You and I both love yacht rock. Yes.
And when during the time Tim and I are writing future proof and
you know, when right after generative AI just came out,
right? And I started to see the
connection between generative AI and the previous forms of AI
like analytical AI and machine learning, all that stuff and how
it all links together. And I had this moment of like,
Oh my gosh, this is it, this is it.
The we get to be the BA as we want to be.
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.
You've seen a lot of talk here and in other spaces about AI and
you might be thinking if you're a business analysis professional
or if you are working with AI, what does that mean for me?
What is the role that I need to be playing and what's going to
change and maybe what won't change?
That's a lot of questions and I've got the perfect person to
answer those. And she has just published an, A
very, it's kind of an academic article on the evolving role of
business analysis in agentic AI. Specifically, I think this
article is going to show you opportunities and possibilities
for that role. And so I'm bringing on stage
Miss Angela Wick. Hey, Miss Angela, how are you?
It's good to see you. I'm, you know what?
I am really happy that you are here to describe what this
evolving role looks like, because I know that you talk
with a lot of organizations and a lot of practitioners and a lot
of people that are doing this kind of work.
So you bring like you bring real examples.
Thanks for having me. It's a fun topic.
The paper was really fun to write and it's exciting to be
able to talk about it and and keep this conversation going.
It's so important for our role. Yeah, I'm, I am really excited
because I think you do such a great job of, of helping us to
understand what it looks like for individual practitioners.
But what warms my heart is how you are speaking to leaders
about what this needs to look like.
I think we we've got to bring them with us as we are thinking
about this evolving role. Absolutely.
And that was one of the goals, right?
Let's help our leaders understand how important
business analysis is to agentic AI.
It was clear to me that as you were putting that article
together, you were thinking about just you were going broad.
These are the skills and the things that a business analysis
needs to do in order to move itself out of requirement
gatherer to the next level. And I could see you thinking
about, OK, so I've said AI literacy and I think where
you're going next is, OK, what does AI literacy mean in
practice? And then going deep separately,
that's really what I was trying to say.
Like I could see you thinking through that in the article.
And I didn't know which came first.
So it sounds like you've been. Thinking.
Yeah, OK. I mean, both have been on my
mind, I'd say for a year. And of course, what happens in
this crazy AI world is that everything's changing so fast.
Once you have a graft done of anything, you question it.
So for a year I kept like rewriting a white paper and
redoing this and redoing class outlines and re and like.
And at one point I just had to say, you know, you just have to
go with it. Yeah.
And then the opportunity came up for that journal.
Journal where I was like, oh, wait a minute, here's an
opportunity to put a little deadline and fire under my feet
to publish something. And you know, and we felt that
way writing future proof too, right?
It was like, Oh my gosh, you know?
You told me when after, I think it was when we were meeting with
you and after the book had come out or just before the book had
come out, you said at that time I've really been thinking about
agentic AI and I'm like, I don't know what that is.
And so that was two years ago. So you've been thinking about
these things. Thinking about it, playing
around with it, working with clients on it.
Yeah, yeah. So let's roll it back a little
bit to just this article that you've put together and it's
available on the IIBA website for our members.
And it's the evolving role of business analysis and agentic
AI, something that you've been thinking about for a while.
My first question for you, it is a really interesting read.
It is a really detailed read, but it's not like, because it's
like, I don't know, 10-12 pages. I mean, it's yeah.
And it's it, it's really good because I think you lay
everything out the case for why business analysis needs to be
involved. And then, you know, what got me
excited was exactly what business analysis looks like
when you wrote that. Who is your intended reader?
Is it individuals? Is it leaders?
Is it somebody else? As much as it's for business
analysts to help us understand our role going forward, I also
wrote it very intentionally for those outside of business
analysis. So thank leaders, IT leaders,
CTOSCIOS, business leaders as sort of a more global
educational piece outside of our own ecosystem.
