Episode - 026: Where we learn from Tejas Parikh, a seasoned finance consultant and transformation specialist, about the art of finance transformation and the power of effective planning. Tejas, with his unique approach akin to a doctor, identifies the pain points in finance operations with a view to stop teams from killing time on non-value adding tasks.

Drawing from his experiences at companies like GE Healthcare, Vodafone, Vanquis, Currys, Unilever, and Yo Sushi, he shares insights on the importance of preparation in forecasting cycles, the role of tools like Power BI in his work, and the need to minimize disruption to operations for finance requirements.

In this engaging conversation, Tejas introduces us to the STAR (Situation, Task, Action, Result) framework, a powerful tool for storytelling in the finance world. He uses this framework to explain how he transformed complex budgeting processes into smooth, efficient operations. This episode is a must-listen for anyone interested in finance transformation, business intelligence, and the intersection of finance and technology. Tune in to learn from Tejas's experiences and gain valuable insights into the world of finance transformation.

Where to find Tejas

Resources Mentioned

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[00:00:00] Tejas: Like you go to a doctor, right? Tell me where it hurts. Where is it painful? What question are you not being able to answer? What is killing the time for your team? What insights you would like to have and you can't see what KPIs you want to see and you can't see. Let's break it up. Let's understand the driver behind it. Okay? Root cause analysis around it. And then, we put together a requirement list of this is what you want, would like a new a system to do for you so that you can do the more value add.

[00:00:29] Adam: Hello and welcome to Tech for Finance, where we help finance professionals leverage technology to level up their lives. I'm your host, Adam Shilton and in this episode we are going to be chatting with Ted Jas Perrick. Ted Jas is an fp and a transformation specialist and is a big advocate of business intelligence.

Tejas comes from an fp and a background working for companies like GE Healthcare, Vodafone, Vanquis, Currys, Unilever, and Yo Sushi. Now Tejas worked as a senior finance consultant and is also founder and director of Ashkar Business Consulting, who helped finance teams use technology to build a high performing finance function.

In his free time, Tejas spends time with his family and loves music, movies, good food and traveling. But before we start, if you like what you hear today, please make sure to subscribe to Tech for Finance on your favorite podcast platform and on YouTube. But it's really great to have you here today, Tejas. I'm looking forward to it.

[00:01:25] Tejas: Thank you for having me, Adam. It's a pleasure to be here. I'm finally on the Adam step for finance, right? I must have done something right.

[00:01:34] Adam: Yes. It's, uh, yeah, it is an honor and yeah, no, it, it is good that people are excited to, to come on the show as, as I always say, you know, I started this to scratch my own itch, you know, have a bit of fun and see whether I could provide value.

Um, it's just gained momentum from there. Um, so it means that I can have guys like you on and we can have some really good conversations, so I really, really appreciate.

[00:01:54] Tejas: it. No, definitely. I mean, actually this is this, in fact, the fact that this show exists and it is so popular itself shows right, that technology is becoming more and more in, more and more pivotal to the finance function.

Uh, if you went like 15 years back, right? It wouldn't be tech for finance, it would be much more of, uh, accounting standards or, you know, something like that, or I F R S or you know, or tax laws or things like that. The fact that we need a tech for finance as a show itself is I think, proof of the pudding, right?

That technology has become, uh, quite pivotal in the, in the finance, finance sector essentially.

[00:02:30] Adam: Yeah. And, and even, even more so now, right? You know, everything has gone completely mad, but, we'll, we'll, we'll come onto that. Um, but firstly, I, I wanna hear a little bit more about you. So obviously now, you know, founder and director of, of, um, Akshar, um, which is great, but you've also led quite a lot of sort of finance transformation projects in, in previous roles.

So if we look at some examples, I mean, at Unilever there was a lot to do with forecasting month end. And I dunno whether you just wanna talk about your approach, you know, sort of the outcomes of some of those projects and the, the sort of stuff that you helped realize as part of those transformations.

[00:03:07] Tejas: I, I, I, I tell you, I generally produce basically a, uh, a star schema to explain these things, right?

So let me, let me give you the star schema. So the situation which I walk into at basically Unilever is that, uh, there are four to five people back office in India, and this is Unilever's own office. It's not even outsourced. It is offshore, uh, in Bangalore, and they are doing basically their planning cycles from there.

Uh, this is for the HR and their property functions, support functions. Cost base is around what, four 50, 500 million. They take basically more than three to four weeks to forecast 3 50, 400, 500 million. And I'm like, this is rubbish. What are you doing? I mean, it is not rocket science, right? Because the fact is, especially the property side of the business, your biggest cost is your rent.

It's not like you are buying your, you are creating new, new properties every quarter. So the, the public portfolio doesn't move as much man collected here. There are changes, but it's not dramatic. So we should not be taking three, four weeks. Uh, and I'll be fair, my first, uh, cycle was a complete failure. Uh, we spent up to 3:00 PM in the morning in the office, uh, worked the whole next day to put some sort of a forecast together.

Uh, no call center director signed it off. It was a complete. Like seriously a bad, bad performance. And I was like never happening on my watch again. Cause this is my first month in the business trying to understand what the processes are and stuff like that. What we then did is, again, that is where the tech for finance is such an important thing and the cap previous conversation is because we put some basic technology, leverage the Power BI that they had little bit of implementation plus basically put access database in place, which I know when I talk like 2020 and access database, I'm well out of date.

But that's the only tool they were willing to at the point in time, you know, without any cost, uh, invest in worked with the net focus cycle was achieved in, I am not exaggerating, seven working days. Okay. We basically ripped up the whole old process, the entire actuals were put in, in a correct structure, validated.

During the month. See, this is the problem. When people do a forecast cycle, they're waiting for the forecast cycle to prepare for the forecast cycle, right? If you plan to fail, you fail, your plan will fail, right? So if you fail to plan it, it is not gonna work. I did do that in month one before my month three comes before my planning cycle kicks off.

So all my home, I was the kid, even in school who would go movies during exams and study during the year. And that's the kind of person I am. And that's what I did. I basically did month one, two, we did all the preparations, uh, actuals, validated templates and new templates. Signed off training on the new templates, making sure every last stakeholder who is receiving the template and expected to fill up stuff, knows what is filling up, where is he filling up everything else.

So when the month end happened and we basically month three finished, and we were like, okay, three plus nine time. Fair enough, press a button. All actuals are in access. Press, uh, pressure, pressure button in pivot tables, all numbers are refreshed. All my templates already, they all went out. Seven working days.

I had including submissions, came back in five working days, reviewed it on the fifth working day with the direct call center directors. They all revision, I don't like this number, can we change this? Blah, blah, blah. All got changed. They fed back to the call center owners or site owners. Everything came back.

Seventh Working Data CFO has a number in his hand. And not only does he have a number, he has a story. He has cost center directors who know what they're talking about. Okay. And we walk in with, with the smartest deck and the smartest like story compared to all of the support functions. And we walk out like we are done.

We did get a challenge still, okay? But given the fact that we were like, you know, more than 500 million if we, we got a challenge, like one and a half or 2 million if I remember correctly. And I'm like, yeah, fine, we'll figure that out. It's not, we'll make that in and we'll get on with it, right? As any person will tell you one or 2 million of challenge, they'll take it any day.

So the point is, If you sit and think clearly about your process, the more of the story is you have to think process first. Then think what technology will support this process? And then think, which are my key stakeholders alongside this process? How do I engage them? How do I make sure that they are all with me?

