February 5, 2025 • 15 mins
💡 AI is revolutionising Business Analysis—but where do BAs fit in?
In this episode of BA Bites, I take a fresh look at AI in 2025 and beyond. We’ve talked about AI before, but things are moving fast. From AI copilots writing user stories to predictive analytics shaping business decisions, Business Analysts need to adapt—or risk being left behind.
🎙️ In this episode, we cover:
✔️ AI’s role in automating BA tasks—friend or foe?
✔️ How AI can analyse stakeholders and predict resistance
✔️ AI vs. human judgement—who wins in data analysis?
✔️ AI-powered decision-making—should we trust it?
✔️ The ethics of AI—bias, transparency & accountability
💬 Is AI a threat or a tool for BAs? Join the conversation on LinkedIn!
https://www.linkedin.com/company/the-better-business-analysis-institute
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
AI is taking over business analysis with AI Co pilots
writing user stories, analysing stakeholders and even predicting
business trends. Are we heading towards an AIBA
world where human analysts become obsolete?
Or is AI just another tool in our toolbox keeping us working
(00:23):
smarter and faster? Now, I've talked about AI before
and its impact on business analysis, but things are moving
so quickly that we need to take another look.
AI tools are advancing, new regulations are coming into
play, and companies are experimenting with AI driven
decision making like never before.
(00:44):
So what does 2025 and beyond look like for business analysis?
The Better Business Analysis Institute presence, the Better
Business Analysis podcast with Kinsman Walsh.
Today we are going to go throughAI powered business analysis,
(01:08):
the future of BAS in an AI driven world.
So let's dive right into it. The big question is will AI
replace business analysis or business analysts?
The short answer is no, but it will change the way we work and
I'm sure of this. So if you haven't done a course
(01:28):
in AOI, get on to it. AOI is already automating time
consuming BA tasks like documentation, stakeholder
communication and data analysis.It's happening right now.
AOI Co pilots like Microsoft Copilot or ChatGPT are built
into BA tools like Jira and Confluence and Power Automate.
(01:51):
AI can generate draft user stories and analyse meeting
notes and even suggests improvement to workflows, right?
So we need to think about what our role means, if AI can do
that and or what a manager's going to be thinking about.
Are they going to be thinking about keeping their BAS or
hiring BAS? If they can get these outcomes
(02:14):
from these AI tools, we need to rebrand ourselves.
Imagine you're in a meeting capturing user feedback.
Stay kind of feedback if you like.
Instead of manually writing summaries, an AI tool like
Fireflies or OSA dot AI will transcribe everything.
It will highlight decisions and even extract action items.
(02:37):
You save an hour of work instantly.
OK, so you need to get ahead of those.
Start using those tools and not taking credit for those
efficiencies, but take credit for learning those tools along
the way and saving time. So what does AI mean when doing
your job? OK, does it mean that AI is
(03:00):
replacing your job? No, it assists, but it lacks the
business context, critical thinking, and most importantly
the human interaction skills that define what a great BA is.
So within the year or two years,AI tools will understand the
(03:21):
business context that if you provided with the right
parameters, it will understand the market and it will be able
to apply more and more critical thinking and deep research.
So what's left more? The soft skills #2 AI for
stakeholder analysis. Can AI predict stakeholder
(03:41):
behaviour? Ever had a stakeholder who seems
engaged but suddenly becomes a blocker?
What if AI could predict that before it happens?
AI driven sentiment analysis canscan emails, meeting transcripts
and surveys to detect frustration, excitement or
(04:03):
disengagement of your stakeholders.
And it's not you having to have that awkward conversation.
The AI is producing the summary report that you can then provide
to management when dealing with difficult stakeholders.
Predictive analysis can actuallyflag high risk stakeholders who
may resist change. And AI can generate customized
(04:26):
communication strategies based on past interactions with that
stakeholder and their behaviour.So, you know, you've got some
CRM tools that do this, great for marketing.
How do you respond to marketing emails?
And you know, it'll e-mail you again if you've interacted, but
it won't if you haven't. It's quite clever in that way
That exists now through automation.
(04:49):
But AI can do more than that. Here's an example.
ABA at a large bank used AI powered sentiment analysis on
emails related to ACRM rollout. The tool flagged growing
frustration among frontline staff, giving the team a chance
to address concerns before resistance escalated and they
(05:13):
disconnected from the project. But here's the challenge.
