SHI Fall Summit 2025 | Vijay Anand, Aible

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Vijay Anand

Chief Marketing Officer

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Vijay Anand, Aible

Vijay Anand

Chief Marketing Officer Aible

At the SHI Fall Summit, theCUBE’s Paul Gillin sits down with Vijay Anand, chief marketing officer at Aible, to unpack how an enterprise AI agent platform moves beyond traditional BI. Anand explains how Aible analyzes “humanly impossible” permutations to rank-order the most impactful decisions across functions like sales and industries from retail to telco. He describes a business-user-first training model – “like onboarding an intern” – where domain experts teach the system in natural language (e.g., clarifying telco terms like “jitter”) so agents speak the c... Read more

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Vijay Anand, Aible

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Paul Gillin

>> This is theCUBE. We are at the SHI Fall Summit 2025, sponsored by SHI with HPE Unleash AI co-sponsored by HPE. Excuse me. We've been talking a lot about AI here and specific uses of AI such as an image processing, medical informatics. But my next guest has AI that is used in a more broad context. Vijay Anand is the Vice President of Marketing at Aible, which is a platform, I understand, general purpose, more business intelligence platform. Thanks for joining us.

Vijay Anand

>> Thank you. We're an enterprise AI agent platform or solution, and we solve business challenges across the enterprise.

Paul Gillin

>> Oh, that sounds pretty general. Give us an example of a problem that you can uniquely solve.

Vijay Anand

>> So if you think about pick a specific industry and take a specific business function like sales for example, most of these business functions depend on data to be able to make meaningful, actionable insights and take those decisions to be able to make changes to their business and actually impact the bottom line. So to drive their most important KPIs, it takes a lot of assessment and analysis of their data to take impactful, actionable decisions and actions that moves the needle. If you take an example around retail, for example, within their stores, everything from stock-outs to theft monitoring to what people are most likely to purchase and to bundle them with other products. All of these factor into assessment and analyzing of data to see how we can actually improve the KPIs around selling. So to analyze this information at what I call a humanly impossible level, it requires AI to be able to sift through every permutation and combination of that data and perform the analysis at humanly impossible scale. And AI is the only one which are able to ask the relevant questions at that scale and produce the answers to be able to rank order the most impactful decisions for that business. And that's where AI comes into collect.

Paul Gillin

>> Now, these are the kinds of problems that business intelligence has been used for decades to try to solve. What new advances does AI bring to the game?

Vijay Anand

>> So business intelligence, having worked in that business for the last couple of decades is a very manually passed process that requires you identifying, first of all, asking the right question, which starts with a human-first approach. And then it talks about understanding which aspect of my business should be on the X-axis and the Y-axis, and to be able to identify what is an outlier or a trend based on the question or the presumption or the bias that I have as a human. Now with AI in the mix, you are analyzing millions of patterns in that same span by allowing AI to ask the relevant questions on your behalf. So you're not even coming with that bias. You're letting AI do the role of asking the relevant questions, understanding what's actually impacting your business, eliminating that entire component of human bias, and then making this an AI first aspect versus a human first aspect that business intelligence has commonly done.

Paul Gillin

>> But humans need to train the model. So what is that process like with your platform?

Vijay Anand

>> Absolutely. So we think about these models as interns, much like you would bring an intern into your business and the business person with their domain experience would train that intern. We would enable the business user to train these models. Using very naturally spoken language, you can train a model to say, "This is what I meant by a specific terminology," or "This is what I meant by a business goal." And to train that model to speak your language with your terms and your specialization so that it is trained to your business, your data, your terminology, et cetera.

Paul Gillin

>> Well, so step us through that. Maybe can you give us an example? Say you're a retail analyst and you're trying to figure out why stockouts are occurring in a particular geography. How might you train that model?

Vijay Anand

>> I'll take a different example in terms of Telco for example. There are very specific terminologies like let's say jitter. And when you are saying, I want to assess the network performance and use the word jitter, and the AI is just going to respond saying "X, Y, Z." You, as a business user, have the opportunity to let the AI model know that I meant X by jitter and not Y. So it is not a binary thumbs up, thumbs down sort of feedback loop. It is an actual explanation to the model. So you really are talking to the model and training it like an intern to be able to deliver specialized results that speak in your language, your specialty, and your process by really understanding what you mean by jitter, which could be very distinct and unique to your business. So you are training that model to speak your language and to respond in a way that an intern would be trained, over time would be specialized much like you would be in your line of business.

Paul Gillin

>> So the training aspect is the generative AI portion. What's agentic about your model?

Vijay Anand

>> So from a generative AI standpoint, you are relying on a large language model to be able to come up with responses based on analyzing language and its information that it has. When it comes to meshing that with the insights from very structured data assets that you have, that requires analysis from a math standpoint, not just from a language standpoint. And to do that instead of a large language model, Aible leverages what we call an information model to be able to sift through millions of patterns and identify what are the most impactful patterns that are going to affect your business. And to combine that with the generative aspect makes it a lot more consumable to business users. So you are not just analyzing language and outputting language that you would do with consumer-oriented tools like ChatGPT, doing that with your data or structured information for the business, which really contains 80% of the value for most organizations.

