True story. A month ago I had a lot more
hair. It has gone that quickly because
I don't know, I don't know about anyone else here, but I feel the
pressure. The pressure's there.
Welcome to Business Analysis Live.
I'm your host, Susan Moore, community engagement manager
with the International Institute of Business Analysis.
I explore topics with industry guests about the work of
business analysis professionals and how their work helps
organizations achieve better outcomes.
And Yep, we take your questions too.
Thanks for listening. Let's get started.
Our world is changing. I bet you are feeling it too as
business analysis professionals. And maybe it's changing in ways
that you expected or maybe it's changing in ways that you aren't
expecting and you are wondering what that means for you and how
you should get ready for that. Well, today we've got a guest
who's been thinking about that and speaking about that
recently. And so I want to bring to the
stage Ryan Foster. Hey, Ryan, and how are you?
I'm. Good.
And yourself. I am doing really well.
So we're going to be talking about what's redefining business
analysis right now because I feel like that's the only thing
that we can wrap our arms around it's right now.
Exactly. Yeah, well, listen, I you are a
face within our community and with within the South African
business analysis community. For those folks who may not know
you, why don't you introduce yourself?
Tell us a little bit about yourself.
So I hate introductions. That's the first thing.
I think the important part is I'm Ryan and I am based in
Chandersburg in South Africa. And I while my title is not
currently business analyst, I am performing business analysis and
I have been Dean so for many, many years and also very
passionate about the role that business analysts or business
analysis is a skill plays in, in, in, in the broader business
sense. So I guess that's all the
relevant bits. The the the rest, people can go
read up about me, but it's not that interesting.
They can find out more about you on your LinkedIn profile or one
of the BIOS from the places where you're speaking.
Exactly. Speaking of that, I actually had
the opportunity to meet you in person, which is such a joy when
I get to meet folks in in person.
We met in Toronto and then had a very chilly walk down a Toronto
St. together. Very, very cold for me.
Neither of us were prepared. You could tell that we didn't
belong in Toronto. Yeah, exactly.
We had our Blazers on and we're thinking, what?
It's so windy? Why is it cold?
It's April. Tell us a little bit about that
talk, the talks that you were doing at BBC, because some of
those what as you and I were chatting before this, some of
those were inspiration for today's talk.
Yeah. So I mean, I think to put it in
perspective that one of the opening slides in my talk was
this is not an AI talk because I think at the moment everything
heads that way. Every conversation heads to AI.
Everything we talk about is AI and it's exciting and we should
be talking about it. But I think what I, what I, what
I want to focus on is the things that are not AI, that enable AI
and, and that was a lot of the, the content that, that I was
chatting around at BBC. And the thing that's the most
interesting to me is that my view on all of this changes
daily. So what I thought last week is
not what I think the week before and the week before that, the,
the, the change in our industry is just so significant and so
quick and happening every second of the day.
It's changing. I come in every day and there's
something new for me. And that's really what what the
talk went through is, is you know, what, what do we do now?
What's changing, what's different, what stays the same?
It was interesting also the side conversations in the halls and
the conversations with people hearing what they thinking.
And the thing that scares me the most personally is you.
If you've been working in this industry for a while, you might
consider yourself an expert. You might consider yourself one
knows what they're doing and all of a sudden we don't.
All of a sudden, you know, 20 years of experience doesn't make
a difference because everything's completely
different to how it was when you were doing it six months ago.
And it's interesting to see how people are adapting to something
so fundamentally different. Yeah.
I I feel like for a profession that is enabling change, in some
ways we as business analysis professionals aren't great at
changing. Exactly.
Yeah.
And. 100%, yeah. OK.
No, you go. Yeah, I was saying I'm gonna, I
said that often we, we resist change and change scares us.
And and right now we, we can't be scared of it because it's,
it's, yeah, whether we like it or not, it is changing.
We are changing and we have to adjust and we've always done
something a certain way. We've got comfortable to in
doing it that way. And now we can't do it that way
anymore. It presents opportunity, but
it's scary. I mean, I was, I was just before
this, I was on a short other conference.
And the first slide that someone put up is what are you?
What are you going to be when AI takes over your role?
And it's quite funny people. This one's going to be a Potter
and this one's going to be a farmer.
And it was quite a lively discussion.
Is it that drastic? I I hope not, but it could be.
We don't know. So, you know, we, I, I talked
about the right now is really the only thing we can, we can
prepare for. What do you think are some of
the skills that we as business analysis professionals can lean
into right now to help us during this change?
And what skills do you think help to move us along with the
change in a productive way? Yeah, so there was in, in doing
my, my talk at BBCI also did a bit of research into, into the
topics and what, what's going on.
And it was a, it was a thing, a statement that I, that I read
and, and it really stood out to me.
It says both humans and AI struggle with unstructured
complexity. And, and our job as BAS is to
take is to turn, you know, something that's chaotic chaos
into something that's not so chaotic.