I wanted it to be spread out further than our IIBA bubbles,
right? Yeah, I agree.
And I clearly got that, which to me was really exciting because I
know one of the myths that you talk about very early is we've
got to move from just being people that write requirements
to this new world. And so that's been an issue that
we've had for a long time is people getting us out into out
of that box. And I feel like this article
gives a little more of a sense of urgency.
Yeah. And, you know, a lot of it stems
from, you know, it's a formal journal and journals are peer
review. And the peers reviewing this
article are not BAS, right? These are genetic AI experts and
AI and robotics machine learning experts where that's what really
forced me to think about what language do I need to use to
help those outside of our business analysis bubble.
Like, yeah, this is exactly it. And so I tested a lot of the
ideas and conversations with similar types of folks
beforehand. Like as I hear them talk about
and discuss their challenges and work with clients on it, it's
like Dove, it's business analysis.
But no one's calling it that in the AI world, right?
So yeah, it took a lot of discussions and work and
research to figure out, OK, how do I talk about this in a way
that those that don't call it business analysis see it that
way? Yeah.
And This is why this article is speaking to me at the moment,
because this is as I am reaching out to adjacent communities, I'm
having to figure out how do I talk about business analysis to
people that don't know the business analysis language,
don't use that term, don't see themselves as doing it.
Yeah. And it's funny because you can
hear someone outside of our world say something like, well,
an agent, you want to break down their task into subtasks and
break that down into subtasks. And then you need to identify
the policies and rules that they need to follow and the
guardrails and boundaries and the data that they need and the
memory context they need at certain points in time.
And I'm just, you know, this is, this is like, and so I'm like,
sounds like business analysis. You can just blank stare looking
back at you and you're like, so then I mean, I remember in some
conversations, I'd stay up till like 2 in the morning, like
drawing it all out. And then I'd come back to an
expert and go, well, OK, when you build this enterprise agent,
you have to identify like every data element and every business
rule and every scenario and the boundaries of each, right?
And I practice like talking about it in the non BA terms
with some stuff mixed in. And then I draw some of our
pictures that we draw and outline things like Gherkin
scenarios and all of this and the source of truth of the data
and where the machine learning is happening in the process.
And they're like looking at me like, yeah, exactly.
And I'm like, but do you ever talk about it that way or draw
these visuals or outline all of this together where we put rules
and data and people and workflow together?
The funny light bulb moment Susan Wright is that for so many
software engineering and AI experts, this is just very
intuitive to them. Yeah.
But a lot of them are also building tech products, startup
culture, So where I feel like a lot of our audience comes in or,
or legacy organizations with a lot more regulation and
complexity. And of course, we hear that all
the time, right? And so that means that our
business analysis skills and techniques become even more
apparent. But it's not just about creating
the document for that model. It's the thinking that all these
models and techniques have always invited.
And then how do we shape conversations around our
analysis doing those models, right?
So that shift from I need to create this model to put in my
document to get approval. We kind of migrate from that to
I need to do this model and partner with AI to do the model
to elevate my thinking so that I shape the right conversations
and then help AI create the right specs to develop this
agent. Like quite a shift, right?
Yeah, it really isn't. And I know that you talk a
little bit about that. We move into more of these
thinking documents that they are not really the thing that some
developer is going to go run off and build towards because
frankly they're probably using AI for that.
But what those thinking documents are doing are helping
us to solidify context, decisions, bigger goals and that
we're working from those, which is another quite different shift
from requirement gathering. Yeah, because specs, specs that
AI creates in the same environment to the development
happens, which is kind of a newer shift right now.
We're all, you know, learning about those are going to be the
new source of truth, which means the work that we do is in
helping prompt and create those and work with those AI systems.
But we have to do this other work of models and techniques
that we've always done to help us think, help others think, and
help validate and review that AI is not missing things.
Yes, because I think that you also talk about with AI going
where AI goes these days. And by the way, you bring up
new, new terms, they may not be new to you, but they I think
will be new to every to some of the readers.