Cause this is not a one man journey. I don't think it is my success. It is not. It's every and everybody, even the call center managers who were like, no, the frankly, they're the the lowest level, right? They are the ones who are actually doing the grant work. They came up to me and they told me this, that this is the fastest planning cycle we've ever done it.

This is the most effective ones we've ever done. And they liked it cause they had to spend less than two hours on the template. They submitted the numbers and they walked away. Their job is not to spend time making numbers of finance. That's not their job. Right? They are site managers, their area managers.

Some of were country managers. I can't do the job that they do. Yes. So us as finance need to put our, you know, the chip on the shoulder away, and please realize that we cannot do what the operations guys do. So how much, how can we minimize the disruption we do to them for our forecasting requirement, our planning requirement, our commentary on our reporting, right?

This is another famous one. We want them to write the biggest commentary ever, boss. How are you helping him ask that the question. And then you have a real business partner. So again, all LinkedIn people talk about business partner all day, right? And I'm like, yeah, I'm not too sure, right? It's not just a behavior pattern.

It comes with your structure, right? With your right. E r P, right? Epms in place, right? BI tools in place, right? You know, behavior pattern in place, right? Processes in place. Some of them want to be a good business partner, they don't even. But they, they just get stuck in the dayday mundane. How do you help them?

It's simple.

[00:09:29] Adam: So, so going back a little bit, what, what was it you referenced there as a star framework?

[00:09:34] Tejas: Yes. So again, so to all my finance colleagues, I say that whenever you're trying to tell a story, you talk about a star framework situation, basically what was the task, what was the action taken and what is the result out of it?

So what I just done, I told you what was the situation basically at Xerox. Then I told you what was the task, task was giving out the budget. The actions I took was access database, power BI templates, training people, blah blah, blah. Result was, we did it in seven working days.

Again, this also is actually your, can be a part of your, you know, uh, what they call finance business partnering and being a better business partner.

Finance is just stop talking about numbers. You need to try talking about stories and how do you tell a story without waffling. Yes. Right. Because there is one extreme. You just speak three sentences and you walk away. Nobody makes any sense of that. The other extreme is you go on for two hours and nobody can make sense of that either.

What the hell do you do? And that is why, you know, you use frameworks like this in the middle for things, things like this to work and you have for you to be able to communicate.

[00:10:41] Adam: Yeah. And it's, you know, we we're always aiming towards that result, aren't we? And I think people need to, to not lose sight of that.

Right. Um, sometimes Absolutely. They caught up in the process and, and they, they lose sight of that. So, no, it's, it's great. And just before we move on, I'm, I'm very keen to learn a lot bit more about, um, that template. And of course you don't have to go into specific specifics, but what you said there was that.

Obviously you needed to make sure that the templates that went out were easy to complete. Cause you know, otherwise you're gonna be met with resistance. Right. So what was the template? Was it like an Excel or, or a spreadsheet that was pre-formatted or, or what did

[00:11:18] Tejas: that look like? So it was Excel, first of all, because as I said, business was not looking to invest into a E P M tool.

We did propose it, but uh, they were not willing to propose, uh, invest at that point in time. So, yes, it was accept though I don't recommend any listener to continue budgeting and planning in Excel. Uh, but yeah, that is what we did. But the point is, again, I had the advantage of obviously years of experience, so I built something out of last 12 months, last forecast cycle, give all the data points necessary for the person providing the input to have that intuition of does this start to make sense or am I just putting in a number and then again putting the chip under my shoulder down selected five, six stakeholders said the stakeholders is not directors.

These are, I'm talking, the analysts who are going to put in the numbers in my template chose five, six of them. And I said, you know what? Run me through this. Why would this not work? Break this for me. Right? Tell me, this is rubbish. Tell me this now. Right? Instead of in middle of the planning cycle. Cause that's gonna be ridiculous.

So let's talk now, tell me what, what can I do better? And look, a lot of good inputs came out. Yes, I have years of experience, but I'm not, I don't know, you need, at the back of our hand, I don't know their ways of working. And obviously we work, I work with, you know, different clients at different points in time.

So I never say that I know everything. I said, look, this is what my best recommendations are. You tell me why this doesn't work. And then genuinely open-minded. Open not only with the open mind, but open mind and open heart. I take the criticisms I get, I say, okay, let's think this through. Lots of logic.

What's the driver? Why are we, why? You know, Simon Sinek. Begin with the why. Why would I do it differently? I understand that. And then, okay. So made changes, made revisions, like made, made what? Eight, nine revisions through the template. Finally, we all signed off on something that, okay, this stands, and then again, those six, seven people were also not my favorite people are who I knew from, you know, five years.

I'm only there for 10 months. So no, I took North America, south America, three people in Europe, two people in Asia. You spread it out because different countries, different continents, different cultures think differently. Behave differently. So I spread them wrong and I said, gimme input. I got those inputs, published the templates.

Then took that and went to the directors and I said, okay, three directors, once your submission comes in, I need you to turn around in 24 hours and tell me, uh, you can sign the number off, or you cannot sign the number off you. What changes you want. Now for you to do this in 24 hours, you need a dashboard.

What do you need to see in that dashboard? Okay, this is my performer. And yes, they obviously threw it out. They said, no, I don't like this. I want this boss. People come to you, they complain about colors. I don't like this color here and I don't like that, you know, line there. I don't get emotional about it.

I do what it, I need to empower them, you know, achieve what they need to achieve. Cause I'm not the one signing off the cost center cost, right? The department cost, I'm not signing off. It's them who's signing off. So whatever it takes for them to sign off, if they want pretty pink over there or they want ponies ands over there, they have ponies ands over there, I don't care.

As long as they're able to analyze the number, have enough data points in there for them to say, okay, yeah, compared to prior, compared to this, the waterfalls are all there. I'm able to now actually stand up in front of the CFO and explain these numbers and I'm able to, you know, get this turned around in 24 hours.

I can tell you yes or no or change this, this, this. Then sure enough, so we signed off the dashboard as well in month two. So when the kickoff happened, yes. It took me, as they say right in English, that it took me 20 years of hard work to become a success overnight. Mm-hmm. So we did like 20, 30 days of hard work before the planning cycle to get that planning cycle done in seven days.

Now that may sound like this. How did you save time? I did. Cause now the process is established. Mm. The templates are established. The vision of working is established. You know, everything is done. So when the next planning cycle comes, I don't have to do 20, 30 days. Again, this is one time investment done.

Currently there there was 5% changes of, okay, can we add actually this one for next time or whatever else. But that's always gonna be there, right? Business evolve. So there's a bit of a chip here and there, which you fixed and then you move on. But then we had a process which started working, and again, I'm not, again, sitting on a high horse.

I very much recognized that every business, the budget cycle or reporting. Mechanism is starts with something which is slick, smart, signed off. Everything else then becomes what I always call a Frankenstein process. Mm-hmm. Cause things get added on left, right hand center and it becomes something which looks like a Frankenstein.

Okay. And that, but a good fpn or or finance leader is somebody who recognizes that, you know what? Stop, this is now becoming a Frankenstein. Mm-hmm. We need to take a step back and this quarter, or this planning cycle or this reporting cycle, we are going to reassess. That's the difference between basically the wood from the trees phrase of you need to be able to stop every now and then and say, you know what?

We are lost in the wood. Let's take a step back, think this through and, you know, work with it. Uh, my time at GE Healthcare, uh, we created a center of excellence as well, right? Uh, and some of the interns, which, which helped us deliver that, they're all become senior managers, head of departments now, and they still in touch with me on, on LinkedIn.