And if you think about explaining something in AI,
let's talk about a prompt. If I give an AIA prompt and I
just go, and I think I used a different example.
But if, if I go right now and I ask Co pilots give me a recipe.
And and I actually did this earlier so I didn't have to type
out now. It just gave me a recipe.
It said, hey, and I'm reading from my other screen of you.
It's got creamy Peri peri chicken bowls.
And the point that I was, I was trying to elaborate this.
I didn't give it any other context besides give me a recipe
and all that's going to spit back to me is something random
because AI will always answer you.
It'll always give you an answer even if there isn't really an
answer. It's not going to ask questions
and say, OK, are you vegetarian? So if I'm vegetarian and it's
giving you peri peri chickens, am I allergic to anything?
What kind of foods do I like and and can go beyond that?
Where do I live? Maybe I live in an area where
where chicken is not available. Maybe the spices that it's
suggesting I don't have available.
What's in my fridge? There's so much of this context
that if you don't give to AI, it doesn't give you a usable
answer. And in my mind, that is very
similar to the work we do right now.
We take a really complex scenario and we, we meet with
our stakeholders and we try and understand what we're looking
for and we do some research and, and we put that together and we
structure that that complexity so that we end up with the right
solution at the end of the day. And I think that is, is where
the skill lies. Just because it's a machine
doesn't make it, it doesn't make it any easier to understand.
And one of the one of the one of the topics or or statements that
that I delved into was around cognitive load theory.
I'm not sure if you've ever heard of cognitive load theory.
I have. Yep, I have.
But, but to me it just, it just, it's just stuck because I was
like, that's exactly what we do. And, and, and in theory what
it's saying is, is that there's, there's different types of load.
You've got intrinsic load, you've got extremist load and
domain load and in, in, in, in, in the way that humans think.
It's very similar to machines. We don't handle if I just throw
everything at you and don't explain it in a very clear and
concise way. So if you in your short term
memory, if I'm just throwing concepts at you and I'm like, AI
does this and AI does that and do this and do that, you don't
have time to process that and put it into your long term
memory. And the ability to take
something like intrinsic load, like we have a really complex
subject matter. If we're talking about rocket
science, rocket science is complex because it's rocket
science. You can't simplify rocket
science. Or you can simplify how you
explain it, but you can't simplify the the subject.
But in the extremist mode is how you break that down into an
understandable way of, of explaining it.
And I think that's fundamentally what we do.
If, if I'm, if I'm sitting with business and they explain
something, I'm drawing, I'm writing something down.
I'm modelling it in a way that simplifies the concept.
And in taking a really complex subject and explaining it and
breaking it down in a really simple way, I, I, I, I enable it
to move from that, that short term memory into the long term
memory. And the same happens with a,
with a machine. A machine also needs to
understand things simply. If I give it a crazy prompt or
crazy example and don't really explain what I'm saying, and I'm
just typing and I'm, I'm saying it in way too many words and I'm
complicating everything, it's going to give me a complicated
output. So I think those things are
similar concepts, even though we're now talking to machines.
Yeah, which I find interesting. Yeah, IA long time ago I did
some graduate work in adult learning and that's where I got
exposed to this idea of cognitive flow theory and how
our brains as adults work. You're and you're right, we've
got that short term memory, which is where we take in all of
that stuff and then our brain is basically a filing system.
It says, do I have I ever heard of this thing?
Oh, OK, Yep, I've heard of this. I'll file that away because I
already know that. Oh, but this is a new thing now
I need to make sense of that. So what I hear.
So by the way, I thought that was one of the most powerful
things that I learned in my adult learning because it helped
me to to understand how people are taking in some of the stuff
that we're doing. So what I hear you say is
something that we can be doing and leaning into now is this
idea that our work is and will continue to be about helping to
deal with that complexity by breaking it apart.
And I actually, I think your talk was on decomposition, if I
recall. Yep.
And so breaking it down for the purposes of helping to make
sense of it in organizations like that, I think is still a
very human skill that we need to bring to the table.
Yeah. And, and I think you're going to
it's going to be how we do it might be different.
But another another point that I brought up is that I believe
that AI will speed up execution. It doesn't speed up thinking.
We still need to do that part and, and, and I'll caveat that
with a yet because I don't know what the, what the future holds
right now. We need to do the thinking.
I need to, when I, when I say to AI and give me a recipe, I, I
need to give it some thinking. I need a recipe for this
purpose. I want to do this.
I need to direct it. It's it's just going to going to
move it move in the direction that I tell it to go in.
But I think, you know, the, the change in the world and the
change in our profession is maybe coming around the
execution side. I think there's a lot of stuff
happening, execution. I can, I, as ABA now can build
an app. I can build something.
I can, I cannot just talk about building it, but I can all of a
sudden start building it. I can get involved in the debt
process. That's exciting.