AI lacks emotional intelligence.Well, right now it does.
So can we trust AI fully to understand stakeholder dynamics,
or do we need that human touch on top?
And of course we do. We know that Sandy might be
having a bad day and that's not a pattern of behaviour that
(05:34):
we've seen, or maybe there's something going on at home.
So we need to distill that information and provide, again,
more humanistic context as AI will only have the information
it's fit into, and a lot of personal information won't be
fit into these tools by companies because of Data
Protection and Privacy act #3 the death of manual data
(05:58):
analysis. AI versus human judgement.
Let's talk about data. OK, so I'm in the data game at
the moment. BAS and data analysts spent huge
amount of time analysing trends,looking at patterns, and making
recommendations. But now AI can do all that in
seconds. So do we even need BAS for data
(06:20):
analysis anymore? So AI tools like Tableau AI or
Power BI, which is now going to be Fabric, can analyse massive
data States and detect patterns faster than we can.
AI can automatically generate dashboards, highlight anomalies,
(06:40):
and also suggest actions. However, again, AI lacks the
business context. It can see the patterns but
doesn't always understand what its implications are.
So again, the business side of business analysis is really
important. And looking at the risks
associated with those recommendations or the actions
(07:03):
and what the cause and effect would be, AI can't necessarily
jump to that. Obviously, it can help you do
that, but connecting those different AI, let's call them
agents for doing different things may well be what ABA
funds they do in the future. Let's take our retail company's
(07:23):
AI, right? It's detected a drop in customer
engagement every October and it recommends reducing inventory.
It says in October we don't sellas much, but the BA looks deeper
and realizes that the deep was due to a reoccurring annual
public holiday. Now the AI might know that if
(07:45):
you fit in holidays and you could ask for reasons, but you
would have to have a human to prompt those kind of cognitive
discovery pieces. So in that case, if we just
purely ask the AI to make some changes, then cutting inventory
would be a big mistake. So while AI speeds up analysis,
(08:06):
it can't really replace human judgement just yet.
And we need BAS to interpret AI insights and ask the right
questions, the right business questions #4 is AI in decision
making OK? Should we trust AI generated
recommendations? And we know about hallucinations
(08:27):
and this is not a technical AI discussion today.
But there are obviously some challenges still with where AI
is at today. AI isn't just analysing data,
however. It is making business decisions.
But how much should we rely on those decisions?
AI can recommend cost saving measures so it can do the lean 6
(08:49):
Sigma for us. Have we fed it with the right
information? It can recommend who to hire.
It can recommend which priorities are important on a
project. Now, the challenge is a lot of
these AI models aren't always explainable.
Sometimes we don't know why AI made a certain recommendation.
(09:10):
It's hidden or it's, it's based on patterns from data that it's
collected and kind of created. There are ethical concerns, of
course, with AI, which can reinforce biases if the data is
learnt from its flaws. So it's just got like the
American data set, then you know, how would those insights
help in India? There might not be some common
(09:32):
patterns there. Cultural considerations.
Amazon once tried using AI for hiring decisions.
It turned on AI, right? And it was biased against women
because it had learnt from historical hiring data that
favoured men because that was its biggest data set.
(09:54):
Without human oversight, AI would reinforce that
discrimination and we wouldn't necessarily get the best
candidate regardless of gender. AI can assist with decision
making but shouldn't make the final call.
I would argue not. BAS can act as a human check
before AI driven decisions are implemented.
(10:16):
So again, we could use that as our engine, but we are
ultimately still the mechanic #5AI and what we call process
minding. So process minding is finding
inefficiencies in workflow. So every week we map out
(10:37):
processes, we find an efficiencyand AI can actually do that in
minutes and we don't use that enough.
It's something I want to see on the market in terms of a product
or product suite of products. Sooner or later, I think this
will be really important for business analysis and business.
However, AI powered process mining tools, there are some
(10:58):
like UI path, they analyse workflows, but you have to know
the tools, you have to use theirlanguage, but it can detect
bottlenecks and suggest improvements.
And these AI automations can streamline repetitive tasks,
reduce human errors and save time.
And it's kind of, we've mensed together business improvement a
little bit with finding inefficiencies, which are
(11:19):
actually two different topics. Let's take a logistics company
that used AI process mining to analyse warehouse operations.
Now the AI found that a simple change in shelf placement could
cut packing time by 30%, and thecompany saved millions just by
(11:40):
optimizing the layout that a human may have overlooked.