And the reason I bring this up, I need to have a phrase over there, any process which takes you more than 45 minutes is wrong. And I stand by that even today, once I designed this whole thing for me to update actuals for Unilever after the month end was less than 45 minutes. That's step one, sending the templates out.

So this is an email, less than 45 minutes when the data comes back. The way the templates were designed, they had a data tab at the back. I had to just copy paste that and put it somewhere else and blah, blah. Yes, there were 20 templates, but 20 times copy paste doesn't take you more than 45 minutes. So, you know, again, any process, which took me more than 45 minutes, we rethink.

So all steps tuck, you know, creation of template, updating of template, sending it out, consolidation, creating the presentation. 45 minutes is a golden rule. If it doesn't take, if it takes more than 45 minutes, either the process is wrong or the technology is wrong, or the skill is missing. Something somewhere is missing, figure it out.

It could be, needs a macro, it needs power bi, it needs, you know, To be broken down needs to be done differently.

[00:18:26] Adam: And I think, I think there's some, there's some really important parts of what you've just said to, to reiterate. So going back a couple of steps when you were talking about, you know, realizing change in 10 months is no, no easy task, right?

You know, um, you've only got a limited window to build rapport and create an impact with, with your team, right? Um, obviously you've got experience, so it was, it was easier for you. But for, for people who are maybe new to transformation or are still in the process of thinking about how they become a, a better business partner, I just wanna stress that asking questions pieces is so fundamental because whether it's a new process that you're testing and in, in your words, I'm trying to break this, you need to give me feedback on why it's not working.

You know, because, you know, As you say, years of expertise doesn't necessarily equate to a process working perfectly first time and in encouraging people to collaborate with you, you're kind of shorting the process, aren't you? You're not first saying this is the way that is proven to work. Cause I've done it before.

You are saying, here is what we've got so far, but we are not ready to deploy it yet because we need you to really get invested in this and we really need you to, to pull it apart before we go any further.

[00:19:39] Tejas: Yeah. I tell you lot of finance people that I work with, I have this challenge as well because they're a chartered and the other departments may or may not have a chartered, right.

They think that they are right. Frankly, my brother and sisters, even if you are right, don't say it out loud. Blunt, honest answer. Okay. Be humble. Go and ask for input. Cause exactly the word you used over there. You know, for them to be invested in the process by me. In fact, sometimes even leaving out something though I know it, I should perfect it.

I will not perfect it. I give it out imperfect. It shows me as vulnerable. And I say, you know what? I don't know. You tell me. And then they feel the the pleasure of, okay, I corrected him, but he's a nice guy. He took it well, it worked out. It builds that capability, it builds that camaraderie. It builds that relationship.

And do it, do it. Honestly, man, no man is perfect. Anybody, anybody thinking that they're perfect. Anybody saying that they've always built a perfect Excel book is, or a perfect model is wrong. Okay? It becomes perfect when you've got multiple people looking at it, working with it, and. Solving for and get them by, get their buy-in.

Single biggest advice for a new business partner is be humble, be be happy, to be vulnerable, ask questions, right. And focus on building credibility. Mm-hmm. Because there are two things which I have noticed. One people, one side people who will want to solve everything before they go and they say, this is what the future is going to look like, or the other pieces is they go and do with nothing.

And I can tell you with experience of business or the operation side, don't like either. Make a thoughtful effort, thoughtful structure thought, bring thoughtful value onto the table, and then be humble about it. I if you, if you, if you recall there all the examples I, I gave, I said I built something, okay.

I built and I said, okay, this is a skeleton in my world. Now let me help me meet it. And that way they're not like, okay, he's lazy. Get, he's just sitting there asking me to do everything for him. I'm not, but at the same time, I'm not saying I know. I know everything I do. Mm. And there's nothing wrong with either.

[00:22:03] Adam: No. It's all valid. The second point there as well is the point that you made earlier about slowing down. Um, so, you know, sometimes people don't get the opportunity to think, right, well it's taken us 45 minutes to do this cuz they get so swept up in, in what they're doing. Right. And I think it's easy to get caught in the trap of if something's not working, we need to add something or we need to evolve the process.

But it's, it's not the case. And, and I've just been trying to find that, the quote because, uh, I think it was last year or the year before, um, I spoke to, uh, a lot of business owners to get their impression of, you know, what are the few things that have helped them achieve the success that they've achieved, right?

And, and one of the guys who I'd, I went to school with a guy called, uh, Raffi Faruk, and he, he owns his own business now called Genie ai. Um, and they do, um, AI in the, the legal space, you know, from, from a con contractual, um, perspective. And one of the points that he emphasized was, As part of their culture, they will always go through a simplification exercise and they will apply equal weight to what do we need to cut out as well as what do we need to add in, you know?

And it's, it is the same for technology. You know, before we adopt a new tool, is there an existing tool that can do that we, that we can evolve? Or is there an existing tool that we're using that we don't need to use anymore because we can evolve a different process. Yeah. So I think that that's, that's worth just emphasizing and not forgetting as well.

[00:23:30] Tejas: Yeah, no, exactly. So like, so for example, do I need a, a accounting system and a CRM and a HRM separately or can I have one e r P which covers all of it, for example. It's honestly and very respectfully, I've been speaking to a lot of people over the last one month, right. Cause I'm on a, on a mission at the minute.

Mm-hmm. And basically a lot of respectfully, lot of finance professionals tend to think E R P is accounting system. Let's get this clear. And I'm sure you know from, from your sales background, background, it's not, it's two very different things. Uh, ERP encompasses and has within itself. Finance as a module and accounting and whatever else is a module, but it is not equal to ERP is not equal to accounting system.

Accounting system is a part of an erp, but can have more bits and pieces in it and that, and things like that. I mean, finance, finance leaders today need to listen to podcasts like this, read articles, look outside their shell, if I may. Mm-hmm. Right? Mm-hmm. I know it's difficult. I know it's not easy. It may be on a train ride, it may be on your, you know, car journey to work or whatever, you know, but this needs to happen otherwise.

Frankly, uh, I think there is a very famous code that you can't, uh, keep in the same things again over and over again, and expect different results. That's a sign of badness. Mm-hmm. Mm-hmm. Right. And a lot of professional students already respectfully, but are behaving that way. They want to do the same thing over and over again, and they want to, they think, why can't we get this in faster?

Mm-hmm. You can't. Mm-hmm. You can't. You have to do it differently. And that, I think, is critical for us as well as professionals to look at that.

[00:25:25] Adam: And the, the other trap as well is, is what I see a lot is, as you say there, you know, sometimes we're led to believe through the marketing or whatever it happens to be.

That, you know, these new systems are the, the be all and, and end all, you know, and, and, and as, as somebody who works in the space, obviously I'm, I'm an advocate of, of technology. Right. But coming back to the point about, you know, sometimes you don't need to add to stuff. Sometimes you don't need to replace stuff.

Yeah. So just because. You're importing into a SQL database and you've got Power BI sat on top of that or, or something equivalent, right? Just because it's a SQL database and it might be seen as a bit dated or, or whatever it happens to be, doesn't mean that you've gotta rip it out just because you're going through this big transformation process, right?

If it takes you seconds to query a SQL database, then don't change it. Yeah. The, the, the business case is then, right. Okay. Well, you know, if we've got the data, how do we, how do we make more sense of it? You know, it doesn't matter where it is, you know, how, how do we use what we've got without sort of break breaking stuff?