It's fascinating. You're still here.
I'm just reading this. There's an interesting comment
here. And do you see the the using
BRDS and FRDS in in the future with AI?
I'll, I'll, I'll read the bigger one now, but like there's a good
example. And this is something as, as I
said, my view on this changes daily.
Originally my thinking was, you know, you had user stories and
user stories. That's that's the be all.
We don't like BRD documents. But this week I found myself
writing an agent that created ABID document because ABOD
document is a great way of it's it, it simplifies complexity.
It's got everything on all in one place.
Yes, it's detailed. So I, I asked an agent that I, I
created to reverse engineer some some code into a requirements
document. I didn't say to go write me user
stories. I thought, well, Abid was an
effective way to do it because it lists everything.
It's got all the, and things like use cases.
They're, they're extremely good ways of putting all the content
together. And the, the, the future skill
is not how we, how we articulate it.
Our skill at the moment is how do we articulate our, our
solutions. We spend a lot of this time
documenting a famous quote I'll use from a good friend, Adrian
Reed is how how many of us spend hours trying to figure out on
our Visio diagrams if that if that line is straight and we do,
we we'll spend time making sure the document is preventable.
If all the lines are straight, the the contents, right, the
spelling's right, the grammar's right.
That kind of stuff in future doesn't take long.
That's the easy part, gathering the the information, gathering
what I call the context, getting that all together so that I can
explain it to a human or a computer so that they can take
that and do something with it. To me, that is the skill that
that that is going to come out. Well, I think you bring up a
good point there, so here I'm going to pop.
This and I think, sorry I'm just reading the second comment.
Yeah. So I'm so this is the one that
that you were just answering. So Richard, thanks for your
question. Do you see using BRDS and FRD?
So I just want to kind of synthesize what you said there
because it, it's it, it is I think 2 pieces.
How, how is our work changing now, but also how are our
deliverables changing now? So what I heard you say is what
we are doing is helping to, to create some structure and
clarity around complex problems and things that are happening.
That's our that's the focus of our work.
And BRDS and FRDS are less about the deliverable that we're
handing off because to your point, AI can help with that.
Those outputs now become thinking documents.
And I think that's what Angela Wick calls them.
They're thinking documents. They represent a way that we are
getting, we are making that complexity visible and we're
breaking it down so that we can have somebody read it and go,
Yep, that's what I was thinking or Yep, OK, I see how we got
there. It is not the end state.
It is just Here's every here's Here's how we've broken it down.
Yeah. And, and if you think about it,
how many times, I mean, when we write sort of a user story,
you'll give it to a business user and and they kind of what
are you on about given what given when then as a user, they
get confused and I get the, the future is I go to a meeting and
I spend that time not taking notes, but I'm talking to
people, I'm gathering information, we're having
conversations. At the end of that, that whole
thing's recorded. It's it's turned into notes.
If I want to turn those notes into a set of BRDABRD document,
I ask it to. If I want to turn it into a set
of user stories, I can convert it to that.
It doesn't take time to do those things in the future.
And I think the skill is what happens in the meeting.
And another one of my favorite quotes is that is Heather Martin
Maine's quote, which is business analysis thinking profession.
And that stands true. Now that's the part we do the
thinking. And so, I mean, there's another
question here around, you know, what skills we, you know, what
skills are we do or we continue to do and what are we replacing
with to meet that thinking part? That's what we need to hone.
I think we need to learn the, the AI tools and, and learn how
to use AI cleverly and how to build, you know, do things like
build agents and, and have conversations with the dev team.
But the thinking part is where, where a lot of work is needed to
produce an output. Now, I mean, I can, as I said, I
can run an agent that can reverse engineer a tiny piece of
code and I have a 30 page document done in 5 minutes
possible. And it's that's not that's not
the skill. It's knowing which which which
thing to tell us to knowing. It's the thinking part that
makes. Sense, yeah.
And, and the thinking, I think includes understanding enough
about the context to know what are the important parts and what
are the, the, the parts that are just noise.
Because sometimes I, I think we, we, we don't need to give our
stakeholders every single thing that we've learned.
We need to give them the important parts that are going
to drive a decision or drive a priority.
And so I think also, I don't know if that's maybe that's
called judgment, judgment and discretion.
I think it's another piece of that thinking competency that
we've got to build. Fully, fully, I mean, it comes
to sort of the, the, the latest comments over there, I'd say.
So AI now writes the BRD and, but does it align or evaluate
the business? And, and, and that's a great
concept because I think in the past we, we sort of did
requirements and we would meet with some developers, convince
them to develop it. They develop it, We got tested
and, and we leave this thing. But now the skill is it's both
before and after and, and we've got extra tools and extra
capabilities that we can handle them before part better.
We, we can synthesize the data that's available with us.
We can, we can do research easier.
