- Does Buzz really concern you, or is it just news for tech startups?
- What exactly is an AI agent in a team chat?
- How does Buzz compare to Slack and Microsoft Teams?
- What changes in practice for marketing workflows?
- Three concrete scenarios: what changes for an Italian company?
- Why it's being talked about now: where do Italian companies stand on AI?
- What's still missing before you can adopt Buzz in your company?
- How do you get started in practice, without waiting for Buzz?
- What mistakes are most common when evaluating a new collaboration tool?
- How do you know if AI collaboration is really working?
- Where does this scenario lead by 2027-2028?
- So, is adopting Buzz worth it for your company?
- How we see it at SHM
- Frequently Asked Questions
- Buzz is built with AI at its core, not as an added layer: agents take part in team conversations as full users.
- The collaboration platform market is being redrawn: Teams has 360 million monthly users, Slack is growing, but only 17% of people who use AI agents say they improve collaboration.
- In Italy, only 8% of large companies have started experimenting with autonomous AI agents in their processes: the competitive advantage is still there to be built.
- The most useful move today isn't migrating to Buzz, but mapping your fragmented workflows and defining governance policies before adoption becomes urgent.
Updated on October 18, 2026
Jack Dorsey has launched Buzz, a group chat platform where AI agents aren't a side add-on: they sit at the table with the team, like colleagues. If you're considering how to bring automation into your workflows, this launch changes market expectations even before it changes the tools.
Does Buzz really concern you, or is it just news for tech startups?
Buzz concerns anyone who uses chat tools to coordinate a team: its launch signals that the market expects AI-native collaboration by 2027-2028, and those who arrive unprepared pay a real competitive penalty.
On July 21, 2026, Jack Dorsey unveiled Buzz, a group chat platform designed for the workplace. The news, reported by TechCrunch, describes a product that puts humans and AI agents — autonomous software systems that carry out tasks without being called on each time — on equal footing within team conversations.
This isn't an assistant in a sidebar. Agents are full participants in channels. For a marketing manager or an SME owner, the practical question is: will this model redefine expectations for collaboration tools over the next 18-24 months?
| Your situation | Does Buzz concern you? | why |
|---|---|---|
| Approval workflow across email + Slack + Drive | Yes | Exactly the problem Buzz aims to solve |
| Team already using Slack or Teams with AI features | Yes | The market is moving: worth monitoring |
| SME without structured collaboration tools | Yes | The right time to choose an AI-ready architecture |
| Company in a regulated industry (finance, healthcare) | Evaluate with caution | Agent governance on Buzz is still to be defined |
| Tech-forward startup with a distributed team | Yes | Ideal profile for early adoption |
What exactly is an AI agent in a team chat?
An AI agent doesn't just answer questions: it carries out tasks, retrieves data, updates external systems and sends notifications autonomously, without anyone having to call on it each time.
Imagine a Slack channel dedicated to a Google Ads campaign. Today, to find out whether the budget is holding up, someone has to open the dashboard, read the data and write an update in chat. With an integrated AI agent, that notification arrives on its own, with an analysis already prepared and an optimization proposal.
This model is called agentic AI: systems that act proactively within defined workflows. Buzz isn't just a smarter chat. It's an attempt to make the conversational layer — that is, the team chat — the operational center of digital work.
To grasp the scale of this approach, it's also worth reading how multi-agent orchestration with Claude and up to 1,000 agents in parallel works: the principle is the same, applied on a much larger scale.
| Agent type | What it does in chat | Practical example |
|---|---|---|
| Monitoring agent | Flags anomalies in real time | Meta campaign budget drops below threshold → automatic notification |
| Reporting agent | Generates periodic summaries | Weekly SEO report shared in the channel every Monday |
| Approval agent | Checks compliance and tracks versions | Checks that a text follows brand guidelines before publication |
| Integration agent | Updates external systems | Closes a Jira ticket when the team approves in chat |
How does Buzz compare to Slack and Microsoft Teams?
Slack and Teams added AI as an extra layer on top of an existing architecture; Buzz is built with AI at its core, which is a difference in paradigm, not just in features.
