Zoho goes all in with AI – bold or inevitable?

by Thomas Wieberneit | Jul 24, 2025 | Analysis, Blog |

The news On July 17, 2025 Zoho launched Zia LLM and deepened its AI portfolio with agents, an agent builder, MCP support and an agent marketplace. Key announcements from the press release include: In-House LLM: Zoho has developed its own large language model, Zia LLM, which comes in three sizes (1.3B, 2.6B, and 7B parameters) to optimize for different business use cases. This allows customers to leverage AI while keeping their data within Zoho’s ecosystem, ensuring privacy. The three models allow Zoho to always optimize the right model for the right user context, striking the proper balance between power and resource management. This focus on right-sizing the model is an ongoing development strategy for Zoho. Speech-to-Text Models: The company also unveiled two proprietary Automatic Speech Recognition (ASR) models for English and Hindi, with plans to support more languages in the future. Prebuilt AI Agents: To facilitate immediate adoption, Zoho has introduced a range of AI agents that are integrated directly into its products. These agents are designed to automate tasks for various business roles such as sales development, customer support, and account management. Global and Private Cloud Deployment: The new Zia LLM will be deployed across Zoho’s data centers in the US, India, and Europe. Continued Support for Other Models: While promoting its own AI, Zoho will continue to support integrations with other popular large language models like ChatGPT, Llama, and DeepSeek. Zoho will continue to scale Zia LLM’s mode sizes. A2A capabilities are on the roadmap. The bigger picture Enterprise software has been a platform game for a long time. AI, in particular generative and agentic AI, have...

LLM Showdown: Comparing ChatGPT, Gemini, and Grok for Automated News Research

by Thomas Wieberneit | Mar 12, 2025 | Blog |

The analyst’s day is full of research. Now, this is the age of AI and AI is here to help, isn’t it? As everyone is talking about copilots and AI agents, why not using the tools at hand to do a little research on research. NB., no one really has a good definition of an AI agent, so this might become an additional topic for research. But I digress. Imagine the following project at hand, which is not only interesting for analysts, btw, but also for a variety of roles in the corporate world. Let’s call it vendor (competitor) monitoring. The job is the following: Research reputable sites for news about a number of vendors, relating to a set of keywords. Reputable sites are high quality news sites, high quality tech publications, high quality analyst sites and, of course the news pages of the vendors in question. Limit the time frame of the search matching to the cadence of my information requirement, e.g., “yesterday” for a daily update or “last week” for a weekly update Provide a summary of the news Give an assessment of how the news affects the positions of the vendors in the marketplace re the key words in question Provide these news with their assessments as a prioritized list, sorted from high impact to low impact Add an executive summary as a preface Send it to me as an email So far, so simple. After all, a lot of folks, yours truly included, do this every day. And it is taking quite some time. So, this job is a perfect one for an automated update...

The ABC of Zoho AI

by Thomas Wieberneit | Feb 19, 2024 | Blog, Sponsored |

During ZohoDay24, Zoho amongst other topics, gave some insight into how the company looks at AI. Raju Vegesna presented Zoho’s AI vision and progress. Additionally, I had the opportunity for a one on one with Zoho’s director of AI research, Ramprakash (Ram) Ramamoorthy. If you want to listen and watch the interview, you can do this here. Both represented a vision that is refreshingly differentiated from the current hype with everyone and their dog talking like large language models, LLMs, are the everything one needs. Well, let me tell you: They aren’t. But let me come to this point later. In addition to not every language model being created equal, and typical for a hype, there is still too much talk about the technology itself, whereas in the words of Raju and Ram the best AI implementation is “when the customer doesn’t know they are using AI but finds value in the output”. This resonates very well with me, as one of my beliefs is that the customer shouldn’t care about the technology that is used to achieve the desired outcome, within some constraints like legality, ethics, and efficiency, of course. Zoho is a technology vendor with a focus on business applications. So, Zoho quite quickly realized that consumer type AI that e.g., helps with spell checks, or nowadays research, suffers from two fundamental flaws: lacking privacy/security and accuracy when it comes to business applications. Both violate some of Zoho’s core tenets, namely their pursue of privacy and business applications that offer a lot of value to the customer. Take the example of improving one’s writing – for some...

Beyond the hype – How to use chatGPT to create value

by Thomas Wieberneit | Feb 15, 2023 | Blog |

Now, that we are in the middle of – or hopefully closer to the end of – a general hype that was caused by Open AI’s ChatGPT, it is time to reemphasize on what is possible and what is not, what should be done and what not. It is time to look at business use cases that are beyond the hype and that can be tied to actual business outcomes and business value. This, especially, in the light of the probably most expensive demo ever, after Google Bard gave a factually wrong answer in its release demo. A factual error wiped more than $100bn US off Google’s valuation. I say this without any gloating. Still, this incident shows how high the stakes are when it comes to large language models, LLM. It also shows that businesses need to have a good and hard look at what problems they can meaningfully solve with their help. This includes quick wins as well as strategic solutions. From a business perspective, there are at least two dimensions to look at when assessing the usefulness of solutions that involve large language models, LLM. One dimension, of course, is the degree of language fluency the system is capable of. Conversational user interfaces, exposed by chatbots or voice bots and digital assistants, smart speakers, etc. are around for a while now. These systems are able to interpret the written or spoken word, and to respond accordingly. This response is either written/spoken or by initiating the action that was asked for. One of the main limitations of these more traditional conversational AI systems is that they are...