Zoho On Balancing AI Innovation With Trust, Control, And Digital Sovereignty

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[00:01:13] And AI is moving into almost every part of the business now, from customer service and CRM to finance operations and just about every internal workflow you can imagine. But as adoption accelerates, so do the questions around trust, data privacy, digital sovereignty, vendor lock-in, and whether an organization truly understands the systems that they're putting into production.

[00:01:42] And my guest today has got a fascinating perspective to this conversation, because he supports around a million businesses globally, including tens of thousands here in the UK. And he's been leading Zoho's UK expansion at a time when cloud fragmentation and AI governance are becoming board-level conversations. So today I want to discuss how businesses can embrace AI responsibly without creating black box systems they cannot explain.

[00:02:12] And learn more about how digital sovereignty is changing tech decisions, and why the future of software competition could depend on transparency, flexibility, and trust. Yeah, we've got a lot to get through today. But enough from me. Let me officially introduce you to my guest now. So a massive warm welcome to the show. Thank you for joining me today.

[00:02:39] Can you tell everyone listening a little about who you are and what you do? Sure. Thank you, Neil. I'm glad to be here. Thanks for hosting me. So I'm the managing director of Zoho UK. For some of your audience who may not know about Zoho, we are a large multinational software product company. About a million companies use our products in 150 countries. And we have a large portfolio of products as well, you know, which covers every functional area of the business,

[00:03:08] such as Zoho CRM for sales, Zoho desk for customer service, and other products for, you know, finance, operations, IT, collaboration, and so on. In the UK, we are headquartered in Milton Keynes. We have a good footprint here among UK businesses as well. About 60,000 companies in the UK use Zoho products. In terms of my personal background, prior to Zoho, I was an entrepreneur.

[00:03:35] I built a SaaS company and took it to exit in about five years' time. Earlier, I did some business development work with IBM, and prior to that, some restructuring and turnaround work. And this was interesting, you know, because I worked on the Lehman Brothers restructuring when the 2008 financial crisis happened. So, you know, close to a billion dollar worth of assets between Hong Kong, Thailand, and Philippines. I was in charge of administering.

[00:04:05] Some of these assets, you know, we called melting ice cubes. The idea was to sell them as soon as possible before they lose value. So interesting times, you know, that period. Earlier, a lot of time in consulting between India and the US. I also saw the dot-com boom and bust at close quarters. Was involved in building some of these e-marketplaces myself. None of which really saw the light of the day. And now I'm here at the helm of our UK business with AI,

[00:04:34] changing the landscape of technology so fast, I would say no less interesting than, you know, some of those historical events that I've been exposed to in the past. Absolutely love it. Such a brilliant backstory. It really brings to life the journey that you've been on. And fast forward to present day, 2026. You've been leading Zoho's expansion in the UK at a time when AI adoption is accelerating rapidly. So I'm curious from what you're seeing here,

[00:05:03] from your perspective, what are UK businesses getting right? And where are they moving too fast? Because there's so much talk about hype and also measurable incomes, real business outcomes, etc. So what are you seeing that they're doing right and maybe not doing so well? Yeah, that's an interesting question. For sure, AI has had an explosion over the last, you know, last two, three years.

[00:05:27] And one would have to have one's head in the sand to not notice the sheer volume of solutions in the market now. The businesses, as I see, you know, which are getting this right, are able to make a realistic assessment of their internal capabilities and focus on what AI can do today and not what it might do in five years time. So that means focusing on practical outcomes such as improving efficiency, reducing costs,

[00:05:55] or moving the revenue needle in some tangible way. Where we saw this, you know, land a bit tangibly was in the customer experience area and particularly in customer service. As you can imagine, there is a constant pressure there, you know, rising volumes, cost control, staff retention. So while earlier chatbots were limited and in many cases frustrating, the technology has matured quite a bit.

[00:06:25] It's a lot more nuanced. It can, you know, understand the intent, complete these routine tasks, and actually improve the experience rather than just deflect these queries. A good example of this is, you know, SysCrope in the UK. They are using Zoho Desk with AI to support their service teams. Things like sentiment analysis, summarizing the interactions, and helping agents respond, you know, faster.

