IdeaScale Product Update — Summer 2026

Most innovation platforms measure themselves by participation: how many ideas came in, how many votes they got. IdeaScale is built for the harder question: what did the program actually produce, and can you prove it?

This summer's releases pushed on that from several directions. Outcomes is here, connecting ideas to the results your leadership already reports on. The AI assistant that answers questions about your own community is worth a look if you have not switched it on, and the AI that keeps your idea pipeline clean got faster and reached the API. The reporting layer that shows where ideas actually go, forward and backward and out, is fully released.

Here's what matters from the work that shipped over the summer.

Outcomes: Available Through Your Customer Success Manager

Most innovation programs are measured on participation and then asked to justify their budget on results. Part of why that is hard is structural: the goals live in one place, whether a strategic plan, a spreadsheet or an OKR tool, and the ideas live somewhere else. Connecting the two is manual work someone does at the end of a cycle, largely from memory.

Outcomes puts them in the same system, in three tiers. Strategic Themes are the handful of priorities your organization actually organizes around. Beneath each theme sit Strategic Outcomes, the measurable objectives that define what progress means. Each carries a baseline and a target. And Impact is the level where an individual idea connects to a specific, near-term result.

How the tiers fit together. Read upward, community ideas each carry an Impact, Impacts roll up into Strategic Outcomes that have a baseline and a target, and outcomes sit beneath a Strategic Theme. Read downward, the targets shape which ideas arrive next. The figures shown are illustrative.

If your team already writes OKRs, the shape will be familiar. A theme reads like an objective and the outcomes beneath it read like key results. The difference is that the ideas contributing to each one are attached to it, not tracked somewhere else and reconciled later.

Concretely: we modeled what this would look like for Tesla, using nothing but its published quarterly results. A theme of Scale Sustainable Energy carries a strategic outcome of Grow deployed energy storage, baselined at the 49 GWh Tesla deployed over the trailing twelve months, with an illustrative target of 55 GWh by year end. Illustrative, because Tesla publishes no storage target of its own. Ideas ladder into it, and each carries an Impact recording the gigawatt-hours it is expected to contribute. Under a second theme, Accelerate Profitable EV Volume, a single idea to simplify the trim lineup ladders to two outcomes at once: reduce cost per vehicle and defend gross margin. That is the shape of it. An idea stops being something a moderator liked and becomes a line item against a number leadership already reports on.

A strategic outcome in Outcomes. Baseline, latest reported value and target sit across the top, progress against the target below them, and the community ideas linked to the outcome underneath. Tesla is not an IdeaScale customer; this is a hypothetical model built from Tesla's published results, used here to show the structure.

The structure changes what you can see, and when. Check-ins record progress against an objective while the cycle is running, so nobody is reconstructing the story in the final week. Objectives close as Achieved or Missed, so a cycle ends with a record you can plan the next one from. Outcomes follow your fiscal year and realign if you change it. And a working set of themes and objectives can be copied into another workspace, which matters when several departments or agencies run the same goal structure.

There are two directions in that, and the second one is the point. Idea-driven outcomes is the direction people expect: the number moves because of ideas, and a completed idea leaves a record of what it actually produced against what it was projected to produce. Outcome-driven ideas runs the other way. When the objective is visible in the same place people submit, contributors write toward it, and reviewers weigh a proposal against a number instead of a hunch. You get fewer ideas that are merely interesting and more that are aimed.

Those two directions feed each other, and that is where the compounding is. Every closed cycle leaves evidence about which kinds of ideas produced results and which did not, and that evidence is what the next round gets judged against. A program run this way should get better at innovating the longer it runs. That is a different proposition from getting better at collecting.

For federal programs, this means innovation activity reports against agency strategic plans and mission priorities, not alongside them. For enterprise teams, it means the program appears on the corporate scorecard in the units leadership already uses: dollars saved, revenue generated, risk reduced, time recovered.

The AI Assistant: Ask Your Program a Question

An innovation program accumulates more content than anyone can read. A community holding a few thousand ideas and ten times as many comments already contains the answer to most questions you would want to ask of it: what has been proposed in this area before, what people objected to, where an idea actually stands. Getting the answer means knowing what to search for, which is precisely what you do not know when the question is new.

