Most organizations solve the first problem with specialized compliance courseware. But for teams already working in Confluence, training can still sit in a separate system from the policies, procedures, and documentation employees use every day.

Smart Courses lets you bring compatible, appropriately licensed BAI/ProSight compliance courseware into Confluence through SCORM, connecting specialized training with the workspace employees already use.

What is BAI compliance training?

BAI compliance training is now offered under the ProSight brand. BAI and the Risk Management Association (RMA) merged in 2024 to form ProSight Financial Association.

ProSight provides compliance courseware designed specifically for banks, credit unions, mortgage organizations, and other financial institutions.

Its training covers areas such as BSA and anti-money laundering, UDAAP, GLBA, cybersecurity, identity theft, lending regulations, deposit and transaction regulations, and corporate compliance.

Courses are built around financial-services roles and can include practical scenarios, interactive activities, and assessments. That specialized content is one reason organizations may prefer licensing established compliance training instead of recreating regulatory courses internally.

The challenge is not only the content

Good courseware solves the content problem. Employees still need an easy way to access and complete it.

For teams working across the Atlassian ecosystem, much of the supporting knowledge may already live in Confluence: internal policies, security procedures, risk-management documentation, department resources, and compliance guidance.

Putting training closer to that knowledge creates a clearer connection between the policy employees are expected to follow and the training they need to complete.

Instead of reading a policy in Confluence and then searching for the related course in another system, employees can access both from the same workspace.

Can BAI/ProSight courses run in another LMS?

Yes. ProSight licenses its programs for use in other learning management systems through its Content Provider Service, subject to the applicable agreement and approved LMS configuration.

Through its Content Provider Service, ProSight can provide licensed programs for use through a client’s approved learning management system.

An important distinction exists between ProSight/BAI-owned courseware and custom content your organization creates.

• ProSight/BAI courseware: You cannot assume you can freely export, copy, or reuse licensed ProSight/BAI training in another LMS. Whether you can deliver a course through an external LMS depends on your licensing agreement with ProSight and the delivery options they provide. Confirm the applicable permissions and SCORM availability with ProSight before requesting or importing course files.
• Custom content created by your organization: If you create and design your own training content within the platform, that content is separate from ProSight’s proprietary course catalog. Where the platform provides SCORM export for that custom content, you can export the resulting SCORM package and use it in a compatible LMS, subject to your organization’s rights to the content and the platform’s applicable terms.

In other words, licensed ProSight courseware and your own custom-created training should not be treated as having the same export rights.

Before planning the integration, confirm with ProSight that your agreement supports external-LMS delivery and that the course is available in a format compatible with SCORM 1.2 or SCORM 2004.

Once you have a compatible SCORM package that you are authorized to use in your LMS, Smart Courses can deliver it inside Confluence.

From ProSight courseware to training in Confluence

Start by selecting the ProSight courses that match your regulatory requirements and employee roles. Training for frontline employees, mortgage teams, managers, or compliance officers may differ, so match the right courseware to the right audience.

Once you select the courses, confirm with ProSight that your license and delivery arrangement support external-LMS use, and request the compatible course files or delivery method available for your account.

In Smart Courses, create a new course, enable the SCORM course option, open the course editor, and upload the compatible SCORM ZIP package you are authorized to use and have obtained through your ProSight licensing or delivery arrangement.

Smart Courses supports both SCORM 1.2 and SCORM 2004, so you can import compatible courseware without recreating the original training as Confluence pages.

Once published, you can assign the course to individual users or Confluence groups with a due date.

For recurring compliance requirements, group-based assignment can also help automate enrollment as employees join the relevant groups.

Administrators can then use Course Insights to follow learner progress, completion status, scores, and due dates.

Connect compliance training with your Confluence policies

This is where the combination becomes particularly useful.

Smart Courses provides a Course Card macro that can surface an individual published course on a Confluence page. The Course List macro can display a filtered collection of courses based on criteria such as categories, labels, or spaces.

Imagine an employee opening a Mandatory Banking Compliance Training page in Confluence.

At the top, the compliance team explains the program and provides links to internal policies. Below that, employees can find the relevant BSA/AML, cybersecurity, lending, identity-theft, or transaction-compliance courses available through Smart Courses.

The same page can also contain training deadlines, instructions, and contact information for the compliance team.

Employees move from policy → context → training without having to search across disconnected systems.

Who handles what?

A practical example

Consider a bank already managing its policies and procedures in Confluence.

The compliance team creates a Mandatory Banking Compliance Training page containing its internal compliance guidance and links to the policies employees are expected to understand.

Courses licensed from ProSight are imported into Smart Courses as SCORM packages and assigned by employee role.

Branch employees may receive BSA/AML and identity-theft training, while mortgage teams receive the lending-related courses relevant to their responsibilities.

Course cards and filtered course lists make that training accessible from the compliance area employees already use.

Meanwhile, the compliance team can use Insights to monitor who has completed the required training and where follow-up is still needed.

The result is a closer connection between what employees are expected to know and the training they are expected to complete.

What does Smart Courses track from SCORM?

Smart Courses captures the completion and score information sent by the SCORM package.

For SCORM 1.2, that includes lesson status and raw score. For SCORM 2004, Smart Courses uses completion status, success status, and raw score.

This allows learner results such as completed, incomplete, passed, or failed to appear in Smart Courses depending on the information provided by the package.

One limitation to keep in mind: Smart Courses does not currently capture individual question-level results from an imported SCORM course. It reports the package’s overall status and score.

Therefore, we recommend testing a new SCORM package before assigning it organization-wide to ensure its completion and scoring rules are reported as expected.

You can also enable certificates for SCORM courses when completion requirements are met.

Bring financial compliance training into Confluence

Compliance training works better when employees can easily find it alongside the policies and procedures it supports.

Once you have a compatible SCORM package that your ProSight licensing and delivery arrangement permits you to use in an LMS, upload it to Smart Courses, assign it to the right employees, and make it accessible from the Confluence pages your teams already use.

The compliance content stays specialized. The learning experience lives in Confluence.

Try Smart Courses for Confluence free for 30 days.

Smart Tables for Confluence now supports SQL Views, giving teams a new way to explore large datasets directly inside Confluence.

Write your own SQL query or simply describe what you’re looking for in plain language. AI Mode generates the SQL automatically, creates an adjustable SQL query, and keeps everything connected to the same dataset.

What’s new?

Smart Tables has always helped teams organize information with sorting, grouping, filtering, global search, pivot tables, charts, and formulas. We’ve added a new advanced feature: SQL Views.

Instead of creating multiple filtered tables or repeatedly combining filters and formulas, you can now create adjustable SQL queries that answer more complex questions, all while working from the same table.

If you don’t know SQL, that’s fine. Simply describe the result you want, and AI Mode generates the SQL query for you.

