In many cases, the biggest challenge of delivering a policy administration system has not been a matter of time – but of predictability. With the increasing intricacies of requirements and growing number of dependencies, the task of keeping everything aligned has become much harder. However, based on recent experience, embedding AI into the delivery life cycle appears to help address these concerns.
Why PAS Implementation Has Traditionally Taken Years
Launch and operation of a policy administration system require careful planning, extensive documentation and coordination across various departments of the insurer. The reason is that such a platform supports a variety of activities, including underwriting, policy lifecycle management, quotation, billing and other functions.
Complexity in PAS implementation is rarely linked to any single technical factor. It emerges gradually as a result of expanding requirements and growing number of parties involved. As the result, inconsistencies that would otherwise be small can easily turn into costly issues once development and integration are started.
In traditional PAS implementations, consisting of development, implementation, integrations and testing, the duration is estimated at 18 to 36 months. Usually, delays emerge when information flows slowly between teams, when inconsistency in one phase results in rework in the next, or when documentation phases add to ticket preparation and testing time.
Where Complexity Slows Delivery
There are certain common patterns where complexities occur. First, structuring business requirements into implementable artifacts always poses its own challenges. Requirements tend to be driven by regulatory constraints, product diversity, regional regulations, and other factors. To structure them into technical documentation and validate them is a complex process, which traditionally relies extensively on manual effort.
Documentation is another challenge that must be addressed before proceeding to ticket preparation. PAS initiatives tend to consist of 3,000 tickets, covering development, implementation, integrations, configuration and testing tasks. Each requires proper description, acceptance criteria, and dependencies on related tasks. Once all this documentation work is performed manually, differences in wording and structure lead to inconsistencies, which can slow down the process considerably. Moreover, the resulting fragmented documentation creates serious obstacles when teams realize that certain aspects could have been clarified before entering the implementation stage.
Use Cases Where AI Makes a Difference
Fortunately, AI technologies enable teams to overcome some of the mentioned complexities, especially if applied consistently throughout the delivery lifecycle of a project. In recent experience, the benefits were not produced by adding artificial intelligence as a separate layer but by integrating it into the day-to-day workflows of analysts, developers and QA engineers.
Requirements analysis
It is not uncommon for requirement documentation to come from several sources at once – Confluence pages, email threads, discussion minutes, and sometimes even informal chat discussions. In their initial form, requirements are unstructured, repetitive and occasionally contradictory.
In an AI-driven delivery approach, AI analyzes requirements received by different channels. It structures them according to the domains that they refer to (quotation, underwriting, billing or policy servicing). The tool groups requirements together, detects potential duplications, contradictions between different versions, and identifies missing information.
Importantly, AI performs requirements analysis, not business analysis. The output generated by AI is carefully validated and reviewed by the analyst team and then used as a basis for further actions. This reduces the time spent on clarifying the contradictions and inconsistencies in requirements before any tickets are created.
Creation of Jira tickets
With requirements structured, the next logical step is to translate them into ticket format. This approach relies on Claude to analyze structured requirements and produce a set of Jira tickets. For each ticket, it provides description, acceptance criteria, and dependencies.
This is still not done in an automated fashion. All tickets prepared by AI undergo validation and adjustment to ensure completeness. The main benefit here is the increase in speed and consistency. Ticket preparation becomes noticeably faster, and developers receive clearer instructions, with less ambiguity and inconsistencies.
Assisting developers
During development, AI is used primarily to provide support to developers, rather than making decisions on their behalf. Developers use it to discuss implementation strategies, ask for code suggestions, check changes in the first draft and solve problems that arise during debugging.
AI does not replace developer judgement or creativity. All its suggestions and recommendations undergo validation and confirmation. However, it substantially reduces the amount of context-switching. Developers spend less time looking for, interpreting and rewriting the same information over and over again. Instead, they are able to focus on solving the problem itself.
