Mani Vembu, CEO, Zoho
Mani Vembu, CEO, Zoho in an interaction with CIOTechOutlook magazine, shared his views on how companies can leverage ML to automate mundane business processes without sacrificing user control, the importance of real-time analytics in the future of enterprise SaaS, how generative AI is reshaping collaboration and creativity within business software and more.
What do you see as the next frontier for AI in integrated business applications?
The future of AI lies in its wise integration with businesses. At present, in the race to adopt AI, organizations are rushing to hyper-integrate AI, consequently compromising on the resilience of the system and magnifying risks. The future lies in the mindful integration of AI contextually within business apps so that enterprises can derive maximum value out of this.
How can companies leverage ML to automate mundane business processes without sacrificing user control?
While AI can automate several processes, humans will continue reviewing the output. Humans are still needed to review the performance of AI and provide feedback. Companies can free up a lot of time wasted on mundane activities and allow humans to concentrate on the bigger picture. It’s also not that wise to automate everything without human oversight. AI still lacks the real-world judgment and causal reasoning of a human expert and grasp of unquantifiable variables that define true expertise. Human insight still remains critical for interpreting AI outputs, seeing their limitations, and making sound strategic decisions.
How important is real-time analytics in the future of enterprise SaaS?
There is no denying the importance of real-time analytics for enterprise SaaS. It helps enhance product value, increase user engagement, and provide a stronger competitive edge. Real-time analytics help companies improve user convenience by delivering insights at crucial decision-making moments. Users are also more likely to stay engaged to a SaaS application that offers all necessary insights within the same platform. Having in-app real-time analytics reduces the reliance on data or IT teams, especially for non-technical business users.
How do you approach explainability and transparency of AI models for non-technical business users?
In most business cases, we try to combine the power of low code with AI and keep the human in the loop for co-creation so that users can see how AI works. We try to keep it simple and show the outcome of AI instead of activities performed by AI.
The benefits of AI models for businesses are indisputable. Data-backed explanations, rooted in real-life positive use cases, can convince even the most non-technical business users. We also leave it to the users themselves to experience AI models for their business purposes.
How do you see generative AI reshaping collaboration and creativity within business software?
Generative AI can save a lot of time and facilitate consistent communication within the workforce. Task delegation and progress tracking can be made automatic. Generative AI can even help provide feedback for our work by assessing past data.
What advice would you give to growing SaaS companies about embedding machine learning into their products thoughtfully?
Emerging SaaS companies must remember to play the long game when embracing AI. It took us 13 years to experiment and see for ourselves how we could optimize our products with the right AI tools. With a customer-first approach, SaaS companies should nurture their R&D operations. Organizations should never disregard privacy and security. A multi-modal approach for AI models help address different business use cases. With the right business context, right sizing models become important to balance privacy, compute and precision requirements.
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