Emergent methods

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The influence of A.I. on project methodologies and process steps is yet to be defined, practised widely, and understood in the context of what most of think of when we think of project methods and processes, skills, risks, and predictable and planned outcomes .

And so, we are told to brace for emergent outcomes, not entirely predictable at the outset. And we are told to adapt our leadership and management skills to this reality (or reality in-waiting)

With that all in mind, I asked chatGPT to opine on emergent methods. Here is what it gave me back:

In the context of project management, "emergence" or "emergent methods" refers to the phenomenon where complex and unpredictable outcomes arise from the interactions and dynamics of various elements within a project. Instead of relying solely on predetermined plans and rigid processes, emergent methods embrace adaptability, flexibility, and the ability to respond to changing circumstances.

Traditionally, project management has followed a linear or sequential approach, where all tasks and dependencies are predetermined in a detailed project plan. However, this approach can be limiting when faced with uncertain and dynamic environments. Emergent methods offer an alternative perspective by acknowledging that projects are often complex, and the future is uncertain.

In emergent methods, project managers adopt a more iterative and adaptive approach, focusing on continuous learning and improvement throughout the project lifecycle. Rather than trying to predict and control every aspect of the project from the outset, emergent methods encourage exploration, experimentation, and the ability to adjust the project's direction based on real-time feedback and insights.

Key characteristics of emergent methods in project management include:

Iterative Planning: Instead of creating a rigid and detailed plan at the beginning of the project, emergent methods emphasize iterative planning. This involves developing a broad roadmap and refining it in smaller increments as the project progresses and new information becomes available.

Continuous Learning and Adaptation: Emergent methods embrace the concept of learning as you go. Project teams gather feedback and data throughout the project's execution, allowing them to adapt their strategies and make informed decisions based on evolving conditions and requirements.

Flexibility and Agility: Emergent methods prioritize flexibility and agility to respond to changing circumstances. This may involve adjusting project scope, reallocating resources, or modifying project objectives based on emerging needs or unforeseen challenges.

Empowered Teams and Collaboration: Emergent methods emphasize empowering project teams to make decisions and encouraging collaboration. By promoting open communication and fostering a culture of trust, teams can respond effectively to emerging issues and capitalize on opportunities.

Embracing Uncertainty: Unlike traditional project management approaches that attempt to eliminate uncertainty, emergent methods recognize that uncertainty is inherent in complex projects. Instead of resisting it, these methods embrace uncertainty and focus on building resilience and adaptive capacity.

Emergent Leadership: In emergent methods, project managers adopt a leadership style that is more facilitative and empowering. They create an environment where team members can contribute their expertise and collectively make decisions to address emergent challenges.

By adopting emergent methods, project managers can better navigate complex and unpredictable projects, promote innovation and creativity, and increase the chances of project success in dynamic environments. However, it's important to note that emergent methods are not suitable for all projects and may require a shift in mindset and organizational culture to be effectively implemented.

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A creativity explosion?

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There are endless headlines and debates about the downside of the A.I. revolution that is (likely) coming, some of which is upon us. Since most project people are knowledge workers or skilled trades workers, the seeming threat of A.I. job replacement is always just hanging a bit off stage-left.

But along comes an essay by Daniel Miessler which posits, in part, that we may be on a cusp of a creativity explosion which by its very nature is going to find its way into projects of all types.

Here's what Miessler has to say [lightly edited], writing in the '1st person':

What’s about to happen to knowledge workers [may be going] to be bleak. And it’s [probably] going to happen so quickly. [..........] in the last several weeks I’ve had a new thought that is blowing me away.

Let me ask you this: what percentage of people are producing creative ideas that are being seen by others and that are good enough to earn them a living? Like, on the planet.

1%? .5%? .01% I don’t know the number, but it’s extraordinarily small. We’ve got 8 billion people now. How many startups are there? How much music is there? How many Hollywoods are there? How many Taylor Swifts? How many Kendrick Lamars? How many Elons? How many Satya Nadellas?

Too few. And here’s the important question. Why? Why so few?

Part of the answer is that talent matters, and intelligence matters, and creativity matters. Sure. Agreed. But how many people have similar capabilities to these people but don’t have the time or the tools to do anything with them?