Dorsey isn't entering an empty market. According to agentmarketcap.ai, Microsoft Teams has about 360 million monthly active users and holds 37% of the collaboration platform market. Slack has about 42 million daily active users and an 18% share. The most interesting figure: 66% of companies that use Teams also use Slack in parallel.
In July 2026, Slack announced Slack Code, an AI coding environment with agents like Claude Code and GitHub Copilot right in the channels. Forbes reports, however, a figure that gives pause: only 17% of people who use AI agents say they've improved team collaboration. It's the lowest value among all the impacts measured.
Buzz is aiming right at this gap: not adding agents to an existing chat, but redesigning the chat around agents.
| platform | AI approach | Users (2026) | Strength | Weak spot |
|---|---|---|---|---|
| Buzz | AI-native (agents as participants) | Launching | Architecture redesigned from scratch | Maturity and integrations still to be verified |
| Slack | AI added on (Slack AI, Slack Code) | ~42M DAU | Broad integration ecosystem | AI as a layer on top of an existing architecture |
| Microsoft Teams | Built-in AI (Copilot) | ~360M MAU | Massive enterprise distribution | Complexity, costs, Microsoft lock-in |
| Google Chat | Built-in AI (Gemini) | Workspace bundle | Native Google integration | Limited adoption outside the Google ecosystem |
What changes in practice for marketing workflows?
The most direct impact is on fragmentation: today approvals, campaigns and reports live in separate tools with constant manual handoffs. An environment with built-in agents can cut this friction significantly.
In many Italian SMEs, a piece of content's approval chain runs through email, Slack messages, Google Drive comments and tickets in project management tools. Average approval time stretches out and traceability gets lost.
Buzz offers an alternative model. Technical details on agent architecture, available integrations and pricing are not yet fully public, so any operational assessment remains preliminary. What is already clear is the direction of the market.
For marketing teams, the areas most affected in the short term are four.
- 1 · Content approval agentContent approval workflowThe agent checks brand compliance, suggests revisions and tracks versions right in the chat, with no need for external tools.
- 2 · Campaign monitoring agentProactive campaign monitoringAutomatic alerts on performance anomalies in Google Ads or LinkedIn campaigns, without opening separate dashboards.
- 3 · Reporting agentAutomated weekly reportsSummaries generated by the agent and shared in the team channel every week, ready for discussion.
- 4 · Editorial agentSEO and copywriting supportContextual suggestions during editorial planning, built into the same conversation where the team works.
Three concrete scenarios: what changes for an Italian company?
The impact varies widely by size and industry: an e-commerce business, a professional firm and a manufacturing company face different challenges, but all of them matter.
Illustrative examples: numbers are meant to show the reasoning, they are not real customer data.
| Company type | Current problem | What changes with agents in chat | Estimated benefit |
|---|---|---|---|
| E-commerce (20 people) | Content approvals spread across 4 different tools, 3-4 days of average waiting time | Agent tracks versions and notifies reviewers in chat; approval happens in a single thread | Waiting time cut to 1 day, fewer version errors |
| Professional firm (8 people) | Client reports compiled manually every week | Agent pulls data from CRM and analytics and publishes the report in the channel | 2-3 hours saved per week per person |
| Manufacturing company (50 people) | B2B campaign monitoring on LinkedIn handled by one person | Agent flags anomalies and suggests adjustments in real time | Faster reaction, less dependence on a single point of contact |
Why it's in the spotlight now: where do Italian companies stand on AI?
In Italy, the gap between large companies and SMEs in adopting AI and automation is huge: most SMEs have not invested yet, which makes right now a window of advantage for those who move.
Data from the Osservatorio Intelligent Business Process Automation at Politecnico di Milano show an Italian market that is still at an early stage. 62% of large companies use at least one process automation technology, but only 8% have large-scale adoption with a strategic vision. 30% adopt solutions with AI features.
For SMEs, the picture is even starker. The Osservatorio Innovazione Digitale nelle PMI reports that 76% of Italian SMEs have neither invested nor plan to invest in artificial intelligence. Only 7% have launched structured AI training programs. 22% of working time is still absorbed by repetitive tasks that could be automated.