[00:06:53] Their human agents are able to now respond a lot faster. So they have seen clear improvements in response times and efficiency. But what's interesting now is that it's moving a bit beyond customer service. We are seeing AI embedded directly into systems like CRM and helping teams prioritize their deals, surface some risks in the deals, guide their next course of action. So in all of this, it's not really replacing people.

[00:07:23] It's augmenting decision-making within their flow of work. And one area that I feel excited about particularly is how AI is lowering the barrier to building applications. So with platforms like Zoho Creator, you know, with AI agents, businesses can now generate applications and workflows much faster. So this means bridging that IT and business divide that always, you know, existed. And which means, you know, greater ability for a business to respond to their,

[00:07:52] the changes which are happening in their business environment. But a common thread across all this, I feel, has to be focus. You know, the successful use cases are defined very specifically. There is a clear problem, a clear application, a clear scope, and a clear way to measure the outcome. I think that's when the projects, AI projects succeed. Now, the area where organizations struggle, as we have seen, is, you know, when they operate out of FOMO.

[00:08:22] Now, just to get something in AI, because everybody is using AI, you know, I'm being left behind. And they forget that digital maturity is a prerequisite to AI maturity. So what I mean by that is that if the business processes within an organization are not digitized, that there is no data to make sense of, then AI can't really do anything. You know, it will be garbage in and garbage out. So step one before embarking on an AI initiative is to assess your digital maturity

[00:08:52] and bring it to a required level of threshold. So to summarize, it's less about moving too fast and more about making sure you're applying the right level of technology to the right problem. Yeah, I completely agree. And I think there are also some concerns that, in many cases, AI is pushing organizations towards almost black box systems. They don't fully understand, especially how it arrives at conclusions or even are able to control.

[00:09:21] So how real is that risk? Are we moving away from that? And what does a more transparent approach look like in practice? Yeah, I believe it's very real. And we are already seeing it play out. A lot of organizations have, you know, adopted AI quickly, often through external tools, without fully understanding how decisions are being made, how consistent those decisions are, and where that data is actually going.

[00:09:49] And that creates risks at multiple levels. So from an operational perspective, you know, when it leads to inconsistent outputs and not so accurate recommendations, this becomes harder to diagnose the issues and improve the process. From a compliance perspective, it becomes a lot more serious. If you can't explain, you know, why a decision was made, it's really difficult to stand behind it.

[00:10:14] And importantly, you know, especially for businesses in the UK, larger European region, as a business, you are responsible for protecting not just your data, but also of your customers and your suppliers. So if the data is being used to train the model, and you're not sure the boundaries within which this is happening, then the sensitive data can go out. And it can lead to serious damage in terms of your reputation and hefty fines which may come your way.

[00:10:42] And then there is the important aspect of the human side of this. If teams don't trust how something works, they wouldn't rely on it. Then they will double check the output. They will revert back to the manual process. You know, they'll work around the system rather than using the system. And then as you can imagine, then AI stops being an accelerator and actually becomes friction. You're doing more work with AI and not really reducing work.

[00:11:11] So coming to the question of, therefore, what a more transparent approach can look like. In our experience, the answer to better trust, control, and traceability is obtained with a combination of right-sizing of AI models and contextual intelligence. So while I'm using some of these words, but let me explain, you know, what I mean by this. See, not every business problem needs a, you know, a big, large language model.

[00:11:40] In many cases, narrower, smaller models are actually better. They are more predictable. They are easier to govern and far more auditable. Less of a black box. And they need a lot lesser data to train. So for even smaller businesses, medium-sized businesses, which do not have huge volumes of data, even they can, you know, take advantage of this. To give you an example, when you are working within your CRM, you wouldn't ask AI to plan your vacation there.

[00:12:11] Right? So it doesn't have to be such an open canvas. It can be a narrower, more controlled context. And then I talked about, you know, contextual intelligence. So AI works best when it understands the business context it's operating in. The customer records, the service history, the finance workflow, and not when it's sitting outside the business as a disconnected layer.

[00:12:37] So that means keeping AI close to your system of record where all the data resides, rather than layering it on top and passing sensitive information back and forth between disconnected tools. And that's when you get true explainability. You can see what data was used, why something was recommended, and maintain a clear audit trail over a period of time. So that's how we approach it at Zoho. Our AI sits within applications like CRM and service, customer service.

