From your perspective, how is AI best and a key differentiator adoption evolving across businesses in the UAE and wider Middle East, from experimentation to real operational impact?
Across the UAE and wider Middle East, AI adoption has clearly moved beyond experimentation into a phase where it delivers tangible operational value. The real differentiator today is not simply using AI, but embedding it contextually into everyday business workflows—across ERP, CRM, finance, logistics, and customer operations. Rather than isolated pilots, organizations are integrating AI directly into how work gets done, which is where its impact becomes immediate and measurable. Everywhere you look now you can see its impact on low-hanging fruits—automating repetitive tasks and streamlining routine processes. Many businesses are already seeing productivity gains in the range of 20–30%, and in some cases significantly higher. This pragmatic, incremental approach is key in which businesses start small, proving value quickly, and then scale with advanced AI solutions. Once these efficiencies are realised, they become more confident in expanding AI adoption across broader functions, using it not only to enhance productivity but also to rationalise software costs.
How are Zoho’s AI capabilities being used in practical, day-to-day business functions such as sales, finance, HR, and customer experience
Zoho’s AI capabilities are increasingly being used in practical, day-to-day business functions, primarily because they are embedded contextually across its suite of applications rather than operating as standalone tools. In sales, for example, AI within CRM systems supports more accurate forecasting, recommends next-best actions, automates follow-ups, and even helps schedule meetings for sales teams. It also enables smarter customer engagement by suggesting timely and relevant responses based on past interactions. In customer experience and support, AI is used for sentiment analysis across channels such as email and WhatsApp, helping agents better understand customer tone and respond appropriately. It can also assist with drafting or approving responses, improving both speed and consistency in support operations. When it cokes to HR, AI streamlines recruitment by screening resumes, shortlisting candidates, and automating interview scheduling. Beyond hiring, tools like Zoho People leverage AI-driven insights from employee surveys and engagement data to help an organisation continuously improve employee experience and internal processes. Similarly, in marketing, AI aggregates data across multiple touchpoints—social media, websites, email, and interactions—to give a unified view of customer sentiment and behavior. This allows sales and marketing teams to approach conversations with deeper context and personalisation.
What differentiates Zoho’s approach to AI, particularly in terms of privacy, data ownership, and integration within business applications?
Zoho has invested more than a decade building its own AI capabilities. Our AI approach is based on three main tenets: first, right-sizing the model, which means that we believe that not all enterprise problems require an LLM. We have built narrow, small and medium language models as well, that can provide results using far less compute, and are sustainable. Second, bringing in business context. Our LLMs have been trained specifically on solving business problems, which again results in smaller compute, making the models sustainable. As Zoho provides software for nearly every business domain, we are able to pull in contextual intelligence that can then help users make business decisions. Thirdly, we embed privacy from the design stage, which means we do not use customer data to train our models.
Our AI offerings are embedded across Zoho’s suite of apps, with focus on practical business impact. In fact, at the moment, the AI capabilities are available at no extra cost to drive adoption. We develop vertically, implementing AI for actionable insights within workflows, rather than pursuing broad experimentation.
Our strategy has always been to build AI that meaningfully enhances sales and business workflows rather than chasing feature parity for its own sake. It is why our AI often operates silently in the background—customers may not even realise a process is AI-powered, yet they achieve better outcomes without worrying about extra costs or data privacy risks.
Many organisations are concerned about the reliability and governance of AI outputs, how can businesses ensure responsible and secure AI adoption at scale?
At this stage, AI has moved past the point of experimentation and has become a core component of the enterprise ecosystem. Governance, transparency and security should all be embedded within every layer of the company’s IT strategy and infrastructure, whether it is on-premise or through a carefully selected cloud vendor. This entails clear accountability, strong and ethical data controls. Successful deployment of AI starts with the right strategy, clean data and clearly defined processes. AI is as good as the existing internal processes, if they are not already functioning properly, no powerful AI or platform will be effective enough to create tangible improvements.
How does AI, when embedded into unified platforms, improve productivity and decision-making compared to standalone AI tools?
AI delivers far greater value for business when embedded into unified platforms because it has continuous access to connected, real-time data across the business, rather than operating in isolated silos. This eliminates common limitations like fragmented data, context gaps, and manual data movement, allowing AI to generate more accurate insights and automate workflows end-to-end. As a result, decisions are faster and better informed, and productivity improves because users don’t need to switch between tools or reconcile information; AI works within the natural flow of operations, with full business context.
For SMEs in particular, what are the biggest barriers to adopting AI, and how can they overcome them without significant cost or complexity?
For SMEs, the biggest barriers to AI adoption are fragmented systems, limited in-house IT expertise, and the best perceived cost and complexity of deploying AI at scale. Many smaller businesses rely on multiple siloed applications—such as separate tools for CRM, collaboration, HR, finance etc —which makes it difficult to unify data and generate meaningful AI-driven insights. In addition, the rise of large language models has created the impression that AI must be resource-intensive and expensive to implement. To overcome this, SMEs should focus on simplifying their tech stack and adopting integrated platforms where data flows seamlessly, enabling AI to work with full context. It’s also important to take a practical approach to AI—starting with targeted, high-impact use cases and leveraging right-sized, efficient models that match business needs, rather than defaulting to complex and costly solutions. This allows SMEs to adopt AI in a scalable, cost-effective way without adding unnecessary operational burden.
Looking ahead, how do you see AI reshaping enterprise software and business operations over the next 2–3 years?
Over the next 2–3 years, AI will move from being a standalone tool to becoming deeply embedded across enterprise software and daily workflows, acting as a real-time collaborator rather than just an assistant. We’ll see the rise of AI copilots and agents that not only generate insights, but also anticipate needs, automate tasks, and support faster, more informed decision-making across functions. Ultimately, the shift won’t be about replacing people, but make mastering its use a highly valuable skill and a key differentiator. Businesses that integrate AI as a partner will see greater productivity and competitive advantage.