But things like, you know, AI drift and us being a part of
ensuring that we are setting proper boundaries for something
so our solution doesn't drift off and, you know, and, and do
things we don't intend for it to do.
So it's yeah, it's, it's in part knowing some new words, but
really having to think about, I forget how you describe it, but
we are working more in the realm of behavior as opposed to I am
defining A predictable box that always does X.
And now we are probabilistic. Is that the word that you used?
Yeah. So, you know, when we when we
introduce AI, we're introducing probabilistic behavior, which if
we back up the antidote that is deterministic behavior, which is
the world we've always worked in, right?
For many of us in terms of the requirements are very little
literal. They're binary.
This input always equals this output.
And probabilistic, when we leverage AI says, I can give you
a same input and you might give me a different output every
time, which is kind of scary for a lot of us as BAS, right?
Because we can't test to a binary outcome at the same time.
What we have to do is elevate our thinking to wait a second.
There's opportunity here, right? So what steps in a business
process would benefit and get a better outcome in a
probabilistic nature? And a really classic example is
a chat, right? So chat box can be deterministic
when the customer says this, say exactly this back or
deterministic where it's, I'm going to infer what I think the
customer is saying and I'm going to respond with a degree of
latitude as well. And it won't be exactly the same
every single time. And that gives some benefits
because we can detect emotion that we AI, right?
Gosh, yeah. I guess we're embedded in part
of the world if I'm saying we always like.
Where are you Angela? Are you in the in reality or are
you in AI? Yeah.
So there's a whole new world of analysis for us to jump into
that says what discrete interactions and pieces of a
process can benefit from a probabilistic nature where we
give an AI to a leeway to decide how to respond, where it can
detect a nuance. And to me, that's a really
exciting part about this, right, is how do we analyze a process,
break it down step by step and say, OK, now which aspects are
we going to have as deterministic, rule driven
binary? And which aspects are we going
to leverage this probabilistic nature of things that will
benefit to get more value, more outcomes?
And it's kind of exciting to think about, right, and design.
Yes, and I think so and you use the word design there because I
and I think that this is really important because we are not
coming up with the static solutions anymore.
So if we are thinking about the design almost of an experience
it we really have to think about where we get involved
differently. And we've always had the, you
know, the argument that we don't get brought in early enough in
our projects where AI demands our presence is as early as
possible. Kind of for the same reason that
we say you want to get your business analysis like you're
around cybersecurity, you need to get that done early, you need
to be building it in. And I think you say the same
thing too about our our involvement in AI agentic AI
projects. Yeah, absolutely.
And you know, there's in some ways you can look at it like
we're going to put this agent into this process.
Other ways you can look at it like we need to completely
redesign the process, right? And yeah, we got to be involved
early on and understand the metrics.
We have to track the evidence and the data to figure out how
is it performing, how has it been performing, Where are the
biggest areas for improvement. And so there's so many realms of
AI involved in all of this, right?
There's all the data analytics that have been around for a
while, right? And machine learning has been
around a long time and leveraging where do you put that
into the back end part of the processes?
Where does that info and results surface and insight surface into
a user experience? And then where do we use all
these other AI capabilities to enhance the journey overall?
So exactly what we're talking about is business analysis being
elevated such that we're seeing the bigger picture of the full
journey, the full value stream because I'll bring back that
term AI drift. Even one piece of AI embedded
into an end to end customer workflow or journey or user can
proliferate and exacerbate downstream.
And as B as we are used to seeing that a deterministic
binary result proliferated, but with AI when it's a
probabilistic result proliferated.
And then what if you have multiple probabilistic results
like? So the analysis end to end is
what's so interesting to me and where we have to level up and
and refrain from analyzing within a silo that a lot of us
are in. Yeah.
Unless there are very. I had a really interesting
discussion yesterday with someone about and they're using
AIA lot and it was about, you know, can you work in silos
effectively or not organizationally with AI.