[00:13:05] So decisions are contextual, explainable, and auditable by design. Because ultimately, transparent AI isn't just about the model. It's about whether the organization can understand it, govern it, and trust the outcome. And there are many leaders and many organizations that are still prioritizing convenience over control, especially when they're choosing cloud and AI providers, for example.

[00:13:31] So I'm curious, again, from what you're seeing, are we reaching a point where digital sovereignty almost becomes a board-level priority? And what is driving that shift, if that is what you're seeing? Yeah, I think we are definitely reaching that point, and quite quickly. Digital sovereignty has moved from being a technical discussion to a board-level priority.

[00:13:54] And I feel it is so because it now directly impacts risk, cost, flexibility, and even the long-term competitive advantage for a business. And this is so because the concept of digital sovereignty itself has evolved. It doesn't include so many more dimensions than just where the data is posted, which is how it was thought of earlier. And I think that is why the word digital sovereignty and not just data sovereignty.

[00:14:25] And what we are seeing is that customers are driving this conversation. It's not just a nice-to-have. And more so, currently in the wake of this current geopolitical environment, where the erstwhile multilateral world order that we were all comfortable with and accepted has just got thrown out of the window.

[00:14:46] So, when our customers are talking about digital sovereignty, what they think about is, one, data residency. That is the basics. In terms of where their data physically resides. So, within the boundaries of the country or within a region in the local data centers. Two, model sovereignty. So, in terms of their ability to choose the AI models that will process their data. Three, infrastructure sovereignty.

[00:15:14] So, in terms of the location of hardware and software stack, which is managing their applications and data. And the jurisdiction governing the operation of that infrastructure. The fourth is intelligent sovereignty. So, in terms of access to tools to train the model, you know, on their own data so that it gets the right context. And getting the assurance that their data will not be used to train the model outside of their organizational boundary. You know, something that I was alluding to earlier.

[00:15:45] And last but not the least is vendor sovereignty. So, in terms of even having flexibility to choose their vendors without getting logged in over a period of time. And related to the last point is also a dimension around concentration. Which is, you know, now being called as tech monoculture. Now, what it means is that a large proportion of AI capability today, as you would know.

[00:16:12] You know, whether it is models, infrastructure, or cloud platforms. Sits within a relatively small number of providers. Many of them are, you know, based in the US. Yes. So, boards are asking this question. What will happen if that access stops? Because of a geopolitical decision or a regulatory, you know, consideration which may come. So, this is not just an IT decision anymore. But an important risk consideration for businesses.

[00:16:40] Especially in the, you know, larger European region. Secondly, given the regulatory environment in the UK and Europe. The boards of the companies are under increasing pressure to demonstrate how the data is being used. How decisions are being made. And whether they can be explained and audited. And more so in some of these regulated industries. Think of public sector, healthcare, financial services, banking, and so on.

[00:17:08] So, in practice, the way I look at it, digital sovereignty isn't about pulling back from global innovation. It's about making more deliberate choices. It means choosing the vendors who are transparent about how data is handled. Who can give you flexibility in terms of how systems are deployed. And who do not lock you in in a single way of working. So, at Zoho, that's something we have focused on for a long time. Giving customers control over their data.

[00:17:36] Incorporating privacy by design in everything we do. And investing in local infrastructure. So, organizations, you know, have the needed confidence and assurance about this urbanity. A big thank you to Donodo for helping me make more than 60 monthly interviews possible across the Tech Talks network. And as businesses move from Gen.AI to Agentic.AI, trusted data becomes everything.

[00:18:03] Everything from Gen.AI to Agentic.AI, Donodo is helping organizations build intelligent, secure, and scalable AI solutions with data access, governance, and explainable results. So, build AI that you can trust. And do it with Donodo. And you can learn more by simply visiting Donodo.com. And there is a lot of talk at the moment around how AI is changing everything.

[00:18:30] There's a lot of exaggerated claims about an impending SaaSpocalypse out there at the moment, for example. But how do you see AI reshaping competition in the software industry? Is it reinforcing the dominance of a few large players? Creating opportunities for more diverse providers to compete? What are you seeing here? Yeah, that's a good question. Neil, I think it's a combination of both as I see it. AI is absolutely reinforcing some advantages for the largest players.

