns technical configuration into something people can review. 

The flow analyzer reads the supported source materials and creates a structured description of the application. It can capture workflow steps, variables, routing behavior, configuration elements, grammar choices, custom logic, and dependencies in language that developers and reviewers can understand. 

Now imagine the difference for the team responsible for the migration. Instead of beginning with source files and institutional memory, business and technical stakeholders have a clearer representation of what the application is doing today. They can review a transfer rule, identify a dependency, trace an exception path, and discuss whether the logic still reflects how the business operates.

AI-assisted analysis turns existing Avaya Experience Portal and Orchestration Designer source material into a plain-language, human-readable description of the workflow and its business logic.

2.  AI generates the Avaya Infinity workflow. 

Once the existing application has been analyzed, the workflow converter uses that structured analysis together with the Avaya Infinity component catalog to generate Avaya Infinity workflow JSON. Where an application contains subflows, the process can also generate linked subroutine workflows seamlessly into Avaya Infinity. 

This is an important distinction from simply summarizing legacy code. The AI is helping create a working modernization foundation that engineers can review and continue developing in Infinity.

Think about a workflow containing a variable set in one part of the application, a decision farther downstream, an audio prompt, and a transfer based on the result. The job is not merely to describe those elements. The workflow needs to be reconstructed using the corresponding components and relationships in Avaya Infinity. 

Structured workflow analysis is used to generate Avaya Infinity workflow JSON for engineering review. 

3.  Thirty-six deterministic checks test the generated workflow.

The generated workflow is put through 36 deterministic validation checks performed by AI agents. Those checks look for structural issues such as:

• Orphan nodes
• Invalid links
• Missing required elements
• Incorrect ports
• Orchestration Designer remnants that should not carry into an Infinity workflow

My team and I deliberately separated the work AI is good at from the checks that should behave the same way every time. AI helps interpret and reconstruct complex workflow logic. Deterministic validation checks define structural requirements consistently. A disconnected node, invalid link, or missing required element can be identified before the workflow moves further through the process. 

4.  Avaya compares the new workflow with the original.

The process scores the generated output against the original AEP WAR source files or Orchestration Designer source, depending on the input used, across eight weighted areas: node coverage, routing and transitions, webhooks, decision logic, variable initialization, transfer configuration, data integrity, and audio and prompts.

The migration score gives Avaya’s modernization team another layer of traceability. A transfer that did not map as expected becomes visible. A missing variable initialization can be investigated. A prompt or decision path that needs attention can be surfaced before the workflow reaches production testing.

For one customer, we recently put 18 existing workflows through the AI-assisted modernization process. Individual workflows and vector conversions that can require days of manual work can now be completed in minutes, depending on complexity. Across the broader modernization effort, this compresses work that traditionally takes months into hours.

We’ve also expanded our CM vector-to-Avaya Infinity work across 35 vectors, with 30 scoring A+ and seven reaching 100. Migration scoring has averaged about 95 out of 100. The results can vary with workflow complexity, so validation and engineering review remain part of the process.

5.  Automation gets another chance before a specialist has to step in. 

We’ve also built a self-healing loop into the pipeline. When validation identifies certain issues, or a workflow falls below the required migration-score threshold, those issues can be fed back into another attempt. The workflow is then validated and scored again, and the strongest candidate is retained for engineering review.

In this self-healing process, the AI agent can retry up to two times while retaining the best result produced. Automation gets an opportunity to resolve detectable issues before a specialist spends time on them, while engineering review remains part of the process.

Generated parent workflows and subflows are prepared for review before import into Avaya Infinity.

6.  People decide what is ready to move forward

The AI-assisted modernization process can prepare parent workflows, subflows, and associated assets for Avaya Infinity, including linking workflow artifacts and uploading audio assets. 

Generated workflows after import into Avaya Infinity.

Developers still review those artifacts before customer deployment. Customer and Avaya teams remain responsible for business decisions, functional testing, governance, acceptance, and rollout.

An imported workflow on the Avaya Infinity canvas, where developers can review and continue development. 

That human-in-the-loop model is central to how Avaya approaches enterprise AI. 

AI is most useful when it shortens the path from complexity to decision. It can help us analyze source materials, generate Avaya Infinity workflows, validate the output, and show developers where they need to look more closely. People still determine what is ready to move forward.

Why Reducing Discovery and Reconstruction Work Matters 

Generating workflow JSON is technically interesting. The business case is the time and expertise required to modernize at enterprise scale. For a large enterprise, modernization also consumes engineering capacity, customer subject-matter expert time, services resources, project budget, and operational attention. 

Consider what teams may have to do manually before a legacy contact center workflow can move forward: inspect source files, determine what the application does, trace routing and integrations, document dependencies, reconstruct the workflow, compare the new version with the original, troubleshoot differences, and prepare it for testing. 

AI can take on more of that repetitive analysis, documentation, workflow generation, comparison, and early validation work. In our automation work, individual workflow or vector conversions can complete in minutes, depending on complexity. Across the broader modernization effort, automating discovery and workflow reconstruction can compress work that traditionally takes months into hours.

Avaya developers and customer teams can spend more of their time reviewing business rules, exceptions, testing, governance, and decisions about what should carry forward.

A clearer view of existing business logic also gives teams a stronger foundation for what comes next. They can see what should be preserved, identify what needs to change, and manage future workflow updates with greater context. In Avaya Infinity, those workflows can extend beyond voice through outbound customer engagement, tandem care, automation and customer journey orchestration, AI performance analytics, and enterprise data and customer context. 

Across the AI work I’ve done, the business case becomes clearest when you look at the work around the model: the manual effort involved, how quickly teams can reach a decision, and where scarce expertise is being spent. 

For large enterprises, time shows up as cost. The opportunity is to spend less expert capacity rediscovering how the existing environment works and more of it deciding what the next experience should be.

Learn more about AI-Assisted Workflow Modernization