And the discussion went into well, it depends on your dev OPS
infrastructure and how well the silos are boundary with
agreements between silos of processes.
I know it's really nerdy, right? And so it's just fascinating and
I'm listening to this and I'm like, most companies don't do
this. And so most companies are in
silos but don't have these well established boundaries between
technical and business process silos.
So we have to look at it more end to end.
Well, anyway, we do what's the value end to end, right?
I think you were talking about, or perhaps I inferred this from
the article, that we might still be thinking about AI development
in the same way that we think about software development
phased. Oh, or in that is like off the
table now. Yeah.
So let me back up. There's a 2 themes that were
kind of intertwining between, right?
So. As BAS, we talk a lot about the
software development life cycle, the SDLC and how AI is changing
that, right? And then the other aspect is how
is AI changing our business processes through all AI
capabilities that we can embed into business processes and
redesign them So it's easy to get them mixed up and put
together in different ways. But the, yeah, the software
development life cycle is changing dramatically and partly
because of the coding tools available with AI.
And in the AI world, this is the first set of professions, the
first set of processes that are being dramatically changed with
AI and we're part of it. Obviously, we're impacted.
So yes, we have to be really careful not to work in a silo as
a business analyst. So what that means is we can't
work in phases anymore. We have to work continuously
more in parallel and in collaboration with developers.
And a lot of people tell me, Oh yeah, I collaborate with
developers, but I mean something like different so.
Can I throw out there like I'm gonna throw out there a vision
that I know there are plenty of BA teams already doing?
OK, I'm giving you the floor. You're not.
But you know, the vision that I think is going to become more
and more common, or I'm already seen it become more and more
common, is that when the business has an idea
immediately, as fast as possible, and I mean, it could
be within minutes, the business technology and BA are in the
room together building a prototype together with an AI
prototyping tool. And then we can reverse engineer
specs from our prototyping working prototyping tool and get
that into production quite fast. That might seem like what,
right? What about the analysis?
And what we're doing is we're facilitating decision making and
analysis in parallel such that with the business using real
working prototypes and then our business analysis, thinking
about the structure discipline of users, scenarios, flows, data
and rules and getting a scenario set together somewhat on the
fly, somewhat right after that meeting back at our desks using
AI to help us. In the meantime, the technical
team is going, OK, I'm understanding what the business
is asking for and thinking about the technical layers and how it
fits into the architecture. And it's just a totally
different way of looking at the SDLC such that the fastest we
can get to a working prototype and then the more we work in
parallel together, that's what it is.
And then the continuous nature is really about evaluating and
monitoring, where our famous knowledge area from the BA box
solution evaluation and monitoring becomes a bigger
circle back into elicitation. Yes, and you've got a great
diagram also for that in the article, which I thought was
really good in that. So you brought up a couple of
things there. I feel like what you've
described was always the dream with air quotes, agile and I
don't know how close we all really got to that goal of I, I,
but now we are talking. That's totally possible with AI.
It's always been possible. I think that the trick is really
going to be like, sure. Are we at risk for it never
happening with some organizations this way as well?
With AI, yes. I'm not sure those organizations
will be around very long because, right, because
organizations are already working in this way.
And so their cost structures and their delivery time frames are
just dramatically different. And the feedback cycle getting
condensed just totally changes the value proposition, right?
So I think what it is the yeah, it is, it's describing a much
more agile, nimble way of working.
And organizations that are not able to shift to that will
suffer in terms of results, right, and outcomes.
And that's where the org design matters.
And but you know, the more organizations I talk to, a lot
of them don't feel like they need to redesign the org.
They just want their team to work together more and do this.
But we all have to understand what that means and get out of
our own way. Yeah, because I do think we are
probably stuck in some patterns, which by the way, I think is one
of the things that your article does well when it speaks to
leaders and people kind of in this spot.