It just didn't come up right. And they say, well, based on my
experience, based on the data I've got other companies and
this is something that we have abias to when work in a company,
we don't look external. And so AI is looking at this
external data and saying, actually people that do packing
when they've got shelves closer to their packing area, they're
(12:02):
more efficient. And so it just automatically
applied that. So it's, it seems like rocket
science, but it really isn't. Now the AI can automate process
improvement. Will be I still need to do the
process mapping or does a role shift to interpreting AI
insights and implementing change?
And I'd say it does because evendrawing those process diagrams,
(12:24):
even like even though I'm a visual person and I like to do
that, that can easily be automated.
And I've already used some toolsto help me do that.
And just a bonus number six. And it's just really important
that there is ethics of AI. There's the biases, transparency
and accountability. And we're seeing this right now
(12:45):
with this battle between China and the US and fake news,
whatever. But you can manipulate these
tools to have the right outcome that you want or your biases
through some kind of geopolitical information war if
you like. We've just got to be careful.
AI is powerful, but it's not always fair.
And these companies that have them the money to build these
(13:07):
tools aren't always ethical. So it will these models will
inherit the biases from the datathey trained on.
And who decides which data they trained on?
The companies. The transparency issue is
growing. We're not sure why AI made
decisions. And then that's told that that's
proprietary information because the training set is proprietary.
(13:28):
So we can't look at that. So for example, Deep Seek that
came out last week, it's open source, so you can actually see
what's behind it. But of course now the US is
looking to shut it down for anything outside of China.
BAS have a responsibility to ensure that AI brings business
value and meets the compliance requirements of your company.
(13:50):
So just be careful as AI takes on more BA tasks, do we need a
new skill set, which is kind of an ethical AI governance?
And I would say, yes, we do, just as we do with data today.
My closing thoughts is that AI isn't replacing business
analysis. This is exactly the prediction I
made maybe six months ago. But it's enhancing how we work.
(14:14):
The best BAS will learn those tools and how to use it
efficiently and effectively to do these things.
But what I think has changed is in terms of how many tools or
how many use cases AI can be used for.
Will just just go mental, OK? And it'll just be massive and
you won't even there will be cases that you can't think of
(14:36):
today that AI will be good for. And so we need to start
experimenting with that and we need to step back now and
actually look as being one of those mechanics.
We're using the tools I've got and ultimately using AI as the
engine, but we're ultimately there as the human element to
(15:00):
business decisions and decisionsgoing forward.
I hope you enjoyed that. I hope you learned something and
or at least it sparked your internal conversation with
yourself about AI and sparked you to learn more.
I will see you next week.
AI is taking over business analysis with AI Co pilots
writing user stories, analysing stakeholders and even predicting
business trends. Are we heading towards an AIBA
world where human analysts become obsolete?
Or is AI just another tool in our toolbox keeping us working
(00:23):
smarter and faster? Now, I've talked about AI before
and its impact on business analysis, but things are moving
so quickly that we need to take another look.
AI tools are advancing, new regulations are coming into
play, and companies are experimenting with AI driven
decision making like never before.
(00:44):
So what does 2025 and beyond look like for business analysis?
The Better Business Analysis Institute presence, the Better
Business Analysis podcast with Kinsman Walsh.
Today we are going to go throughAI powered business analysis,
(01:08):
the future of BAS in an AI driven world.
So let's dive right into it. The big question is will AI
replace business analysis or business analysts?
The short answer is no, but it will change the way we work and
I'm sure of this. So if you haven't done a course
(01:28):
in AOI, get on to it. AOI is already automating time
consuming BA tasks like documentation, stakeholder
communication and data analysis.It's happening right now.
AOI Co pilots like Microsoft Copilot or ChatGPT are built
into BA tools like Jira and Confluence and Power Automate.
(01:51):
AI can generate draft user stories and analyse meeting
notes and even suggests improvement to workflows, right?
So we need to think about what our role means, if AI can do
that and or what a manager's going to be thinking about.
Are they going to be thinking about keeping their BAS or
hiring BAS? If they can get these outcomes
(02:14):
from these AI tools, we need to rebrand ourselves.
Imagine you're in a meeting capturing user feedback.
Stay kind of feedback if you like.
Instead of manually writing summaries, an AI tool like
Fireflies or OSA dot AI will transcribe everything.
It will highlight decisions and even extract action items.
(02:37):
You save an hour of work instantly.