Whether you’re managing project portfolios, sprint backlogs, financial reports, asset inventories, or compliance registers, Smart Tables now lets every team explore data with the level of precision they need.

Why do native Confluence tables eventually reach their limits?

Native Confluence tables are excellent for documentation and lightweight tracking.

Most teams don’t just want to find information anymore; they want to answer questions like:

• Which projects are overdue and over budget?
• Which high-priority risks still don’t have an owner?
• Which assets expire next quarter by department?
• What’s the average completion rate per team?

Smart Tables already solves many of these scenarios through sorting, grouping, filtering, search, pivot tables, charts, or formulas.

SQL Views don’t replace those features. They complement them.

For everyday work, filtering, sorting, or grouping is often the fastest solution.

When datasets become larger, or questions require multiple conditions, aggregations, or advanced reports, SQL Views provide another layer of analysis without leaving Confluence.

Why SQL views?

Many teams already think in queries, even if they’re clicking through filters instead of writing SQL.

SQL Views make those questions easier to answer.

They allow you to:

• Query multiple conditions in a single request.
• Aggregate data using counts, totals, averages, and calculations.
• Create other views without duplicating your tables.
• Generate multiple perspectives from one dataset.
• Save SQL Views for future reporting.

The biggest advantage isn’t writing SQL; it’s avoiding duplicate tables across your page.

One dataset and multiple views

One Smart Table can support multiple teams and use cases without filling your Confluence page with separate macros or duplicate tables.

With Tab Mode, you can display different views of the same Smart Table as tabs within a single macro. Each team can create its own SQL View based on the same source data and switch between views without leaving the table.

For example:

• Project Managers can monitor overdue tasks and project status.
• Finance can focus on budget variances and spending.
• Leadership can review high-level portfolio summaries.
• Engineering can track sprint progress and blockers.
• Operations can review resource allocation.

The source data stays the same. Each team simply gets the view it needs, all organized within one Smart Table.

AI mode makes SQL easy for everyone

SQL isn’t difficult for technical users, but for everyone else, AI Mode eliminates the learning curve.

Instead of writing a query, simply describe the information you’re looking for.

Examples include:

• Show all overdue tasks assigned to Marketing.
• Display projects exceeding their planned budget.
• Count high-severity risks grouped by owner.
• List assets that expire within the next 30 days.

AI Mode translates your request into SQL, automatically creates the SQL View, and lets you review or edit the generated SQL query if you’d like.

Technical users save time, and non-technical users gain capabilities they never had before.

Use case: How to manage Company Risk Register with SQL query?

Imagine a Project Manager Officer (PMO) team managing a company-wide risk register.

For daily work, they use:

• Sorting to prioritize risks by status.
• Grouping to organize risks by team or category.
• Global Search to locate a specific risk by ID or description.
• Filters to review only open, active risks.
• Pivot Tables to summarize risk counts by severity.
• Charts to visualize exposure across teams.

As the register grows, different stakeholders need different perspectives. Rather than maintaining multiple copies of the same table, the team creates SQL Views.

The Finance team creates a view showing every open risk with a cost impact above $20,000:

“Show me all risks where cost_impact_usd is greater than 20000, and status is Open.”

The Project Office creates a view of overdue, still-open risks to make sure they’re caught and acted on quickly.

SELECT `risk_id`, `description`, `owner`, `due_date` FROM `T1` 
WHERE `status` = 'Open' AND `due_date` < CURDATE() ORDER BY `due_date` ASC

Engineering and IT/Ops review a view of unmitigated risks, filtering to anything where mitigation hasn’t started yet, so effort goes where it’s needed first.

SELECT risk_id, description, mitigation_status FROM T1 
WHERE mitigation_status = 'Not Started' AND status = 'Open';

Every SQL View is based on the same Smart Table, ensuring everyone sees the latest information without duplicating or manually maintaining data.

How to use SQL?

If you’re new to Smart Tables, you’ll find the interface intuitive and easy to navigate. Whether you’re starting from scratch or importing an existing Excel file, you can begin organizing and analyzing your data in just a few clicks.

As your dataset grows, use familiar features such as sorting, grouping, global search, filters, formulas, pivot tables, and charts to explore your data from different perspectives.

When you need a more advanced view, simply click + View and select SQL table. From there, you can write your own SQL query or switch to AI Mode and describe the results you want in plain language. Smart Tables generates the SQL query for you and creates an adjustable SQL View alongside your existing Table, Pivot Table, and Chart views.

You can switch between these views at any time without modifying your original dataset, allowing different teams to work from the same data while focusing only on the information that’s relevant to them.

Why does this matter?

Every team works differently, and some questions can be answered with a simple filter.

Others require grouping, a pivot table, or a chart.

And now, as your data grows and your questions become more complex, SQL Views offer another way to explore the same information without exporting data or leaving Confluence.

Instead of choosing among spreadsheets, databases, and Confluence, Smart Tables brings them together into a single view on one page.

Key takeaways

SQL Views expand what teams can do with data in Confluence by adding powerful querying capabilities to the Smart Tables experience. Rather than replacing existing tools like filtering or grouping, they provide more ways to explore larger datasets and answer more complex questions.

• Analyze large Confluence tables more efficiently with SQL queries designed for advanced reporting and data exploration.
• Keep one source of truth by creating multiple SQL Views from the same Smart Table instead of maintaining duplicate tables for different teams.
• Generate SQL queries with AI by describing the data you want in plain language, no SQL expertise required.
• Combine SQL Views with existing Smart Tables features, including formulas, sorting, grouping, global search, pivot tables, and charts.
• Work entirely inside Confluence Cloud, with Smart Tables running natively on Atlassian Forge for a seamless and secure experience.
• Give every team the view they need, whether they’re tracking projects, reviewing budgets, managing assets, or monitoring operational data.

Frequently asked questions

Can you use SQL with Confluence tables?

Native Confluence tables don’t support SQL queries. Smart Tables adds SQL Views, allowing you to query table data directly inside Confluence without exporting it to another tool.

Do I need SQL knowledge to use SQL views?

No! Smart Tables includes AI Mode, which automatically converts plain-language requests into SQL queries. You can use the generated query as-is or edit it after running it.

When should I use SQL views instead of filters or grouping?

Filtering, sorting, grouping, and global search are the quickest way to work with everyday tables. SQL Views are best suited for larger datasets or more advanced reporting scenarios that require multiple conditions, aggregations, or complex queries.

Can different teams create different views from the same table?

Yes! One Smart Table can support multiple SQL Views, allowing project managers, finance teams, executives, and operations teams to work from the same dataset while focusing only on the information relevant to them.

Can I save and reuse SQL queries?

Yes! SQL queries are saved as SQL Views, making it easy to return to the same analysis without rebuilding filters or writing the query again.

