I am Daniel Armanios,

I am the academic director
of this programme,

the BT professor and chair of
major programme management.

And in close collaboration with Guy,

we have the distinct pleasure

of rallying all the passion and energy

that you'll bring to our cohort

into eight modules and a thesis.

And so as part of this,

I thought we would give
you a little taster

of kind of what goes on in the classroom,

the kind of ways we
kind of think critically

about our existing major
programmes and the like,

just to give you a feel
of kind of the experience

as best as we can do
it on a Zoom interface.

Next slide please.

So just to give some thoughts, we usually,

when we think of major programmes,

we usually like to start
with definitions or models.

As I always tell our students
models and definitions,

they are similarly in
that they're always wrong,

but some could be useful.

And so the way you can kind of
think about major programmes

and the philosophy we
have in this programme

is that major programmes are
really big scale initiatives

that involve different
organisations, communities,

institutions, delivery
partners and the like,

where what goes into them,

what comes out of them

and all the time in between

is not always clear.

For a more practitioner view,

we can think of this in terms of metrics

of usually projects that
are 1 billion or more.

But again, we can debate that.

For example, there's quite
a few really complex,

very large scale IT projects

that will never hit a billion pound mark,

but are still very
complex, interdependent,

have the similar features

or have major programmes
in their own right,

affect millions of people and
take five plus years to build.

If you look at HS2, it
takes decades to build.

And so what that means is
often the programme manager,

the project manager that starts this

is not the one that's going to finish it.

And so you have to build
in handoffs in between.

And so with that on the next slide,

I may ask those in the
audience participants,

what kind of projects for
you or programmes for you

come in mind when you see that definition?

Any projects, programmes?

Feel free to type it in.

I'll synthesise as I see them.

No one's allowed, as I
always say in my classroom,

I don't take silence to mean
agreement or understanding.

So which brave soul is going to be willing

to maybe put in a few
words here and there?

No.

- Daniel, there's a couple of answers

in the Q and A.

- Oh, in the Q and A.

So they're all in Q and A, excellent.

Yes. Infrastructure, perfect.

Government, software implementation,

rollout, railway programmes.

Nice, so we're seeing, oh yeah, the chat.

Okay, put perfect HS2,
Olympics, airports, oil and gas.

So you're thinking large,

usually the answers I'm seeing here,

Adrianne brought up a good one
on humanitarian programmes.

We'll talk about this

or kind of engage with in a minute,

but we're thinking of kind of large scale,

either physical hardware that's designed

for physical connectivity
or a hard infrastructure

that's designed for digital connectivity

like broadband and the like.

So if we toggle through
the slides a little bit,

what you get essentially is,
you know, London, stadia,

bridges, you know, the kind of large scale

infrastructure programmes.

If we could toggle the slides please.

Yep.

So these are the kind of things

we also talked about the digital side.

So what I usually do to kind of,

and when I'm in the classroom,

I usually try to instil some provocation,

not necessarily that this means

what we're going to talk
about in a few minutes here

are major programmes in their own right.

They're mainly designed to be provocations

to make us rethink what elements

could be in a major programme or not,

or even to debate whether what we see

is new trends are just fads
or fashions of the time.

And so if you may go to
the next slide please.

And so when I talk about
these trends coming up,

I want you to ask yourself,

do you see elements of major
programmes in here or not?

Is this just a fad or fashion?

And if you do, what kind of opportunities

and challenges come to mind?

If we can toggle some slides please.

Yep.

So the next slide,

how many of you have
heard the company Icon?

Some of you may have,
some of you have not.

They are a startup that
3D prints infrastructure,

so they did their first
permitted house in Austin.

They've now built an entire
community in Tabasco, Mexico,

because they can print layer by layer

using a proprietary printing
machine called the Vulcan

and a proprietary
substance called lavacrete.

They can also do really quick tailoring

of different structures.

So there's a lot of military
applications of interest,

because you can quickly print
out bridges infrastructure

to really mobilise forward
deployment operations.

And in space, if we need
to look at unique things

for the Martian Lunar climate

that can tailor those kind
of printing applications.

The valuation, if you
want to use valuation

as a metric of a large scale
infrastructure programme

or major programme, it's about $2 billion.

And if we go to the next slide,

you can kind of see how this looks.

So this was their first house on the left.

It was built in 2018.

It has two, it takes
two days of print time.

The base and the
foundations are all print.

So the fittings, the windows and stuff

are all traditional build.

350 square feet, $10,000 to build it.

Fast forward one year later
they cut the print time in half,

they expanded the size of 500 square feet

and the mortgage for these houses

is about $20 per month for seven years,

which is pretty remarkable
in this instance,

if you can go back please,

in this instance because this community

is built on $3 a day.