You know, if it ain't right, don't fix it, is the common phrase,

[00:26:27] Tejas: right? Yeah. Right. And horses for courses, so what you are hinting on there is again, also horses for courses. So you basically look at what the question is. Even today, my consulting clients, when I speak to them, I always question to them is, what is the problem you're looking to solve?

Mm. Okay. Don't tell me I want our bi, or I want a plan, or I want a solutions, or I want, uh, new e r p, or I want. Dynamics. Don't tell me that. I don't care. Mm-hmm. Respectfully, I don't care. Cause you are a finance professional. You are not a tech professional. So don't tell me what tech you want cause you dunno what what you want.

Ok. Tell me what problem you want to solve. Tell me where, where does it hurt? Like you go to a doctor, right? Tell me where it hurts. Where is it painful? What question are you not being able to answer? What is killing the time for your team? You know what insights you would like to have and you can't see what KPIs you want to see and you can't see.

Or they take five days to make that KPI because everything is, you know, scattered all over the place. Tell me that. Okay. Let's understand that use case. Let's understand the five I's around it. Let's break it up. Let's understand the driver behind it. Okay? Root cause analysis around it. And then we put together a requirement list of this is what you want, would like a new a system to do for you so that you can do the more value add.

Mm-hmm. Now whether that is your current system tweak for something or a new system or what we talk after. Mm-hmm. But if, because you don't go to doctor and he, he tells you, okay, go and take this medicine. I hope your doctor doesn't do that. Right. Uh, and you hope your doctor doesn't do that. So same way.

Right. You don't tell or you don't tell him that, by the way, you give this medicine to my friend, so gimme the same medicine. Hmm. Do you do that? No. Right. And how many times still a financial professor will come and tell you, oh, we had plan in that business so I want that. Or we have power, we there so I want this, or I want the data lake.

Cause we had Azure in my last place. Or we had, you know, in my last place I. Your last place. This place is different. The ways of working are different. People are different. Skillsets are different. Processes are different. Even between Unilever and png, even though they're more, more or less the FMG businesses, the processes will be very, very different.

Even the SOAP departments both make soaps will be producing soap very, very differently. Procuring property very differently, you know, processing it very differently. Marketing very differently. Shipping it very differently. You know that distributor contract will be very different, so you cannot, for God's sake, right, take the last job knowledge and say, I want exactly the same system.

What you can do though, is take your last jobs questions and how those systems help. What questions you answered, what KPIs you had, you know, what insights you have and how do you create it in this environment. That is definitely a transferable skill, which you take across.

[00:29:26] Adam: And, and you, you've got me thinking now because you used the doctor analogy and it's always a dangerous game because there's, there's a, there's a lot of doctor analogies, right?

And I, I think you did right. That, you know, for, for you to just turn up to a doctor and without them asking any questions, just say, take this and go away. You obviously that that's, that's definitely the wrong approach. But what I find, and again, in the uk obviously we're based in the uk, we have nhs, we're forever grateful for it, you know, free healthcare and, and you know, I, I will never grumble about that.

But if we take the example of a busy doctor Yeah. A busy gp, general practitioner, um, they've got so much on their plate. They're seeing so many appointments. They get into a rhythm of asking a few questions with a view to prescribe and get rid of right now. What I say there in the majority of instances is the default is to prescribe something.

Yeah. What I think is lacking in some instances, and of course every doctor is drastically different. And again, not not bashing any doctors out there, but what I've seen in in other cultures in other countries is before the prescription, it's the right, well, let's work back, you know, when did this start?

You know? Are there any other factors that have contributed to this illness? Because it might not be that we need to prescribe something. Right? You know, um, I'm gonna prescribe you extra strong antihistamine fur allergies could actually be right. Well actually you've probably got a severe intolerance to this food.

Do you see what I mean? So, so looking at things holistically in some instances and taking that step back is really useful. And that example is transferable. When we look at evolving finance process, when we look at evolving finance systems, because we. Our default wants to prescribe something. Our default looks to what tool is gonna save me, what process is gonna save me.

But what we really need to do is try and your point there about root cause analysis, which is where is this stem from? Is there anything at the start of this process or related to this process that may be sending us off course that we need to solve before we stick a plaster over it and say that this is gonna be

[00:31:35] Tejas: the solution?

And that is the reason why I ask people, when you're looking for somebody, not even me, any consulting business to come and help you out on stuff like this, you should look at what, what are, what are the kind of questions are they asking? Okay, what is the level of questions are they asking? Are they only asking you questions?

Are they asking more than you questions? Cause realistically, a CFO director very respectfully knows there is a problem. They don't know the line item level D problem. Okay. They're not supposed to, they're not meant to. Hopefully they're not meant to. Their team is the one who takes care of all these things.

It's the, this morning we were talking to somebody and we were like, we can, we are not gonna do this with you. We would, let's put a discovery session together, get your key stakeholders, let's have this conversation together. Let's get a larger team and then have this conversation. Cause you're not going to solve this by asking one person that aren't all the questions.

Ok. And plus the frameworks that we use. For example, you look at, uh, you know, uh, uh, establishing Nalytics culture. There's a book by Andrew. Uh, he's a, he's a guy on LinkedIn as well, and he says it's a very nice book, boss. He also talks about people and process. Technology, and he talks about mindset, right?

Mm-hmm. That is, again, such an important element of thing. Look at your people, look at what mindset they have, and then see if it is just a question of retraining, reorganizing those people. Are they working with this strength or their weaknesses? There's a reason why I did, did GC index when I did, right?

Mm-hmm. I'm a geologist as well. I mean, I'm a finance person. To become a geologist, uh, is absolutely like counterintuitive, but for what I do, I need to be able to work with people and understand what are the motivators, you know, what gets them going, and otherwise I can't transform teams essentially, and I cannot transform stuff.

So it is about, you know, look at the frameworks and applies even independently if you want to do it. There are enough books out there. Be the five, you know, establishing a tic culture. You know, there are so many books on finance transformations and things like that. Read a little, okay. If you can't physically, like I am a very bad reader.

I can't read a book. I listen. Okay. Uh, I, I like to listen to things, so I'll do it on audible. Don't care how you do it. Do it and then see whether you can do it in house or get independent advice. One of the reasons where we, again, say that you should always get independent advice, which I'm sure you would agree with, when you go into a business, everybody has a hidden agenda, but they're all employees.

There is office politics going on. I want, and everybody wants. My process is per fact. I want everything else to change, but not my process. Okay. I literally just came off a call before this one where I was literally hearing the lady going on and on about, yeah, but this is how we do things. Yeah, but that's what you were doing for the last 10 years.

You can move, right? Things can change, but she was not willing to change. You could clearly hear it in her voice that she was basically like, you all have to adjust. I'll not adjust. And eventually we'll get her to change. But in your journey it's a, it's a challenge and we have fun along way. It's

[00:34:53] Adam: little, it's, it's little by little, isn't it?

It's, it's baby steps, you know, and going back to the point you made earlier, sometimes you've gotta slow down before you speed up and, and sometimes seeing is believing as well, you know? Um, and, and initially, you know, as you say, you dunno what the hidden agendas are. You know, you don't know what the, the personal personal challenges.

The personal ambitions are of, of every individual, you know, cuz obviously you can't get into people's heads. Um, And, and you probably find a lot of this in, in the, the BI space. With the bi work that you do, it only takes one example dashboard of visualization for people to change their tube. Um, and then that, that then creates a raft of other questions.