Can SQL Views be used alongside Pivot Tables and Charts?

Yes! SQL Views become another view type within Smart Tables and work alongside Table, Pivot Table, and Chart views. You can switch between them at any time while keeping the same underlying dataset.

Does SQL work with imported Excel and CSV data?

Yes. After importing data from XLSX or CSV to Smart Tables, you can query it using SQL Views, build pivot tables, create charts, or continue working with the rest of Smart Tables’ features.

Is Smart Tables built on Atlassian Forge?

Yes! Smart Tables is a Forge-native Confluence app, meaning it runs directly on Atlassian’s cloud platform and integrates with the Confluence experience.

Which teams benefit most from SQL Views?

SQL Views are ideal for any team managing structured data in Confluence, including Project Management Offices (PMOs), Product teams, Engineering, IT Operations, Finance, HR, Compliance, and Operations. They’re particularly valuable when datasets become too large or complex for standard filtering and grouping alone.

With features like Atlassian AI definitions powered by ROVO, employees can select an unfamiliar term, ask for an explanation, and get an answer instantly. Some acronyms are even defined and highlighted automatically, so you don’t always have to look it up. It is fast, convenient, and often surprisingly accurate.

So if AI can explain terms on demand, do organizations still need a glossary?

The short answer is yes.

Because explaining a term and managing organizational terminology are two completely different challenges.

AI helps people understand information in the moment. A glossary ensures everyone across the organization is working with the same definition, the same language, and the same source of truth.

The most effective knowledge management strategy is not choosing between AI and a glossary. It is understanding where each one delivers value.

Where do AI definitions shine?

Imagine a new employee reading a technical document for the first time.

They encounter a term they have never seen before. Instead of opening another tab, searching through documentation, or messaging a colleague, they can simply define that word from a Confluence page using Atlassian AI definitions.

This is where this feature excels.

AI-generated definitions are contextual. They can analyze surrounding content, interpret intent, and provide explanations tailored to the situation. They require little maintenance and can scale across large amounts of information automatically.

For quick clarification, AI is often the fastest solution.

When the question is: “What does this mean right now?” AI is usually the right answer.

Where does AI reach its limits?

The challenge begins when terminology becomes specialized, or when the same word means something different depending on who you ask.

Consider terms like:

• RAID log (Risks, Assumptions, Issues, Dependencies), a staple for any PMO, rarely explained the same way twice
• WSJF (Weighted Shortest Job First), core to SAFe prioritization, and genuinely hard to look up in plain language
• Product Owner, a title that exists natively in Jira, yet means something different on nearly every team that uses it
• CAB (Change Advisory Board), meaningless as three letters unless your org has already defined it
• DORA metrics: deployment frequency, lead time, MTTR, change failure rate, often tracked differently team to team
• RTO / RPO (Recovery Time Objective / Recovery Point Objective), high-stakes compliance terms that are easy to mix up
• Definition of Done (DoD), famously interpreted differently by nearly every Agile team

These aren’t just vocabulary. They’re business-critical concepts, and in several cases, the inconsistency itself is the problem, not just the definition.

Different teams may interpret them differently. Different departments may create competing definitions. Over time, documentation becomes inconsistent, and onboarding becomes harder.

AI can explain these terms, but it cannot guarantee that every employee receives the exact same official definition every time.

AI explanations are probabilistic. They can vary depending on context and available content.
That flexibility is useful for understanding and less useful for governance.

The role of a glossary app

A glossary exists for a different reason. Instead of generating definitions dynamically, it establishes approved definitions that everyone can reference.

When someone asks:

“What is the official meaning of this term in our organization?”

A glossary becomes the authoritative source.

With Smart Terms, organizations can:

• Create and manage glossary entries
• Identify who created a term
• Control who can create and edit definitions
• Standardize terminology across teams, spaces, pages…
• Reuse definitions across multiple spaces
• Maintain a central glossary repository
• Ensure consistent definitions for every employee

AI helps people discover information.

A glossary helps organizations govern it.

A glossary is more than a definition.

One of the biggest misconceptions about glossary tools is that they simply store definitions.

Modern glossary platforms do much more.

A Smart Terms entry can include rich formatting, images, links, related terms, synonyms, acronyms, labels, multilingual definitions, and related vocabulary structures.

This transforms a simple definition into a knowledge object.

Instead of explaining a term, you are building relationships between concepts and creating a navigable knowledge network.

That is something AI-generated explanations do not inherently provide.

Why does highlighting matter more than definitions?

Definitions are only useful if people see them. This is where many AI-based approaches fall short.

Users must actively ask for a definition or trigger an AI interaction. Or, in the Atlassian AI definition case, it selects and decides which terms will be highlighted.

Smart Terms takes a different approach.

Terms are automatically highlighted throughout Confluence and Jira, making knowledge discoverable exactly where people are working. Hovering over a term immediately reveals its definition without interrupting the workflow.

More importantly, highlighting can be controlled.

Organizations can customize:

• Where terms are highlighted
• Which glossaries apply to specific spaces
• Global glossary behavior
• Term visibility
• Highlighting rules

This creates a structured knowledge layer directly inside Confluence rather than relying solely on ad hoc AI interactions.

AI and glossary management work better together.

The future is not AI versus glossary software; it is AI-enhanced glossary management.

That is exactly the direction Smart Terms is taking with Lexi, the new AI assistant for Smart Terms glossary.

Lexi helps organizations accelerate glossary creation while maintaining governance and control.

Instead of manually creating every glossary entry from scratch, teams can use AI to identify terminology, generate definitions, and build glossary content faster based on a space or page content. You can also upload a file, and Lexi will determine all the terms that should be defined.

The difference is that these definitions become managed assets rather than temporary AI responses.

Once created, terms can be edited, approved, enriched, translated, and governed like any other term created with Smart Terms.

Lexi helps create the glossary; Smart Terms advanced configuration helps manage it.

When to use Atlassian AI definitions and when to use Smart Terms Glossary?

Use Atlassian AI definitions when:

• Users need quick contextual explanations
• Information changes frequently
• You want immediate answers with minimal setup
• Understanding is more important than standardization

Use Smart Terms when:

• Definitions must be consistent across teams
• Terminology requires ownership and governance
• You need a central glossary repository
• Compliance and documentation accuracy matter
• You want rich definitions and structured relationships
• You need advanced highlighting and discoverability
• Multiple languages must be supported consistently

The strongest knowledge strategy combines both. AI helps people understand what they are reading, while a glossary ensures everyone is speaking the same language.

Final thoughts

AI has made knowledge more accessible than ever. But accessibility is not the same as consistency.

Features like AI definitions are excellent at helping employees understand unfamiliar concepts in context. They reduce friction and improve productivity.