Again, I don’t know the answer to that, but I’m betting it’s vast. Not hundreds of people. Not thousands. Millions.

But they can’t go to a studio. They can’t talk to their producer friends and get a break. They don’t have an art table to work on. They don’t have a beat machine.

AI is about to change that. We’re about to remove many of the advantages that Steven Spielberg has over Takashi Noshimira, who lives in a small rural town in Japan, who is a creative genius. With these new models coming out, with the ability to create music, create video, create screenplays, create scripts, etc—we’re about to equalize the playing field massively.

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Working in the penumbra of the rules

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Process and methodology are the name of the game in projects

We all know that

Select a methodology and it undoubtedly comes with its rules, procedures, gates, decision points, you name it ....

Any worthy methodology also comes with a historical record: what works; what doesn't; compromises; deviations; and exceptions, of course.

Take in all the dots

So, that's a lot of dots: all the methodology dots and all the historical record dots. What if, when you stand back a bit and look strategically at the sum or integration of the dots, you find grey areas where principles and tactics seem to conflict? You may even draw conclusions about the process that were only apparent when you took the integrated view.

Greater than the sum:

Another way to see this: a strategic view of the dots is a not that different from the concept that the "sum is greater than than the parts". And so the "uplift" is found in the shadow of the dots.

Penumbra workings

Using, applying, or working the stuff in the shadow of the main dots is what many call "working in the penumbra", where a penumbra as we know is the shadow area behind or adjacent to something more opaque. 

Working in the penumbra is working with conflicts, competitions, and perhaps choices that we really don't want to make: personal, cultural, technical, quality, schedule, scope trades and choices that are hard and not really win/win.  

But there can be an upside: You may discover an entirely new way forward to a better outcome if only you are allowed to use some of the outcomes and conclusions which are "off the beaten path" or even on a path formerly denied.

Some even go so far as to say that new process emanates from what can be seen in the penumbra of the main process and methods. If so, you might say that penumbra emanations are tantamount to an emergent outcome.

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Grasping a big number

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We don't think about numbers linearly

That is, as numbers get large, their "largeness" kind of flat-lines in our thinking, like a logarithmic scale (*)

Grasp the future

We can't grasp the future very well either when it is really far removed from the present. 

Daniel Miessler posted this bit of schedule data in a recent blog:

The difference between a million and a billion is counter-intuitively large.

As an example, a million seconds is 12 days, and a billion seconds is 

32 years.

I had to look it up to confirm. That's bonkers.

Risk management

The phenomenon of a flattening of the future possibilities affects risk perception big time. 

If you're trying to finance your project, "2 year money" is more expensive than "10 year money". And "90 day" money is more expensive than either of those. 

Why?

The "cone of uncertainty".

We can 'accurately' gauge the next few weeks, perhaps even a year is not too far off. 

The finance guys think the near-term risks are more certain and more expensive than those we can't really forecast (so we fall back on historical models)

 

Just think about how the 'cone of uncertainty' widens as we view the future, but is it wide enough? Can we really bring our mind to consider the limiting case?

A million deaths in U.S. from COVID; no one would have accepted such an outlandish forecast of the cone of uncertainty when it all began.

"They say" that one really bad mistake is tragedy, but a thousand similar mistakes is just a lot of mistakes. We just can't muster the impact of one mistake multiplied up to that of a thousand.

And so it's generally understood that we view the future more optimistically that the present (there's time to fix things), or we have a bias towards optimism, as we cannot grasp the extent of the risk possibilities, even as someone gives us a number (one billion seconds: the mind numbs; there's no grasp of reality). Climate science is a present example.

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(*) A log scale is plot of exponents, rather than that of the base number. As an example, the base number might go 10, 100, 1000 and be plotted as an ever-extending curve; but on a log scale, the plot of exponents is 1, 2, 3, and the plot would be linear, just a straight line. So whereas a phenomenon might change by a 1000:1, in our mind's eye we might flatten that to 3:1.  

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Don't ask me about the worst case

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Perhaps the most common question in risk management, if not in project management is this one:

"What's the worst case that can happen?"

Don't ask me that!

Why not?