You know, if I want to understand, I did it last week,
I want to understand that the piece of functionality we're
working on how other banks in South Africa are doing it, for
example. And I was able to produce a
comprehensive research document really quickly.
So before I can, I can pull it in, so I can evaluate, I can
make some decision to your point, I can synthesize, I can,
I can use judgement. But then afterwards there's,
there's a new thing. Now with the tools that are
becoming available, I can potentially see the impact of,
of the piece of work I'm doing. So I could in theory have a,
have a direct database access that I can go and see how many
people use that functionality and I can consume that data.
Previously I, I would need developers to build reports.
And now I, you know, with things like MCPS over data, I can go
and see the data myself and have a retrospective look at that
functionality and continuously evaluate it, or build an agent
that continuously evaluates it and spits an outcome out to me.
And then I use my judgements to decide what to do next.
So I think those are all the possibilities of the role going
forward. And all of those things that you
described are happening probably on the same day.
So this idea that a lot of us have been working with either in
sprints or iterations, whatever you call those things that that
have been time boxed. Now instead of it being two
weeks now, we're talking about like a couple of days where we
are able to to do the thinking and decision making and
implementation and the testing. And that that is happening at
rapid speed. Yeah, you can do a lot more in a
shorter time. And I think that brings up other
skills that are going to be important.
So not to speak badly about project managers, but they're
always on our case. And now we need to probably get
some more of those project management skill sets because at
the moment our teams will be working on 3-4 different
requirements in future. Because it's it's easy to
prototype, it's quicker to deliver, the execution's
quicker. It's not, it's not good enough
to be able to only work on three or four and you're working on 34
different features at any point in time.
Absolutely, there's a lot more going on.
Even though it's quicker, I'm not spending as much time
writing the document, I'm spending more time out there.
So I think, you know, I don't necessarily think the skills are
being replaced, but I think some of them are becoming more
prevalent than they used to be. The skill was often, you know,
this one, this person writes a great document.
They know how to articulate things.
They're not to synthesize data. And that becomes less important.
And you, you should think more to the thinking side.
And then I see other comments, I think so YouTube comments over
there, which, which is quite an interesting one about entering
AI as the full project context. And I think this is a fantastic
place that we can play. So we look at some of the modern
AI and tech that's out there. You you build agents and you
build things called skills. So you've got clawed that you
build skills. And then you could we use sort
of VS Code. So we'll we'll build skill files
there. And to me, skills are are kind
of packaged and instructions. So you could put your
architecture as a skill and, and it can understand how your
system is designed and how things fit together so that, you
know, when I'm sitting with business and they say, hey, I
want a thing that does XY and Z right then and there, You don't
need to take that back to developer.
You can see up front. So this might not fit our
architecture or there's a different way we can deliver it.
And I think AI gives us on the ground access to, to information
as opposed to retrospective. And we can make better decisions
and we can, we can also test things, the ability to to build
an app while you and I are talking.
We could be right now and I could just be building a little
quick app, show it to you while we're sitting here and you go,
yeah, that's, that's on the way there.
It fits. And then and then we can move
forward. That's fascinating to me.
Yeah, it when you introduced yourself today, you said I'm a
product leader, but that's kind of where I'm at today.
I, you know, I don't who knows what tomorrow will bring.
I feel like we are starting to exist in a world where maybe
those job titles don't mean quite as much.
It's really the collection of skills and competencies that we
can bring. Where, where do you see, do you
think that the term business analyst, do you think that
evolves along in these rapid rapidly changing times?
So I do so, so to be controversial, are we still
going to have a business status in the future?
I think if if we don't take control of our our industry and
our profession and our skill sets, we might not.
But I think there's an opportunity for us to do that.
And it's to me, it's less about, I've got a, there's a problem
in, in a business, let me call a business analyst and rather
becoming the person people want to call, Oh, there's a problem.
Let's call Ryan. I don't care what Ryan's called,
but I know Ryan's got the skills to solve the problem.
And if we all think with that mindset, it doesn't really
matter what you called, it really doesn't.
And yes, they are, you know what your title and that is
important. And they are, they are things to
that. But I think it's, it's, it's
less about what you call yourself and more about what,
what you do. And I think there's a really
interesting question here around, are we going to have
less BAS? So, you know, if you're a
project, you currently have 5 BAS and now we're going to only
have one. And I've seen two different
views on this. View 1 was, is saying, because
we speed up execution now. So the development life cycle,
the ability to take the thinking and make it into a delivered
product is quicker. You're actually going to switch
the other way around. You're going to have one BA to 1
developer or one engineers. The new term, everyone's calling
it. So one product role to one
engineering role is going to be 1 to 1, which makes sense
because that person can execute very quickly.
And then I've seen other things saying, well, maybe we're going
to cut down and we're going to have less of everything.
And I think both could be true in a way.