Yet organizations still need a trusted source of truth for business terminology; that is where Smart Terms continues to deliver value, not because it competes with AI but because it provides the governance, structure, discoverability, and consistency that AI alone was never designed to provide.

And with Lexi bringing AI-powered glossary creation into Smart Terms, organizations no longer have to choose between intelligence and control.

This is the reality for most growing teams, and it’s usually handled with a mix of onboarding docs, Slack messages, and a manager’s best guess about when someone is “ready.” That approach breaks down because knowledge and workload live in two systems that don’t talk to each other: a wiki page doesn’t know how loaded someone’s sprint is, and a sprint board has no idea whether they’ve finished security training yet.

Why do most onboarding programs fall short?

Three failure patterns show up again and again in growing teams:

• Readiness gets measured in time, not knowledge. Three weeks in is treated as a milestone regardless of whether someone has absorbed what they actually need to know.
• Training and delivery are treated as separate phases. Once someone touches their first ticket, onboarding is considered “done,” even if half the learning material is unfinished.
• Capacity is managed informally. “We’ll ease them in” rarely gets written down anywhere a manager or teammate can see it, so it doesn’t survive contact with a real sprint.

Fixing this doesn’t mean replacing Confluence or Jira; it means connecting the tools you already use so learning progress and workload planning inform each other in real time.

Structuring the Learning Path with Smart Courses for Confluence

The first half of a scalable onboarding system is making sure every new hire follows a consistent Learning path, not a scattered mix of pages and informal handovers.

With Smart Courses for Confluence, HR and team leads build a dedicated onboarding Learning Path that sequences everything a new hire needs into the right order: company policy, security training, product knowledge, engineering standards, and role-specific material. Learning Paths let you group multiple courses into a complete program, so a developer, a customer success manager, and a project manager can each follow a tailored track while still sharing common company-wide training.

A few things make this especially effective for onboarding at scale:

Courses can combine Confluence pages, PDFs, images, videos, and even HTML or SCORM packages so whatever format your documentation already lives in, it can become part of the learning journey without being rebuilt from scratch. Since Confluence pages stay connected, any updates your team makes are automatically reflected inside the course, so nobody is ever learning from an outdated version.

Assign courses directly, with due dates. Managers can assign individual courses to specific users or groups, complete with due dates and automatic notifications. This makes it easy to deliver mandatory training exactly when it’s needed: security training before a first login, product knowledge before someone joins their first customer call, instead of hoping people find it on their own.

Skills and quizzes turn learning into evidence. Interactive quizzes at the end of each chapter or course confirm a new hire actually understood the material, not just clicked through it. Combined with prerequisites and skills, onboarding becomes competency-based rather than time-based. Teams may require specific knowledge or a completed quiz to unlock the next stage, so nobody advances on assumption alone.

Insights give managers a real-time view. Built-in insights dashboards show course completion, quiz results, and overall learner progress across every new hire at once. Instead of chasing status updates in a 1:1, a manager can see exactly where each person stands and catch gaps before those gaps affect day-to-day work.

By turning existing Confluence knowledge into structured, trackable training, Smart Courses answers the first key onboarding question: what does this person actually know, and are they ready for the next step?

Managing real workload with ActivityTimeline for Jira

Knowing what someone has learned only solves half the problem. The other half is knowing how much real work they can actually take on right now, and that’s a question learning tools alone can’t answer.

This is where ActivityTimeline for Jira comes in. It’s a resource and capacity-planning app built directly on top of your existing Jira projects, giving everyone, including new hires, a personal timeline showing what’s assigned to them, along with a workload indicator that reflects how loaded they are relative to their real capacity.

A few features make it particularly useful for managing new team members:

See real workload distribution. The workload indicator, color-coded from light green to red, is calculated from both Jira estimates and custom events such as bookings or training blocks. For a new hire, this means a manager can see at a glance whether an “easy onboarding ticket” is actually filling their whole week or leaving them mostly idle, rather than finding out during a retro two sprints later.

Control allocation for new hires. Rather than assigning tasks the same way you would for a tenured engineer, ActivityTimeline lets you set individual involvement (capacity) per person, for example, configuring a new hire at 4–5 hours/day of plannable work for their first two weeks. Booking events lets you reserve time for shadowing, pairing, or training sessions without needing a Jira ticket for each one, so that time shows up on the timeline and counts against capacity instead of disappearing into an invisible gap.

Avoid overloading during ramp-up. Because the workload indicator accounts for vacations, partial days, and custom involvement schedules, it’s much harder to accidentally schedule a new hire for more than they can handle. If a manager tries to stack tickets onto someone whose involvement is intentionally set to a lower level, the indicator visibly tips into yellow or red before the team finds out the hard way.

The result is planning grounded in actual capacity rather than assumptions. A manager isn’t asking “do I think this person is ready”; they’re looking at a timeline that already reflects training time, partial involvement, and current workload, and making a scheduling decision from there.

Bringing both systems together

Handing someone their first real piece of work comes down to two questions: has the relevant part of their learning path actually been completed, and does the timeline show they have the capacity for it right now? Smart Courses answers the first. ActivityTimeline answers the second. Put together, a manager isn’t guessing anymore; they’re looking at evidence of both readiness and availability before making the call.

Customer Success Manager onboarding

The same model works just as well outside engineering teams. Imagine a company hiring five new Customer Success Managers ahead of a major product launch.

Each new hire is enrolled in a Smart Courses LMS Learning Path that covers product knowledge, support workflows, communication guidelines, and internal tools, with quizzes to verify their understanding before they start working directly with customers. Managers track progress through the insights dashboard, just as an engineering manager would.

At the same time, ActivityTimeline helps team leads reserve time for product shadowing, customer call observations, and coaching sessions by reducing each new hire’s available capacity during their first weeks. As training milestones are completed, managers gradually increase their workload so new Customer Success Managers gain confidence without becoming overwhelmed.

Whether you’re onboarding developers or customer-facing teams, combining structured learning with capacity planning creates a repeatable onboarding process that scales as your organization grows.

Try Smart Courses for Confluence free for 30 days.

Try ActivityTimeline for Jira free for 30 days.

Why do native Confluence tables fail as status tracking tools?

Some teams use Page Properties Reports to create portfolio views. Others rely on large native Confluence tables. Both approaches can work, but as the number of projects grows, filtering, sorting, updating, and maintaining multiple stakeholder views becomes increasingly difficult.
On Confluence Cloud specifically, the Page Properties Report exists in a stripped-down form called the Content Properties Report. Macro nesting isn’t supported, which breaks the more advanced configurations that work on the Data Center. Cloud teams are largely left with a plain native table, and the limitations compound fast.
There’s no way to filter by team or RAG status in view mode, no sorting by deadline or priority, and no grouping by phase. The table shows everything to everyone, which, for a portfolio of 15 or more projects, usually means it shows too much to be useful.