Because I don't know, and I'd only be guessing if I answered.

So what questions can you answer about the future case?

• I can tell you that a projection of the past does not forecast the future because I've made the following changes (in resources, training, tools, environment, prototypes, incentives, and .....) that nullify prior performance ......
  
  
• I can tell you that I can foresee certain risks that can be mitigated if I can gather more knowledge about them (Such being a Bayesian approach of incrementally improving my hypothesis of the future outcomes). So, I have the following plan to gather that knowledge ......
  
  
• I can tell you that there are random effects over which I have no control and for which there is no advancement in knowledge that will be effective. These effects could affect outcomes in the following ways ......
  
  
• I can tell what you probably already know that the future always has a bias toward optimism (there's always time to fix it), and that there's always a tactical bias toward "availability" (one in hand is worth two in the bush ....) even if what's available is suboptimum.

Heard enough?

So go away and let me work on all that stuff!

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Arithmetic with randomness? Doable?

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When your office analyst is working with randomness or uncertainty, getting a numerical answer to a question is tricky business.

It's just a box

Here's one to ponder: if the length of a specific "box" is uncertain, but from observations of similar boxes the length seems to lie between Lmin and Lmax, and similarly the width seems to lie between Wmin and Wmax, how then does your analyst provide you with a number for the area of the box? 

There are at least three methods that apply to arithmetic with uncertainty:

1. You could multiply each value of L and W, pair by pair. If there were 6 unique observations of L and six of W, then there would be 36 combinations, like Wmin X Lmax. You could then average them. (*).
   Of course, if you are dealing with volume of a cube rather than an area, then the situation gets quite large quickly. You might have LxWxHeight, and that would bring in 6 times more combinations
   
   
2. You could do away with the randomness by first calculating the average W and the average L. If you calculate from observed data, then the average of the observations is a statistic, and it is not random. In effect, you've done "reduction" on the randomness.
   
   
3. You could calculate the 'expected value' by adding up the probability-weighted values --- value of L or W times the probability of L or W. You sum all the probable L's and W's, and then do the L*W multiplication on their weighted sums. Probability would be calculated from the observed frequency that each value of L and W occurs.

Now, think about risk management

What if the L and W were not length and width, but rather risk impact and risk probability? Typical parameters on a risk matrix. 

Can you multiply them? 

You don't know impact and risk probability for certain; there is randomness about each parameter.

Well, the right answer is that your risk analyst should apply one of the methods described for the box at the outset of this posting.

Here the bell ring

What if your observations more or less show that the distribution of Ls and Ws (or the impact and risk probability) is more bell-shaped than uniform (as in the previous example). The three methods still work, but there is a lot of work to do to get the answers from the bell-shaped distributions. And, if there is a lot of production data, then all three methods are really calculation intensive, best left to a computer. 

Arithmetic in the face of randomness: You have to deal with the distributions of the observed or forecasted data, convolving distributions, or otherwise doing "reduction" on the randomness to reach a calculated result.

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(*) You might recognize that this is pretty similar to the process of adding two uncertain numbers demonstrated by the example of rolling two six-faced die, and averaging the sum of the outcomes, which of course, is 7.

Also note that whereas the probability of any one die value is uniformly between 1 and 6, the probability of the individual 36 sums is decidedly not uniform. The average, 7, has the highest frequency of occurrence among the 36 outcomes. 

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Maximizing project value

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Which are you? 

Government department; non-profit or voluntary; or a "for profit" business?

Whichever. 

Have you ever thought closely about these ideas (*) when thinking about the value of your project? 

Business Category and Project Objectives
Category	Business values	Project Objectives
Government department	Mission satisfaction for public constituency Protection of taxpayer interests	Best value outcomes (most bang for the buck)
Non-profit or voluntary organization	Betterment of target constituents Organizational sustainment Donor satisfaction	Simplest outcomes that serves constituent interests
For profit business	Improvement of business scorecard Shareholder (owner) benefit	Maximize scorecard impact

Is there ever an argument with this?

Projects are most successful when executives, sponsors, stakeholders, and project managers all share an understanding that projects only exist to promote and benefit the organization at large

And no, I didn't leave out beneficiaries, customers, or users. The stakeholders and at-large organization cover those bases. So, no busy work; no painting rocks to absorb excess labor. If it doesn't add value, then do something else.

