The Human Side of AI Enablement: Leading an Organization Through the Change Nobody's Ready

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Transcript

[0:00] Host (Rajnish Ranjan)

Hello, ladies and gentlemen. Welcome to another episode of AI Machines and Marketer.

Today’s conversation is about the part of AI transformation that isn’t widely talked about, but that everyone is trying to make work. It’s an open secret that in marketing organizations, leaders are approving budgets, buying sophisticated tools, building AI strategies, and declaring their teams “AI-ready.” But in most cases, these initiatives fail to even move beyond the pilot stage.

Let me be clear: the problem isn’t the technology. AI enablement has never really been about buying more tools or automating more processes — it’s about changing the way people work. It’s about trust, adoption, acceptance, new challenges, and fear. Yes, you heard that right: fear that technology will replace the very people being asked to embrace it.

That’s what today’s topic is: the human side of AI enablement — leading an organization through the change nobody’s ready for.

Quick disclaimer: this isn’t a product pitch or a theoretical debate about what AI can or can’t do in the future. It’s an honest conversation about what happens when AI is introduced into a real team, a real pipeline, a real workflow — with real outcomes.

Joining us today are two people who’ve led this change in their own organization: a CEO who championed the idea, and a leader who actually brought it to life across product, digital, and data teams. Please welcome Mr. Taran Nandha, CEO of Growth Natives and DiGGrowth, accompanied by Mr. Arpit Srivastava, Vice President of Growth Marketing and Analytics at Growth Natives. Welcome, Taran. Welcome, Arpit.

[2:06] Host (Rajnish Ranjan)

Taran, this is your first time on AI Machines and Marketer — how are you feeling today?

[2:12] Taran Nandha

I feel energised.

[2:17] Arpit Srivastava

I think this one’s special for me, because sharing this frame with Taran means a lot — he’s been like an elder brother, a mentor, an investor, a boss, so many parts of my life rolled into one. So this is going to be a powerful conversation. We’re looking forward to it.

[2:39] Host (Rajnish Ranjan)

Let’s start with you, Taran — what did you realise, and when did you realise, that becoming AI-ready was more about people than technology?

[2:52] Taran Nandha

Any change you bring into a business has always been more about people than about technology — that’s not a new concept, and it didn’t start with AI. Any time there’s a tech revolution, any time there’s a giant leap forward, people have always been the enablers of that change, because at the end of the day, it’s the same people who have to adapt to the new way of working. So it’s less that I “realised” it at some point, and more that I always knew: if we were going to embrace AI, we first had to make sure our people were ready to embrace it too.

[3:43] Host (Rajnish Ranjan)

What’s your take on this, Arpit?

[3:45] Arpit Srivastava

I completely agree. From a technology standpoint, we were actually ahead of the curve — we had an AI team before AI became fashionable. So in some sense, the technology side was already in place. From a people standpoint, the mandate came directly from the CEO, and that was the push we needed. That’s generally how these things work, and it worked for us.

[4:12] Host (Rajnish Ranjan)

So where did resistance actually show up — across product, digital, and data teams?

[4:19] Taran Nandha

I was enabling the change from the top, but it was people like Arpit who were actually leading these teams day to day — making sure adoption wasn’t just a checkbox exercise. He was the driving force, he was instrumental. Honestly, this question is really more for him — what kind of resistance did he run into?

[5:00] Host (Rajnish Ranjan)

This one’s for you, then, Arpit.

[5:03] Arpit Srivastava

I won’t point to anything specific, but in general, any change comes with resistance — that’s just how we’re wired. You have to accept upfront that change brings repercussions and pushback, and that’s exactly what we faced. For example, our brand team’s focus is on consistency, voice, and positioning, while our SEO team is focused on discoverability and traction — and there was real disagreement over whether and how to use AI. So what we ended up doing was letting some content be geared toward brand and the rest toward SEO. As we produced those pieces, we analysed the data and used it to decide what was actually working — and that’s how we brought the two teams into alignment.

[6:04] Host (Rajnish Ranjan)

So how do you build trust in AI before asking people to use it for important, high-stakes work?

