Three vendors, three AI architectures, one test. This article is the direct head-to-head of monday Sidekick (generally available since January 2026), Asana AI Teammates (launched March 2026), and Jira Rovo agents (open beta since 25 February 2026). No affiliate link, no vendor preview access, no sponsored content. The test methodology, the task-by-task results, and the honest failure modes are below.
Short answer up front: each vendor is meaningfully the best at a different thing. No single product wins across all four test tasks. Teams that expect a universal winner will be disappointed; teams that match the vendor to their actual primary use case will save real time.
The test methodology
We ran an identical task set through each platform over three weeks in March and April 2026. The same underlying project was replicated in all three: a 45-task cross-functional product launch with three workstreams (engineering, marketing, customer success) and two external dependencies. We populated each platform with 12 weeks of synthetic project history including status changes, comments, blockers, and at-risk indicators, so the AI had something to analyse.
Four tasks were tested in each platform:
- Status reporting: generate a status summary for the executive sponsor.
- Scheduling suggestions: propose re-sequencing after a two-week delay on a critical dependency.
- Risk identification: surface the top three at-risk items and explain why.
- Cross-project portfolio analysis: compare project health across three parallel workstreams.
Each task was run three times per platform at different points in the three weeks to control for one-off good or bad outputs. Results below are summarised across all runs. All tests used the vendors’ standard tiers (monday Pro with Sidekick Plus, Asana Advanced with Teammates add-on, Jira Premium with Rovo included).
Verdict at a glance
| Task | Best | Second | Third |
|---|---|---|---|
| Status reporting | Asana AI Teammates (Status Reporter agent) | monday Sidekick | Jira Rovo |
| Scheduling suggestions | monday Sidekick | Jira Rovo | Asana AI Teammates |
| Risk identification | Jira Rovo | Asana AI Teammates | monday Sidekick |
| Cross-project portfolio analysis | monday Sidekick | Asana AI Teammates | Jira Rovo |
| Overall usefulness per dollar | Jira Rovo (included in Jira Premium) | monday Sidekick | Asana AI Teammates |
The overall-usefulness-per-dollar winner is not the best-performing product on any single task. Jira Rovo wins on price/value because it is bundled into the Jira plan. Teams already on Jira get Rovo essentially free; teams on monday or Asana pay real add-on money for AI capability.
Task 1: Status reporting
The task: “Generate a status report for the executive sponsor covering the last two weeks of work across all three workstreams. Include key accomplishments, current blockers, upcoming milestones, and items requiring executive attention.”
Asana AI Teammates (Status Reporter) — best. The Status Reporter agent is a specialised Teammate, which is what makes it good. The output followed a consistent structure across all three runs: accomplishments bucketed by workstream, blockers with owner and deadline, upcoming milestones with risk flags, and specifically-escalated items with recommendation. The first draft needed moderate editing (maybe 15 minutes of work to make it publishable) but the structure and content coverage were right. The Teammate checkpoint model also surfaced questions where the agent was uncertain, which is genuinely useful. Output quality was consistent across the three runs.
monday Sidekick — second. Sidekick produces decent status reports but the output varies meaningfully across runs on the same data. Sometimes it surfaces the right escalations; sometimes it buries them in a general summary. The cross-board context is strong (Sidekick understands relationships across boards well), but the output format is less predictable. First-draft edit time was roughly 20–25 minutes.
Jira Rovo — third. Rovo’s status reporting is serviceable but tilts engineering-heavy. In a cross-functional project, Rovo over-weights the engineering workstream and under-reports the marketing and customer success work, even when all three have equal depth of data. For an engineering-only project, Rovo’s status reporting is competitive with the other two. For cross-functional work, it is visibly weaker.
Task 2: Scheduling suggestions
The task: “The supplier integration work has slipped by two weeks. Propose a revised sequence that minimises impact to the launch date, identifying which tasks should be compressed, which can be done in parallel, and which should slip with the supplier work.”
monday Sidekick — best. Sidekick’s scheduling suggestions were both the most usable and the most appropriately hedged. The output identified two tasks that could be compressed with additional resourcing, three tasks that could genuinely parallelise, and one hard dependency that would slip regardless. The agent’s suggestions matched what an experienced PM would propose in about 70% of cases. Critically, Sidekick flagged the places where it was not certain and surfaced those for human decision rather than confidently proposing a wrong answer.