You and we have all been just like we were all promised speed
with agile like here AI gives, truly gives us speed.
Do you think that have you seen with the any of the companies
you've worked with? Are they giving any?
Are they only focused on speed in?
Is that impacting at all the outcomes that you're seeing?
Oh, absolutely right. Some are, and they're paying for
it, right? Especially with agentic AI, one
of the common things you see is teams to build AI agents, but
not building something called human in the loop and governance
processes around them, which is super important and another huge
opportunity for BAS, right? So AI agents in a business
process, the amount of monitoring and proactive
monitoring of the agents work increases hugely.
And that means workflows and people who have to monitor and
be empowered to make decisions and monitor and that those are
additional workflows, right. So it's not just a report that
someone gets every week or every month or an escalation thrown to
their inbox. I mean, there has to be much
more robust monitoring and roles of people supervising the agent
with transparency and explain ability into it and processes to
view, manage, pull things out of the agentic process, put them
back in, escalate, change, right?
Like whole processes, Who's going to do it?
What permissions do they need? What data do they need to see?
What actions do they need to take?
What rules and boundaries do we have?
How does it get integrated back into the automation process?
That's a. Whole project itself.
Yeah. And it, it is it, it's A and you
know what? It's almost as you describe it.
It is a new reality because I don't know that we all, even
when we build these deterministic systems, I don't
know that I've been involved in a lot of projects where I think
about the human stuff that's got to happen afterwards.
Yeah, here that's exclusively a whole piece of work because now
I am excluding humans from parts of it.
Now I need to know where the humans have been involved
because I've somehow got to replace them.
You talk about implicit knowledge that one of the things
that we will have to do is business analysis professionals
is not just help to determine all these guidelines and rules
of engagement, but also we've got to go find the knowledge
that nobody has written down, that people just know to do.
Like that's a the whole reality of things that we've got it now
find and build. It's more prevalent in legacy
large organizations too, where you typically find business
analysts. Yeah, right.
Like a lot of the big tech companies will be like, Oh yeah,
it's super easy to build an agent.
You just do this, drag and drop this, describe this.
But then somewhere in that presentation, somewhere they
will mention ever so slightly, as long as your process is
perfectly documented and known, which any of us in the VA world
know that that's a far cry from reality and most organizations
that we work with. Right.
Well, and again, another opportunity for us that you talk
about is all of this does depend on there being documentation.
And if we have legacy systems, nobody updated that
documentation. You probably don't have the
whole thing documented end to end anyway.
And you certainly know, don't know about Barbara's checklist
that she keeps on her desk where she works around stuff or knows
the secret codes to get a transaction through.
It's a whole different. It's a whole different reality
that somebody us needs to be accountable for and it has the
skill set. But this, I think one thing
that's really important for our community to understand is that
doesn't mean that we're necessarily should or going to
get some current state phase to document it all right?
Like I know we're used to that after decades and decades, but
even about 15 years ago I started seeing that fall apart
where projects were being cancelled if BAS took too long
to document the current state because people wanted to see
progress, they don't want to keep paying to document the
current state. Well OK, that pain point just
got a lot easier because we can have AI read the code bases and
tell us what the current state is.
Wait a second though, we still have that checklist on Barb's
desk. You know, the non systematic
things that are happening. So then it's like, well then OK,
do we need a phase to then, OK, let's reverse engineer the code.
AI tells us what the current state is of the code.
OK, now we have to go figure out where's the human things
happening. But there's something else
because I still think we can do it faster than a phase.
And that is scenario and user group identification, which has
always been about practice in our world, but not practice
commonly enough. OK.
We tend to start with systems instead of people, but if we
take a, a people user 1st and then we can say, OK, we're going
to focus on this user group and these scenarios.