All your projects, one table

Smart Tables for Confluence (Table Filter & Charts) approaches the problem differently. Instead of spreading project information across multiple pages and reports, it keeps everything in a single structured table and adds the interactivity needed to manage it effectively.
One row per project, all data in one place, different stakeholders interact with that same data in different ways without creating additional pages, exports, or versions.

Some of these capabilities are available in other Confluence content formats: inline editing in Live Docs and filtering in Databases.

RAG status that you can actually act on

RAG stands for Red, Amber, Green, a simple health indicator that tells stakeholders at a glance whether a project is on track, at risk, or off track and in need of immediate attention. It’s the first thing anyone looks for in a portfolio review, and the first thing that becomes meaningless when a Confluence native table goes stale.
From there, the whole table responds.
Filter to show only Red and Amber projects before a steering committee meeting and sort by RAG status to surface risk at the top.
Group by status to get an instant count of how many projects are on track versus struggling.
The RAG column stops being a label someone remembered to update and becomes a filter you can query.

Grouping and sorting your portfolio view

As portfolios grow, flat lists become difficult to interpret. Grouping reorganizes the same table instantly by owner to understand workload distribution, by phase to see where projects are concentrated, or by priority to surface what needs immediate attention.
The data stays unchanged; only the view does.
Before a portfolio review, sorting delivers the same speed: by deadline to bring upcoming work to the top, by RAG status to lead with risk, or by last updated date to catch stale projects before they become reporting issues.

Inline editing directly on the page

The biggest reason Confluence tables become outdated is friction. If updating a project requires opening the editor, locating the correct row, making the change, and saving the page, the update gets delayed. And delayed updates compound until the table is no longer trusted.
With Smart Tables, updates happen directly in view mode. A project manager changes the status during a standup. A delivery lead updates a phase during progress review. Small updates take seconds, so they actually happen, and the Confluence table stays current.
Jira remains the right home for tracking work items and operational reporting, but project context, stakeholder updates, and strategic notes live in Confluence, and Smart Tables for Confluence (Table Filter & Charts) make that page interactive enough to serve as the portfolio view too, no switching tools required.

Visualize portfolio health

Smart Tables can also generate charts directly inside Confluence from the same project table, no export, no separate reporting tool needed.
A program manager can visualize projects by phase to see where the portfolio is concentrated.

A department head can view workload distribution across teams; an executive can pull up a RAG status breakdown of how many projects are Green, Amber, or Red as a chart alongside the table, ready for a board update. The chart updates automatically as the underlying data changes, so it always reflects the portfolio’s current state.

It stays connected to the rest of the page

A program manager opens the Confluence table before a portfolio review and sorts by RAG status. Every Red project appears first. An engineering lead filters to their team and groups by phase within seconds; they can see what’s in delivery and what’s still in planning. An executive opens the same page and sorts by priority to focus on the highest-impact initiatives.

Smart Tables also work naturally alongside the rest of a project page: Jira issue embeds, smart links, user mentions, decisions, and other Confluence content, the table remains connected to the broader project context rather than becoming a separate reporting destination.

The best project status table is not the one with the most metrics; it’s the one people actually keep up to date. When filtering, sorting, grouping, and editing are frictionless, the Confluence table stops being documentation and starts becoming an operational tool teams trust.

Building a project status table in Confluence that actually stays up to date is not a process problem. It’s a tooling problem, and one structured, filterable, inline-editable table is usually all it takes to solve it.

Workday manages employee records, approvals, and HR workflows. OnRamp orchestrates onboarding processes across teams and systems. Jira keeps onboarding tasks organized and moving. Together, they replace spreadsheets, scattered Slack messages, and forgotten follow-ups with a structured process.

But there is a problem many teams eventually run into.

A new hire can complete every onboarding task, receive every account, attend every scheduled meeting, and still not actually be ready to do the job.
That is because onboarding logistics and onboarding learning are two completely different things. You can process an employee without properly preparing them.

Where operational onboarding ends

Most modern organizations already rely on a stack of tools to manage onboarding at scale.
Workday handles employee records, approvals, and HR workflows.
OnRamp coordinates onboarding journeys across teams and systems.
Jira tracks tasks, tickets, and operational execution.
Together, they remove the chaos of manual onboarding, replacing spreadsheets, Slack threads, and ad-hoc coordination with structured, automated workflows.

But even when all of this works perfectly, a gap still remains.
A new hire can complete every onboarding task, receive every account, attend every scheduled meeting, and still not actually be ready to do the job.
That is because operational onboarding and learning onboarding solve two fundamentally different problems.

Operational Onboarding vs Learning Onboarding

Operational Onboarding:

These systems measure whether onboarding tasks happened.
A completed Jira ticket confirms that access was granted.
A completed HR workflow confirms that approvals were processed.
But neither confirms whether the employee is actually ready to perform their role effectively.

Operational onboarding is about execution:

• Accounts created
• Access granted
• Equipment delivered
• Workflows completed

It ensures the employee can enter the system.
But it does not ensure they can perform inside it.
Learning onboarding fills the gap left by operational systems.
Most organizations already have onboarding knowledge documented inside Confluence. The problem is structure.
New hires are often handed dozens of pages without knowing:

• Where to begin
• What matters most
• How information connects

So instead of learning a role, they end up having to navigate documentation.

Operational onboarding creates access.
Learning onboarding creates understanding.
Operational onboarding answers: “Are they set up?”
Learning onboarding answers: “Are they ready?”

Smart Courses for Confluence: turning documentation into learning

Smart Courses for Confluence transforms Confluence from a documentation platform into a structured learning environment built around the knowledge your team already maintains there, not a separate LMS you have to migrate content into.

That distinction matters.

In most organizations, onboarding knowledge evolves constantly alongside the business. Smart Courses keeps the learning experience connected to the same Confluence environment employees will use long after onboarding ends.

Instead of navigating isolated wiki pages on their own, new hires progress through a well-organized catalog with course cards and structured learning paths with clear progression and measurable outcomes.

Assign courses: New hires are automatically enrolled based on their group, so learning starts on day one without anyone needing to set it up manually. Once they join the group, they will receive a notification to take the course.

Exams and quizzes: comprehension gets verified at each stage, which matters most for compliance, security, and product knowledge, where misunderstanding has real consequences

Certifications: employees earn a record of what they have completed, and managers can see who is genuinely ready, not just who has finished the tasks

Learning paths: onboarding is organized into a guided progression so new hires move through knowledge in the right order, from foundational concepts to role-specific expertise, instead of navigating pages on their own

Skills and prerequisites: courses can require specific knowledge or completed modules before unlocking the next stage, ensuring no one skips steps they are not ready for.

How Workday, OnRamp, Jira, and Smart Courses work together

These tools complement each other so effectively because each solves a different onboarding problem.