Oh, you mean we can do this? You mean we can do this? So my recommendation for anybody who realizes that something needs to change is that it's now a lot easier than it used to be to, um, spin up a free tool, you know, experiment some dashboards, and then then share that, you know, and, and that that can help.

Whereas previously, you know, the, you know, the cost of building some of these applications was prohibitive to the point where you did have to go through a, a, a real big change process. Now it's a lot quicker because you can realize at least some of the benefit early stages with some of the tools that that exist.

Right. Yeah,

[00:36:07] Tejas: so see, again, you make a be beautiful point about data visualization. So again, uh, I am working on a, on a reasonably large company at the minute, uh, and I was categorically told, and even previous one, like Cedia is on my, on my LinkedIn. So you look at city media, I categorically was told that there is a department in Cedia, which has, where the c the departmental CFO has made her own, basically dashboards her own Power bi and she will never change it.

Ok. You cannot even change one color on her dashboard apparently. And I was like, okay. I did not argue, said okay, I carried on doing what I did for the other department. I built the structure, but I always built it in a manner that we covered the whole business, not just one part of it. We did the whole thing, built what I felt added value.

I kept an eye on what dashboards she had, uh, Obviously took inspiration as I said, right? I learned I, there's nothing wrong in learning. So I took inspiration, built what I thought was good, got all of this sorted. The first department bought the new dashboards, which I put together. Then I set up a call with that, that particular department, the cfo, and I said, look, I know you have your own stuff.

I respect that. I built this. Can you please help me understand why this doesn't work? Cause mm-hmm. Clearly, right? You have your own stuff. Believe me. Counter to ev what every other advice I was given in that business. She like, tell us, why will I not use this? You save me the headache of maintaining all this nonsense by myself.

I, she was, in fact, she literally went the other end of the spectrum. She, like, I am the only woman in this entire finance. That department who understands what I have done, and therefore I have to maintain all of it. It's all the, everything is on my neck. Uh, and like, you know, I feel stress outta it.

Whereas now that you centralize this whole thing, I can benefit outta your reports having to worry about, you know, maintaining all of this. I'm like, yes, I wanted to have the fruits of the labor. I don't wanna do the labor. She like, bloody perfect. Yes, she had some questions. I want you to add these three calculations.

I want these two pie charts, or whatever else. That's her bloody right man. I mean, I mean, if I don't listen to that, then Im not a good business partner or good customer service person, right? I have to do what my customer asked me to do. I said, look, fair enough. Gimme, gimme five days, I'll deliver. Five days.

Done it. She's looked at it. She looked at my number, her number in a few months, blah, blah. Verified, picked and tied, audited my number. All numbers look the same. She's like, I'm not gonna use my reports anymore. I'm not gonna maintain them. This is good. Yeah. Cause I got basically scheduled reporting in Power BI in everything else.

Every three hours. Exactly as you said, via data and everything else. She like, oh my God, why do I want do this by myself? You don't have to.

[00:39:03] Adam: Yeah. Yeah. That's, and, and you know, ju just to emphasize again for people that, that are listening is obviously you, you and I work with businesses, um, but of course there's a lot of finance pros that work within businesses that obviously need to sell internally as well, but the principles are exactly the same.

You know, you've gotta win hearts and minds internally as well as, you know, third party consultants coming in to, to support you as a business as well. So I'll just, I'll emphasize that the seeing is believing. It doesn't take five minutes to, you know, create an example dashboard and then get feedback as, as you're saying there, whether or not you're an external consultant or you are already working in the business.

Right. But I'll, um, I'll tell a story, and I can't remember whether I've told this on the, on the podcast before, but, Um, with my business, um, I was tasked with giving a presentation on ai, uh, when chat g p t first came about. Right. Um, because it was one of those, and I'm always saying we don't have to get into the weeds, but obviously the level of AI we now have is, uh, a fundamental shift, right?

It's like, um, I don't think people are exaggerating when they're saying it's like a, another industrial revolution. Like we're, we're essentially leveling up right now because the, the, the ca capability is so vast, right? Um, so I did the presentation and, um, I used a slightly different tool to, to PowerPoint, so, so, um, PowerPoint obviously great cause we've got the, the themes and all of the branding and all the templates set up.

But I started the, the meeting by saying, I appreciate this is a bit off-brand, but you'll see why as we go through the presentation. So it was about, I know, 10 slides, something like that. And it talked through, um, changes were seen in the market use cases for some of these AI tools. Um, it had images in it, you know, um, the slides were in slightly different formats, you know, formatted according to what visually came across the best.

Um, and everybody was really obviously bought into the presentation. Um, and the second or last slide, um, was just a one bullet point that said 90% of this presentation was created using ai. And that, that was kind of that aha moment because all of the people in the room that were thinking is a gimmick.

You know, we, we'll never use this. There's no applications for this. It's too early. Immediately saw, right? Well, in 30 seconds somebody's managed to create a presentation using ai. That was the equivalent of anything else that would take me an hour or two hours to do. And the thing that was that it wa the thing that's scary actually, is it, it wasn't just the visual and the slides that were done during using ai.

Um, I used chat gpt to create the agenda or the outline of all of the slides. Um, and then I fed it into the tool called Gamma. And there's loads out there now, I think Gamma app. And I've done the LinkedIn presentations about it, but it did the text and the bullet points as well. Yeah. So, so, so not only did it do the presentation out outline it did all of the, the text as well.

I only had to strike through three points that were incorrect. And I left them in, crossed out as a, as a like cliffhanger to say, you'll notice they're crossed out. But I'll tell you why that is at the end. Right? Even the images were AI generated, right? Um, so,

[00:42:04] Tejas: so this is beautiful. Now again, so to the data visualization point, like people always ask me like, why only Power bi?

You know, why not something else? Microsoft invest so much in Power bi. Power BI has AI as well. It generates insights for you. It creates commentary for you. It's not always right, exactly as you just said, three bullet points needed to be canceled out. I am not saying it's perfect, but hey, given where we were say 10 years back or even three years back, it's, it's miles forward to where we were, right?

And it starts showing you stuff like, okay, you know, did you realize that your vendors, these are your top 10 vendors, or you know, these are your top five vendors or this department are spending like, Uh, like one of the projects I'm working on, the CFO statement was, I didn't know this department uses Microsoft for, uh, data, data space separately.

So there was this one department buying Azure space by itself, though, they had corporate licenses for the entire thing. So this is basically wasting money essentially, right? Oh, wow. I did not, obviously, I mean, come on. It's a very big business and I don't know the name names on the, on the show, but it's a very big business, so obviously I don't expect him to know every line item, every vendor.

So he would, people would see Microsoft and they would just pay it and blah, blah, but this is duplication of cost. Trust me, by that one dashboard we saved, saved him a few hundred thousand. He's like to stop. Why the hell doing call right now? Yes. And AI can do stuff like that. It'll help you see stuff which you'll not see.

Yes, there are certain things which as a human mind, you will co be able to correlate. And then there are certain things you cannot correlate. Okay? And AI can help you with stuff like that. Uh, it is about applying common sense though, okay? Mm-hmm. This whole, uh, other side of, on LinkedIn of AI gonna take over the world, it's not mm-hmm.

Somebody at the minute. The AI is very, very smart, but somebody still needs to comment, sense. Check it exactly as you did. You read it, you understood it, and you said, okay, these three bullet points don't make sense. I am gonna cross them out, but they don't make sense. So somebody will have to do that, okay?

And that means somebody will need to understand what that AI is telling you, and therefore, we need to keep being smarter than ai. Otherwise they'll not understand.