Concepts of Value

Along the way, value takes on multiple meanings.  At one level, value—or values—is (are) what we believe in, a “truth” of sorts that needs no proof.  It’s just there, and we believe it without reservation. 

Value, defined as beliefs, is often extended to a belief system constructed of beliefs and principles.  Principles are actionable statements of doctrine; they give beliefs behavior. Thus, like any other system, a value system is a structure of elements supporting defined behavior.

At another level, value is worth we place upon something that is really meaningful to us for which we are willing to give up something else.  In the transaction, it’s possible to acquire value-add; indeed, adding value is often, but not always, a transactional objective.  

For value-add transactions to be possible value cannot be a zero-sum game; the value pie can be made larger.  There is no “conservation of value” principle like there is for energy.

Perspectives

At the end of the day, there are these perspectives for the PMO to take into account:

Value Perspectives
Sponsors	Project Managers	Beneficiaries
Value is achievement of business scorecard objectives	Value is delivery of outputs for the intended business investment	Value is being better off, more effective, and more efficient than before

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(*) This posting's material taken from my book "Maximizing Project Value, A project manager's guide". See the cover photo below.

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Working on a proposal? Read this ....

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Do you read Mike Clayton? You probably should.

Here is his idea of the 12 points of a perfect proposal:

What are the elements of a perfect proposal? Here are 12.

 

You

No one wants to hear all about you. See the next subtitle. That's who your audience wants to hear about... themselves. But (and it's a big 'but') they do need to know enough about you to know you are worth paying attention to. So, for a cold proposal, this means using the introduction or cover letter or some other means to establish your credibility - what my dad used to call your bona fides.

Them

This is what they want to know... What's in it for them? Show how you have their best interests in mind with this proposal. You understand their needs and desires and know how to satisfy them better than any alternative solution can.

Focus

Keep your focus on the specific situation. Any sign of standard 'boilerplate' descriptions, arguments, or evidence will make it look less about 'Them' and more about 'anyone'. 

Value

How will your proposal deliver and maximise value to them? The vast majority of business decisions revolve around the capacity to either make money or save money.

Benefits

But there can be other benefits too. Describe the non-financial value your proposal offers - and what this means to them. This, of course, means you need to take time to understand them and their requirements and priorities. 

Emotions

All that hard evidence gives them a reason to make the decision to accept your proposal. But it won't motivate them to do so. For that, you need to tap into their emotions. Find emotional hooks into pride, fear, duty, desire, ambition, loyalty, passion... Emotions drive decisions: reasons justify them.

How

So, you also need to show how your proposal will solve their problem, deliver their joy, or enhance their reputation. Make the link between what they want and what you are proposing as clear, simple, and short as you can. A 15-step sequence from the cause (your proposal) to the effect (their outcome) won't cut it. 

Process

Next they need to know what will happen if (when) they say yes. What will you do, what will they do, and how long would it all take? For confidence that your is the right choice, they need to see a plan that clearly works.

Context

This section lets them understand how your proposal fits into your life and theirs - your business and their own. This shows that you and they are compatible and is the equivalent of the more traditional (cheesy) 'how we are different to the competition'. Of course this differentiates you. It shows how this proposal is right for them and for you.

Business

Don't go all techy on a technical proposal. Remember who you are speaking to. If your audience is a business person or a group of businesspeople, focus on the business. If your audience is software engineers, focus on the business of software engineering: not the hardware. What is their business? That's how to frame your proposal.

Structure

I get it. You have a lot of ideas to get down. But, before you start, develop a structure that makes it compelling for them to follow. If they asked six questions in a sequence, then a great structure is to answer those six questions... in the same sequence. Make it easy for them to say 'yes'.

Quality

Finally remember Mark Anthony's advice: 'The evil that men do lives on. The good is oft' interred with their bones'. People remember your mistakes and easily forget all the good stuff. Make sure you check the quality of your proposal, not once, not twice... Better still, get the pickiest, most pedantic person you know to do it for you. You invested all that time. Now add a little more investment, to avoid throwing it all away!

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