But if we're ensuring that we're constantly adding value, I don't
think we're going to. I don't think there's going to
be a problem. And there will always be a role
for us. What it's called, who knows?
Is it a skill set? Yes.
Is it a title? Maybe not, who knows?
And you know, I think a lot of people are worried.
A lot of people are worried what happens to my project?
Are we going to need so many people?
Maybe not. But I think the people that are
left or not even the people that are left, people that are going
to be able to do a lot more. So that's that to me is the
interesting part. Right now I can only do so too
much. Get get AI on, I can do a whole
lot more. I can add a whole lot more
value. So maybe there's just going to
be a lot more change happening, which is good.
Yeah, so I. Invest more in the things that
they're doing here. So I, I hear you saying that
there is also some comfort and literacy around AI that's
required. And whatever this, this role
becomes, if, if in fact it is a role, it, it really sounds like
as you're describing it here today, it is kind of a hybrid
mashup of different kinds of skill sets.
It's a little business analysis, it's a little project
management, it's a little developer, it's a little tester.
Understanding all of those domains because they will be
happening so rapidly, I think is kind of a a, an ideal mixture
for somebody that is a really important part of projects in
organizations. I think.
I think that's kind of how I interpret what you're saying We.
Bring things together. Yeah, yeah, we, if we, the
people that bring it all together.
And now we have a little bit of we have some extra skill sets.
So I'm not technical, but all of a sudden I have access to code.
I don't need to be able to recode anymore because something
that in between me and the code can recode for me.
And I don't have to go to developer and say, hey, tell me
how this field works. I can just go ask AI and it's
got access to things. So there's a lot.
There's there's a lot, a lot of exciting things on horizon for
me that enable me to be more effective.
And I think that's the exciting part.
So I'm not going to be the person that sits here and say
everyone's safe. Don't worry, everyone's got a
job in five years time. And you know, I am planning, I'm
checking, can I do plumbing? Can I do electrician?
What are the what are the skills that I can do that are going to
be around if my job gets replaced and I'm not doing too
well? So I bet I best learn a hard
skill. But at the same time, I'm
excited because I feel like these now more than ever, I've
got more in my in my arsenal that I can go and I can use, I
can use to my my ability and I can do a lot more without asking
40 people, which I really, really enjoy.
I really enjoy that I can be more valuable because I've got
access to the stuff doing my same job, but now I can do it
quicker. I think there are endless
opportunities ahead of us. And if you are someone who wants
to see change in an organization, who believes that
you can be part of that change, like this is your time to in a
way skill up. But also tune into those skills
around adaptability. Because what I hear you say big
time is what it is going to require on a personal level is a
large amount of adaptability because we don't know.
And so you've got to be comfortable with the not knowing
and being able to see the opportunities in what is
happening literally on a day-to-day basis.
No, exactly. And yeah, see in the chat
there's another question around what tools we can use and what
AI tools and and yes, it means less about the tool, it's more
about the concepts. So the way that I heard it
explained recently is for every instruction you get, the first
thing in an AI first environment is to think how could AI help
you do this better and try that. Try it for 5 minutes.
If it doesn't work, leave it and guarantee it the whole way you
did it. But I think there's so many
tools available to us and it depends on your organization.
You know, I work, I work at a bank.
We can't just do anything we want.
We've got, there's lots of controls and information
security and things like that. We need to be very careful about
that kind of stuff. But we have access to a whole
lot of things. So even if it's research,
research a simple thing like that, try, try, incorporate into
everyday lifestyle. I mean, so it really depends.
We are and we've got access to a whole lot.
So like Jira and Jira Rovo and the Rover identity capabilities.
Fascinating at the moment for me, Claude, obviously there's
some really interesting stuff happening with Claude.
There's a whole lot of tools that we make heavy use of.
And I think the game that's also going to change.
Today we use this one thing, tomorrow we use something.
I mean, copilot. We've got copilot.
And the way that copilot has changed in a year is phenomenal.
I mean, if I ask it a question now, it knows everything that I
know and every e-mail I've ever received and every document I've
ever worked on, it has context. So it provides pretty good
answers. Yeah.
I went from hardly using AI two years ago to today.
I use it all day and every day, all day long.
But it's important not to lose track of our actual skill.
Yes, yeah, because it still is giving us AI wherever you use it
is still not giving us great answers.
So we so it is going to be important that judgment is going
to be essential because if you do take an AI first approach and
you get some sort of an output, you are accountable for whatever
that thing for, however you use that output.
And so it still has to be accurate.
It has to be representative of the facts, the context.
So that's, that's going to be really important.
And I think you, you said something there.
And so to answer to, to sort of add on to Denise's question, a
lot of the tools that we are using in our organizations are
now they have AI built into them.
You use the JIRA example, I just got my latest Microsoft Office
update and Copilot now shows up at the top of my word and says,
what are you trying to do? Try those things.
