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? 00:00:52,120 The Better Business Analysis
Institute presence, the Better Business Analysis podcast with
Kinsman Walsh. Today we are going to go through
(01:05):
AI powered business analysis, 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.
(01:27):
So if you haven't done a course in AOI, get on to it.
AOI is already automating time consuming BA tasks like
documentation, stakeholder communication and data analysis. 00:01:40,720 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? 00:02:03,080 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?
(02:12):
If they can get these outcomes 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
(02:35):
even extract action items. 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?
(02:58):
OK, does it mean that AI is replacing your job?
No, it assists, but it lacks thebusiness context, critical
thinking, and most importantly the human interaction skills
that define what a great BA is. So within the year or two years,
(03:19):
AI tools will understand the 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 behaviour?
(03:44):
Ever had a stakeholder who seemsengaged but suddenly becomes a
blocker? What if AI could predict that
before it happens? AI driven sentiment analysis can
scan emails, meeting transcriptsand surveys to detect
frustration, excitement or disengagement of your
(04:05):
stakeholders. And it's not you having to have
that awkward conversation. The AI is producing the summary
report that you can then provideto management when dealing with
difficult stakeholders. Predictive analysis can actually
flag high risk stakeholders who may resist change.
And AI can generate customized communication strategies based
(04:29):
on past interactions with that stakeholder and their behaviour. 00:04:36,240 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.
But AI can do more than that. Here's an example.
(04:53):
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
disconnected from the project. But here's the challenge.
(05:17):
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
we've seen, or maybe there's something going on at home.
So we need to distill that information and provide, again,
(05:41):
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
analysis. AI versus human judgement.
(06:02):
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
analysis anymore? So AI tools like Tableau AI or
(06:26):
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,
and also suggest actions. However, again, AI lacks the
(06:47):
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
and what the cause and effect would be, AI can't necessarily
jump to that. Obviously, it can help you do
(07:09):
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
AI, right? It's detected a drop in customer
engagement every October and it recommends reducing inventory.
(07:33):
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
you fit in holidays and you could ask for reasons, but you
would have to have a human to prompt those kind of cognitive
(07:53):
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,
it can't really replace human judgement just yet.
And we need BAS to interpret AI insights and ask the right
(08:14):
questions, the right business questions #4 is AI in decision
making OK? Should we trust AI generated
recommendations? And we know about hallucinations
and this is not a technical AI discussion today.
But there are obviously some challenges still with where AI
(08:34):
is at today. AI isn't just analysing data,
however. It is making business decisions. 00:08:44,120 But how much should we rely on
those decisions? AI can recommend cost saving
measures so it can do the lean 6Sigma for us.
Have we fed it with the right information?
It can recommend who to hire. It can recommend which
(08:56):
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. It's hidden or it's, it's based
on patterns from data that it's collected and kind of created.
(09:17):
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 patterns there.
Cultural considerations. Amazon once tried using AI for
(09:39):
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. Without human oversight, AI
would reinforce that discrimination and we wouldn't
(09:59):
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. So again, we could use that as
our engine, but we are ultimately still the mechanic #5
(10:25):
AI and what we call process minding.
So process minding is finding inefficiencies in workflow.
So every week we map out processes, we find an efficiency
and AI can actually do that in minutes and we don't use that
enough. It's something I want to see on
(10:45):
the market in terms of a productor 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 like UI path, they analyse
workflows, but you have to know the tools, you have to use their
language, but it can detect bottlenecks and suggest
(11:06):
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 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
(11:32):
change in shelf placement could cut packing time by 30%, and the
company saved millions just by 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 a
bias to when work in a company, we don't look external.
(11:54):
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 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
(12:17):
shift to interpreting AI insights and implementing
change? And I'd say it does because even
drawing those process diagrams, 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 tools
to help me do that. And just a bonus number six.
And it's just really important that there is ethics of AI.
(12:40):
There's the biases, transparencyand accountability.
And we're seeing this right now 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
(13:01):
always fair. And these companies that have
them the money to build these tools aren't always ethical.
So it will these models will inherit the biases from the data
they 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.
(13:22):
And then that's told that that'sproprietary information because
the training set is proprietary.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
(13:43):
ensure that AI brings business value and meets the compliance
requirements of your company. 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
(14:06):
isn't replacing business analysis.
This is exactly the prediction Imade maybe six months ago.
But it's enhancing how we work. 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
(14:29):
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 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
(14:52):
and ultimately using AI as the engine, but we're ultimately
there as the human element to business decisions and decisions
going forward. I hope you enjoyed that.