[00:44:30] Adam: Yeah. And, and, and as always, as we always say, you know, the, the biggest missing element in AI at the moment is context and the training piece.

And it will get there, I'm sure. But, um, yeah, the, the domain expertise and that, that niche insight that a human takes, you know, 20, 30 years to build at the moment, and I say at the moment, can't be emulated by ai. So that there is a sense check. But just wanna go back to the point that you made there about, um, Basically spotting something that a human then went on to action, right?

And I, and I think we need to focus on that. So previous podcast guest was, uh, Tamer, c e o at causal, who, who do a modeling platform. Um, very, very, very good insights from, from that session. And, and his, his viewpoint is that, uh, traditional AI in the form of, you know, machine learning and the stuff that's been around for years is good.

You know, um, it can provide predictions, you know, based on these scenarios. It could be that, you know, this is gonna be your revenue next year based on these variables, or here's an outlier because it doesn't follow the trend of the previous data, right? But his point, and I think he's dead right, is that it still takes somebody to go in and decide what's actionable and what's not.

So in that example that you gave there, the breadcrumbs were there, you know, why is the amount here bigger than the amount over here? But then the human goes in and says, actually they're pretty much the same thing. So the action from that is we can then reduce that, that spend and that is

[00:45:56] Tejas: it. Right? So, so that is the reason why when every time when somebody says on LinkedIn that, you know, AI is gonna take over the world or whatever else, or all finance jobs are gonna lose to ai, I don't think so.

If your job is to key in numbers in a system that might go, okay, yes. But if you are doing something, again, what I tell my Steve all the time, sorry to, sorry, to stop in between you get paid to think you don't get paid to do. If you are getting paid to do today, you're gonna be in that. Mm-hmm. But if you're getting paid to think, to decide to analyze and understand your job is as safe as it can be.

Okay. And Cause yeah, you think AI will show you stuff, it'll show you the correlations, regressions, very, very smart, you know, commentary and stuff like that. But exactly as you just said, somebody will have to read it, understand it, process it, and then take action out of it. And that will still have to be a human.

We will not automate that just yet. IBO is not coming just yet. It might come in 20 years. I don't think it's coming today. Yeah.

[00:47:05] Adam: And I, I think we'll get to the point where the, the AI will start getting better at highlighting what is and actionable. But I think the decision piece is the, is the crucial bit, you know, so, so here's three bits.

That you need to focus on. Here's some potential actions that you could take. What do you wanna do? And, and this is, this is the whole concept of, of AI as a co-pilot, right? You know, and again, Microsoft building it into Microsoft 365 with, you know, their, uh, G P T based, uh, open AI based, uh, co co-pilot there.

So I really hope, um, And this is what my new tagline's all about, right? Turning systems into su superpowers, you know, helping finance teams turn, uh, systems into superpowers. If you can get the right blend of human competencies and tech technological competencies, I, I think you're onto a winner.

[00:48:01] Tejas: Exactly.

But that is the thing, the keyword there is the right. Blend. Right. It is a blend. It's not a replacement of each other. Okay. Uh, and obviously the taxes. Taxes always helped you. There was a time, right, 30 years back or 40 years back, so the listeners will now call me old, but they were doing basically pen and paper accounting.

There was no, there was no laptops. Right. Computer was not, was not existent. Even Excel or DS Excel came in nineties. Correct. So realistically, calculators replace pen and paper, Excel replace calculators. Tomorrow something else will replace Excel. And whether all the Excel levels out there, I'm sorry, but it'll get replaced eventually.

Whether you like that answer or not, it'll get there. It might take 30 years to get there. Mm-hmm. Right. That's just life. It's about, I think, human spirit of being able to adapt to that new changing environment, which is a winner. Cause we, humans are basically the biggest, uh, basically, you know, parasites on the planet.

We know we know how to adapt. Okay, we'll kill what we need to kill to survive. So, yeah, yeah, yeah.

[00:49:10] Adam: And yeah, but before it escapes my mind, and, and again, I think this, this is very relevant. Um, it's why I love these conversations because so, so many cogs end up wearing. So I've, um, I've spoken before about, um, notable, uh, N O T E A B L E I think.

So note able, um, and I found out about it because it's a, it is a chat G P T plugin. It existed before they created the chat G P T plugin. But it's, it's basically, um, Visualization and, um, coding tool essentially, you know, so, so you can upload data or connect to data, and similar to Power bi, um, you can use code to, to basically generate visualizations and, and query your data, right?

But instead of like in Power bi, whereby you've got your q and a right, where you go in and say, show me X over Y or show me this axis. And this axis, um, is a little bit different because you ask chat gp, PT the question chat, G P T then creates the code to query the data. So it's not, it's not quite as succinct as just doing a data q and a with the likes of Power BI or something similar.

But, but the reason that I'm mentioning that comes back to that human interpretation because I was doing some analysis of some data, um, and this, this was call data, so not, not a, a direct finance use case I guess, but I had one and a half thousand calls, um, and I needed to d determine, um, which calls were connected at what times, because the action that came off that is, Should we be making calls at times where there are more connection rates?

So logic dictates yes. Looking at 80 20, you know, you want to be trying to contact people when they are more susceptible to, to answering the phone, right? So, so I ran this through notable, and it was literally raw data, so it was a call date, call outcome, you know, connected voicemail, so on and so forth. Um, and then there was a, a couple of other parameters in there.

Now it was really badly formatted data, right? So, so the, the date and time, so it was date and time. So if I just asked it outright, you know, tell me, um, the best times to connect with somebody, it had come back and say in a regular scenario, I can't understand that column because it's a combination of date and time information.

But in having the chat interface. Chat, G B T and notable were clever enough to say, right, well that data's not gonna work, so I need to create extra columns to separate the date and time. Oh, wow. And it generated, it generated the code to do that for me. So then I just needed to ask, right now, I've got a time column that you've separated out for me.

Show me those stats. But the, the reason for the story, apart from the core stuff that you can do with chat gpt and notable is it produced a visualization, which was weekday am pm Yeah. With a, with a biograph, very simple bio graph. And it was very clear that, um, mornings had better success rates. Yeah. And that Mondays, Wednesdays, and Fridays had the highest percentage connection based on total cause.

Right Now I then asked it to produce a written commentary of that graph, and the written commentary it produced was incorrect. Totally incorrect because I said probably my prompt was wrong, but I said, look at the graph and provide a written commentary, but the AI can't look. Do you see what I mean? Yeah.

So it was trying to look at something and it was producing incorrect results. If I'd said, do a written commentary of the data, then I probably would've got a better result. Yeah. But I think that's one of the limitations that we have to bear in mind. You know, you need, still need to ask good questions whether you're speaking to people or whether you're talking to ai.

Um, and we need to be mindful that AI doesn't see things in. Pictures like we do some AI do. Right? And with chat GBT four, if you use the api, it can be mo multimodal where it recognizes images, right? But before that, it's still text-based. So I just wanted to emphasize that where we are at the moment, unless you are using some of the really advanced stuff, um, there is still a limitation with what it is able to see.

So that human interpretation of the visual is still very relevant.

[00:53:11] Tejas: Yeah. No, but that is it, right? So this is where the fact that you were able to read it and whatever the commentary came out and say, yeah, no, but this doesn't make any sense. So that sense check was needed and, and that is the reason why you a human is still important.

And second is the fact that exactly as you pointed out, ask the right question. Mm-hmm. And those are skills with us as humans need to maintain, whether you're doing it with Excel or whether you're doing those. Core skillset. I don't think honesty are changing. Uh, now been in the industry for 20 years.