And so this is remarkably affordable

even for really low income communities.

And when a new technology gets built,

we oftentimes to help people
understand how it's used,

whether it's a startup
or a major programme

or a manager in such a programme,

they usually do things
that are scaffolding,

build demonstration projects to see that.

So if we can go to the next slide.

And we can look at the video here.

I think if you just, yep, perfect.

Oh, didn't go.

Okay, perfect.

So essentially what's happening here,

this is a final project of their housing.

There's no need for sound here.

The sound is just music.

So that's why I've asked
to narrate it myself.

You can see the flat,
whoops if we can, yep.

The flat base of this called
the Vulcans a 3D printer

and it prints out line by line,

they're proprietary
substance called lavacrete.

And you can see the kind of tailoring

they can do with their designs.

And then we'll see
shortly at the end here,

the way the final layout looks.

And so after this is how
they build the foundation

and within you can fit all
sorts of different designs,

have really open design features

besides the community in Tabasco, Mexico.

They also print really luxury houses,

because people can
literally tailor the house

the way they exactly want it.

So it also meets a very
high end clientele as well.

We'll go to the next slide.

The next example we have here is Zipline.

And Zipline is a drone
based delivery company,

predominantly working in Africa.

And what they do is, is that, for example,

if you're in an area that
doesn't have any infrastructure,

you also see, and let's say someone

is in need of assistance,
medical assistance,

and we know their blood type,

they'll take a drone
and ship the blood type

to that person via a
launcher based system.

The valuation of this company
is also about 3 billion.

And if we go to the next slide,

give you a sense if any of
you that know about drones,

some of them are vertical
takeoff and landing,

some are launcher based

and this one's launcher based,

it puts a small payload and that payload

is dropped or parachuted
down into another part of

another area of the community.

So this idea here is,

kind of the idea with
these kind of examples

is not, you know, you may say,

oh this may make me
rethink major programmes.

Perhaps it doesn't.

But it gives you a sense
of what's happening now

at the interceses of what's
historically major programmes.

We go to the next slide.

Some of the work I've done in this space

is try to say where can we find

the most extreme examples
of major programmes

that would have this

kind of distributed
decentralised name field.

And so some of my work has been in Alaska

and the reason I picked Alaska

is these towns are extremely
small, extremely isolated.

You have to fly on milk runs
to essentially get there.

So these projects have to stand alone.

Each of the projects in the portfolio

of the major programme have to stand alone

and work on their own.

I'm not asking you to read the text here,

but if you're familiar with
Alaska, some of you may,

is that there's quite a
few indigenous tribes.

So each of the texts
you see here in Alaska

is a different indigenous community.

So you have different
languages around water,

different cultural experiences,

different relationships
with the land around

as guided by the tribal communities there.

And so you have different
language extremes,

you have different spatial extremes

and even extreme conditions.

Literally they have to
heat the water lines

to make them flow.

And so what you can see
is, we'll talk about,

so in this sense you're seeing really

a whole wide sense of extremes

that's really having to be at the frontier

of a lot of environmental change.

A lot of the ways in
which we're rethinking

even the traditional major
programmes around infrastructure.

Next slide please.

And so you can see also
some of the extreme damages

that can happen.

So this actually happened during Covid

where a fire hit
precisely the water system

that could extinguish the fire.

They had a huge amount of
trouble bringing people in,

because there was also
concern of spreading Covid,

because when you're dealing with

small village communities
on the outskirts,

they also are not equipped

to handle potential outbreaks of Covid.

And so you also get in the extreme

and acute interdependencies
between different programmes

like those in healthcare
versus water and the like.

Next slide.

We can look even in other
kind of distributed nature

around search patterns.

If you look at Google and or Alphabet

and their search algorithms

underneath it are entire
sets of major programmes

in server technologies,
cloud migration technologies,

data analytics.

Each of the dots we see
here are data centres.

As of last count that's
been publicly available,

two and a half million servers.

And what you can see that happens

from that in the next slide

is that it requires a
tremendous amount of water

to cool billions of gallons of water.

And even the emissions from
the search algorithms that use,

for example the the workhorse algorithm

they use to better tailor search ads

to your user experience.

And we haven't even by the
way talked about chat GPT

and how this would disrupt
these kind of things.

But even to test their
workhorse models just once

it's equivalent to a single trans American

trans-European flight,

let alone the amount of money it is

to train even to two common languages.

If you click once more on the animation

from German to English, it takes $150,000

just to increase the BLU score,

which is the performance
metric of the search,

even by a marginal amount,

by two languages are very similar.

So you can start seeing

when you have these kind of distributed

really large kind of major programmes,

the costs in a variety of ways,

whether it's not just monetary

but also environmental and
even other supply inputs

drastically multiply.

And so what kind of world
are we thinking about here?