It doesn't all have to be done before the project starts to
make progress in development. So really learning how we can
work differently to chunk out and continuously analyze in a
structured disciplined way through users, user group
scenarios and scope incrementally through this is a
lot another example of that continuing of continuous
analysis. Yeah, I'm, as you're talking
about some of the realities of business analysis with AI, the
good news is I think I don't hear you talking about a lot of
crazy new techniques or skills. In fact, I hear you saying,
well, those things that we've been doing, we'll still need to
do them. I hear you saying though we
might, we need to do them earlier.
The focus of where we start something needs to be different
and there might be new areas of analysis that we need to focus
on that we have not previously thought of.
Do you think is that fair it and do you think there are new
skills competencies we need? Yeah, it's tricky because as
much as it is about leveraging all these current techniques and
techniques we've always used, the biggest danger to that
statement is people think they don't need to change because the
shift is massive. Yes, we're going to still use a
lot of these thinking patterns and cognitive patterns we've
always used to get through complexity and to understand the
relationship between people, work flows, data, rules and
technology. The trick is we have to execute
on them very differently. It's, it's not about a phase and
our PC with a Vizio license, right.
It's about using AI as a thinking partner to get that
done and often doing that live in the room with business and
technical folks present and knowing when that's happening
and the reaction to it. What parts of those
conversations matter to shaping decision making at velocity?
It's funny, 'cause I'm, you know, like anything with big
change, aren't we all new to articulating all of.
This right? Well, that's OK.
Also, you you used the term decision velocity a long time
ago and I quote you on that because I like that concept.
I wish it would have made it in the future.
Book. It's almost a.
Term that came out of my mouth while describing the book after
the final proof had been submitted for print AM.
I seeing a second edition, maybe I.
Don't know about that, but like decision velocity, right?
That's really huge term because there's a lot of places I CBA is
needing to use it. And the first one is an intake
all the ideas that come in. So it's AI doing the
development. Wow.
We can take every idea and build it quickly, but what actually
matters? What?
So I feel like in some organizations, business analysis
is part of the intake process. Intake, right?
Analyzing all the requests that come in, which ones should we be
doing and in what order and in what priority and analyzing them
holistically for their relationship to other ideas,
projects, etcetera. The intake process, the question
is, OK, wait, so we could literally take an intake item
and have it developed in a couple of hours conceivably,
should we? So I feel like our business
analysis is going to have to shift left up into this intake
process to say what is our process to evaluate ideas
quickly and quickly identify which ones we should be doing
and get to that rapid AI prototyping phase as fast as
possible. But we need to make sure we have
a good problem or opportunity to find an evidence behind it.
And so that whole process to me is a big area for us.
And then knowing that the requirements piece in the middle
is highly automated. Now with AI, it's that bringing
that downstream with developers and making sure that strategic
alignment and the detailed scenarios play out the way we
want them to, as intended, evaluating it as it goes through
development, testing and in production.
And so decision velocity comes into play with like, well, how
are we helping others make decisions fast on which projects
and features and defects and enhancements to do?
And then which scenarios are in scope and in what order?
And how do we incrementally and iteratively bring them through?
Like all those decisions, where do we have this rule conflicts
and how are we going to make those decisions?
Like the amount of decisions? Oh my gosh.
Well, OK, Susan, you talked to any group of BAS today and what
do they struggle getting decisions made?
They can't get decisions made from their business leaders that
makes the requirements late and blah, blah.
So this is a skill we're really going to have to get into is our
new skills. So we have AI literacy.
We've got to think about how to do our work differently.
We have to use different tools. We have to.
So there's thought threads of our legacy BA techniques that
run through it, but how we thread them through all of the
new things we need to be thinking about and tools and
literacy and the way projects run is the shift.
That to me is really big. Yeah, it is because it leans so
heavily into, I hate the term soft skills, but human skills,
let's call them human skills. What I hear you saying is that
the new reality for us is leaning heavily onto our human
skills of things like discretion, validation, so
critical thinking, systems thinking.
We've got dealing with ambiguity.
We've got, you know, I don't know in any number of these
skills that I think a lot of legacy organizations have said,
I just need for you to write stuff down.
