How To: Managing the AI Team Data Hub and Pipeline Updates

SOP: Managing the AI Team Data Hub and Pipeline Updates

Key Steps

1. Understand what the Data Hub is for 0:15

generated-image-at-00:00:15

• Treat the Data Hub as the central place to view team data and progress.
• Understand that the Data Hub is not limited to one function; it adapts to the team type.
• Use it to see the work being done by AI agents and the status of that work in one place.

2. Identify the right data model for the team 0:39

• Recognize that different teams need different versions of a Data Hub.
• For recruiting, the Data Hub may function like an ATS.
• For project management, it may resemble a tool like Linear or Atlassian.
• Confirm the team’s goals before deciding what data should appear in the hub.

3. Use the Data Hub as the team’s operational record 1:33

generated-image-at-00:01:33

• Let the platform generate the appropriate data tools based on team function.
• Review what data the team members need to understand the work being done.
• Use the Data Hub as the main source of truth for the team’s activity and progress.

4. Review the SDR Data Hub as a CRM 1:43

• If working with the SDR team, treat the Data Hub as the CRM.
• Expect the system to populate records continuously, 24/7.
• Check the hub regularly to see new data, updates, and pipeline changes.

5. Monitor prospect and deal stages 2:13

• Review prospect statuses such as contacted, qualified, replied, or disqualified.
• Track deal stages such as discovery, proposal, negotiation, won, and lost.
• Use these stages to understand where each lead or deal currently stands.

6. Verify meeting records and source information 2:47

• Check that booked meetings appear automatically in the hub.
• Review the source of each record, such as Gmail or voice calls.
• Confirm that the system is capturing the full pipeline history from start to finish.

7. Switch between teams to view different data sets 3:19

generated-image-at-00:03:19

• Select another team to see a different version of the Data Hub.
• Expect the displayed data to change based on team goals and responsibilities.
• Use the hub to make sense of each team’s work in context.

8. Rely on AI agents to update records automatically 3:48

• Allow AI agents to move records through stages without manual updates.
• Understand that the system can update lead and deal statuses automatically.
• Use this automation to reduce manual CRM maintenance and improve transparency.

9. Use the Data Hub for team visibility and action planning 4:36

generated-image-at-00:04:36

• Review the hub to see what is happening today across the pipeline.
• Use the displayed statuses to decide what actions need attention.
• For example, if meetings are booked, make sure the right team members attend them.

10. Add records manually when needed 4:57

• Add new prospects manually if they are not already in the system.
• Use bulk prospect upload when entering multiple records at once.
• Add new campaigns when launching a new target or initiative.
• Let the AI agents take over from there and continue updating the records.

11. Let the system support both automation and manual control 5:22

• Use the platform’s self-learning and self-improving behavior to reduce repetitive work.
• Remember that AI agents can work independently while still accepting manual input.
• Combine automation with manual updates when needed to keep the Data Hub accurate and useful.

Cautionary Notes

• Do not assume the Data Hub is the same for every team; it changes based on function and goals.
• Avoid relying on manual updates alone, since the system is designed to automate stage changes.
• Make sure manually added prospects or campaigns are accurate before submitting them.
• Review records regularly so automated updates do not go unnoticed or unverified.

Tips for Efficiency

• Check the Data Hub frequently to stay ahead of new meetings, replies, and stage changes.
• Use the automatic updates as the primary workflow and reserve manual edits for exceptions.
• Add prospects in bulk when possible to save time.
• Create new campaigns only when there is a clear target or use case.
• Encourage the team to use the Data Hub as the shared source of truth for pipeline visibility.

Link to Loom

https://loom.com/share/004f0e0e9e8e4779bba38a89900d446b

Overview of the Data Hub: Dynamic CRM-Like Workspace for AI Teams

1. Start with the Inbox as the control center for AI and human collaboration 0:00

• The inbox is where you can:
• Talk to your AI agents
• Talk to the human loop
• Debug agent behavior
• Ask what agents have done
• Change agent routines if needed
• This sets up the broader idea: the platform is designed for active collaboration and oversight, not just automation.

2. Understand what the Data Hub is meant to be 0:15

• The next feature is the Data Hub.
• In a sales context, this is essentially a CRM.
• The key idea is that the platform keeps it function-agnostic so it can adapt to different team types.
• Instead of hardcoding one tool, the system generates the right kind of data workspace based on the team’s purpose.

3. See how the Data Hub changes based on the team 0:39

• Different teams get different versions of the Data Hub:
• Recruiting team → ATS-style hub for candidate tracking
• Project management team → Linear/Atlassian-style workspace
• Sales team → CRM-style hub
• The Data Hub is dynamically created based on:
• What the AI team members are doing
• What data a user needs to understand that work
• The goal is to generate the right data tool on the fly for each team.

4. Use the SDR team as the first real example 1:43

• The platform starts with an SDR use case.
• For this team, the Data Hub functions as the CRM.
• It is continuously populated in the background, 24/7.
• As time passes, new records and updates appear automatically without manual entry.

5. Track prospects, deals, and meetings automatically 2:13

generated-image-at-00:02:13

• The Data Hub shows the full pipeline in a structured way.
• For prospects, you can see stages like:
• Contacted
• Qualified
• Disqualified
• For deals, you can see stages like:
• Discovery
• Proposal
• Negotiation
• Won
• Lost
• You can also see booked meetings and where they came from, such as:
• Gmail
• Voice calls
• This gives a full view from first outreach to closed deal.

6. Use the Data Hub to understand what is happening across the team 3:19

• The Data Hub is the place to see all the data needed to understand the team’s work.
• If you switch to another team, the Data Hub changes to show different relevant information.
• The system is not built by saying, “We need a CRM.”
• Instead, it builds the team first, and the CRM-like Data Hub emerges from that team’s needs.

7. Let AI agents keep the CRM updated for you 3:48

• A major benefit is that the Data Hub updates itself.
• Unlike traditional CRMs like Salesforce, HubSpot, or Zoho, you do not need to manually move records through stages.
• AI agents automatically update statuses such as:
• Prospect → Contacted
• Replied
• Qualified
• Disqualified
• Meeting booked
• This makes the pipeline transparent and always current for the whole team.

8. Still allow manual input when needed 5:00

generated-image-at-00:05:00

• Even though AI handles most of the work, users can still act manually.
• You can:
• Add new prospects
• Add prospects in bulk
• Create a new campaign
• Once added, the AI agents pick up the work from there.
• This means the system supports both automation and human control.

9. Key takeaway: the system is self-learning and self-coordinating 5:22

• The main message is that the agents are not only autonomous.
• They are also:
• Self-learning
• Self-improving
• Self-coordinating
• The Data Hub is the visible layer that shows all of this work in a clear, dynamic way.

Link to Loom

https://loom.com/share/004f0e0e9e8e4779bba38a89900d446b