This scenario explains why the launch of Buzz, and more broadly the shift toward AI-native collaboration, is such a strong signal: the market is moving, and Italy's delay can still be made up.
What's still missing before you can adopt Buzz in your company?
Buzz is in its launch phase: product maturity, integration stability and agent governance still need to be verified in the field before any adoption decision.
The platform is not yet available in a stable version for the Italian market. Details on localization, enterprise support and pricing are not confirmed. Any operational assessment is therefore preliminary.
The most critical issue is agent governance: in regulated business environments, having agents operate autonomously in shared channels requires clear policies on permissions, action logs and accountability. Companies will need to define these frameworks before moving ahead, whichever tool they choose. On this topic, it's also worth reading our analysis of the governance risks of AI agents that we've already covered in the cluster.
Another concrete factor is migration. Moving a team off Slack or Teams has real costs: training, reconfiguring integrations, losing conversation history. The decision can't be driven by enthusiasm for something new.
| Risk | Level | How to manage it |
|---|---|---|
| Product maturity | High (recent launch) | Monitor for at least 2-3 quarters before evaluating |
| Autonomous agent governance | High | Define policies on permissions and logs before adoption |
| Cost of migrating from Slack/Teams | Medium | A concrete cost-benefit analysis, not just enthusiasm |
| Localization and Italian support | Medium | Check availability before making any plan |
| Competition from established players | Low-medium | Slack and Teams are speeding up: the gap is narrowing |
How do you get started in practice, without waiting for Buzz?
The most useful move today isn't migrating to Buzz. It's preparing your organization: map your fragmented workflows, review the integrations you already have, and define governance policies.
There are four concrete actions a marketing lead can take right now, regardless of how Buzz evolves.
- 1 · Marketing / operations leadMap your fragmented workflowsIdentify the team processes that pass through more than two different tools. They're the ideal candidates for AI agent integration.
- 2 · IT / digital managerReview the integrations you already haveSlack and Teams already integrate with analytics, SEO and CRM tools. Making the most of them is the first step toward AI-ready collaboration.
- 3 · Management / legalDefine agent governance policiesDecide who can activate an agent, what data it can access, and how its actions are logged. Doing this before adoption becomes urgent is much easier.
- 4 · Marketing teamKeep an eye on how Buzz evolvesFollow the next few quarters without rushing to migrate. The market will become clearer: today it pays to prepare, not to run.
To structure these paths more systematically, our deep dive on autonomous AI agents and the budget-team integration model offers a useful operational framework. Reading about how Microsoft Copilot is evolving into a super app also helps you understand where the established players are heading.
What mistakes are most common when evaluating a new collaboration tool?
The most common mistake is choosing the tool before understanding the problem: the right platform depends on your existing workflows, not on the hype of the moment.
When something new like Buzz arrives, companies tend to make the same mistakes. Spotting them early is worth more than any analysis of the tool.
| Common mistake | Why it is a problem | How to avoid it |
|---|---|---|
| Migrating just to follow the trend | Real training and reconfiguration costs with no guaranteed benefits | Wait at least 2-3 quarters for the product to mature |
| Not defining governance first | Agents operating without logs or clear permissions create legal and operational risks | Policies written before adoption, not after |
| Evaluating features only | Value depends on integrations with the tools you already use | Analyze available integrations as the first criterion |
| Ignoring migration costs | Loss of history, training, reconfiguration: often underestimated | A concrete cost-benefit analysis based on real data from your own context |
| Expecting immediate results | Only 17% of people who use AI agents say they improve collaboration (Stack Overflow) | Plan a break-in period of at least 3-6 months |
How do you know if AI collaboration is really working?
The indicators to track are concrete and measurable: approval time, number of manual handoffs between tools, frequency of version errors, and team satisfaction.