If you are worried about where can I get some experience with
AI, open up any of the tools that you're using for your job.
They have AI in them. Start there.
Yeah, yeah, it use what you've got access to essentially and,
and, and play around with it. And I think it also ties in
quite nice to the to the next question around.
And do we, do we think that the tools in the current form are
actually generally going to help?
And, and I think it's, it's an interesting question because in
my opinion, this this is, it's how you use that tool.
So I would use the tool. If I've got a problem, I might
use it to brainstorm ideas. Say if you had this problem,
what would how would you solve it?
But again, it comes down to the context you give it.
If I don't give the right context, what country am I in?
What environments am I working in?
Are you in a highly regulated environment?
Are you not in a highly regulated environment?
There's so much information that's available that if you
don't give the correct context, you're going to get bad answers.
And, and that's why I said stuff like copilot is becoming much
better because it has context. It knows exactly who I am, what
I work on, who I speak to. It knows my team's messages, it
knows my emails that come in. So it has a pretty good idea of
context. And if I provided the correct
context and can guide it, I think it's possible that it can
give you nice answers and then it's judgement.
Then it's deciding you know what, what, what can I do with
this? Should I use this?
Shouldn't I use this? Is it valuable?
There's that term at the moment called what's AI slop?
And we see a lot of them. I mean, every second video is,
is, I mean, you know, that thing is AI and it's, it's, it's,
you're bombarded by it. And you know, the e-mail that
the person that cons, you know, really constructed the way that
they speak and they've sent you such a well worded e-mail.
You know, it's not them. You know, it's just AI that's
done it. And you know, they haven't
checked it. And I think we've got to guard
against a got to guard against that.
Humans are imperfect by nature. So you're getting something
absolutely perfect out. You wonder is the, is the, is
the authenticity behind it? And sometimes the way we solve
problems need that human, that human connection.
It's not robotic. You know, the, the, the, even
though I work in banking, the solutions that we deliver and we
need to deliver, they work. They're for people.
They're not for machines that the end users a person and so
you lose the humanity out of things.
If you're just using AI, it's the screen is the most efficient
the screen could be and it doesn't have the nice welcome
screen when you when you load in because it's not seen as
efficient. So I think it's it's what you do
with AI and and you're the judgement aspect.
The thinking part is, I think going to be the the next barrier
for us. Yeah, I think so.
AI, what I've realized in, in a lot of the AI stuff that I've
produced is how much AI strips out authenticity and makes a lot
of stuff sounds nice, but it is very generic.
And you're right, organizations are building products and
services for, for humans. And so you, it, you, you can
read something and know when it is not generated by a human.
And that can impact trust, reliability, when something
sounds so perfect that it can't be true.
I mean, I think we still have that, that capability as humans
that we know when something doesn't read as authentic.
So, so being able to create things and ensuring that they
represent real, real human experience because we do still
need to connect with people, we do still need to bring in hearts
and minds for the for the products and services that we're
building. Yeah.
And and it's the thinking part. I mean, any if, if, if I can go
to a meeting recorded and from that I can go all the way from a
recording to a requirement to automatically delivering it to
pushing to production. And I'm not needed in that.
It's going to be a very sad world, not only for, for, for
the people that are not no longer involved in the process,
but I don't think the outcome is going to be as good because just
because you're told to do something doesn't mean it's the
best thing to do. And we need to use judgement to,
to, to bring that in and, and be better and to help clarify
things and help shape the direction.
So I still think there's a very bright future for us.
Yeah, I, I do too. I know we are getting lots of
questions. Ryan, you're doing such a good
job of bringing in these questions.
I'm going to answer Vivian's question really quickly.
Hey Vivian, we live stream to YouTube as well.
So as soon as we're done today, you'll be able to, to, to get to
this recording. So thanks.
Thanks for your question. Do you see a we've, we've got
lots that we've got three more questions.
Do you see one that you want to take or should I just pop it up
and? We get do you?
Do you see the BA Mall that one? So I mean this 22 separate
points here and, and, and really want to address both of them.
So the funny one is the, the PO versus PM versus BA role.
I think in, in the, in the lifetime of business analysis,
there's been like these three big debates and it was, you
know, maybe 10 years ago, it was waterfall versus AI versus
agile. And then it, and then it was BA
versus PO. And now it's to AI or not to AI.
And, and I think do I see the BA role merging?
I see many roles merging. I see the, the, the, the, the
skill sets. I, I see less, less people doing
more things because you don't have to, you know, I see a
genetic AI being involved in things.
So I'm not going to have to do some things in future because
there's a lot of capabilities within AI.
So I do see a lot of roles emerging.
And then the technical question is, is an interesting 1.
So if you'd asked me many years ago, are we in a technical role
or are we in a in a business role, my answer would have
always been business because even a technical solution is a
business solution. It's there for business.
We don't build for computers, we build for people.
