New year, new features: Level up your Stack Overflow for Teams in 2025 - Related to stack, year,, challenges, level, how

New year, new features: Level up your Stack Overflow for Teams in 2025

The first release of the year is packed with capabilities to make your knowledge-sharing community enhanced.

As we step into 2025, we’re kicking things off with a series of powerful updates designed to make your Stack Overflow for Teams experience even improved. Whether you’re celebrating the milestones of the past year or gearing up to tackle new challenges, these enhancements are here to support your knowledge-sharing community in meaningful ways.

This release is packed with tools to help your community thrive in 2025 and beyond. Dive into the details below to explore everything we’ve rolled out!

Let’s take a moment to acknowledge the incredible contributions that kept your community thriving in 2024. Your 2024 Stacked, available for qualifying teams only, goes beyond the numbers, offering an interactive snapshot of engagement and impact. It’s more than a retrospective—it’s a chance to celebrate the collaboration, curiosity, and camaraderie that define your team.

Stay connected with improved weekly digests.

Our redesigned weekly digest emails bring actionable, personalized insights right to your inbox. These new, personalized digests keep you in the loop and empower individuals to contribute more effectively and include five key components:

Summary: Each user will see a persona-driven wrap-up of how they helped their community during the prior week. SME Progress: If SME auto-assign is enabled, people will see the top two tags they are progressing on toward becoming a SME. Your Reminders: people will see a list of service product nudges and reminders so they can follow up and take the primary actions to support a thriving community. Unanswered Questions: Leveraging the algorithm used on the homepage, the top unanswered questions will be surfaced to the user based on their activity and tag preferences. Account Configuration Nudges: people will receive smart recommendations on account configurations—like setting up notifications for MS Teams/Slack or watching tags—based on where people are in their journey with Teams.

Search smarter with OverflowAI enhancements.

We’ve fine-tuned OverflowAI to deliver precise, relevant summaries and to guide customers toward the best possible answers—or help them craft superior questions when needed.

Prompting for OverflowAI Enhanced Search has been updated to ensure that results are both accurate and relevant. If relevant context is found, OverflowAI Enhanced Search will deliver a summary. However, if no relevant results are available, the system will prompt consumers to post their question.

This will create clarity in the search summary experience by indicating when OverflowAI is answering a question versus when it is summarizing and by encouraging them to post a new question if the summary doesn’t answer their question.

In addition, OverflowAI thread summarization in both Slack and Microsoft Teams integrations has been updated to be more personalized and focused, eliminating generic phrasing and unnecessary content. These updates will give clients clearer, more concise outputs when asking questions and receiving summarized answers. Once summarization has been completed, an updated success message will unfurl the summary and encourage clients to review, verify, and add tags to OverflowAI answers to ensure knowledge integrity.

Seamless integration with Microsoft 365 (public preview).

In this release, we’re bringing Stack Overflow for Teams to Microsoft 365. The Stack Overflow for Teams Microsoft Graph Connector allows organizations to bring trusted, team-validated knowledge from Stack Overflow for Teams directly into Microsoft 365, where it can be accessed seamlessly by development teams and other technical people.

With this connector, content such as questions, answers, and top answers from Stack Overflow for Teams is indexed and made searchable within Microsoft 365 Copilot. Developers can simply ask technical questions in natural language within Copilot and receive summarized responses sourced from their organization’s internal Stack Overflow knowledge base. Each answer includes links to the original Stack Overflow for Teams content, making it easy for customers to dive deeper into topics if needed. This setup excludes data from the public Stack Overflow platform, ensuring only internal, organization-approved knowledge is referenced.

For organizations using Microsoft 365, this integration improves the accuracy and accessibility of developer resources, enhancing the efficiency of internal technical support and knowledge sharing. Developers benefit from reduced context-switching, as they no longer need to jump between applications to find reliable, organization-specific insights. With Stack Overflow for Teams content readily available in the Microsoft 365 experience, teams can streamline workflows, access accurate knowledge, and boost productivity directly within the tools they use every day.

NOTE: The Stack Overflow for Teams Microsoft Graph Connector is currently in public preview. Please direct any questions or feedback to the Microsoft team.

For additional details on the improvements above and other updates with the latest release, view the [website] release notes.

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The Promises of Agentic AI and How to Sidestep Challenges

In boardrooms worldwide, top decision-makers are likely asking their IT executives, “What more can we do with AI?” It’s a reasonable question as artificial intelligence investments continue to skyrocket. , the global AI market is expected to reach more than 800 billion dollars by 2030.

The truth is that AI is set to permeate most aspects of everyday life. We’re already using it in hospitals to help with diagnosis, in coding to streamline product development, and even in industrial settings to improve supply chains. In 2025, the next evolution of AI will come in the form of AI agents. This latest evolution of AI might even bring about the return on investment (ROI) that many stakeholders are desperately seeking. However, there are some steps that enterprises must take to realize the promises of agentic AI and avoid potential perils.