Workday manages the employee record and HR workflows

OnRamp orchestrates onboarding journeys and cross-team coordination

Jira manages operational execution and task tracking

Smart Courses manages the learning layer that transforms documentation into structured onboarding and measurable capability development

A new employee can be created in Workday, automatically enrolled in onboarding workflows through OnRamp, assigned operational tasks in Jira, and guided through structured learning paths in Confluence via Smart Courses.

Operational onboarding and learning onboarding become connected rather than isolated from each other.

The Use Case: Onboarding a senior backend engineer

Move the engineer from “signed contract” to “shipping their first line of code” in 10 days, while ensuring they understand security protocols and system architecture along the way.

Phase 0: Workday the HR trigger

Everything starts in Workday.

Once HR marks the candidate as “Hired,” the employee record is created and synchronized across systems. Role, department, and onboarding metadata are now available to the rest of the stack.

This is what officially triggers the onboarding process.

Phase 1: OnRamp the orchestration layer

From that moment, OnRamp takes over coordination.

It connects HR, IT, and engineering teams into a single onboarding flow:

IT is notified to prepare provisioning
HR receives compliance tasks
Managers get onboarding checklists
The new hire receives timelines and communication
Jira onboarding projects are automatically prepared

At this point, onboarding is no longer manual it is orchestrated across systems.

Phase 2: Jira, the operational execution layer

Next comes execution.
A Jira onboarding project is automatically created and populated with tasks across teams.
IT provisions a MacBook Pro, GitHub access, and AWS permissions.
Managers are reminded to schedule onboarding sessions and introductions.
Security teams handle access reviews and compliance approvals.
Everything that needs to be physically and operationally set up is now tracked and completed.
But even when everything is marked as done, a gap remains.
Jira confirms the engineer is equipped not that they are ready.
They may have the laptop, the access, and the tickets completed… but still not understand deployment safety standards or backend architecture principles.

Phase 3: Smart Courses for Confluence, the learning layer

This is where onboarding shifts from execution to understanding.
Instead of navigating a long list of Confluence pages, the engineer enters a structured learning path built directly from the company’s existing documentation.
They are guided step by step through the knowledge they actually need to perform the role.
At the end of the “Security Standards” module, they complete a short quiz to validate understanding.

Once completed, Smart Courses generates a certification:

“Learning Path Complete: Security Certified.”

In Confluence, training materials often end up scattered across pages, inconsistently formatted, and difficult to navigate. As content grows, users lose visibility, and administrators lose control over structure and consistency.

Smart Courses for Confluence solves this by turning Confluence into a structured training system where teams can build a fully customized learning catalog, define clear Learning Paths, and control how content is discovered, filtered, and consumed.

Building a structured training system

The main shift Smart Courses introduces is moving from unstructured documentation to a designed learning environment.

Instead of isolated pages, training content is organized into structured learning elements, including courses, exams, Learning Paths, and a centralized catalog. This allows organizations to design training intentionally, ensuring consistency across teams while maintaining flexibility to accommodate different learning needs.

As a result, training becomes easier to manage, easier to scale, and significantly easier for users to follow, since content is no longer fragmented but part of a clear learning journey.

Building a customized training catalog

One of the most important aspects of delivering a strong training experience is customization.

Smart Courses allows teams to customize its catalog views based on their specific needs. The catalog can be adapted to reflect internal structures, training priorities, and audience segmentation.

This flexibility is essential when thinking about how to build a learning system in Confluence that scales across teams and departments.

Filters

Discoverability is where most training systems fail and where Smart Courses delivers the most impact.

The catalog includes a powerful filtering system that allows learners to find content quickly based on:

• Type (courses, exams, Learning Paths)
• Category
• Difficulty
• Language
• Status (assigned, ongoing, done, and favorites)

These filters transform the catalog into a dynamic discovery tool. Instead of searching manually, learners can navigate training logically and efficiently.

This directly addresses the core issue: making training content not just available, but easy to find.

Beyond structure: customizing the training experience

Customization in Smart Courses for Confluence goes beyond organizing content; it defines how training is experienced.

Teams can brand the catalog with logos and titles, and add announcement messages (info, note, or warning) to guide users directly within the interface. This transforms the catalog from a simple page into a dedicated training environment.

Administrators can also control how content is prioritized by setting default sorting and featuring up to 20 courses, ensuring the most important training is immediately visible.

On the visual side, course cards can be customized to show or hide specific information, while layout settings such as thumbnail size and scaling keep the catalog clean and consistent.

At the same time, categories and difficulty levels bring structure to the experience. Categories can be created without limitation, allowing teams to organize content by topic, department, or training objective. Difficulty levels, defined up to ten levels, introduce a clear sense of progression by indicating course complexity.

Together, these capabilities turn the catalog into a structured, branded, and fully controlled training experience, not just a list of content.

Improving discovery, structure, and learning experience with ecosystem tools

Beyond Smart Courses itself, the training experience becomes more powerful when supported by complementary tools that enhance the Confluence environment.

Rovo AI by Atlassian improves content discovery by helping users surface relevant training materials and documentation through intelligent search and contextual recommendations across Confluence and connected apps. Instead of relying solely on manual navigation, users can ask natural queries and quickly find useful knowledge across their organization.

Refined sites by Refined transforms Confluence into a fully branded training portal by improving navigation, layout, and overall user experience. This creates an environment that feels like a purpose-built training platform rather than a traditional documentation space, which increases engagement and usability.

On the content-structure side, scroll content manager by K15t helps teams manage training documentation in a scalable, consistent way. It ensures that content remains properly structured, version-controlled, and easy to maintain as training programs evolve.

To reinforce consistency, Synced Blocks Macro for Confluence allows teams to reuse content across multiple training pages while keeping it synchronized.

For presentation and usability, Aura Content Formatting  Aura Apps (Seibert – appanvil) improves the visual structure of training content by introducing layout components that enhance readability and reduce cognitive load, making learning materials easier to consume.

Finally, Composition Tabs by Appfire for Confluence helps organize complex content into structured sections using tabs, allowing learners to navigate information more easily without being overwhelmed.

Together, these tools enhance the broader Confluence environment in which Smart Courses operates, improving discovery, structure, consistency, and usability around the training experience.

Most teams start using Confluence tables with high hopes. They create pages to track KPIs, monitor performance, and share updates across teams. At first, it works.

But over time, things break down.

Data gets outdated, pages get duplicated, and metrics such as KPI lose consistency. Eventually, teams move their tracking back to spreadsheets.

This isn’t a team problem. It’s a tooling gap.

And it becomes especially visible as organizations try to scale Confluence KPI tracking and project management in Confluence.

