Damian BerghofHead of R&D Compact Hydraulics| HAWE Hydraulik SE - MunichMünchen, Germany
I’m experimenting with a RAG-based AI chatbot to support project work in an R&D environment. It builds on structured "Jour-Fixe" / weekly meeting notes, team profiles, and project data to suggest next steps and highlight how to better enable team members. I see this as an early stage toward a more autonomous AI agent-based framework.
From your perspective: What are your biggest pain points in project meetings today?
And what concerns would you have, especially regarding data privacy or transparency of AI-driven recommendations?
Product Operations Program ManagerBarcelona, Cataluña, Spain
Great topic!
I recently read an interview with an AI expert (I can’t recall his name) who pointed out that AI is evolving from being an assistant to becoming an agent. This chatbox seems to be moving in that direction.
One of the biggest pain points in meetings is leaving without clear decisions or next steps, specifically the what, who, and when. Current AI tools often fail to capture the nuances of a discussion, missing key decisions while highlighting less relevant content.
One way to address this could be to provide the assistant with better inputs upfront, such as the topics, attendees and their roles and responsibilities, expected decision points, and desired outcomes. This would help guide the AI toward delivering more useful and actionable results.
Privacy must also play a critical role, as meetings often involve sensitive topics and data. I’m not sure how best to address this, and I look forward to learning more about it in this thread. Saving Changes...
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
One of the biggest gaps I see is exactly that, leaving without clear ownership or next steps, even when the discussion was good. On the concern side, I’d add trust. If people don’t understand how the AI is interpreting the conversation, they’ll hesitate to rely on its recommendations, especially in sensitive contexts. Saving Changes...
Imran AfzalAuthor| The Strategic PMOCary, NC, United States
This is a really interesting direction—and I think you’re pointing at something deeper than just “AI suggesting next steps.”
Most project meetings don’t fail because information is missing.
They fail because meaning isn’t aligned.
Two people can hear the same discussion and walk away with completely different interpretations of:
what was decided
what matters most
what happens next
That’s the real gap.
Where a RAG + agent approach becomes powerful isn’t just summarizing notes…
It’s acting as a consistency layer across conversations.
For example:
Highlighting where today’s “decision” conflicts with last week’s
Surfacing unresolved assumptions that keep reappearing
Tracking how priorities are actually shifting over time (vs what people say)
Calling out when “alignment” in the room doesn’t match execution signals
That’s when it moves from assistant → governance capability.
On pain points in meetings, the biggest ones I see:
False alignment (everyone nods, execution tells a different story)
Decisions without clear ownership or tradeoffs
Re-discussing the same topics because prior context wasn’t retained or trusted
Most tools capture what was said.
Very few help interpret what it means over time.
On concerns—privacy is part of it, but I think the bigger issue is trust in interpretation.
If the system suggests next steps, people will ask:
Why this recommendation and not another?
What inputs did it prioritize?
Is it reinforcing the loudest voice in the room or the most accurate signal?
If that’s not transparent, adoption will stall quickly.
So I’d frame it this way:
RAG helps you remember.
Agents help you suggest.
But the real opportunity is helping teams stay consistent in how they interpret and act on decisions over time.
That’s where this gets interesting. Saving Changes...
Consultant| Timely Nexus Project LLPGreater NOIDA, Uttar Pradesh, India
This sounds like a very useful idea, especially in R&D where a lot of context gets lost between meetings. Common pain points are too much focus on status updates, unclear action items, and poor tracking of decisions and responsibilities. A system like this could really help by keeping continuity and suggesting next steps. However, there are some concerns mainly around data privacy, who has access to sensitive information, and whether people can trust and understand the AI’s recommendations. There’s also a risk of relying too much on AI if the input data is incomplete. Overall, it’s a great direction, but success will depend on good data quality, clear controls, and user trust. Saving Changes...
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Very interesting direction, especially the shift from documenting meetings to shaping decision quality.
In practice, the main pain point is rarely lack of information, but lack of decision traceability:
What was decided, why, based on which assumptions, and with which trade-offs.
RAG can help, but only if it goes beyond retrieval. The real value emerges when the system:
Surfaces conflicting interpretations
Makes assumptions explicit
And links recommendations to evidence and downstream impact.
Otherwise, it will simply scale existing biases with more speed and confidence.
On concerns, two are structural:
Transparency of reasoning – teams must understand how outputs are constructed
And decision accountability – AI can inform decisions, but must never absorb responsibility
If these are well governed, this is not just a better meeting tool. It is the foundation of a decision system. Saving Changes...
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