2020 years. Right. And I don't see that changing over time. So

[00:53:54] Adam: yeah, I, I think you're absolutely right. You know, and again, coming back to, to baby steps, you know, you don't have to master ai, you know, in a day it's, it is gonna be baby steps. And, and the recommendations that I make is, you know, and, and I talk quite a lot about P Power BI, to be fair, because it only takes uploading a CSV into click on the little insight button, and already you, you're halfway there, right?

But with some of these other tools, there's no end of tools now where you can literally upload, um, a CSV and talk, talk to the data. So, so start there and, and that will build your skills in asking better questions of ai because I think if we are focusing on skills to develop, we need to develop obviously the, the soft skills that enable us to be better communicators and better business partners.

As we were talking about earlier. But we also need to have in mind that the, the better questions that we can ask of these new tech tools, especially if it's a chat interface, is also a skill to be built

[00:54:44] Tejas: as well. Yeah. But see, again, on, on the other side, these are individual skills that individuals can build for themselves.

But for any finance leaders that are listening, they would, they should also look. Think about scalability of whatever solutioning you are trying to do. So like doing this as an individual on my, you know, on my spare time on a Saturday is a different thing as compared to scaling it for a 500 million business or a 500 million business or a no multi-billion dollar business is a whole different ballgame, very respectfully.

And that needs to be played differently. Understand. So that, again, that is when, even with Power BI or with any other AI that businesses try to implement, I always tell them that guys play with it. And I use, actually use the words, play with it. So the, the Group F guy, which I was talking about, you know, he takes this now by power, be a dashboard to every meeting.

When I trained him for three, three different sessions that I showed him, the dashboard I built, I said, look, this is how you can do this. This is how you can do that. This is where you've got filters here, you've got filters there. This is how you can make it bigger, smaller, whatever else. The keyword I kept telling him was, Please go and play with this.

Come back to me with more questions. Break it. I challenge you, break it, but come back to me with more questions. Cause that when finance leaders start encouraging their teams to ask questions, be open is the time when you will have really highly efficient and effective finance functions. Okay. Yeah. Don, whatever, right?

That whole open door policy is a myth. Okay? Just because you keep your door open doesn't mean that you're a friendly finance person. It's not, okay. Sorry, but that's DS, and I'll call it fps. Okay? It's about genuinely being open-minded. Open-hearted with your people, support them when they fail, help them to learn, invest in their development, and suddenly you'll then have your finance teams bring solutions to you.

One of the things that we used to do at GE again, was the fact that in every team meeting we would put up a problem and I wouldn't solve it. We would solve it. There's a difference, and I almost certainly in most meetings, try and hold myself back that, okay, for 15, 20 minutes, let the group break their head, let them come up with something.

If they can't come up with anything, and if I have something in my back pocket, I will pull it out. But I will try and get people in that uncomfortable silence position and say, think. You don't get paid to play. You get paid to think you're a finance charter. Think and there were interns there. There were experienced people there.

There were everybody. We would all sit seven, eight of us. I mean imagine seven, eight finance people at GE Healthcare. GE doesn't hire stupid people. So you know, you sit there and you think, and we come on. If such seven, eight intelligent people sit in their home, they should be able to crack more stuff.

Right? It's not that difficult. Yeah. It's about developing that open culture and behavior in your business. Then if you are looking to implement ai, E R P or anything, nothing can defeat you. The problem happens is you have hired all these people and you're telling them what to do. Okay. Richard said you hire intelligent people and to, for them to tell you what to do.

Hire intelligent people and tell them what to. Yeah, there is what, what do both of us do? We hire them when we tell them what to do. That doesn't work, man. Yeah, yeah.

[00:58:24] Adam: No, that's, that, that's it. And, and again, to, to put the tech spin on it. Um, so yes, your, your job is to think and to get other people to, to think.

Right. Um, and to come back to previous examples where, you know, you said some people work in it in a certain way. Right. Um, and I just want to enforce what you said there about having a play with things. So in the same way that I had a bit of a play with AI and then is pricked up when I presented it, it's a good way to build influence and credibility in an organization because whether it's something like notable Power BI or whatever, they, they're free to start.

Right? And, and, and if you can present something with a tool that is not familiar to the business in the space of however long it takes you. The question is then how have you done that? You know, we need to listen to this guy. This guy or girl knows their stuff. You know, so, so I think there's a lot of power that comes with being able to, to quickly utilize a tool, even if it's just playing and even if you don't use it forever, as you say, because to get a whole business using this sort of stuff could be a, a challenge, right?

But there's nothing to stop you providing. It's within your company guidelines. Right. And, and I'll always say, if you are experimenting with a disclaimer, I'll get closer to the mic here. If you are playing with AI tools, read the data protection policy. And anonymize data wherever you can, because even if they've got an ironclad data policy, there's, there's still no guarantee.

You know, even last pass, you know, as one of the best password managers that I use, for example, I get, I won't say frequent, but maybe once a year I get an email from Last Pass saying, just need to inform you of a data breach, right? So if you can anonymize the data and still get insight from it, it's better.

So I'll go back to that call data. The original data had caller staff member information on there, but it was anonymized, so it just came up as a number. Yeah. So I can still search for it because I can search for that number when the results are spat out. But to the platform, it's, it's not, it's, it is unusable.

There's no personal data, there's no company data there. Do you see what

[01:00:26] Tejas: I mean? Yeah. And, and see what you've just done there. And what you've just talked about is understanding data and having a data send data sense. Most people don't have any sense about data. They just think it is information and they can dump it.

They don't realize the impact it has and hence it. Very careful. Read and read and read and listen to people before you go around, especially with the corporate data and play with it. If you want to play, I, I can tell my guys, go Google up some free data, which is freely available and play with that data.

Cause that data, frankly, is nobody's data. That's just dummy data. And today, today's day agent time to get dummy data is not very difficult. So if you wanna have a play, don't play with data, play with dummy data, uh, or don't know. Play with your complete data data and figure that out. Otherwise, youll get yourself into legal trouble, which very frankly, I don't think Adam.

[01:01:32] Adam: Yeah, no, no, no responsibility for anybody being silly with data. Know that that for sure. And, and just while we're on that subject as well, um, so I'm obviously a big fan of chat, G P t I've, I've got the pro version, um, cause it gives me the plugins and, and all of that sort of stuff. Now, um, in the guide, um, ultimate GT G GPT framework for finance, shameless plug there, um, there is a, a beginning bit on data.

So, so there's an opt out on open AI where you can essentially say, I don't want this data being used to train the model. Now they've since improved that. If you go into the settings and chat gpt, there's now a privacy area. Dunno whether you've seen it, but it's cheeky because if you turn off the model training, so if you say, I don't want you using this data, you can't use the chat G B T plugins, you, you can only use the, the standard model and you lose all of your chat history.

You see what I mean? So, so there's no way of turning off that training and, and, and data analysis to open AI take whilst using some of the advanced features. So I dunno whether it's deliberate or not. It's probably because if you are using uh, G P T plugins chat, G B T can guarantee that the plugin that you are using isn't going to do things with the data.

So I think that's the logic there. But yeah, I just wanna remind people that there is a setting in chat GBT where you can go in and turn off, um, the training model data. So don't forget

[01:02:51] Tejas: that as well. Yeah. So that is the reason why very respectfully, Adam, yes, it, I can clearly tell that you are as passionate about chat gpt as I'm about Power bi.