I hope you learned something andor at least it sparked your
internal conversation with yourself about AI and sparked
(15:14):
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? 00:00:52,120 The Better Business Analysis
Institute presence, the Better Business Analysis podcast with
Kinsman Walsh. Today we are going to go through
(01:05):
AI powered business analysis, 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.
(01:27):
So if you haven't done a course in AOI, get on to it.
AOI is already automating time consuming BA tasks like
documentation, stakeholder communication and data analysis. 00:01:40,720 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? 00:02:03,080 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?
(02:12):
If they can get these outcomes 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
(02:35):
even extract action items. 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?
(02:58):
OK, does it mean that AI is replacing your job?
No, it assists, but it lacks thebusiness context, critical
thinking, and most importantly the human interaction skills
that define what a great BA is. So within the year or two years,
(03:19):
AI tools will understand the 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 behaviour?
(03:44):
Ever had a stakeholder who seemsengaged but suddenly becomes a
blocker? What if AI could predict that
before it happens? AI driven sentiment analysis can
scan emails, meeting transcriptsand surveys to detect
frustration, excitement or disengagement of your
(04:05):
stakeholders. And it's not you having to have
that awkward conversation. The AI is producing the summary
report that you can then provideto management when dealing with
difficult stakeholders. Predictive analysis can actually
flag high risk stakeholders who may resist change.
And AI can generate customized communication strategies based
(04:29):
on past interactions with that stakeholder and their behaviour. 00:04:36,240 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.
But AI can do more than that. Here's an example.
(04:53):
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
disconnected from the project. But here's the challenge.
(05:17):
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
we've seen, or maybe there's something going on at home.
So we need to distill that information and provide, again,
(05:41):
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
analysis. AI versus human judgement.
(06:02):
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
analysis anymore? So AI tools like Tableau AI or
(06:26):
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,
and also suggest actions. However, again, AI lacks the
(06:47):
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
and what the cause and effect would be, AI can't necessarily
jump to that. Obviously, it can help you do
(07:09):
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
AI, right? It's detected a drop in customer
engagement every October and it recommends reducing inventory.
(07:33):
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
you fit in holidays and you could ask for reasons, but you
would have to have a human to prompt those kind of cognitive
(07:53):
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,
it can't really replace human judgement just yet.
And we need BAS to interpret AI insights and ask the right
(08:14):
questions, the right business questions #4 is AI in decision
making OK? Should we trust AI generated
recommendations? And we know about hallucinations
and this is not a technical AI discussion today.
But there are obviously some challenges still with where AI
(08:34):
is at today. AI isn't just analysing data,
however. It is making business decisions. 00:08:44,120 But how much should we rely on
those decisions? AI can recommend cost saving
measures so it can do the lean 6Sigma for us.
Have we fed it with the right information?
It can recommend who to hire. It can recommend which
(08:56):
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. It's hidden or it's, it's based
on patterns from data that it's collected and kind of created.
(09:17):
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 patterns there.
Cultural considerations. Amazon once tried using AI for
(09:39):
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. Without human oversight, AI
would reinforce that discrimination and we wouldn't
(09:59):
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. So again, we could use that as
our engine, but we are ultimately still the mechanic #5
(10:25):
AI and what we call process minding.
So process minding is finding inefficiencies in workflow.
So every week we map out processes, we find an efficiency
and AI can actually do that in minutes and we don't use that
enough. It's something I want to see on
(10:45):
the market in terms of a productor 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 like UI path, they analyse
workflows, but you have to know the tools, you have to use their
language, but it can detect bottlenecks and suggest
(11:06):
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 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
(11:32):
change in shelf placement could cut packing time by 30%, and the
company saved millions just by 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 a
bias to when work in a company, we don't look external.
(11:54):
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 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
(12:17):
shift to interpreting AI insights and implementing
change? And I'd say it does because even
drawing those process diagrams, 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 tools
to help me do that. And just a bonus number six.
And it's just really important that there is ethics of AI.
(12:40):
There's the biases, transparencyand accountability.
And we're seeing this right now 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
(13:01):
always fair. And these companies that have
them the money to build these tools aren't always ethical.
So it will these models will inherit the biases from the data
they 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.
(13:22):
And then that's told that that'sproprietary information because
the training set is proprietary.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
(13:43):
ensure that AI brings business value and meets the compliance
requirements of your company. 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
(14:06):
isn't replacing business analysis.
This is exactly the prediction Imade maybe six months ago.
But it's enhancing how we work. 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
(14:29):
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 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
(14:52):
and ultimately using AI as the engine, but we're ultimately
there as the human element to business decisions and decisions
going forward. I hope you enjoyed that.
I hope you learned something andor at least it sparked your
internal conversation with yourself about AI and sparked
(15:14):
you to learn more. I will see you next week.
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