OK, so you need to get ahead of those.
Start using those tools and not taking credit for those
efficiencies, but take credit for learning those tools along
the way and saving time. So what does AI mean when doing
your job? OK, does it mean that AI is
(03:00):
replacing your job? No, it assists, but it lacks the
business context, critical thinking, and most importantly
the human interaction skills that define what a great BA is.
So within the year or two years,AI tools will understand the
(03:21):
business context that if you provided with the right
parameters, it will understand the market and it will be able
to apply more and more critical thinking and deep research.
So what's left more? The soft skills #2 AI for
stakeholder analysis. Can AI predict stakeholder
(03:41):
behaviour? Ever had a stakeholder who seems
engaged but suddenly becomes a blocker?
What if AI could predict that before it happens?
AI driven sentiment analysis canscan emails, meeting transcripts
and surveys to detect frustration, excitement or
(04:03):
disengagement of your stakeholders.
And it's not you having to have that awkward conversation.
The AI is producing the summary report that you can then provide
to management when dealing with difficult stakeholders.
Predictive analysis can actuallyflag high risk stakeholders who
may resist change. And AI can generate customized
(04:26):
communication strategies based on past interactions with that
stakeholder and their behaviour.So, you know, you've got some
CRM tools that do this, great for marketing.
How do you respond to marketing emails?
And you know, it'll e-mail you again if you've interacted, but
it won't if you haven't. It's quite clever in that way
That exists now through automation.
(04:49):
But AI can do more than that. Here's an example.
ABA at a large bank used AI powered sentiment analysis on
emails related to ACRM rollout. The tool flagged growing
frustration among frontline staff, giving the team a chance
to address concerns before resistance escalated and they
(05:13):
disconnected from the project. But here's the challenge.
AI lacks emotional intelligence.Well, right now it does.
So can we trust AI fully to understand stakeholder dynamics,
or do we need that human touch on top?
And of course we do. We know that Sandy might be
having a bad day and that's not a pattern of behaviour that
(05:34):
we've seen, or maybe there's something going on at home.
So we need to distill that information and provide, again,
more humanistic context as AI will only have the information
it's fit into, and a lot of personal information won't be
fit into these tools by companies because of Data
Protection and Privacy act #3 the death of manual data
(05:58):
analysis. AI versus human judgement.
Let's talk about data. OK, so I'm in the data game at
the moment. BAS and data analysts spent huge
amount of time analysing trends,looking at patterns, and making
recommendations. But now AI can do all that in
seconds. So do we even need BAS for data
(06:20):
analysis anymore? So AI tools like Tableau AI or
Power BI, which is now going to be Fabric, can analyse massive
data States and detect patterns faster than we can.
AI can automatically generate dashboards, highlight anomalies,
(06:40):
and also suggest actions. However, again, AI lacks the
business context. It can see the patterns but
doesn't always understand what its implications are.
So again, the business side of business analysis is really
important. And looking at the risks
associated with those recommendations or the actions
(07:03):
and what the cause and effect would be, AI can't necessarily
jump to that. Obviously, it can help you do
that, but connecting those different AI, let's call them
agents for doing different things may well be what ABA
funds they do in the future. Let's take our retail company's
(07:23):
AI, right? It's detected a drop in customer
engagement every October and it recommends reducing inventory.
It says in October we don't sellas much, but the BA looks deeper
and realizes that the deep was due to a reoccurring annual
public holiday. Now the AI might know that if
(07:45):
you fit in holidays and you could ask for reasons, but you
would have to have a human to prompt those kind of cognitive
discovery pieces. So in that case, if we just
purely ask the AI to make some changes, then cutting inventory
would be a big mistake. So while AI speeds up analysis,
(08:06):
it can't really replace human judgement just yet.
And we need BAS to interpret AI insights and ask the right
questions, the right business questions #4 is AI in decision
making OK? Should we trust AI generated
recommendations? And we know about hallucinations
(08:27):
and this is not a technical AI discussion today.
But there are obviously some challenges still with where AI
is at today. AI isn't just analysing data,
however. It is making business decisions.
But how much should we rely on those decisions?
AI can recommend cost saving measures so it can do the lean 6
(08:49):
Sigma for us. Have we fed it with the right
information? It can recommend who to hire.
It can recommend which priorities are important on a
project. Now, the challenge is a lot of
these AI models aren't always explainable.
Sometimes we don't know why AI made a certain recommendation.