Analyzing releases and detect conflicts
When multiple developers work in parallel, conflicts may occur. After tasks are assigned, completed and bundled into a release candidate, merge conflicts and overlaps of changes are identified in the version control system.
AI assists in detecting conflicts and resolving them faster. Change descriptions, notes made by developers, and logs of conflicts in version control are analyzed, and AI summarizes them in clear terms. It suggests possible ways to resolve the conflict and checks whether the change proposed will not cause problems elsewhere. However, the final decision belongs to the developer.
Generation of test cases and assistance in troubleshooting
Generating test cases and preparing test scenarios and data is one of the clear-cut use cases where AI helps achieve tangible results. On the basis of structured requirements, AI generates test cases, including rare edge cases that may be overlooked without the help of automation tools. Additionally, for testing APIs and frontend solutions, it prepares test cases and test data.
Once tests are carried out, another use of AI appears to provide the best results – it analyses test results and helps identify possible root causes of failures. This allows testers and developers to find the root causes much faster and thus avoid wasting time on resolving unrelated problems. While it does not eliminate errors, it helps speed up the test cycle and increase its coverage.
Code quality check and SonarQube integration
AI is also used to assist the work of SonarQube in analyzing code and finding issues. Upon scanning the code automatically, SonarQube identifies issues in the code and creates a Jira ticket with an explanation of the problem. Then, it is passed to AI, which provides clear information on the nature of the problem and potential suggestions to deal with it.
Developers use this information and decide on what suggestions to follow. This creates a semi-automated feedback loop. Code is analyzed automatically, AI helps make sense of the problem, and the issue is closed. The code does not correct itself, but the process becomes much more streamlined and consistent.
Case Study
Typically, a PAS implementation project involving 2,000-3,000 tickets and 10+ specialists has its own peculiarities. Traditional development approach implies that such a project, with development, integration, testing and implementation, will span approximately 18 to 36 months.
In one recent implementation, the same scope took a completely different course. By integrating AI into almost every aspect of the delivery process, the team managed to maintain continuity and prevent accumulation of delays.
Approximately within six months, the core phase of PAS implementation reached its first production-ready release. Notably, this period includes only implementation and does not cover the end-to-end life cycle, including integration and further activities after go-live. Nevertheless, this period can be considered impressive in comparison with conventional delivery methods.
What helped in accelerating this phase? First, preparation processes were significantly shortened. Requirement preparation and validation took less time. Development was more efficient due to lack of unnecessary context switching. Integration and testing also progressed smoothly, without unnecessary interruptions. This translated into less rework and, ultimately, faster development pace.
Key Learnings From Implementation
Here are some key learnings from our experience.
First, complexity should be addressed as a process problem, not necessarily a technical one. Many bottlenecks originate from ineffective information exchange, and well-designed workflows can eliminate much of them.
Second, good documentation practices pay off. While AI helps with achieving more consistency, it is essential to have proper documentation discipline, in order to achieve reliable results.
Third, early-stage clarity pays off exponentially. The clearer and more structured the requirements are, the better and smoother development, integration and testing go.
Managing Complexity as a Strategic Capability
Shorter timelines alone do not define success in PAS delivery. Reliable implementation depends on both speed and predictability. What became clear from this experience is that AI does not simply accelerate development. Its greater value lies in helping teams manage project complexity in a more disciplined and predictable way. When requirements are structured earlier, communication becomes clearer, and dependencies are visible sooner, teams spend less time correcting misunderstandings later. In practice, this translates into less rework, better alignment between business intent and technical execution, and fewer delays caused by issues discovered too late.
For insurers planning modernization efforts, this is perhaps the most practical takeaway. Large PAS initiatives will remain complex by nature. The difference comes from how that complexity is handled. When it is surfaced earlier, structured clearly, and addressed at the right stage, projects become more predictable and easier to control. In that sense, the real contribution of AI is not speed alone, but the ability to manage complexity with greater confidence throughout the lifecycle.
