I'm include to agree, augment not replace - what is always needed is the technically informed project manager combined with some diplomacy skills. That comes with experience.

I already see computerization, of which AI is just the latest phase creeping in to do the repetitive stuff - it already creates project plans, but in unskilled hands these can easily become so much rubbish in so much beautifully presented rubbish out.

The ability to recognise similar previous projects and say ' aha the last time on project xx we overran by six weeks and had a penalty payment of XX because we missed a thing - this time we should allow for it' is the sort of thing that a grey haired human does very well - but a machine can be trained on hundreds of past projects, not just one lifetimes worth.

M.

You were saying that AI is not a gaffer, but a mate that can help the human gaffer do their job better. AI can give you good info, ideas, and tips, but it can’t do the stuff that only humans can do, like making choices, leading folk, having a natter, and caring about feelings.

You also said that AI doesn’t mean that human gaffers are not needed anymore, but that they have to work in a different way. AI can help human gaffers deal with the bother and changes that happen in their work. AI can also help human gaffers focus on the important bits of their work, like getting on with others, managing risks, and being clever.

But you also said that AI has some bother and chances for human gaffers. For example, how can human gaffers use AI to do their work better and give value to their outfits and customers? How can human gaffers make sure that AI is used in a good and fair way in their work? How can human gaffers learn the skills and abilities needed to work well with AI tools?

You were saying that AI is not a gaffer, but a mate that can help

Indeed - the phrase Microsoft are using for AI in software development is "co-pilot".

At the moment it feels like an enthusiastic, well presented, but ultimately a pretty dim apprentice. It's good a finding things other people have done and can repeat them parrot fashion, even pretty good a dressing them up to look very plausible indeed. What it's still seems to be very poor at is actually understanding if what it's found actually useful for the problem in hand.

What I've seen so far is perhaps 80% accurate and helpful - but the remaining 20% is not only dross, but so well presented that it takes an inordinate amount of human time to filter it out and find a useful substitute, meaning it was probably quicker just to do it yourself in the first place.

I suspect normally we rely on many subtle human clues when talking to people to gauge the value of what they're telling us and so can intuitively home in on useful information fairly quickly (e.g. if you ask a stranger for directions, and when answering if they appear drunk or say left while their hand makes a gesture to the right, you're probably not going to trust what they say; or when reading text if the spelling and grammar seems all over the place it might raise a worry, or if it contains the odd snippet that just doesn't add up, or you know it's from someone who has proved unreliable in the past. Stuff generated by AI seems to be anonymised and stripped of all such clues.

   - Andy.

Regardless of what we think, AI is inevitable and unstoppable, and that it will change the world in ways that we cannot control. Although I am bit pessimistic and resigned about the future of AI.

I tend to agree with you.  I find that I am using AI more each day.  One particular application that I use it for is Learning From Experience.  It provides valuable insights and trends which can then be used to inform project risk registers but as always it is only as good as the data that can be mined.  It becomes therefore essential to conduct adequate, and dare I say, honest LfE sessions throughout the project lifecycle.  I find here comes the overlap with the Quality Management Body of Management and Six Sigma methodologies.  Correct and robust root cause analysis using such tools as ISHIKAWA and 5 Whys, with proper data entry enable the collection and assimilation of lessons learned that can be consulted using AI algorithms.  

L

I agree with you.  AI is gaining traction and will definitely affect the way we all work in Project Management.  Contrary to the PMBOK, no project is truly unique.  Whilst context can be unique a project is a collection of subsystems.  For instance, if looking at a building one sees a collection of various systems and subsystems which are always generic in nature.  Take a HVAC system.  It has a motor, filter, pump/fan, dryer, ducting etc.  On a generic level each should have a generic DFMEA etc which may not be bespoke to the project in hand but is a valuable source of risk management.  From such a dataset a model is constructed (i.e digital twin) from which with variable design, operational, and MRO parameters are input...thus parallel engineering though concurrent simulation results.  The use of AI here greatly speeds up the identification of the optimum design and relevant safety case.  In addition, AI also greatly speeds up the Critical Path once the option WBS is identified.

Personally I see not only AI driving greater efficiency for the Project Manager, but the PM will also have to greatly raise their game.  This will no doubt be reflected in any competency framework for Project Managers in the future.

I can certainly see the benefit in scenario modelling, given typical project managering of trying to balance the three strands of running late, and/or over spending, and/or failing to fully deliver, an aid to realistically modelling the "what if" cases would be good. However I can see the challenge of having valid data for the AI to work with - for occasional large infrastructure projects (we don't build 1000 HS2s a year) or occasional innovation projects it's an interesting question as to where the AI will get its source data from.

However, I agree with the comments above that I would still expect the final decision to be a human one, just because a lot of project management decisions are going to be the "wrong" answer for at least one stakeholder, and so I guess that we'd want a human to be able to be held accountable for that final decision. Even if it's based on AI data - but I'd hope that an effective AI system wouldn't give a single "correct" answer (which there never really is in PM) it would give a series of possibilities and resultant consequences to choose from.

I agree Andy.  AI certainly is not the 'be all and end all' in project management and never will it be.  Scanning technology is a great aid to build a 'digital twin' from which to simulate and can prove to offer significant advantages but as with all such architecture it must be verified and validated.  The reaction of the 'model' must therefore be aligned to the real world with suitable known calibration parameters.

You cite HS2 as a possible example for challenge.  Agree that AI could not be applied to the whole project but it certainly does have applications with bespoke elements of the project.  For example:

Tunnelling equipment optimisation and maintenance repair and overall (MRO) strategies.  This links well to the work being done in the Integrated Vehicle Health Management (IVHM) agenda and the work being done in prognositic health management (PHM),

The solutions offered by IVHM, PHM, and Through-life Engineering Services (TES) lend themselves well to the application of AI when looking at High Value, High Risk, complex projects.

Whilst AI offers a paradigm shift I would not advocate the use of 'closed loop' thinking or decision making within such architectures and models for obvious reasons.  Systems should always be 'open loop' with the human as the moderator.

feels like an enthusiastic, well presented, but ultimately a pretty dim apprentice.

(Ah well we have all worked with someone like that at some point. Now it is a good job we do not meet in real life.. stories that could be told..)

Perhaps like so many examples of automation, it is rather worse than a good competent person, but better than an incompetent or absent one.
Once this limitation is recognised and the fog of hype thins, it may be a very useful sidekick rather than project lead.

Mike.