IdeaScale's AI assistant answers questions about your own community in plain language. It is available from the apps you already work in: Idea Portfolio, Moderation, Members, Reporting, Whiteboard, Admin, and Ideation. Ask where you are, without going somewhere else first.

Answers are grounded in your community's own content. The assistant retrieves against your ideas, comments, and attachments, and answers from what it finds, not from a general model's impression of the question. Every answer cites the ideas and comments it drew on as clickable references, so you can open the source and check it yourself. That matters more than it sounds. An assistant that cannot show its work is one you have to take on faith, and faith is not a position you can defend when the answer goes into a report for leadership. The same discipline holds when the material is not there: if your community's content does not support an answer, the assistant says so instead of inventing one.

It answers with data, not only with prose. Ask for a table of active campaigns with idea, vote, and comment counts, or a bar chart of how ideas distribute across your community's tags, and the assistant queries your workspace and renders it. This is the half people do not expect from a chat panel, and for a program office it is the quiet differentiator: questions that used to mean building a report or exporting a spreadsheet become something you ask in passing.

The assistant asked to create a bar chart of idea distribution across community tags. It discloses its reasoning and renders the chart in the panel.

It can search the answers stored in your custom fields. Asking for "ideas from the Northeast region tagged for cost reduction" works even when region and category live in custom idea or profile fields, not in the idea text. The assistant resolves those fields itself. Results carry stage-specific detail too, including assessment values and refine-stage answers, so it can tell you where an idea stands in your workflow, not just that it exists.

It answers questions about IdeaScale itself. Ask how to set up a review stage and the assistant answers from IdeaScale's product documentation, so admins get setup guidance without leaving the tool. When a question needs both, a single answer can combine documentation with live data from your workspace.

It reads; it does not act. The assistant answers questions and retrieves records. It does not create campaigns, move ideas between stages, or alter your data. It also reads with your permissions: every lookup runs as you, so it never returns anything you could not already open yourself. That boundary is deliberate. Turning the assistant on carries no risk to the integrity of your program's records.

It opens with suggested prompts, so someone meeting it for the first time sees what it can do instead of an empty box and a blinking cursor.

Your content stays inside your environment. For federal customers, the assistant runs inside the FedRAMP boundary. Indexing and retrieval happen there, and your idea text is not sent out to an external service to be answered. That distinction is the difference between an assistant you can use and one a security review will not let through the door.

Search and AI: Finding What People Meant

Keyword search misses paraphrased intent. "Reduce wait times" doesn't match "shorten queues." For an innovation program that matters more than it sounds. If people can't find related work, ideas duplicate, comment threads fragment, and the institutional memory the platform is supposed to provide never materializes.

IdeaScale's semantic search matches on meaning, not exact phrasing, and this summer the engine underneath it was upgraded to a current version, improving precision and reliability across every workspace without making search slower or costlier. For federal customers, this analysis runs inside the FedRAMP boundary and idea text never leaves it. For an agency running a large multi-bureau program, better recall is the difference between "this has been proposed before" surfacing for a reviewer and the same idea arriving six times from six bureaus because nobody could find the first one.

The same engine feeds two things moderators live in every day: duplicate handling and sentiment. Most of this summer's work went into making both operate at the scale of a whole campaign, not one idea at a time.

Duplicate handling is now a complete workflow. Detection got noticeably faster: results in the moderator review list are cached, so the potential-duplicate view loads quickly enough to work through in one sitting. Idea Portfolio advanced search can filter by duplicate status, so you can pull every flagged potential duplicate in a campaign and work the list. And once found, duplicates can now be merged, and unmerged. Consolidating closely related ideas into one carries over the votes, comments, and attachments of each. Nothing is lost, and unmerge restores the separated ideas to their original standalone state. Everyone who contributed to a consolidated idea receives an email explaining what happened, so consolidation stops being something that silently happens to a member's submission.

The Potential Duplicates panel on an idea. Each match carries a similarity score, here 85% and 70%, and a checkbox: the moderator picks which ones to consolidate and merges from the panel. Member names in this example are placeholders.

That completeness is the point. Detection flags a possible overlap, merging resolves it, and because the action is reversible and contributors are told, a moderator can clean up confidently instead of hesitating over a decision they can't undo.

