The data is huge. Today's organizations face big decisions about how much data to capture. Capture, manage, analyze, store, and promote to other systems of record, knowledge, and intelligence. Almost every action (by humans, machines, software agents, or broader applications) creates a log file-level record with a rich information footprint that tracks what a data source did on a given day. Companies must now decide what information they want to let go without being tracked. (Literally thrown down the drain) and what it decides to hang on to.
It would be easy to say, “Let's have it all” and try to handle all the data that a digital business generates, but cloud computing can be expensive, and you can't have it all (just in case). Retaining them is costly, physically impractical, and strategically difficult. It's swollen.
You need a way to know which data is important, which sources to pay the most attention to, and how to navigate different information domains. The answer, as we take for granted these days, lies, at least in part, in the use of generative artificial intelligence (gen-AI) and the ability to filter and select patterns more accurately (and faster) than humans. It is in its ability to help us recognize. Traditionally it is possible.
Company search and knowledge discovery
Atlassian, a team collaboration and productivity software platform company, is leveraging Atlassian Intelligence technology to leverage human-AI collaboration to more directly understand which data sources organizations should prioritize and focus on. I think there is. First launched a year ago, Atlassian Intelligence (the central name for his AI brand at the company) helps teams use AI to improve productivity and solve the most complex problems of enterprise search and knowledge discovery. It's designed to help you.
With so many technology platforms installed in a typical enterprise (a large company with over 2,000 employees uses an average of over 230 apps), a core requirement today is ERP Or the ability to not only analyze data within your CRM system, but also: Analyze data across all domains, all applications, all the time.
“Our goal is to enable businesses to turn Atlassian and third-party data from across their organizations into actionable insights and informed decisions. In this era of generative AI, All team members must have the following capabilities: [data] Jamil Valiani, head of product AI at Atlassian, said:
Analyst firm Gartner suggests that around half of digital knowledge workers struggle to find the information and data they need to effectively perform their jobs. Why is a seemingly straightforward job so difficult? With so much enterprise data out there, valuable knowledge gets trapped in information silos, creating access log jams, data chasms, and cost disadvantages. will occur. To make the job even more difficult, employees using one application often have to switch between applications to get data and alerts from another software in order to understand what to do. It's often there. Is it reality? You know the experience when you're talking to a call center and the agent asks, “Can I put you on hold?” and the operative wonders where she went while she was fiddling with “other systems” for 10 minutes. There will be. Yes, that's reality. .
Please enter… Atlassian Rovo
Atlassian Rovo is proposed as an antidote to the data discovery problems that have been portrayed so far. This is a new product designed to accelerate finding, learning, and acting on information distributed across various internal software tools within an organization. It works with specialized features designed to search data, tools, and platforms for contextually relevant results. It learns and understands your company’s data through AI, discovering patterns, and deeper data exploration using “knowledge cards” (instant, fact-based answers to search queries) and AI chat. It's built to. They can also help you “act” through the use of specialized agents who reside within your workflow to handle time-consuming tasks, complete projects, and solve complex problems.
To get a broader context here, Knowledge Cards provide an in-context snapshot of specific information about projects, goals, new teammates, and more, based on your organization's enterprise data. Teams can get answers instantly as they work, and knowledge cards get smarter as data is added to Atlassian's teamwork graphs.
“Today, we need to realize that AI is only as useful as the data available to us. We are in the business of understanding how teams work. [so that our platform is] Understand common patterns, anti-patterns, organizational structures, and lines of communication. All this knowledge is funneled into a unique common data model we call the “Teamwork Graph” and it’s the secret sauce that makes Rovo and other AI capabilities special. Our teamwork graph technology ingests data from Atlassian tools and other SaaS apps to reveal a comprehensive view of your organization's goals, knowledge, teams, and work. With each new tool connection, team action, or project event, Teamwork Graph draws more connections, expands its knowledge, and delivers increasingly relevant results,” said Valliani. explained.
There are important information management trends here, across data analytics software vendors, across enterprise resource planning (ERP) and customer relationship management (CRM) specialists, as well as low-code platform companies, automation specialists, and everything in between. You can see it unfolding across people. . Organizations love leveraging and retrieving data (remember the keep-it-all option from earlier?), but they end up with complex structures of data repositories in multiple formats, shapes, and velocities. and is often difficult to navigate. Atlassian says it saw this modern-day “phenomenon” happening and built Rovo Search, which displays the most contextual and relevant results, regardless of where the data is stored or in what format. . Understand what your team wants and provide insights from across your enterprise systems.