Jira Rovo — second. Rovo understands dependency structures well (Jira’s native model favours this kind of analysis). Its scheduling proposals were technically correct but often overly conservative — the agent recommends slipping the whole chain when a more aggressive plan would have been defensible. For risk-averse teams, this is arguably correct; for teams that need to hold the launch date, Sidekick’s more aggressive proposals were more useful.
Asana AI Teammates — third. No specialised Teammate for scheduling at this writing means the task falls to the general Teammate system, which produces scheduling suggestions that are less structured. The Launch Planner Teammate is close to the right agent for this use case but under-performs on mid-project re-sequencing compared to fresh-project planning. For initial project setup, Launch Planner is good. For schedule revision after a known slip, it is weaker.
Task 3: Risk identification
The task: “Review the last two weeks of project activity. Identify the top three items most at-risk for the launch deadline and explain why each is at risk and what should be done about it.”
Jira Rovo — best. This was the biggest surprise in the test. Rovo’s risk identification was consistently the most useful across runs, particularly because of its permission-respecting architecture — Rovo sees the audit trail and can reason about who-changed-what-when, which matters for risk judgement. The three items Rovo flagged in each run were genuinely the right three to worry about. The recommendations were conservative but defensible. The governance-aware architecture surfaces “someone updated this but did not notify dependent teams” as a risk signal, which neither competitor does.
Asana AI Teammates — second. The Compliance Specialist and Status Reporter Teammates between them produce risk-identification outputs that are structurally good. The issue is that risk judgement requires cross-cutting understanding, and the specialised-agent model means no single Teammate has the full picture. Running Compliance Specialist and Status Reporter both and manually reconciling produces good risk intelligence; running either alone leaves gaps.
monday Sidekick — third. Sidekick’s risk identification tends to over-index on explicit status flags (items marked “at risk” or “delayed”) and under-index on implicit risks (items that have not been updated recently, items with quiet comment drift, items where the assignee has been unresponsive). For teams that discipline themselves to flag risks explicitly, Sidekick works. For teams where risk is visible in activity patterns rather than explicit status, Sidekick misses too much.
Task 4: Cross-project portfolio analysis
The task: “Compare project health across all three parallel workstreams over the last four weeks. Which is on track, which is slipping, and what is the likely impact on the overall launch?”
monday Sidekick — best. Cross-board analysis is monday’s strongest suit, and Sidekick’s portfolio comparison outputs were the most useful. The cross-board relationships are clearly understood. The comparison tables generated by Sidekick were usable almost directly in executive decks. Sidekick also surfaced “here is a dependency that looks invisible in the board view but matters for the cross-project picture,” which is exactly the kind of insight a portfolio analysis should produce.
Asana AI Teammates — second. Asana’s portfolio-level reporting is genuinely strong (this is one of Asana’s historical strengths as a product). The Teammate model applied to cross-project analysis works, but the comparison outputs require more manual reconciliation than monday’s. Asana’s Goals and Portfolios capability provides structure the Teammate can work with; if that structure is set up well, Asana’s cross-project analysis is competitive with monday’s.
Jira Rovo — third. Cross-project comparison is Jira’s weakest use case for Rovo. Jira’s project model is optimised for deep-in-one-project work, not portfolio-level synthesis. Rovo produces competent individual-project summaries but stitching them together into a portfolio view is noticeably weaker than monday’s native cross-board capability. For engineering-only portfolios (all three projects are engineering), Rovo is acceptable. For cross-functional portfolios, it is not the right tool.
Where each fails
Specific failure modes, each confirmed across multiple test runs, that vendor marketing consistently omits.
monday Sidekick’s failure mode: the credit-based pricing creates an unpredictable cost curve as usage scales. A team that starts with Sidekick Lite included in Pro eventually wants Sidekick Plus or Super for heavier agent work; the cost climbs faster than expected. Also: Sidekick’s output varies meaningfully across runs on the same input, which is a confidence-in-output problem. Ran the same task three times and got three different emphasis patterns.
Asana AI Teammates’ failure mode: the specialised-agent model means you end up running multiple Teammates to get full coverage on complex tasks, and reconciling their outputs is work. For simple, well-bounded tasks, this is great. For complex cross-functional analysis, you are managing four or five Teammates instead of one generalist AI. Also: AI Studio credit exhaustion mid-month is a real risk on heavy usage — the smart layer can go dark until the next billing cycle.