And so some of us have been overburdened by, OK, my role is
to show up with a pen and a piece of paper.
And now what we're saying is that's we don't even need you to
do that because we've got something that can do that.
We need you to bring your highest, best set of human
skills to help us think through this non human thing that is
changing our organization like that.
Yeah. So like when this comes to life,
it's like you're shaping conversations in order to make
decisions. And when our stakeholders
struggle to make decisions, there's usually a few things at
play. A the wrong decision is when
you're asking to be made. So when leaders and business
stakeholders won't make decisions for us, when we're
waiting on those decisions, we often get frustrated and think
I'm waiting on them. Oh my gosh, how am I going to
get all this done by my deadline?
I didn't get all these decisions.
And a lot of times we simply haven't put the right decision
in front of them. So we think it's the one thing
holding us back. But in reality, they read the
decision in the e-mail or whatever and they're like,
either they don't understand the decision to be made or they
don't agree it's the right decision, but they were too busy
to reframe it and meet with you. And the other thing is decision
timing. So it could be either too early
to make the decision or maybe too late to make the decision
and dealing with those things and how to build consensus and
get alignment and right. So the right decision at the
right time with the right people, is it the right person
to be making the decision with the right people as input?
And then do we have the right context for them to make it?
So I think decisions don't get made off and because we don't
have the right context in front of the decision maker when they
don't have the right data, the right risk information.
So yeah, all of those. And when you think about that
weaves in with everything else we do, right?
We are going to need to do process models and analysis and
decision rule analysis and all these things that we do to get
that context and to evaluate and shape the timing and path of
these decisions. It's a lot of leadership skills.
It is a lot of leadership skills.
And so I, you know, I wonder where do you see new BAS, entry
level BAS fitting into this? Because so much of what you,
what we've just talked about to me are pretty senior skills, but
we need, we, we've got to grow those junior BAS from somewhere.
How do they fit into all of this?
It's a very good question, you know, and it's a discussion that
comes up a lot. I think I've had this question
come up three times this week already in various
conversations, right. So the good news is knowledge is
more accessible than ever with AI and then AI connected to all
the enterprise knowledge. The learning curve to get at the
knowledge our junior BAS will find a much easier time with.
OK. One aspect now the judgement
about that takes many years to develop, but then we think about
what about all these special TBA skills?
How are we going to teach someone to model well, AI can
model for them. Question is, do they recognize
when the model is not optimized? Like lots of process models, for
example, that I've done with AI, when I sit back and look at
them, it's like, that's a horrible process.
It's so good, the research. Can get into this, right?
Yeah, it modeled it for me. But the tricky part's going to
be teaching the juniors how to think like our thought process.
So we have kind of a new mandate as senior BAS to bring junior
BAS along with thinking out loud in front of them.
And so the I think the better that type of thing happens, the
quicker they're going to learn. But they don't need the
knowledge uptake that has all, you know, been present for a lot
of folks. They won't have to spend time.
I mean, like when I was a junior BA, what kind of work did I get
given? Yes, it was work that taught me
these skills. But it was also a lot of grunt
work that AI can now do. Yeah.
So I think it's really hard for us to imagine junior BAS going
through it quicker than we did, but I think it's totally
possible. Yeah.
And so for leaders that are listening to this, that is
something for you to be thinking about is building those
opportunities for those to develop, those junior folks that
take advantage of the technology, but also don't short
circuit important learning, especially around the soft
skills like in fact, creating programs that develop soft
skills and build confidence there and getting the knowledge,
you know, because they can get knowledge at at a good pace.
It's the human stuff that becomes the focus, I think.
Yeah, I mean. How we learned it too, right?
We had to have role models like to learn how to facilitate
highly interactive, engaging requirements workshop.
I don't know about you, but I had to see it and experience it.
Yes. That's how I learned.
