An AI tool that helps product managers prioritize features. It analyzes customer feedback, support tickets, market trends, and engineering effort to score potential features based on their likely impact on key metrics like user growth, retention, or revenue.
Most software companies have far more feature requests than engineering capacity. Requests arrive through support tickets, sales calls, app store reviews, customer interviews and the founder's inbox, and they rarely land in one place. When roadmap planning comes around, product managers end up relying on whoever argued loudest, the biggest customer's latest email, or a spreadsheet that was last updated months ago. The result is a roadmap that is hard to defend and features that ship without clear evidence that anyone needed them.
Frameworks such as RICE (reach, impact, confidence, effort) and ICE help, but filling them in by hand is slow and subjective. Someone has to read hundreds of tickets to estimate reach, guess at impact, and chase engineers for effort estimates. An AI product roadmap prioritisation assistant does the reading and first-pass scoring, grouping related feedback into themes, counting how many customers and accounts each theme affects, and producing a draft score that a product manager can challenge, adjust and explain to stakeholders.
Connect the feedback sources: Zendesk or Intercom tickets, sales call notes, survey responses and a feature request board. Each item is pulled in with its customer, plan tier and date attached.
A language model reads each item, tags the underlying need rather than the literal request, and clusters similar feedback into candidate features so duplicates and variations are counted together.
The assistant joins those clusters to Jira epics and to account data, so each candidate feature shows how many customers raised it, which segments they belong to and whether any are at risk of churning.
Engineering leads add or confirm effort estimates. The tool then calculates RICE or ICE scores, highlights where confidence is low, and shows the evidence behind every number.
Product managers review the ranked list, override scores where strategy or dependencies matter, and export a roadmap summary with linked source feedback for leadership and customer-facing teams.
A product manager is deciding between three major features for the next quarter. The AI assistant analyzes all available data and scores 'Feature A' as having the highest potential impact on user retention, providing a data-driven justification for the roadmap decision.
NLP for feedback analysis, Scoring Algorithms (RICE, ICE), Integration with Jira, Zendesk, etc.
Support tickets and call notes usually contain names, email addresses and sometimes health or financial details that customers volunteered. Under the Privacy Act 1988 and the Australian Privacy Principles, use of that personal information for internal product analysis is generally fine when it relates to the purpose it was collected for, but strip or mask identifiers before sending text to an external language model where you can. Check where your LLM provider processes data; several offer Australian or regional hosting options if customers or contracts require data to stay onshore. If you sell to government or regulated clients, review their contract clauses on subcontractors and offshore processing before connecting their tickets.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
No. It produces a ranked, evidence-backed shortlist and makes the reasoning visible, but product managers still decide. Strategy, sequencing, dependencies and commercial commitments sit outside what the model can see. Teams generally use the scores to structure planning discussions and to explain decisions to stakeholders, not to replace judgement.
Expect around one to two months. The early weeks go into connecting Zendesk, Jira and account data, cleaning up tags and agreeing scoring definitions. The remaining time is spent checking the theme clusters against what the product team already knows, adjusting weights, and running one real planning cycle with the tool alongside the existing process.
The main drivers are the number of feedback sources to integrate, how messy the historical data is, and whether you need a custom interface or can work inside Jira and a dashboard tool. Ongoing costs are mostly LLM usage for reading new tickets, which scales with ticket volume, plus a small amount of maintenance when source systems change.
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