But now I do see that the, the analysis role, I see a slight, A
slight technical shift in it and we actually involved in the
building execution and not because we're going to code, but
because the capability is there to do things like vibe coding.
If I want to build a mock up UII don't I no longer need to ask
someone who's specialist to do it.
I can go do mock up UIS and get that specialist to check and,
and to bring in some of the character into it.
But I do see us having to understand a little more of the
technical side. So as I mentioned, you know,
having access to stuff like Claude where you you can give it
access to your code and ask it questions if you don't ask the
right questions. So like information security is
a big topic at the moment. And I can go implement a pattern
on, on a website where it says, OK, well, I'm going to store
your security credentials in the code.
And any information security person will tell you that's not
a great pattern. But if you don't understand
enough about that, you're just going to go, oh, do it, it
works. You know, the credentials are
stored in the code. It works when I use it.
The functionality is what I want it to be, but the security
patterns are not there. So I do believe it helps to to
start to think in a more technical way.
Although the the and. I think it's just because the
bridge between technical and non-technical is becoming less
because this is a gentic interpretation layer.
Yeah, it it, things are becoming really blurred so that those
skill sets we are building are really transferable and almost
like Lego blocks where we can kind of stick them together and
we can we can build a role for ourself that it really meets the
moment. So I think this next question is
an interesting one. This is from Safar.
How do you manage the rising expectations?
Right. Our organizations are like,
you've got AI. It makes you 10 times more
productive. Go do stuff here, he says.
Now everyone believes any analyst analysis can be done in
two to three days instead of months.
It doesn't really seem realistic.
Yeah. How do you answer?
That so true story. This is a very true story.
A month ago I had a lot more here.
It is gone that quickly because I don't know I don't know about
anyone else here, but I feel the pressure.
The pressure's there. You know, it's, you've got AI
and I'll do it quicker and we need to figure this out.
And if, if, if, if anyone on the call or listening in is saying,
oh, that doesn't worry me, then I then I'm a bit confused
because it worries me. It it gives me sleepless nights.
I'm not sure. How do we accelerate things?
I think we're in in, in a, in a very difficult transition zone.
But I also think that that's where people throw and that's
we, we, we people that are really good at what they do can
thrive. I always say, if we worked in an
environment where everything just worked, it was easy.
I went into work every day. I, I, you know, everything just
slid into place. What role would we have?
We are, we are there to simplify things.
We are there to, to do the thinking.
If it all just worked and then people are going to say, well,
we don't need you. So yes, we need to manage the
expectations and yes, they will always be the supply and the
demand are not going to match. People are always going to want
more than they. Than they get and the way that
I'm handling at the moment, I just ignore the things I can't
control. I focus.
If I've got 500 things to do and someone's expecting me to do 500
things, I focus on the 10th things first and then I move on
to the next 10 and move on to the next 10 and move on to the
next 10. I think there's an expectation
that we need to do a little bit more than our normal, you know,
9 to 5 and to pick up these skills and to come to sessions
like this or conferences like BBC or local IBA chapter events
where people are talking about this stuff and learning about
this stuff. So that I can figure out how do
I take the thing that is repetitive in my day and
automate it in some way. We've done a lot of automation
on our side. Things that I always say, even
when I'm not working now, I'm still working because I've built
things, not just me, my people in my team and people that my
colleagues and peers have built. Things that, that are operating
while we we are not at work. Processes that are, that are
ongoing, things that are happening, agents that are
running in the background that are doing little bits and
pieces. Because something that if I can
save myself 10 minutes a day, do that 10 minutes a day.
And then the next day, if we go to another 10 minutes, all of a
sudden you have another hour free.
So it's, there's no good answer. There's there's really no good
answer. There isn't you.
You're always going to be expected to do more than you
can. Yeah.
I think pressure is becoming the normal part of our, of our work.
And, and I want to add on to something that you say, I very
often talk about, we are professional learners.
Like that's, that is part of the role that we offer in
organizations. We get thrown into situations
where somebody's like, I, I don't know, can you just make it
work? And I think when we apply that
lens to our own development, because learning is a muscle,
you are somebody that is good at learning or you aren't just like
if you went to the gym them and built a muscle, if you use it,
you'll build it up. If you don't, it atrophies.
And so I think that skill is make learning a thing that you
do just natively pull up a you know, I read Harvard Business
Review journal that that helps me.
Whatever those activities are that help to keep your learning
muscle working are going to be essential right now.
Yeah, I mean, they they don't call it continuous learning for
nothing. It's it's definitely and that
hasn't changed. It hasn't changed in the loss.
I think maybe it's become more relevant, but it hasn't changed.
Yeah, I think now more than ever, ever we should be we
should be trying learning a new skill, trying something
different, you know, extend yourself.