[00:19:00] You know, they have the scale, the infrastructure, the data, and importantly, the ability to invest in highly capital-intensive side of the AI. You know, the models, the compute, the data centers. All of the underlying infrastructure that makes it possible. So, these are, you know, the big tech guys that we are all aware of. Some of the model builders, you know, of whom we are hearing about a trillion dollars in market cap with some of these planned IPOs.

[00:19:28] And some of these big infrastructure builders, you know, the chip makers and all that. Now, that's not something smaller providers can replicate. But at the same time, AI is also creating real opportunities for a diverse set of providers who are building applications using AI as a service.

[00:19:50] If you think from a business's point of view, that real value is not in the size of the model, but in the capability to apply the model towards an outcome that they desire. So, in that context, customers are making some conscious trade-offs currently. While larger platforms can bring a general purpose capability and, you know, more power, but they often come with complexity, higher cost, and a degree of lock-in.

[00:20:21] Smaller players, on the other hand, can bring a deeper, more comprehensive solution to solve specific problems, you know, for an industry or for a use case. So, AI is widening the field, but it's also raising the bar for what customers expect. And they are starting to ask how well this AI solution fits into our operations, how transparent it is, and how much control do we retain.

[00:20:50] And that's how they are making their decisions. And outside of AI, another challenge that many organizations continue to face today is, of course, cloud fragmentation. So, how are you at Zoho helping UK businesses stay agile while also maintaining compliance across what looks like an increasingly series of complex environments out there? Yeah. Yeah.

[00:21:15] The reality is, Neil, that, you know, most organizations haven't deliberately created these fragmented clouds. Yeah. It has happened over a period of time. You know, as a business grows, different teams, you know, bring in tools to solve specific problems. So, the sales team will go out, find a CRM, start using it.

[00:21:37] Customer service team will, you know, do their research, find another, you know, customer service support software and start using it. And that is the beauty of SaaS. It allows you to just start using something on pay as you go. You know, you can even put in your credit card, have a couple of users to start with. And slowly you realize that, you know, you have just a big set of tools and nobody even knows how many and which all are they.

[00:22:05] While individually they work well, but they don't always join up. That becomes the problem. And the result then is duplicated data, inconsistent reporting, and no single reliable view of what's actually happening across the business. And that slows decision making, increases operational costs, and makes compliance as difficult. Because governance cannot be done tool by tool.

[00:22:35] You know, it has to be done across the business. And that's where really Zoho comes in. Our approach has always been to simplify that environment by bringing core business functions on a connected platform. Whether that's CRM, that's customer service, finance, or operations. And with something like Zoho One, which is all in one bundle, you're not stitching these tools together.

[00:23:01] That you are working from one unified system so that the data flows across the business, across the functional areas. In practice, what that means is that teams are working from that same data. Processes run across functions and reporting becomes consistent. And that also makes a big difference from a business's ability to be agile. You know, because it's a single source of truth.

[00:23:30] You can adapt your processes, introduce automation, or layer in AI much more quickly. Because everything is aligned. And from a compliance perspective, it's just as important. It's easier to apply the governance consistently. Whether that is access controls, audit trails, data policies. When everything fits within that one single environment.

[00:23:55] So the way I see it is that most organizations don't have too many tools. They have too many disconnected ones. And when talking about AI, another big topic right now is trust and data privacy. Both are becoming central to every tech decision now. And I always like to try and give people listening valuable takeaways. Are there any practical steps that organizations could be taking to ensure that that AI strategy

[00:24:25] strengthens trust rather than undermines it? Because I know it's a big concern for many leaders right now. Yeah, it is. And there are some organizations, you know, which are getting this right. And the ones which do, they treat trust and privacy as something operational and not just a nice to have. Because once trust is lost, as you can imagine, it's very difficult to win it back.

[00:24:51] You know, whether it's with your employees or your customers or your regulator. So I would say the first step in this is simple. Find out where AI is already being used. A lot of leaders would assume that AI adoption is happening through official projects. But quite often it starts with individuals.

[00:25:16] You know, someone is using an external AI tool to write customer emails, summarize the contracts, analyze some spreadsheets, or building, you know, a small internal workflow. And now what is being called as wide coding. And, you know, with a plethora of tools available, you can really build an app so quickly or a workflow very quickly. And it can solve an immediate problem.