Sentiment tells you how the conversation went. Detection says two ideas are the same. Sentiment says what the community made of them. It is now a column in Idea Portfolio, so admins and moderators can see whether response to an idea reads positive, negative, neutral or mixed without opening it. Mixed is the useful one: it means the comments are genuinely split, so a contested idea reads as contested instead of averaging out to nothing. Sentiment is also a filter target in advanced search, so you can pull every idea trending negative and work that list too. And the reading stays honest as ideas move: it recalculates after a merge or unmerge, and runs consistently across copied ideas and reindexed content, so a score reflects the current record, not a stale one.

The Sentiment column in Idea Portfolio, alongside net votes and date submitted. Each idea carries a reading derived from its comment thread, and ideas with no comments to read show no badge.

Underneath both, AI processing is faster and more reliable. The content analysis engine was rebuilt for speed, cutting response times for duplicate detection and sentiment, and the scheduled background jobs that keep AI insights current were fixed to run without interruption, so results reflect your current content, not the last successful run.

Reporting: See the Journey, Not Just the Snapshot

Most innovation dashboards answer one question: what stage is this idea in right now? That hides the three things that matter more for a program decision: what is actually moving forward, where ideas move backward (which is a process defect telling you something), and what quietly leaves the funnel altogether.

IdeaScale's Journey and Snapshot reports were built for exactly that: native time-in-stage tracking, flow visualization, and bottleneck detection, not another view of the current state.

Journey and Snapshot reports are fully released. A Journey report shows how ideas move: a Sankey diagram of forward transitions paired with a data table covering backward movement and drop-off, with drill-down to the individual ideas behind any transition, so you can see where your process slows down instead of inferring it. A Snapshot report shows how things stood: your pipeline as it looked at points in time you choose, daily through yearly, grouped by workflow and stage with time-in-stage alongside, so "what changed since last quarter" is a report you run, not a story you reconstruct.

A Snapshot report at monthly frequency. Each snapshot records how many ideas sat in each stage at that moment, charted across the months and broken out in the table beneath, so the pipeline's history reads at a glance.

Dashboards and reports display in each user's language, so a program spanning several countries can look at one dashboard without anyone translating first. And date-range queries are faster across large datasets, historical and forward-looking alike.

An Open Platform: APIs and Integrations

Innovation data is only as useful as the systems it can reach. The closer it sits to where work actually gets done, the less of your program runs on manual status updates.

Stage history is now available through the public API. The full record of an idea's progression, covering which stages, in what order and how long in each, is retrievable programmatically, so time-in-stage and funnel-movement analysis can run in whatever tool your organization already uses. Every transition carries its timestamp, so audit and oversight questions about when an idea moved and how long it sat are answerable as a side effect of the operational tracking.

Duplicate detection is now available through the REST API. The engine that surfaces potential duplicates inside IdeaScale can be queried programmatically, so external systems and custom dashboards can identify similar content without a person in the loop. Potential-duplicate data no longer stops at the platform edge. For federal customers, exposing detection through the API does not change the FedRAMP boundary. The analysis still runs inside it, and idea text still never leaves.

Two-way Jira sync is live, and got steadier this cycle. Innovation lives in IdeaScale and execution lives in Jira; the bidirectional integration closes that loop so status, comments, and activity history flow back into a dedicated IdeaScale view. Executives and program managers see implementation reality without needing Jira accounts of their own. This cycle's work on it was maintenance, and the useful kind:

• Editing and removing connections is clearer. Delete and update flows now state explicitly what happens to linked external records before you confirm, so removing a connection is no longer a guess about consequences.
• Admins can clear out broken external links. When a linked record in an external system no longer exists, the dead reference can be removed from the idea's detail page, so idea records stay current instead of accumulating links that go nowhere.

How the loop closes: a decided idea is pushed to Jira as a linked issue with status, comments, and activity syncing back; the measured result lands on the idea; and variance against projection rolls up to the Strategic Outcome. The two steps marked coming soon are on the roadmap, not yet released.

Bug fixes are excluded from this update. The 18 issues resolved in this window are documented separately, including two authentication security fixes, several localization corrections, and a reporting read-only enforcement fix.

Questions About Any of This

Your Customer Success Manager can walk through anything in this update against your own program — including Outcomes, which is enabled per account.