If if you never thought I'm I'm not a developer, I'm never going
to look at code. Maybe you want to give it a try
and have a look at it. And even if it's just to
understand it better and understand how the roles to come
in at that. And you know, I see there's
another comment around BA roles requiring data and testing 100%.
I think one of the most exciting things is the ability to, to,
to, for AI to handle big amounts of data and to give you insights
out of the data. Back in the day, if I wanted to
get an insights out of data, it was it was not hard.
If you millions of rows of data you can't check in Excel.
Now I can have AII can ask AI question.
Please tell me out of the data these XY and Z.
Maybe it's going to give you the wrong answer and hallucinate,
but I've got the ability to synthesize data in a better way
and some things are just not possible.
I can't review 5000 documents in 24 hours, but AI can can gain
some insights out of those documents in that amount of
time. So I've got the ability to do
it. And in testing, yes, I think
there's, I mean, testing has never been in the I mean, if you
even when you did, when I did my CBAP, you couldn't say, well,
some of the my role is testing that you couldn't use that for
your hours for, for CBAP. And now it's it's because
because the role is sort of expanding.
I think you, you're not you, maybe you're not the tester, but
you, you need to know what you're doing.
And Bob coding is essentially produced version A and continue
iterating on it until you've got a version that that works.
Yeah, yeah. That's I think that's a.
Good, a good one. This is another interesting
comment, yeah. Oh, this last one, maybe this
will be our last question because I think this is a good
one as well. So sounds good.
Do we need to keep in mind the compliance part of using AI?
Do we need frameworks on how to involve AI and how?
I bet that's working at a bank. I am sure that is something that
you guys are also thinking about.
Yeah. So I mean, the short answer is
yes. The long answer is I don't know
how we get there, but I think there's this human judgement and
thing. And that's why I'm saying in
that example, I thought where I was mentioning about the
security, you know, storing the credentials in the code.
I know that that's not a good pattern.
And we need to get, we need to understand these things.
And one of the, one of the best skills of ABA is not necessarily
that they can solve the problem, but that they know who to call
to solve the problem. I'm not an information security
expert, but when something comes on my, to my desk and I'm like,
no, this doesn't look right. I know who to, to call.
And I think that often, often is, I mean, you would say that
you're not solving the problem, but I think you are.
You're solving the problem by knowing the right people to pull
together the right people to that the right decisions make is
to get the right data and to know not to just go forward to
something and to prevent risks because it's too easy these
days, right. You know, a year ago when you
wanted to develop something, the time it took to develop it meant
there was a lot of time to test. Many people were looking at it.
There was, you know, months and months of rigorous testing of
demos and views. Now that's thinking go to
production in a day. All of a sudden we've got to
slow down and go well, hold on, hold on.
I know I know this needs an infosec and persons have a look
at it. I know that and there's poppy
concerns or privacy concerns on this.
So I think there's the roles, just it's scary at the same time
because a lot of trust, a lot of responsibility sitting on our
heads, but that produces it's an opportunity in my mind.
Yeah, it, it is. It gives us, I think the kind of
visibility that we have wanted in organizations for a long
time. Because I do think the ability
to connect people in organizations is also a really
important part of our role. Because you're right, I, you
know, I can sometimes know that I'm not the person to ask about
this, but that I know who is, is a really important role that I
can play on a project because I, I understand how processes work
and how we get things out the door safely.
I think that's important. All right.
Well, boy, this has been a great, a great conversation.
Now you were talking about all of the talking that you've been
doing and I feel like if I don't ask you about the South Africa
summit before I before you leave today, I will not have done my
job. So you are instrumental in
planning that. And I was asking you before we
went Live Today how that's going.
And you said, well, there's a little bit of a change.
So what's So tell us a little bit about that summit and what's
recently changed. Yeah.
So, so I think, I mean, our summit is generally in November
and we had, we were aiming for the beginning of November, but
unfortunately we have a voting day that day and it's only just
been announced that it's going to be a public holiday.
So it's not a great day, another great day to run a conference
because we should all be out there voting.
So we'll be looking at new dates.
I do believe the IBA is a board is on this call and they they,
they're looking at we working together to figure out dates,
but it'll be sometime in November.
It's always a highlight for us, first of all, to, you know, to
get all people from around the world that come, come and visit
us here and we can show them what we're doing in South Africa
and learn, but also that we within South Africa learn from
each other. It's it's always a highlight of
the calendar for me. Yeah, I, I have always heard
great things about it. And you do you pull in a lot of
great speakers. All right.
Well, Ryan, it's been good to catch up.
I think you've, you've got our our audience thinking today.
So thanks for all this great insight.
It's a pleasure, I really enjoyed it.
Yeah, yeah. Thanks for joining us today.
All right. Well, that is it.
I know we've got you thinking about about what's going on with
business analysis right now and maybe some things that you can
be thinking about to help you to bridge that gap.
We will be back in two weeks and you can find us right here.
Thanks for listening. Do you have any questions,
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