[00:25:43] While there is real value in that speed, you know, which allows you to do this. But if it happens outside governance, it creates risk very quickly. Now, customer data can get copied into open models and, you know, that can go outside the boundaries of your organization. Sensitive financial information can add up in tools, you know, in tools that nobody is really monitoring.

[00:26:07] Some of those temporary fixes which somebody did to solve that gap can expose a business, you know, in terms of security. Cyber security threats are increasingly, you know, very, very real. And then it can break the business process. It can break the audit trail. So those are the things that can happen. So the first thing I would tell any leadership team is really to run an AI usage audit. Where is AI already being used?

[00:26:37] What tools are people using? What data is being shared? Who owns these processes? My sense is most organizations are surprised by the answer. The second step is to create a clear policy. Not necessarily a 50-page document that nobody will read, but simple practical rules. What tools are approved? What should never be entered into an open AI model?

[00:27:03] When does an AI-generated output need a human review? Who is signing off on the AI-driven workflows or the apps which are getting built? So that clarity is important. If you don't define the boundaries, then the employees will define them for you. The third step is keeping AI inside your code systems, you know, wherever possible.

[00:27:29] If the teams are constantly copying information from your systems that they use every day into some disconnected tool, which is, you know, outside, you will lose visibility and you will lose control. If AI operates inside your CRM, inside your customer support, inside your accounting, data stays, you know, within your environment. It, your existing permission set allows the required level of controls and decisions,

[00:27:58] you know, which are made can be tracked and audited. So that is the difference between something that feels clever for a week versus something that can actually scale. And finally, managers need to be trained to also challenge AI, not just use it. They need to know when to trust it, when to question it and how to spot when something doesn't look right. Can't blindly just believe AI. We all know it hallucinates.

[00:28:27] So, so the goal is not just speed, but more importantly, to generate confidence. Fantastic advice. I love that line there as well about challenging AI, not just using AI. So important. And for leaders listening who are also trying to balance innovation with regulation, what, what does responsible AI adoption actually look like on the ground inside enterprises, especially beyond those high level principles? What does that look like?

[00:28:55] So, the way I see it is that responsible AI adoption starts with getting your data foundations right. That means structured, well-governed data, not just large volumes of it. And we talked about not necessarily in every context you need a very large model. A medium or a small size or a narrow model may just require a smaller data set and very well do the job.

[00:29:26] The main point being if your data is inconsistent, duplicated, or poorly defined, AI will simply amplify those issues. So, organizations need to think carefully about how their data is organized. You know, some common definitions, clean inputs, consistency across systems. That's where the real work is. And honestly, it's usually the least glamorous part of AI initiative.

[00:29:55] Everybody wants to be part of defining that AI strategy. Nobody is ever excited about cleaning your CRM. But that is the part that matters the most. That's where it starts. Next, you need a clear oversight of where AI is being used, what decisions it's influencing, and who is accountable for those outcomes. That includes putting controls in place, like access controls, you know, approval workflows,

[00:30:23] clear boundaries around where AI can be used and can't be applied. And then, importantly, auditability. Organizations need to be able to trace the decisions. What data was used, how something was generated, and how it has changed over time because of, you know, AI's intervention. So, that's not a good practice. It is a regulatory expectation.

[00:30:49] Particularly in the UK and Europe, there is an increasing focus on explainability and being able to justify these automated or AI-assisted decisions. Whether it can be to your regulators, your customers may ask for it, or even internally, you may have governance, you know, from your board demanding this. A good example I can think of is from one of our healthcare customers in the UK. They are a large healthcare staffing organization.

[00:31:18] They use Zoho solutions with AI embedded in the solution to power their recruitment, their operational processes, and the care management processes. So, the care plan for a patient can be a 30 to 40-page document. And a care worker would typically work with 8 to 10 patients in a day. So, their AI solution summarizes this care plan for each patient for that care worker so

[00:31:46] that they have the relevant information at hand and they can be more productive and it improves their effectiveness to handle each and every patient. And it does so while ensuring that the patient-sensitive information remains accessible only to the right audience and meets those stringent compliance requirements of the NHS. And the reason this works is because they haven't added AI.

