Darren HastingsPrincipal Consultant| Finnite Pty Ltd creators of Provua.comBrisbane, Australia
Right now everyone is asking how best to apply and control AI in project management so that it delivers genuine value without losing oversight.
I’ve been fortunate to be part of a year-long deep debate on this question, and along the way I’ve gathered some highly practical and implementable insights — especially in the areas of risk, stakeholder alignment, and WBS development.
One of the key themes found is that AI is most effective and efficient when coupled with a strong supporting framework - this avoids much of the 'workslop' trap.
I’d love to hear from others in this community:
Where are you finding AI most useful in practice?
Where do you feel its risks or limits outweigh the potential benefits?
If there’s strong interest in the topic, I’d be glad to share more of what I’ve learned and open a deeper discussion — but first I’d really value your perspectives.
Thanks,
Darren Hastings, PMP
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Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Excellent reflection.
“Workslop” is indeed the trap when AI is introduced without structure.
In my experience, AI delivers real value in project delivery when anchored in three essentials:
- Decision Frameworks
Tools like RCPCV™ or DA’s Choose Your WoW help us frame, validate, and not just execute AI outputs.
AI amplifies cognition, but humans decide.
- Trust & Traceability
Especially in risk and stakeholder alignment, outputs must be transparent and auditable. Otherwise, trust erodes.
- Human-AI Role Clarity
Treat AI as a “team member” with clear roles, boundaries, and handoffs.
This avoids chaos and promotes adoption.
I’m cautious with AI in areas like deep stakeholder analysis or problem framing where nuance and ethics matter most.
Thanks for sparking this timely conversation!
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1 reply by Darren Hastings
Oct 03, 2025 1:51 AM
Darren Hastings
...
Hi Luis Branco,
Thanks for taking the time to reply, and I certainly agree with your points. My observation is that in the race to try to adopt AI for productivity gain these points aren't yet well enough understood or applied.
Excellent point, Darren. I’ve seen AI add the most value in automating reporting, risk forecasting, and knowledge management, freeing PMs to focus on strategy and people. The risks, however, arise when teams adopt tools without a clear governance framework, resulting in ‘workslop’ rather than genuine impact. The balance lies in aligning AI with structured PM practices, not replacing them.
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1 reply by Darren Hastings
Oct 03, 2025 1:55 AM
Darren Hastings
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Hi @Syed Ashir-Riaz,
Great points, I'm interested - what tools of methods have you used for risk forecasting? your final point 'The balance lies in aligning AI with structured PM practices, not replacing them' I think is particularly strong at the moment.
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Darren HastingsPrincipal Consultant| Finnite Pty Ltd creators of Provua.comBrisbane, Australia
Excellent reflection.
“Workslop” is indeed the trap when AI is introduced without structure.
In my experience, AI delivers real value in project delivery when anchored in three essentials:
- Decision Frameworks
Tools like RCPCV™ or DA’s Choose Your WoW help us frame, validate, and not just execute AI outputs.
AI amplifies cognition, but humans decide.
- Trust & Traceability
Especially in risk and stakeholder alignment, outputs must be transparent and auditable. Otherwise, trust erodes.
- Human-AI Role Clarity
Treat AI as a “team member” with clear roles, boundaries, and handoffs.
This avoids chaos and promotes adoption.
I’m cautious with AI in areas like deep stakeholder analysis or problem framing where nuance and ethics matter most.
Thanks for sparking this timely conversation!
Hi Luis Branco,
Thanks for taking the time to reply, and I certainly agree with your points. My observation is that in the race to try to adopt AI for productivity gain these points aren't yet well enough understood or applied.
...
1 reply by Luis Branco
Oct 03, 2025 3:39 AM
Luis Branco
...
I’ve noticed that without a shared thinking framework, teams often jump into AI tasks with enthusiasm but no compass.
Thanks, Darren Hastings That’s exactly the challenge I’ve seen too: adoption often races ahead of reflection.
I wonder, in your experience, what’s been the most effective structure or principle to help teams slow down just enough to apply AI with intention?
For instance, have you found success with any specific framework, checklist, or even cultural norm that prevents the "workslop" spiral before it starts?
Happy to keep exploring
This feels like a conversation that could evolve into a joint learning thread!
Saving Changes...
Darren HastingsPrincipal Consultant| Finnite Pty Ltd creators of Provua.comBrisbane, Australia
Oct 02, 2025 7:46 AM
Replying to Syed Ashir Riaz
...
Excellent point, Darren. I’ve seen AI add the most value in automating reporting, risk forecasting, and knowledge management, freeing PMs to focus on strategy and people. The risks, however, arise when teams adopt tools without a clear governance framework, resulting in ‘workslop’ rather than genuine impact. The balance lies in aligning AI with structured PM practices, not replacing them.
Hi @Syed Ashir-Riaz,
Great points, I'm interested - what tools of methods have you used for risk forecasting? your final point 'The balance lies in aligning AI with structured PM practices, not replacing them' I think is particularly strong at the moment. Saving Changes...
Luis BrancoCEO| Business Insight, Consultores de Gestão, LdªCarcavelos, Lisboa, Portugal
Oct 03, 2025 1:51 AM
Replying to Darren Hastings
...
Hi Luis Branco,
Thanks for taking the time to reply, and I certainly agree with your points. My observation is that in the race to try to adopt AI for productivity gain these points aren't yet well enough understood or applied.
I’ve noticed that without a shared thinking framework, teams often jump into AI tasks with enthusiasm but no compass.
Thanks, Darren Hastings That’s exactly the challenge I’ve seen too: adoption often races ahead of reflection.
I wonder, in your experience, what’s been the most effective structure or principle to help teams slow down just enough to apply AI with intention?
For instance, have you found success with any specific framework, checklist, or even cultural norm that prevents the "workslop" spiral before it starts?
Happy to keep exploring
This feels like a conversation that could evolve into a joint learning thread!
Program Manager| HARPER SRLSanto Domingo / Distrito Nacional, Dominican Republic
Great question, Darren. AI delivers its best value when paired with a strong governance and process framework, not used in isolation. For example, I’ve seen teams use AI to accelerate WBS drafts and risk scenario modeling, but the real success came when those outputs were reviewed against established PM standards and stakeholder input. The “workslop” risk is real, without structure, AI can create noise instead of clarity.
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