[6:12] Arpit Srivastava

Adopting AI, or getting an organisation to actually follow through on AI enablement, is a lot like a major product release — you can’t just declare it’s in production. You need environments like staging and UAT, and there are cases where you run the old and new systems in parallel and see what’s actually working before you commit fully. That’s exactly what we did. It wasn’t imposed top-down; it was a gradual, step-by-step process. We identified people who were a little ahead in AI skills and curiosity, and they became role models for the rest of the company — three or four people across departments who were slightly ahead of the curve. They built a roadmap and came up with use cases that showed instant results, and that’s how we got traction from everyone else.

It really comes down to experimenting with new ways of doing things. Even without AI — say you’re moving a team from Google Docs to Microsoft Word — there’s always that experimentation curve: things you like, things you don’t. People are used to doing things a certain way; you show them a new way, let them experiment, and pretty soon they realise which way works better and take it forward themselves. You don’t have to force it down anyone’s throat — you just have to make sure they’re experimenting.

[8:32] Host (Rajnish Ranjan)

Taran, as AI takes on more of the work, how have people’s roles changed — and how are they perceiving that shift?

[8:33] Taran Nandha

That’s one of the biggest things — people have to come at this with a genuinely open mind. With AI, what used to happen is you were good at certain things and you stayed in that lane. If you were on a team building web applications, you had a designer, a developer, someone doing content, someone doing UX. Now, with AI, a good designer, developer, or content marketer can do all three or four of those roles by leveraging it. So people have to get out of their comfort zone and stop thinking “I only do design” or “I only do development” — because that’s no longer true. That’s the biggest shift they need to understand: a lot of roles are collapsing into each other, and people actually end up feeling more empowered because of it.

I think that’s one of the reasons we’ve seen strong adoption — people feel more empowered, there are fewer barriers. Earlier, if I asked a project manager to get a website live in two weeks, they’d have to coordinate with six different people. Now it’s down to two or three, because a lot of that work can be handled by fewer people — and the fewer people in the mix, the less complex it is to manage. So there’s real efficiency that comes from multiple roles being handled by a single person in the age of AI.

[10:38] Host (Rajnish Ranjan)

So — my next question to both of you. Since one person can now do multiple jobs, how do you tackle the fear that AI will actually replace people’s jobs?

[10:51] Taran Nandha

I’ll be honest — AI is going to take away some jobs. Anything that brings efficiency means fewer people are needed to do what’s become more efficient. But at the same time, it creates new opportunities and entirely new types of jobs that are just as important, if not more so. So the overall impact on the workforce isn’t as simple as “job loss” — certain roles will become redundant, but new ones will emerge. What used to take five bookkeepers might now take two, but on the flip side, there are new opportunities in financial analytics, in data science, and in the implementation and use of AI itself.

The parallel I’d draw is the internet economy: people thought the world would go paperless, but every household ended up with a printer. Printing companies didn’t go out of business — they’re booming — and neither did paper companies. We’re still far from a paperless world; every classroom, every home, still has a printer. Those predictions were just notions; the real impact takes time to play out. Same with brick-and-mortar: yes, some stores closed, but for every five that closed, ten online stores opened, and that spun up an entire logistics revolution — millions of people now work in delivery and supply chain because of online retail. So if one area goes down, another comes up, and we’re seeing exactly that with AI.

Things that weren’t possible before AI are possible now — crunching a large dataset used to take weeks or months, now it takes days. So people who used to shy away from analysing data are analysing more of it, getting more insight, and taking more action. I don’t think the net impact is going to be bad for people overall — but certain roles will become redundant because of it. Sorry for the long answer.

[14:04] Host (Rajnish Ranjan)

No, not at all — that answer gave me real highs and lows. So on one hand, people may lose jobs. On the other, there are new opportunities — you just have to stay vigilant about it.

[14:16] Taran Nandha

Exactly. You have to be ready to adopt the new opportunities as they show up — adoption and disruption move side by side, and we’ve seen that play out in our own company. Roles are becoming redundant, sure, but tasks that used to take hours or days can now be done in minutes. Take a typical product manager: they used to spend a lot of time on product requirement documentation and managing Jira. Now, we’ve moved those same people into more client-facing work as well — someone who was purely a product manager on the data-and-AI side is now facing clients directly on analytics delivery. So think about it: they’ve actually moved up the value chain, with no negative impact on the company. It really just comes down to how you see the glass — half full or half empty.