How Rovo works
When users want to know if a project is on track, Rovo searches all Atlassian content and selected third-party content, from Jira issues to the countless files stored in Google Drive and Sharepoint. can provide the answer. Documents that the user has forgotten. The company says it is building Search to query custom internal applications developed for finance and human resources to provide answers relevant to any team or industry.
“When users add Atlassian Rovo to Jira or Confluence, they get enterprise search with semantic intelligence that can leverage information resources across the enterprise. In essence, Rovo helps teams unlock more power. “It's an enterprise knowledge tool that can help you,” said Rajeev Rajan, Chief Technology Officer at Atlassian. “Rovo Chat enables users to perform contextual searches across a company's knowledge base, which means everything from Jira tickets to employee contacts to marketing campaign information resources. Again, it's important to note that users get Atlassian Rovo Agents, or software agents. [functioning sub-components of software code] Work alongside human owners as virtual team members. For example, software agents can be used to perform code reviews for developers, and can be set to a specific “tone of voice” at any level, from passive to personal. Literally a pirate. We provide 20 His Rovo agents ready to use, but users can create more His Rovo agents in the Atlassian Marketplace, and of course you can develop your own. We've been working on AI on our platform for years, and the latest version of Atlassian Intelligence is a game-changer for businesses across all industries. ”
Rajan also pointed out that even customers who don't buy Rovo but use Jira or other Atlassian products still have Atlassian Intelligence embedded throughout the platform. Search in Rovo is (obviously) managed by administrators during setup for each user, so they are aware of their permissions so that employees can only search within the appropriate boundaries. Atlassian's CTO describes Rovo as one of the company's “most ambitious” products to date. That's why he has assembled what he calls a “world-class engineering team” to tackle the most complex challenges in enterprise search and knowledge discovery to accelerate the way customers find, learn, and act on information. It explains that it is building.
Users can use this technology to identify team members with the necessary expertise to contribute to project success, and to uncover relevant topics to better understand a project. Search helps teams find the exact information they need from vast amounts of complex data. Rovo Search can pull information from popular tools such as Google Drive, Microsoft Sharepoint, Microsoft Teams, GitHub, Slack, and Figma to provide comprehensive answers.
Permission-aware control
As suggested above, search results are personalized and contextual. Permissions are fully respected, so employees only see the information they should see, and restricted data remains private. Administrators have full control over enabling search connectors and data access. Rovo understands your company's knowledge, people, processes, and goals, so your team can approach your company information in innovative ways. Rovo not only helps teams find information, but also supports interactive learning for deeper understanding.
With Rovo Chat, your team can engage in interactive conversations to ask questions until they get the answers they need, generate new ideas, get helpful feedback, and solve problems as they work. . Chat is based on your company's data and gets smarter as your team continues to use it. When answering a question, Rovo suggests related topics and follow-up questions that your team can use to uncover more details. It also provides easy-to-understand explanations of technical terms. The agent, Atlassian claims, is more than just an improved version of a chatbot. In other words, agents bring specialized knowledge and skills to various workflows and processes.
We're at an interesting tipping point with these technologies. It's not because data-centric human-AI collaboration platforms aren't advanced, it's clear that they already are. Many of the features on offer here are arguably quite progressive, and in some cases quite radical and experimental, in terms of how they impact human workflow. We know that AI is being applied to transform teamwork, but that part is not the issue.
Software agents next to humans
What's even brighter is how employees are progressing with these technologies and how they can “port” and integrate them into their previous jobs to improve performance. At Atlassian, Rovo agents enable teamwork with the ability to integrate large amounts of enterprise data, decompose complex tasks, learn on the fly, and collaborate with human teammates to make important, complex decisions. Remember I said transform.
This means it's up to us humans to embrace (or at least appreciate) these tools or let them pass us by.
No one wants to lose their job to AI without at least trying out the new automation features offered by modern enterprise software platforms. Many of them promise to enhance our workflows, make our roles more productized, and actually do more. It's fun. Hug the bot and make friends with its entire family.