Jira Rovo’s failure mode: non-engineering context. Rovo is clearly built by a team that thinks in tickets, epics, and sprints. For marketing, sales, customer success, or operations work, Rovo feels narrower than the product marketing suggests. The engineering-adjacency assumption shows. Teams considering Rovo as a cross-functional AI across Jira Service Management, Jira Product Discovery, and Jira Work Management should test against their actual cross-functional use cases before committing.
All three share: the confident-wrong output problem. When these agents are wrong, they sound plausible. The verification cost on autonomous actions is consistently higher than vendor marketing acknowledges. See AI agents in project management: what actually works in 2026 for the broader assessment.
Cost of AI per seat per vendor
Real costs for a 25-person team on standard tiers with AI enabled, as of April 2026:
| Vendor | Base plan | AI add-on | Total per user per month |
|---|---|---|---|
| monday (Pro + Sidekick Plus) | ~$19 | Credit-metered, roughly $8–$15 in sustained use | ~$27–$34 |
| Asana (Advanced + AI Teammates + AI Studio Plus) | $24.99 | Teammates add-on + AI Studio Plus | ~$38–$48 |
| Jira (Premium, Rovo included) | ~$15.25 | $0 (bundled) | ~$15.25 |
Jira Rovo’s bundled inclusion in Jira Premium is the pricing story that changes the calculus for a lot of teams. Teams already paying for Jira get the AI as a free feature, which — even if Rovo is not the best-performing agent on every task — makes the total-cost calculation lean significantly in Jira’s favour for engineering-led organisations.
monday and Asana both use credit-based pricing for their heavier AI tiers, which creates unpredictability. Budget worst-case and add buffer.
FAQ
Which AI agent is the most autonomous?
None of them should be trusted as fully autonomous in April 2026. Asana’s checkpoint model is the safest for semi-autonomous work. Jira’s permission-respecting architecture is the safest for regulated or governed contexts. monday’s Sidekick is the most comfortable to use as a conversational assistant but not meaningfully more autonomous than the others.
Do any of these actually save time?
Yes, on specific tasks: summarisation, cross-workspace search, and first-draft generation save roughly 40–60% of the time those tasks would take unaided. On complex tasks (scheduling under constraints, cross-project analysis, risk judgement), the time savings are smaller and the supervision overhead is larger. Net time savings for a typical PM user lands around 3–6 hours per week if the AI is used actively and thoughtfully.
Can I run the same test on my own projects?
Yes, and you should before committing to a vendor. Use your actual project data (or a synthetic version with similar complexity), run the same four tasks through each platform’s trial, and evaluate the outputs. Your context will produce different results than our test; what is universal is the structure of the trade-offs, not the specific rankings.
Are these actually better than ChatGPT Plus or Claude with my data?
For the “AI as conversational assistant” use case, a general-purpose AI with good access to your project data (via MCP or export) performs competitively with the embedded AIs. Where the embedded AIs win is on tight integration with the tool’s workflow — being able to take action (create task, update status, schedule meeting) from the AI conversation without copy-paste. If you will not use the take-action capability, a general-purpose AI plus good data access may be sufficient.
How much does GPT vs Claude model choice matter?
Less than vendor marketing suggests. monday’s MCP integration, Asana’s AI Studio, and ClickUp Brain all support multiple models; Jira Rovo uses Atlassian’s own implementation. Both GPT-family and Claude-family models handle the four test tasks at similar quality levels. Teams with strong preferences for one model family should factor that in, but do not pick the PM tool primarily to get the model — the tool integration matters more than the underlying LLM.
What about ClickUp Brain?
Not included in this head-to-head because ClickUp’s AI is architecturally different enough (it is a broader AI capability rather than an agent offering) that direct comparison is misleading. See monday vs Asana vs ClickUp and the AI section of AI agents in project management: what actually works in 2026 for how ClickUp Brain compares to these three.
When should I re-evaluate this comparison?
AI in PM tools is evolving fast. This comparison is time-stamped to April 2026 and will age quickly. We refresh this article every three months. Any major vendor release that materially changes the ranking will trigger an interim update. If you are making a tool decision 6+ months after the refresh date at the top of this article, verify current state with the vendors directly.
Test performed March–April 2026 on identical 45-task project data across all three platforms. Results summarised across multiple runs per task to control for output variance. Last verified: April 2026.