(09:10):
It's hidden or it's, it's based on patterns from data that it's
collected and kind of created. There are ethical concerns, of
course, with AI, which can reinforce biases if the data is
learnt from its flaws. So it's just got like the
American data set, then you know, how would those insights
help in India? There might not be some common
(09:32):
patterns there. Cultural considerations.
Amazon once tried using AI for hiring decisions.
It turned on AI, right? And it was biased against women
because it had learnt from historical hiring data that
favoured men because that was its biggest data set.
(09:54):
Without human oversight, AI would reinforce that
discrimination and we wouldn't necessarily get the best
candidate regardless of gender. AI can assist with decision
making but shouldn't make the final call.
I would argue not. BAS can act as a human check
before AI driven decisions are implemented.
(10:16):
So again, we could use that as our engine, but we are
ultimately still the mechanic #5AI and what we call process
minding. So process minding is finding
inefficiencies in workflow. So every week we map out
(10:37):
processes, we find an efficiencyand AI can actually do that in
minutes and we don't use that enough.
It's something I want to see on the market in terms of a product
or product suite of products. Sooner or later, I think this
will be really important for business analysis and business.
However, AI powered process mining tools, there are some
(10:58):
like UI path, they analyse workflows, but you have to know
the tools, you have to use theirlanguage, but it can detect
bottlenecks and suggest improvements.
And these AI automations can streamline repetitive tasks,
reduce human errors and save time.
And it's kind of, we've mensed together business improvement a
little bit with finding inefficiencies, which are
(11:19):
actually two different topics. Let's take a logistics company
that used AI process mining to analyse warehouse operations.
Now the AI found that a simple change in shelf placement could
cut packing time by 30%, and thecompany saved millions just by
(11:40):
optimizing the layout that a human may have overlooked.
It just didn't come up right. And they say, well, based on my
experience, based on the data I've got other companies and
this is something that we have abias to when work in a company,
we don't look external. And so AI is looking at this
external data and saying, actually people that do packing
when they've got shelves closer to their packing area, they're
(12:02):
more efficient. And so it just automatically
applied that. So it's, it seems like rocket
science, but it really isn't. Now the AI can automate process
improvement. Will be I still need to do the
process mapping or does a role shift to interpreting AI
insights and implementing change?
And I'd say it does because evendrawing those process diagrams,
(12:24):
even like even though I'm a visual person and I like to do
that, that can easily be automated.
And I've already used some toolsto help me do that.
And just a bonus number six. And it's just really important
that there is ethics of AI. There's the biases, transparency
and accountability. And we're seeing this right now
(12:45):
with this battle between China and the US and fake news,
whatever. But you can manipulate these
tools to have the right outcome that you want or your biases
through some kind of geopolitical information war if
you like. We've just got to be careful.
AI is powerful, but it's not always fair.
And these companies that have them the money to build these
(13:07):
tools aren't always ethical. So it will these models will
inherit the biases from the datathey trained on.
And who decides which data they trained on?
The companies. The transparency issue is
growing. We're not sure why AI made
decisions. And then that's told that that's
proprietary information because the training set is proprietary.
(13:28):
So we can't look at that. So for example, Deep Seek that
came out last week, it's open source, so you can actually see
what's behind it. But of course now the US is
looking to shut it down for anything outside of China.
BAS have a responsibility to ensure that AI brings business
value and meets the compliance requirements of your company.
(13:50):
So just be careful as AI takes on more BA tasks, do we need a
new skill set, which is kind of an ethical AI governance?
And I would say, yes, we do, just as we do with data today.
My closing thoughts is that AI isn't replacing business
analysis. This is exactly the prediction I
made maybe six months ago. But it's enhancing how we work.
(14:14):
The best BAS will learn those tools and how to use it
efficiently and effectively to do these things.
But what I think has changed is in terms of how many tools or
how many use cases AI can be used for.
Will just just go mental, OK? And it'll just be massive and
you won't even there will be cases that you can't think of
(14:36):
today that AI will be good for. And so we need to start
experimenting with that and we need to step back now and
actually look as being one of those mechanics.
We're using the tools I've got and ultimately using AI as the
engine, but we're ultimately there as the human element to
(15:00):
business decisions and decisionsgoing forward.
I hope you enjoyed that. I hope you learned something and
or at least it sparked your internal conversation with
yourself about AI and sparked you to learn more.
I will see you next week.
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