[00:32:14] They are using AI within their systems context. It's sitting within the operational system with a clear ownership of data and clear visibility of how decisions are being supported. So, on the ground, responsible AI isn't really about doing more with AI. It's about putting the right structure, the right governance and accountability in place so that AI can be applied safely,

[00:32:41] it can be scaled properly, and it is trusted in the long term. So much gold in your answers today. And finally, if we were to look ahead a little into the future here, how can organizations maybe better maintain their independence and control over their technology stack while also benefiting from that global innovation in AI and everything that we're seeing here? Because it's an exciting time, but we don't want to lose independence and control over it.

[00:33:10] Any advice on that? Yeah. There is a perception that, you know, you have to choose between control and innovation. And therefore, you know, this question is very relevant. But I believe that's not really the case. You just need to be more deliberate in how you build your tech stack.

[00:33:30] So rather than building it around a single ecosystem, organizations as we see today, the progressive ones, you know, they are thinking more about flexibility. How easily can they integrate new capabilities, adapt processes, or change direction as the landscape evolves? And that's very important with AI. As we know, the pace of change is the fastest that we have seen so far in the technology landscape.

[00:33:58] So in this context, customers, you know, organizations looking to take advantage of AI and the innovation, they should prioritize for their digital sovereignty and ask their tech vendors to demonstrate it to them. So the reality is that different vendors may fare differently in terms of their capability on the various dimensions I had talked about earlier.

[00:34:20] And based on the relative importance of these various criteria, they can decide what is the right vendor for them and then how that can plow into their tech stack. And the other aspect of this is to keep a track of one's internal capability as well. The organizations that maintain independence are the ones that really understand their own data and their own processes.

[00:34:50] They're not entirely reliant on vendors to tell them what's happening. They have enough visibility and knowledge to make informed decisions about what to adopt and what not to. So the way I see it, it's less about global innovation and more about how you engage with it. Because AI is not a single solution that you implement once. It's something that is constantly evolving. And I think that is a powerful moment to end on.

[00:35:20] And your expertise and passion for all the topics we've discussed today, I think, really shines through. And for anybody listening, feeling somewhat inspired by listening to you today and how Zoho is helping organizations remain agile and compliant amid growing cloud fragmentation and AI, the pace of technological change, etc. Where should I point everyone listening to want to find out more information about how you and your team might be able to help? Yeah, absolutely.

[00:35:48] So, Neil, they can look up our website, www.zoho.com. That is Z-O-H-O.com. Lots of information about how AI can help organizations. A lot of customer stories that they can look at and learn how they have found success, which could be inspiring for many of the businesses. They can also look at LinkedIn, you know, Zoho's LinkedIn pages, Zoho UK's LinkedIn pages.

[00:36:16] Well, one can look at my LinkedIn page. I share a lot of stories and some of these thoughts that we discussed today. Awesome. I will add links to everything. And we did cover a lot today from how businesses can embrace AI responsibly without fueling a black box tech monoculture. And also the rise of AI, what that could mean for improving competition from diverse software providers.

[00:36:44] And again, your passion shines about how AI and digital sovereignty can actually empower businesses without compromising trust or data privacy. See, if this set off any light bulb moments in anybody listening, please reach out to the guys at Zoho. All the links will be in the show notes, which you can find also on a blog post at techtalksnetwork.com. But more than anything, just a big thank you to you for coming on here today, sparing some of your time and sharing your insights. Really appreciate you. Thank you.

[00:37:14] Thank you, Neil. Glad to be here. So many big takeaways from our conversation today, especially around rather than chasing hype. My guest talked about the importance of solving clear business outcomes, understanding your data and making sure AI sits within trusted systems rather than disconnected tools.

[00:37:35] And that message feels especially relevant as more teams experiment with AI outside of formal governance, sometimes without even realising where that sensitive data is going. And I think we also explored digital sovereignty, cloud fragmentation and that growing concern around tech monoculture. As he said, this is no longer just an IT conversation. It's becoming a board level risk discussion about control, resilience, privacy and long term independence.

[00:38:04] So a big thank you to him for joining me today. And remember, you can find out more at Zoho.com. Follow Zoho on LinkedIn or connect with him directly on LinkedIn as well for his thoughts on responsible AI, digital sovereignty and the future of business software. But that's it for today. So thank you for listening as always. And I'll speak with you all again very soon. Bye for now.