Is one where major
infrastructure projects,

oil and gas, traditional infrastructure

clearly are important,

but they themselves are also

becoming more distributed, decentralised.

Think of modular
infrastructure construction,

think of 3D printing,

think of other kinds of data
analytics, digital twins,

building information models
that are increasingly making

even major infrastructure
projects of this sort.

And you're dealing with very
kind of acute localization

as well as globalisation.

So if you take cloud computing,

cloud data migration,
cloud computing programme,

you can deal with any
attack on a single entity,

but it also could cascade
to the entire system.

So you constantly have to think about

every single edge local point

as well as what that means
for the bigger data set.

And then also another extreme,

if you think of such as

this distributed solar
microgrid farm in Zambia,

these are usually last
mile kind of options

for those who don't have
electricity and energy

as their ambitions grow
and they need more energy,

how do I connect other last
mile communities together

to increase the amount of
electricity I have available?

And so we're increasingly
one of the provocations

and critical thinking we can have

and hopefully in the question and answers,

and I know some people
have raised their hands

in the interim,

happy to engage with this
further as as we go along.

I just want to make sure
we get time at the end

for your questions.

Is that increasing on the next slide

what we'll see is we're increasingly

dealing with major
infrastructure programmes

that have centralization,

but are now increasingly distributed.

You're dealing with
entities that are about,

instead of thinking about one
project and making it bigger,

you're thinking about how
do I tailor the project

for maybe a big size city

such as Chicago, London, or Kampala

all the way to something smaller

such as the villages in
Alaska we talked about.

It's no longer just taking the
same kind of standard tools

and procedures and
replicating the outcomes.

We have to think about the methods,

because they know they need to be

tailored to different contexts.

And so we're getting into a much more

kind of large scale portfolio of projects

that have to be situated
to different contexts

and social environments.

And so as we go through the discussions,

as we have the provocations,

I see that some others have
already discussed things

around AI and leadership and such

and who is effective in
making the kind of differences

and and forms of this.

We'll definitely discuss aspects,

We're happy in the Q and
A to discuss aspects,

but this gives you a feel of kind of

just a diverse theoretical
and academic perspectives

that are now coming to bear

in this exciting frontier
that's evolving and changing

as we deal with net zero,

as we deal with social grand challenges.

And also the technologies
that Ravi Shankar

and others have mentioned

that are also becoming quite influential.

And we'll discuss this more in a bit

and I think that's all for me.

Oh, yep.

Okay.

Actually we have a little bit more.

Good, okay, so good.

I still have some time.

So in terms of the introduction,

what things can we think about?

And this is in terms
of, I'll bring up two.

One is scaffolding and one
is what we call sensing.

'Cause increasingly when you
have distributed programmes,

what ends up happening
is that who knows what

and who needs what is
not so clear anymore.

Formerly we're all in the same field site,

we could all see what each
other's doing, now we can't.

So how do you deal with that?

And then secondly, we're
increasingly dealing with

stakeholders that we have
to increase their benefits,

increase their understanding
of what's going on,

even if they're not involved directly

in the delivery of the programme.

How do you find those communities?

How do you engage with them

to ensure that these programmes

are really delivering
the benefits we'd want

to such communities?

So next slide.

So we could think of
scaffolding in two ways.

One is re-envisioning the possible,

and this gets a little bit
into some interesting things

that Ravi Shankar and others

in terms of logistics that are happening,

is that people are
starting to use AI and AR

both on an individual project

like you see on the lens here

around visualising what
a project would look like

with augmented reality,

but even entire urban
landscapes such as digital twins

to kind of reimagine what's possible.

Now, why does this matter so
much for major programmes?

It's because if you're trying to achieve,

let's just take a goal like net zero,

we're supposed to reach
half emissions by 2035,

Zero emissions by 2050.

The issue is, is that the
projects coming online now

are going to take five
to 10 years to build.

So literally the projects
that we're building right now

starting to build right now

have to have at least emissions.

The problem is they're
entering a world five, 10 years

from now that we know is
going to have climate issues,

but is also warming and
we're not clear of it.

And so how do you bridge that
kind of collapse time scale?

In some sense there's a lot
of interesting companies

that are trying to visualise that

to see if it helps managers

better anticipate these problems.

So one of the technological
leadership things I've seen

going on is how to
build these technologies

to assist in anticipating
or forecasting these issues.

And so that's kind of rendering

the kind of what could be possible.

At the same time though,

you have to situate it

within existing
understandings of communities

and people in your organisation
that are delivering this.

So a very simple example that R used

when they delivered the Beijing
water cube on budget on time

was something as simple
as slack and other things.

And so, and what they decided to do there

just simply looking at kind of

what are the responsibilities,

the roles at such an organisation

and making sure everybody
knew that when they came in,

irrespective of who the individual is

that's representing the organisation.