[15:23] Host (Rajnish Ranjan)

Half empty, noted. So — where do you let AI decide, and where must a human stay involved?

[15:31] Taran Nandha

The human absolutely must stay involved. I haven’t seen a single process yet where AI runs entirely on its own — keeping a human in the loop is critical, and how much depends on the process and what you’re trying to achieve. But you can’t let it run completely unsupervised. You need governance, checks and balances, in place. At the end of the day, there’s always a human who pushes the “go” button. Even machines need people to run them.

[16:09] Arpit Srivastava

I think it really comes down to the stakes involved in a given task. For something small and repeatable — like updating a contact — it can be fully automated. But where the stakes are higher, like building rapport with a client, you definitely still need a human in the loop. We’ve got live examples of that too — you’re actually one of them, Rajnish. You used to spend a whole day writing a single 500-word piece of content. Now you can do that in two to three hours, research and drafting included — and that’s exactly what’s freed up the time for you to host a podcast like this one.

[17:14] Host (Rajnish Ranjan)

So — what did you actually measure to prove people were adopting AI?

[17:23] Taran Nandha

Like I said, it started with the mandate to move in this direction. From there, it was about experimentation — what tools people were using, and what impact that was having on overall efficiency. The approach is simple: don’t overcomplicate it. Make sure that wherever AI can be used, it is being used, then look at the before-and-after, the actual cause and effect, and decide whether it’s worth it. There have also been times we realised humans are more efficient than AI, or make fewer errors — in some situations, AI genuinely gets more wrong than a person would. Where the human matters more, we lean into that; where a task can run more independently through AI, we shift the balance that way.

[18:29] Host (Rajnish Ranjan)

Last question of this segment, starting with you: what’s one thing leaders and teams should do this week to actually get started with AI transformation — to move off static, business-as-usual work and make a real shift?

[18:49] Taran Nandha

Mainly, it’s about understanding everything a team does — what part of it can actually be done by leveraging AI — and then understanding what it will take for AI to succeed there. The mistake I’ve seen consistently with our customers and partners is this: they read a case study about some company automating accounts receivable with AI, and they assume they should do the same. So they hand their team a mandate: “go automate accounts receivable using AI.” What they don’t stop to ask first is whether their company is actually ready for that. Is the data organised? Is accounts receivable data in a state where an AI agent can actually take action on it? If it’s still sitting in paper stacks, scattered filing systems, or a patchwork of spreadsheets, you’re not ready to put AI on that function yet.

So that’s the number one thing — it’s not just about what can be automated, it’s about what part of your business is actually ready to be automated. If it isn’t, your job is to make it ready first. That’s where we see a lot of customers and partners spending real time: realising there’s genuine value in AI, but that a foundation has to be built before you can capture it. Any leader pushing AI needs a way to actually measure how ready they are.

[21:00] Arpit Srivastava

And that foundation point is key — a big part of AI success just comes down to how clean your data is. If it’s not clean, and you’re already reaching for the next shiny AI object, that’s a recipe for disaster.

Going back to your earlier question on measurement — if you really want to know whether it’s working, you have to look at the time you’ve actually saved through AI automation, and what you’re doing with that time. If work gets automated and people aren’t putting that saved time toward anything additional, it’s not a gain, it’s just added cost. We’ve seen real examples of the alternative in our own organisation, where people have stepped up and expanded their scope — our digital analytics team, for instance, can now deliver implementations much faster, so we’ve expanded that same team into new service lines as well. In the same spirit, a product manager took on more client-facing delivery work, and a content writer became a podcast host. Those are the real markers of AI’s impact — not just hours saved, but how you’re moving your people, and the value they’re creating because of it.

[22:44] Host (Rajnish Ranjan)

That’s a great place to close this section. Today’s conversation made one thing very clear: AI transformation doesn’t happen because of the platform — it happens because of the people. It needs trust, clear leadership, practical support, and a vision that understands this kind of change can’t be forced. Thank you so much for joining us today, Taran. Thank you, Arpit — the pleasure is all ours. And thank you to everyone listening. I hope this discussion helps you approach your own AI transformation with a bit more clarity and confidence.

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