Before adopting any AI-native tool, it helps to set a baseline, meaning a starting measurement, for the processes you want to improve. Without a reference point, you can't tell whether the change worked.
| Indicator | How it is measured | Positive signal |
|---|---|---|
| Average content approval time | Days from brief to publication | 30% or more reduction versus baseline |
| Number of tools used for a single workflow | Manual count or tool audit | From 4+ tools to 2 or fewer |
| Version errors in documents | Team reports or post-publication reviews | Eliminated or significantly reduced |
| Time spent on manual reporting | Hours per week per person | Measurable reduction after 4-6 weeks |
| Team satisfaction | Quarterly internal survey | Perceived improvement in process clarity |
Where does this scenario lead by 2027-2028?
The market is moving toward AI-native convergence: those who don't prepare their processes and governance today will be behind when adoption becomes standard.
The most interesting horizon isn't Buzz itself. It's what its launch signals for 2027-2028. Three plausible scenarios.
Scenario 1 – Convergence on common standards. Buzz gains traction among startups and tech-forward teams. Slack and Teams speed up native AI agent integration to defend their market. The result is convergence on shared interoperability standards between agents and platforms.
Scenario 2 – Acquisition or replication of the model. Buzz doesn't take off as a standalone product, but a larger player acquires or replicates its model. The impact could be indirect but significant, as has already happened with other Dorsey innovations.
Scenario 3 – A split market. On one side, enterprise platforms with robust governance for regulated industries. On the other, more agile tools for creative and marketing teams. Choosing a tool becomes a strategic decision tied to the company's profile.
In all three cases, organizational readiness matters more than the specific tool you pick. It's also worth exploring how Shopify is opening checkout to AI agents: another sign of the same direction, applied to e-commerce.
So, is adopting Buzz worth it for your company?
Worth it if you have marketing workflows fragmented across multiple tools, a team of at least 5-10 people, and the willingness to experiment with a product that's just launching. The advantage of getting in early is real, but it takes a tolerance for uncertainty.
A middle path if you're already on Slack or Teams and are evaluating the AI features available. Before migrating, make the most of existing integrations and define agent governance: it's the most useful work you can do right now.
Not yet if you work in a regulated industry, need guaranteed stability, or haven't yet defined your team's core processes. Wait at least 2-3 quarters and keep watching how the product evolves.
How we see it at SHM
For us, AI should be integrated to free companies from routine work, not to chase the latest trend. Buzz is an important market signal, but the real value isn't in the platform: it's in the process mapping and governance work every company has to do before choosing any tool.
We're already working on architectures where automated agents and human teams collaborate seamlessly, from campaign management to reporting and content approval. Buzz's arrival is no surprise: it confirms the direction we've long recommended to the companies we support on their journey of strategic AI integration.
If you want to know where to start, our training on business process automation is the most concrete entry point. If you want to explore custom agent development, our chatbot and virtual assistant development service offers a structured path. All our deep dives on this topic are collected in our AI and process automation cluster.
Frequently Asked Questions
No. As of launch (July 2026), Buzz isn't available in a stable version for the Italian market. Details on localization, enterprise support and pricing haven't been confirmed yet. It's worth watching how it evolves over the next few quarters before planning any adoption.
Slack added AI as a layer on top of an existing architecture: agents are add-on features. Buzz is built with AI as a founding element: agents are full participants in conversations, not separate tools. It's a difference in paradigm, not just in features.
Migration costs include team training, reconfiguring existing integrations and losing conversation history. There are no universal estimates: they depend on team size and the complexity of your integrations. A concrete cost-benefit analysis is the first step before any decision.
Governance requires written policies on at least three aspects: who can activate an agent, what data it can access, and how its actions are logged. Defining these rules before adoption is much easier than doing it after the fact, especially in regulated industries.
Data from the Politecnico di Milano Observatory shows that 76% of Italian SMEs have neither invested nor plan to invest in AI. 22% of working time is still absorbed by activities that could be automated. The gap is real, but it's also an opportunity: those who move now still have a competitive advantage to build.
Three concrete actions: map the team workflows that go through more than two different tools, evaluate the AI integrations already available on Slack or Teams, and define internal governance policies for agents. These steps are useful whichever tool you end up choosing.
Your competitive advantage doesn't come from the tool you choose, but from the clarity of the processes you put into it.
Related articles
Discover more articles exploring similar topics, selected to offer you a more complete and stimulating perspective. Each piece of content is carefully chosen to enrich your experience.