This is even useful in areas

where historically they
haven't maybe necessarily

built that knowledge.

So for example, the
dairy farm you see here,

this is actually Nicaragua

where they tried to formalise
their dairy farming work.

And so the idea there is how
to take informal ventures

or informal dairy farms

and help them build into a programme

that could be formalised
to increase Nicaragua's

competitive advantage and milk.

What they literally did
was build a model farm

that people could walk through

and see what would it look like

if I was running in this way.

So scaffolding both
helps envision the future

in different ways than previously

and helps it situate in the
local knowledge and experience

of communities that are involved

as well as delivery professionals.

Next slide.

And then on the sensing aspect,

we've been thinking about
how to use this to better,

how we can use our existing
frameworks to detect gaps.

And so some have asked, like Kath,

Catherina asked about Covid,
how this is dealt with.

We actually have a study

where we looked at how
broadband infrastructure

influenced the degree
to which unemployment

was affected during Covid.

And one of the issues is,

is that you want people to stay at home,

but if they're going to stay
at home they need broadband.

And what we found is that
unemployment increased,

particularly in counties and areas

where the the inaccess of broadband

was very concentrated to a few people.

The reason for this is because

when others around you have broadband,

you assume everyone does and there may be

a very substantial minority that don't,

and those people get really
harmed from employment.

And so if you can map out
your broadband network

during Covid and you know precisely

the small pockets that
don't have broadband,

you can start targeting services,

emergency expenses and others

even as a delivery professional

that could help shore up those gaps.

And so I think where
Covid was acutely aware

in the major programme
space was really forcing us

to think about the interdependencies

between major programmes
and also try to see

what we can use in our existing data,

our existing networks of
programmes to detect gaps.

And so that's what we've done here.

So for example, on this
one to the left briefly,

is that if you look at
the small dash lines,

these are known as trivial streets,

these are pedestrian streets

and it turns out certain configurations

of infrastructure lead to
certain social activity.

So those within, let's
say the box number one

below or two or three or four

where they have a lot of
pedestrian streets inside,

they have a lot of cohesion,
a lot of solidarity,

but the really dark black lines,

which are non-trivial
streets, which mean they go,

they get crossed by automobiles,

they really stop communities
from talking with each other.

So for example, one in two,

they don't actually talk
to each other that much,

because there's no streets

that kind of cross them
pedestrian streets.

Even though they're
right next to each other.

And so with the example I
mentioned with broadband,

if you expand this out to a city,

you can then start
seeing pockets of people

that we may not be aware of.

That really matters.

Now to the question of of
Ravi Shankar and also an AI,

this also recognises that if
our physical infrastructure

has gaps and not everyone has access

to the same infrastructure,

then the problem becomes

is that there could be
bias in our AI networks,

because of physical assets,

not because of the
representativeness of even data.

So we take this example of this picture

of let's say a smart
traffic control system.

You'll notice that all,

most of the the things
being optimised are cars.

Only in the very far distance
you might see one bus.

And what a AI algorithm will do

if it's updating in a evasion manner

is going to assume this
is a path that roads like

and they'll keep optimise cars like,

and they'll keep optimising for cars.

The problem is, is if you
have a restrictive bridge

or a set of roads that
are a gap in the pack,

in the distance, buses
may not be going through,

not because they don't
want to be on that road,

but because they can't.

And so what could happen
is if the physical assets

are not evenly distributed,

then your AI system will be biassed

based on physical networks,
not just the data networks.

And so then we've been thinking about,

or people have been thinking about

could you penalise or control
in the algorithms bias

due to infrastructure networks as opposed

to just the representativeness
of the data itself,

which we also know has some biases.

And so with that next slide,

the kind of ideas that I think
this programme is enhancing

is that major infrastructure
projects, oil and gas,

digital transformations, logistics,

other things are still crucially
important major programmes

and you'll continue
studying them in this MSc.

What we're looking at
is how do we prepare you

both for the current world

and also thinking about
trends into the future.

How are these historically
infrastructure programmes

becoming more modular?

How are they handling different
technological disruptions?

How are they handling pressures

from governments and others
to deliver, you know,

not benefits much more immediate

and in the short term
communities around them

as well as new areas,

new frontiers for major programmes

such as humanitarian logistics,

such as cloud computing,

such as distributed
renewable energy programmes

and others that need the kind of thinking

that is brought into this MSc.

And so tomorrow's events are going to need

that kind of distributed
decentralised thinking.

And I've mentioned a couple of ways

to stress test out with
scaffolding sensing,

but there's eight other modules

for which you'll get very different takes

and perspectives and
really wide your toolkit

for how you can think of with

of which this is only kind of a taster.