The problem with native Confluence Tables

For some teams, a typical KPI table in Confluence looks like this: rows for campaigns or initiatives, columns for owners, deadlines, statuses, and metrics. It’s a reasonable starting structure. But the moment the dataset grows, the cracks appear fast.

Native Confluence tables offer limited filtering and grouping capabilities. You can’t easily filter by owner or status, group by team or priority, or dynamically sort large datasets. Updates rely entirely on people remembering to come back and edit the page.

So teams compensate in predictable ways, duplicating pages for different views, exporting to spreadsheets, and maintaining multiple versions of the same dataset just to answer simple questions.

Smart Tables: a structured layer inside Confluence

Smart Tables addresses the root limitation directly. Rather than treating Confluence pages as documents with embedded tables, Smart Tables turns those tables into interactive, structured datasets without requiring teams to leave the workspace.

The shift sounds subtle. Its effects are not.

• Filtering
  Each stakeholder sees only their slice of data, by team, status, quarter, or priority. What makes this powerful is that filters are persistent, reusable views built on top of a consistent schema, so they never break and always reflect live data. One table replaces what used to require multiple duplicated pages, each drifting out of sync.

• Grouping
  As KPI data grows, a long flat list of metrics becomes impossible to scan for different teams, statuses, and priorities all mixed together with no visual hierarchy. Grouping solves this by organizing data into meaningful categories on the fly. Group by team to see how each department is performing at a glance. Group by status to instantly separate what’s on track from what’s at risk. The data doesn’t change just how you navigate it.

• Sorting
  Without sorting, the answers to the most critical questions, which KPIs are underperforming, which campaigns are delivering, and what needs immediate attention, are buried inside the data. Sorting brings them to the surface instantly. Sort by lowest performance to identify risks before they escalate. Sort by highest impact to highlight wins worth sharing. Sort by deadline to stay ahead of what’s coming. Any field, any direction, actionable in seconds.

The foundation of effective Smart Tables tracking is a consistent data structure. Each row represents a single unit of measurement: a campaign, a sprint, or a product initiative. Each column is a defined, standardized field: metric name, owner, team, status, priority, reporting period, performance value.

When every row follows the same schema, the entire table becomes a queryable dataset; that consistency is what transforms a table from a formatted list into a system.

The Atlassian Teamwork Collection: tools becoming a system

With the Atlassian Teamwork Collection, the company is making a deliberate architectural shift: away from individual apps used side by side toward a unified system where planning, documentation, and execution are deeply connected.

This reflects something real about how work actually happens. A decision made in a Confluence page triggers a project tracked in Jira. A sprint outcome updates a KPI that lives in a doc. When tools are isolated, context is lost at every handoff. The Teamwork model is designed to close those gaps.

Even within a better-connected Atlassian ecosystem, Confluence had a specific problem: the moment documentation became operational, tracking deliverables, monitoring KPIs, and managing live project data, its tables became a liability. Static by default, prone to drift, dependent on manual exports. The Teamwork vision needed Confluence to do more.

The bigger shift: from documentation to operations

Together, Smart Tables and the Teamwork Collection represent a broader transformation in how teams manage work.

Reporting consistency, one of the most persistent pain points in project management, gets addressed at the source. KPIs are defined once in a structured format in Confluence. Every stakeholder sees the same data in the same format. Reports are generated from the live source, not assembled from exported fragments.

Just as importantly, this shift doesn’t require teams to change how they work inside Confluence.

Smart Tables works effectively with native Confluence macros and elements, including smart links, attachments, code snippets, decision macros, and user mentions. Tables remain fully integrated with the rest of the page, whether it’s linking to a Jira issue, displaying media, or exporting to PDF.

What this means in practice

A team lead filters the KPI table in Confluence to view only their team’s metrics. A manager groups data by status to spot risks, while an executive sorts by performance to identify priorities before a review. No exports, no duplicate reports, no need to search for the latest version.

This is the experience that Smart Tables enables and that the Atlassian Teamwork Collection is designed to support.

Confluence KPI tracking isn’t about adding more tools or pages. It’s about making existing data structured, consistent, and interactive, with Smart Tables providing the foundation and Atlassian delivering the system that connects it all.

Today, that problem is even more visible. Teams are becoming leaner, expectations are higher, and new hires are expected to get up to speed faster than ever. There’s less room for delays, missed steps, or inconsistent training.

If you’ve ever managed onboarding, you’ve probably seen how easily things can slip. A task gets delayed, training isn’t assigned on time, or follow-ups depend on manual reminders. Even with a clear plan, execution becomes inconsistent.

That’s where monday.com starts to make a difference. It brings structure to onboarding by turning it into a workflow where every step is tracked, assigned, and visible. Instead of relying on memory, teams can rely on a system.

But even with that structure in place, onboarding still lacks the extra layer that makes it truly effective.

How does monday.com help you manage employee onboarding?

Onboarding tends to get messy faster than expected. Tasks are spread across emails, documents, and various tools, making it easy for things to fall through the cracks.

That’s exactly where it comes in. It gives you a way to organize onboarding into a clear workflow where every step is assigned, tracked, and visible. You can see who is responsible for what, what’s been completed, and what still needs attention, all in one place.

Why does onboarding need more than tasks and items?

Onboarding isn’t just about checking off tasks. It’s about helping new hires understand how things actually work.

They need to learn processes, tools, and expectations, which usually means going through structured learning modules. Without that, even a well-organized onboarding process can leave people confused or dependent on constant support.

The challenge is that while monday.com is great for managing workflows, it doesn’t natively deliver training or show how someone is progressing and performing through it. That’s why many teams end up using separate tools or manual tracking, which brings back the same problems onboarding was supposed to solve.

How does Smart Courses LMS fit into monday.com?

This is where Smart Courses makes a real difference. It adds that learning layer directly inside monday.com, so training becomes part of the same workflow rather than something separate.

What makes this especially powerful is that Smart Courses doesn’t introduce a new interface or external system. Instead, it uses the same board structure, navigation, and visual design that teams already use in monday.com. Everything feels familiar from the start, which means there’s no learning curve and no need to switch between tools.

Because Smart Courses is built on top of the same board logic, you can use monday.com’s native capabilities directly within your training setup. This includes creating workflows, triggering automations, and leveraging AI automations to handle repetitive actions like assigning courses, updating statuses, or sending reminders.

Course creation follows the same intuitive approach as any other board in monday.com. You can build learning modules by combining different types of content, such as videos, documents, and assessments, and organizing them into structured sections that guide learners step by step. Everything lives within the item view, which serves as a dedicated training piece for each course.

As users go through their training, their activity is captured automatically. Instead of relying on manual tracking, teams can use course insights to see progress, completion, and performance directly within monday.com.

The result is a system where onboarding doesn’t just happen alongside your workflows, but inside them.