Let’s first take a moment to understand what I mean by agentic AI. AI agents are software programs that can autonomously take actions to achieve pre-assigned goals. This software is meant to continuously collect and leverage data and constantly learn ways to improve upon those preset goals. Like most AI software, they require training and intervention when necessary.

Agentic AI’s Promises and What To Watch Out For.

The concept of an AI agent isn’t new — we’ve just come a long way from early iterations. For example, chatbots and Siri have been around for a while. Newer iterations, like ChatGPT and Gemini, and even updated versions of Siri, have progressed each day and transformed how we interact with AI agents.

With newer AI agents’ ability to learn, it democratizes AI use to non-experts and allows a perpetual improvement of processes that aren’t always dependent on an expert. Despite these promises, organizations must avoid some potential pitfalls as they seek to leverage Agentic AI.

The Relationship Between Talent and Agentic AI.

Many of us in the IT/cybersecurity sector know about the ongoing talent shortages. Agentic AI can streamline processes, helping mitigate these talent gaps. If appropriately trained, AI agents can initiate cybersecurity alerts and begin remediation. They can also enhance observability operations and smooth out customer interactions through intuitive chatbots.

The potential pitfall arrives if IT leaders look to agentic AI to solve their talent shortages completely. As their dependence on AI grows, organizations will need to hire talent fluent in using AI so they can intervene to improve its functionality. In addition, as AI frees up specific talent to focus on other tasks or departments, business leaders may have to reconsider their organizational structure.

Like Any New Technology, There Are Security Risks.

Remember to introduce data into a self-deterministic entity connected to your IT environment and data when using AI agents. Depending on the agent, the data may even interact with third parties. A potential pitfall is a breach that occurs and expands using AI agents as a medium. This could put personally identifiable information (PII) at risk and decrease customer confidence in your ability to protect their privacy.

There are several ways to avoid these mishaps. First, sound data management is essential. When AI is trained on quality data, it can produce quality results. Quality data management includes properly securing data, retrieving valuable data from legacy systems, and safely making data accessible to relevant stakeholders. It also means establishing sound data governance policies.

Upskilling and training staff on responsible AI use will also set an organization up for success. AI Agents may offer opportunities for new roles that won’t be filled outside the organization. This means offering training or upskilling paths to hire from within. Certain employees may need training on how to use Agentic AI to improve their current roles.

Lastly, AI mustn’t operate on its own all the time. Therefore, provide intervention methods. Establish a program for constant check-ins to ensure the agent serves as intended. Following these suggestions could help today’s organizations fully realize the promises of agentic AI in 2025 and beyond.

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OpenAI Launches Deep Research: Advancing AI-Assisted Investigation

OpenAI has launched Deep Research, a new agent within ChatGPT designed to conduct in-depth, multi-step investigations across the web. Initially available to Pro customers, with plans to expand access to Plus and Team customers, Deep Research automates time-consuming research by retrieving, analyzing, and synthesizing online information.

Unlike standard chatbot interactions, Deep Research operates independently for 5 to 30 minutes, browsing the web, interpreting content, and compiling reports with citations. Powered by a specialized version of OpenAI’s upcoming o3 model, it is optimized for reasoning, data analysis, and structured research. The tool is intended for professionals in knowledge-intensive fields such as finance, policy, and engineering, as well as consumers looking for comprehensive insights on complex topics.

Early evaluations indicate that Deep Research outperforms previous AI models in tasks requiring deep contextual understanding. On Humanity’s Last Exam, a benchmark that assesses AI across expert-level subjects, it scored [website] accuracy—more than twice the performance of previous OpenAI models.

Despite its capabilities, the tool is not without risks. AI-generated research can still be misinterpreted, especially when dealing with specialized subjects. Peter Ksenič, a designer and quality manager, cautioned:

Keep in mind, that if you do not know your topic, there is a huge risk of errors. Also if you don't understand the topic, you can make misleading statements by bad interpretation of obtained knowledge.

Concerns about AI's reliance on education and professional development have also been raised. Moses Maddox emphasized the importance of AI literacy:

We are spending so much time talking about what AI can do that we are not teaching students how to actually use it. Right now, students and young professionals are letting AI control them instead of the other way around. They’re blindly trusting AI instead of learning how to refine its outputs... AI is not going to replace them. Someone who knows how to use it advanced will.

OpenAI acknowledges these concerns and plans to refine Deep Research through iterative deployment. While it is designed to streamline complex research, the organization emphasizes that AI should be used as a tool to enhance human expertise rather than replace critical thinking.

Access to Deep Research will expand in phases, with a more efficient version in development to support a broader user base. For now, it marks another step in AI’s evolving role as a research assistant.

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