Uh, but yes, the fact is that is the reason why I, I am of the individual opinion. And again, there will be a lot of, this is a controversial statement, uh, coming up. I think AI is the future, but it is at the horizon if that kind of makes sense. Now the sun has risen, it has come. Mm-hmm. Wrong way in last five years compared to last five years back and stuff like that.

So yes, the sun has definitely risen. We can all see the race and we are all impacted by the light, but it's not there in our day-to-day lives yet. And I use the word yet very, very firmly. I'm not saying it's never going to be there, but I don't think it's there yet because exactly. As in your example that you gave earlier, right?

About your called dataset, if the data is cleaned, then the, the, the, the, the, the AI needs to first set a program to clean the data or organize the data before it starts giving you insights and stuff like that. And for that, you would've had to ask the right question to the chat GT for it to be able to do that.

And you know, things there and very, especially there's just four or five column data that you, it sounds like you had, whereas when you normally go to corporate level, right, you are talking multiple tables and multiple data tables and multiple stuff like that. So again, right now we are having a conversation with one of our clients and there is an AI team competing with me on EPM and me with EPM and Power bi and Power bi, whereas they have got AI and they have come with, oh, we have 98% data accuracy.

If. These, these, these, these, these conditions are met. I'm like, mate, it's like, you know, a car company saying if there's a glass roads and the temperature 38 degrees and blah, blah, blah, then my mileage is, you know, that's bullshit man. Real life work like that. Right? Real life is real life and, sorry, but I live in real life.

I work in, I, cause I've got to where I've got to, I have data entry. Right. So I understand the journey and I'm like, wait, you are Yes on a board presentation or on a, you know, on a forum. It sounds beautiful when you put up those slides tomorrow as SP and a, I can't use those that forecast and say, okay, that can use this as a forecasting tool.

It doesn't work. Okay. You cannot say that you are taking 60% of my data and you are percent of my data. You're forecasting it. 90%, 98% accuracy. Yeah. Worth lot. My business that cost 4 billion, if you can forecast efficiently, very good. What do I do with billion? I'm still but screwed, right? That ain't gonna, so like, that's like me flipping a coin and saying, oh, I'll get heads.

Yes, you'll get heads half the times, you know, yes, stop clock is right twice a day. So that's not the way to look at it. So yes I am, I do keep an eye out on these technologies as they come along, but when I talk about. Sta I am slightly much more still, uh, machine learning, RPA automation space because that I think is where rubber has yet to still completely meet the road.

Okay. How many businesses today are still doing reporting in Excel? Okay. Mm-hmm. Right. In uk, UK itself, forget, zero, forget the word, right. How many businesses are still doing basically, uh, consolidation in Excel. Okay. How many businesses are doing budgeting in Excel? I talked of Unilever doing budgeting in Excel.

I mean, Unilever. Okay. Like it doesn't get bigger than that. Yeah. Right. So, so all I'm trying to say is in the tech space, there is still a long journey to go for Excel forecast, manual forecast, disparate systems to still do the automation organization journey first. Because then is when you can then really leverage AI and do something really cool with it, right?

But that J and by that time, hopefully this whole data privacy issues and the data issues within AI as well, and I know in the US and stuff like that, in Europe as well, they're talking about regulation of AI and things like that. So that should evolve in next five odd years by the time businesses can do this.

Because otherwise, you know, I could even come to me and say, I want rpa. Why? Right? Mm. No. Cause everybody else has it. That's nonsense, right? Everybody, why everybody else has it is irrelevant. What are you trying to solve? How does that help you? You know, those basics don't change. So get, go from your manual, disparate, uh, processes into a structured, automated, you know, process.

And then think about, okay, now I'm here. What's my next frontier? And that might be ai, that might be company-wide, you know, talking something more, uh, advanced or sensible. But you cannot, like, you know, I can't have my six months old or one two year old and tell him to go and get my levels. It's not, it's not gonna work.

You have to go through that journey and that journey, that maturity journey, the data maturity journey, data, policies, all of that needs to happen. Otherwise it didn't gonna work.

[01:08:07] Adam: That. Yeah. Yeah, yeah. You've gotta start at the beginning. You can't, you know, sometimes you can shortcut when it comes to these fundamental processes.

You can't, you know, um, there might, you know, shortcut is different to a quick win. There might be some things that you can do to, to get a bit of an edge, but it won't change the fact that good people, good data, good processes, the foundation of any other shiny tool that, that you put on top of it. So, no, it's perfect.

It's, it's good stuff. So we're, we're coming up to time. Um, Ted, we, we've already overrun cause it's been such a good conversation, but I'll ask you the question that I ask everybody before we sign off. So the show's called Tech Refinance, and this can be either in your personal or your professional life.

An app gadget software tool, and you can't say Power bi. Um, why not, not, can't say a smartphone. Why not? Because we know Power bi. We know Power BI is already at the top of your list, but it, it could be an app on your phone, you know, it could be a, a Google Chrome plugin. You know, just an app tool or gadget that you literally use every day that you couldn't live without.

[01:09:16] Tejas: Yeah, but then I, I don't wanna say something like WhatsApp because that's very lame. Uh, no, I'll still say Power BI because there is fine, literally, genuinely, I go and do Anaplan implementation. I do, and I implementation, I do a lot of E P M implementation as well. Right. Or ERP implementations alongside that.

It's like that, you know, it's like Korean on a, on a, on an Indian curry. It just goes with it, right? Indians on the call will know what I'm talking about. You take any Indian or any Indian dish, you put Korean top and it just works. And

[01:09:53] Adam: that's what Yeah, but it's gotta, it's gotta. It's gotta be fresh coriander though.

None of this dried stuff. No, no, no.

[01:09:59] Tejas: Fresh, fresh, fresh.

[01:10:01] Adam: Homegrown cups. Right. That's a great

[01:10:03] Tejas: place to start. Power BI is my coriander. Everything.

[01:10:10] Adam: Where can people find out more about you? Te Jess?

[01:10:12] Tejas: No, LinkedIn. I am on LinkedIn pretty much 24 7. My wife makes fun of me that I'm like a small girl on Instagram, but uh, I am on LinkedIn all day. Uh, and you'll hear from me on at odd hours on responses because I definitely live on

[01:10:30] Adam: LinkedIn. Fine. And, um, you are also, uh, now a Gartner Peer Community Ambassador as well, aren't you?

[01:10:37] Tejas: I am. Very much so. Very, very honored to share stage with people like yourself and Chris Ortega and, uh, you know, everybody great out there.

[01:10:46] Adam: Yeah. And, and uh, to be fair, it's, I haven't given them a shout out yet. Um, cuz I'm still finding my feet a little bit for, uh, with the platform, but, um, Gartner Peer Community is free.

Yeah. And, and it's, it is basically, um, like-minded individuals. I mean, te Teds and I are part of the, the finance community. So obviously if you work in finance, join the finance community and it's just an opportunity to ask questions, share ideas, and just connect with industry leaders. So, you know, it's kind of more like a hyper focus version of LinkedIn, I guess, um, related to certain topics.

So yeah, check out the Gartner Peer Community, connect with Ted Jas and I on there and, uh, yeah, we can start another conversation there too. But yeah, no LinkedIn. I'll, I'll put your link in the show notes ess and, uh, yeah, just finished by thanking you for coming on. It's been a really, really good conversation.

[01:11:31] Tejas: Thank you for having me. Thank you so much for listening to me and I really hope we catch, uh, online and offline again. Yeah, absolutely.

[01:11:38] Adam: Thanks Tejas see you a bit. Cheers. Cheers.