A simple onboarding flow using monday.com and Smart Courses

Let’s look at what onboarding actually looks like when everything is connected and automated.

It starts the moment a new employee is added. Instead of having someone manually assign training, the system immediately assigns the appropriate onboarding courses. The new hire doesn’t have to wait or ask what to do next, because everything is already in place from day one.

As they go through their training, course insights give visibility into their progress. You can quickly understand whether the onboarding is moving forward or if someone is falling behind, without needing to check in manually.

At the same time, onboarding content doesn’t stay static. When a course is updated and its status changes from draft to published, it is automatically assigned to the right users. This ensures that every new hire always receives the most up-to-date training without additional effort.

And because people get busy, reminders are sent automatically, and users will be notified. Instead of managers chasing employees, the system keeps things moving on its own.

Why are teams moving toward automated onboarding?

The shift toward automated onboarding isn’t only about scaling teams anymore. In many cases, teams are becoming leaner, and expectations from each new hire are higher than ever.

Companies are investing heavily in AI and automation, sometimes reducing headcount, but that makes onboarding even more critical. Every new employee needs to be productive faster and equipped with the right, up-to-date skills from day one.

This is where onboarding can no longer rely on manual processes. When training depends on follow-ups, scattered materials, or inconsistent delivery, it slows down time to productivity and creates gaps that teams can’t afford.

By combining monday.com workflows with Smart Courses and automations, onboarding becomes a system that ensures every hire receives the same structured training without additional effort. Instead of managing onboarding step by step, teams can rely on a process that runs consistently and adapts as content evolves.

In a context where companies are optimizing resources, investing in people remains one of the most important decisions. Automated onboarding ensures that investment is not only made, but fully realized.

That’s the gap Smart Tables and Smart Terms close.

Smart Tables gives structured data a functional home inside Confluence.

Smart Terms ensures terminology is defined and consistent across pages.

Used together, they turn Confluence from an archive into a working knowledge system.

What Confluence does well and where it stops

Confluence excels at documentation. It’s a collaborative wiki where teams capture decisions, brainstorm ideas, and store information. Spaces and pages make it easy to organize work, and integrations with tools like Jira keep everything loosely connected.

But when your documentation becomes operational, Confluence shows its limits. Tables in Confluence are fine for static content, but you can’t filter, sort, or calculate within them. If you need to slice data by priority or generate a quick chart, the answer is usually “no.” Teams end up exporting to spreadsheets, and those spreadsheets become the new source of truth while Confluence pages go stale.

Teams often try to address this with glossary pages, tooltips, or expand macros. These solutions can work temporarily, but they rely on manual linking and individual page authors remembering to apply them. Over time, definitions drift, duplicates appear, and terminology becomes inconsistent across spaces.

Smart Terms solves this by introducing a centralized terminology layer for Confluence. Instead of defining terms manually on individual pages, teams maintain a single glossary in which each term includes an official definition, synonyms, and related documentation. Smart Terms then automatically detects these terms across pages and highlights them with consistent tooltips. Because the definitions are managed in one place, updating a term once updates it everywhere, ensuring that teams across the organization use the same language and understand the same meaning.

This is the ceiling: Confluence captures knowledge well. It doesn’t operationalize it at scale. Smart Tables extends Confluence’s data capabilities. Smart Terms extends language governance.

Use case: Transforming onboarding with Smart Tables and Smart Terms

Consider how a growing technology company uses Confluence to onboard new employees. Before adopting Smart Tables and Smart Terms, the onboarding process looked like this:

• A long page titled “New Hire Tasks” listed steps like “Set up your laptop,” “Complete security training,” and “Read the architecture runbook.” Managers copied this list from a spreadsheet, so some formatting broke, and deadlines and assignees disappeared.
• Acronyms such as “HLD,” “QA,” and “SOC2” peppered the document with no explanation. New hires had to ask someone or search the wiki, hoping to find a page that defined them.
• Managers couldn’t see who had completed which tasks without reaching out individually, and any updates had to be made in multiple places to keep the spreadsheet and the Confluence page aligned.

Step 1: Converting the task list into a Smart Table

The team decided to turn their New Hire Tasks page into a Smart Table. Instead of a static list, they created a structured table with columns for the task description, the responsible person, due dates, status, and priority. Each column used the appropriate field type dates for deadlines, a user field for assignees, and a status field for tracking progress. Because the data was now structured, managers could filter tasks to see which ones were overdue or assigned to specific departments. They could also sort tasks by priority and generate quick reports without leaving Confluence.

Step 2: Defining internal language with Smart Terms

While setting up the Smart Table, they noticed that many tasks referenced specialized terms like “SOC2 training,” “HLD review,” and “MFA enrollment.” To avoid confusion, they compiled these terms into a Smart Terms glossary. Each term included a clear definition, a link to the relevant policy or documentation, and, where appropriate, synonyms or acronyms so that different variations of the same concept could still be recognized and explained consistently. For example, “HLD” was defined as “High-Level Design document used to describe system architecture and components.” The glossary also provided translations for teams working in multiple languages.

Once the terms were defined, Smart Terms automatically highlighted them in the Confluence page. New hires could hover over “HLD” or “MFA” to see the full definition without navigating away from the task list. Because the glossary was centralized, updating a definition once ensured consistency across all pages and spaces.

Step 3: Enforcing consistency and tracking progress

With Smart Tables handling the structure and Smart Terms handling the language, the onboarding page became a living document. Managers could watch task progress in real time, filtering by status or assignee. New hires understood every term they encountered, and if a new acronym cropped up, it was easy to add it to the glossary and have it highlighted everywhere.

By keeping the task list in Confluence rather than exporting it to spreadsheets, the team reduced duplication and version-control issues. Smart Tables’ built‑in formulas automatically calculated completion percentages, and a pivot view summarized onboarding progress across departments. Meanwhile, Smart Terms maintained the company’s internal language. Even as processes changed, the glossary could be updated once and reflected throughout Confluence.

The result

Combining Smart Tables and Smart Terms turned a confusing onboarding checklist into a structured, self‑explanatory experience. New hires completed tasks faster because they knew what each step required. Managers saved time by filtering and reporting directly in Confluence. And everyone began using the same language, reducing miscommunication and reinforcing company standards.

Bringing it all together

The problem of outdated pages, copied spreadsheets, and inconsistent terminology is not unique to onboarding. Any process that involves structured data and specialized language, from compliance tracking to product roadmaps, suffers when Confluence is used as a static archive. Smart Tables solves the data side, giving you a place to filter, sort, and calculate without leaving the page. Smart Terms solves the language side, ensuring that everyone reads and uses the same definitions across the wiki.

By adopting both, your Confluence space evolves from a collection of documents into a working knowledge system. Data stays fresh, language stays clear, and your team stops losing knowledge to siloed files and context‑less jargon.