And then eventually, hey, I really like how that person's
doing it. I want them to be my mentor with
it and then they might invite me to be a note taker at one just
so I can see a few more of these things and how they play out.
And then the next release of a project, it's like, Hey, will
you leave this small portion of this workshop, right?
Like you have to kind of peel your way into those skills.
And I think that's going to have to be a lot more focus to to
learn those leadership skills and, you know, continuous
mentoring, continuous feedback. We are super excited that we're
going to be able to share this article because it's really, I
mean, I was, I took like 5 pages of notes, Angela.
So I like, yes, I loved every piece of it.
I really did because I think it's, it is the kind of article
that I think if you are a leader particularly that is thinking
about how do I need to help the BAS in my organization really
get ready or be ready for our AI initiatives, our agentic AI
initiatives, like where do we need to help them focus, but
also where can we give them confidence that they they've got
their skills. Your article does that.
It's a start. It's a start.
It's a, it's a lot of shift. I've been, you know, teaching
more about AI stuff and way beyond the surface of generative
AI stuff. And it's really fun to Cbas have
the light bulb moments and go, oh, and anything from literacy
to the role shift with the SDLC process changes to understanding
that AI integration and the agentic AI and really
understanding and see the light bulb click of the complexity and
the analysis and go, whoa, yes, we have to be part of this.
But it's writing requirements documents like it used to be.
There's it's truly analysis. And that's why I got excited.
I made all those noises, by the way, when I was reading the your
article, because I was that excited about the opportunities.
And in some ways I was like, this is this, these are the
kinds of things that we should always have been doing and
always involved in. And now we have, I think, real,
not just permission, like we really have a duty and it's
evident. Yeah, I I agree.
It's you and I both love yacht rock.
Yes. And when during the time Tim and
I are writing future proof and you know, when right after
generative AI just came out, right?
And I started to see the connection between generative AI
and the previous forms of AI like analytical AI and machine
learning, all that stuff and how it all links together.
And I had this moment of like, Oh my gosh, this is it.
This is it the we get to be the B as we want to be.
And I remember having a very strong desire to to for my next
conference session to play the Kenny Loggins song This is it
from the yacht rocket genre. But I peeled back on that idea
because I just didn't think anyone would quite be there with
me on it. Like, right.
But I feel like you. You're a singer, You're a
singer. And I don't know if everybody
knows that you sing. I mean, you don't seem like
professionally or anything, but in the.
Car in the shower occasionally currently, yeah.
And. By myself a little bit I did in
high school, I was that was kind of my big sport was yeah, I
mean, yeah, but yeah, I think this is it.
This is our moment, this. Is our moment.
It is make no mistake who you are, make no mistake where we
are. This is our time as BAS to like
be exactly who we think we should be, who we want to be,
the analysis, the influence decision velocity.
I mean, we have a chance to step into a governance role.
And I don't know if it's going to end up in in the business
analysis or project management practices or where it'll end up.
But you think about, I mean, of course I'm partial to business
analysis, but like the intake of ideas, the feasibility,
viability, the value and prioritization, decision making
and the monitoring of solutions and figuring out where to focus
our energy as an organization, that's all business analysis at
many detailed levels too, right? Yes.
Yes, and that that's really the stuff that I have always loved
in this work is being at that level where I felt that I was
most useful, which is what came through very clearly in this
article. So if people read this article
or if you're thinking about downloading it members and
you're thinking you're going to be afraid this is not that kind
of article. It is to me, it's really
uplifting and there's lots of opportunities and possibilities.
All right. Well, thank you for joining us
today. If you are an IIBA member, I
just want to let you know that article that we have talked
about today, The evolving Role of Business Analysis in the Age
of Enterprise Agentic AI. It is available for
membersonlythroughiba.org. So download it.
It is a great read. It's an easy read, and I think
that you're going to feel really positive about where our skill
set can take us into the age of AI.
Thanks for joining us today. Bye.
Thanks for listening. Do you have any questions,
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