Paul Gillin

>> So you're analyzing these very large data sets and trying different ways to correlate data. We know the correlation is not the same as causation. How do you protect against the machine I guess sort of going off the rails, maybe making assumptions or correlating factors that don't necessarily relate to each other?

Vijay Anand

>> So hallucinations are actually a very common issue with large language models. If the large language model does not have an answer, it tends to make something up and that's a hallucination.

Paul Gillin

>> You've seen that. Yup.

Vijay Anand

>> Right. And so with the information model, you're actually preventing that from happening. You have the ability to create very deterministic answers, which have very factual charts that support that answer or insight. So it's all very fact-based. So there isn't a question about hallucination because everything is factual or deterministic in its nature. With Aible, we use something called blue is true. So anything that's highlighted blue, you can be very confident that it is factually verified responses and something that's not highlighted blue, you'll know that there isn't a backbone that has been verified so you might require additional cross-references. So by giving you that certain verification mark or certification mark or whatever, you're instilling a lot more confidence in users saying, "Okay. This can be verified" or "This is factual" and you are double-checking the veracity of it.

Paul Gillin

>> The CIOs aren't necessarily always comfortable giving development access to business end users. Your platform enables that. What steps do you recommend or what tools do you provide to make sure that the applications they produce are reliable?

Vijay Anand

>> One of the key strengths for Aible at least, is to be able to run everything within the customer's environment, whether it's the cloud or their edge devices or their on-prem systems but data never leaves their control or possessions. Aible is performing the analysis within that environment and the data never leaves it. So it's instilling a lot of confidence from a data privacy, data residency and security standpoint so that CIOs are very comfortable saying, "Okay. Data's always going to be in my possession."

Paul Gillin

>> What do you think the future of business intelligence looks like as AI revolutionizes this field? In the future, will BI be entirely Agentic? Will we not have to ask questions of our system, BI systems, it will come to us when there's something we need to know? What does that future look like?

Vijay Anand

>> I think much like anything else, it is going to be AI first. It's absolutely impossible to let humans drive the entire process. That doesn't mean you're eliminating the humans. You're augmenting their efficiency levels to an order of magnitude that we never deemed possible. The idea of mapping variables on an X-axis and a Y-axis and trying to find the needle in the haystack is an old-fashioned way of doing insight finding anyway. So imagine throwing everything at AI and allowing AI to decide these are the things you should be looking at and then combining that with human intelligence to help prioritize that for your business and also getting a glance of what is the potential impact of that action going to be even before I take it, right? So that aspect of being able to look at information at a humanly impossible scale and to organize that in terms of what is impactful for my businesses is unprecedented, and I think it's going to be hard to ignore.

Paul Gillin

>> Can you work across all different types of business domains, everything from finance, to HR, to manufacturing?

Vijay Anand

>> So Aible is a industry and function agnostic platform. It is defined by the business user to their specific business challenges. So if you're in sales, or whether you're in finance, for retail, or for healthcare, or for the travel industry, you, as a business user, will be in control in defining what is the most important KPIs for your business, what is the goal you're after, and you're talking to the system in a very business-centric manner. So you are now bringing that information and you're translating that into Agentic workflows that support your process in your day-to-day and you're really augmenting the way you efficiently work through today.

Paul Gillin

>> And what does your membership in the HPE Unleash AI program, what unique value does that provide you, and by association, your customers?

Vijay Anand

>> So we are very early in the stage of integrating with PCAI to figure out our integrated stack, meaning as a combined solution, we have a pre-tested, pre-configured solution that has been shown high levels of efficiency and lower total cost of ownership to run these specific AI workloads and compute on that system. So we are doing that pre-testing prior to an end organization acquiring this at the most efficient, the lowest cost possible way of owning a technology like an enterprise AI that has to perform at that scale. Now, if I'm doing analysis at millions of patterns being analyzed at one goal, the cost can be uncontrolled. So by pre-testing for efficiency and the lowest possible total cost of ownership, we are ensuring that we are performing this at the interest of that end organization so that it's coming as a packaged integrated AI stack that can be ready to be unleashed, if you will, within these end core organizations.

Paul Gillin

>> So maybe again, look into your crystal ball a little bit because your company is kind of at the forefront of redefining the user experience, right? Moving away from dashboards and SQL queries to something that is more Agentic. What does software look like three years from now? Will we still be using menus? Will we still have dashboards? Or will software work in the background and come to us when it needs to tell us something?

Vijay Anand

>> I think we're definitely going to be in a very give and take relationship still. I don't think it's going to be completely owned by AI or completely owned by humans. It's going to be always interrelated. I think we're already moving words automating as much as we can the things that we don't want to do, but we are empowering someone to do 10 times or a hundred times as much they could in a past era. We definitely are working through systems that can automate end to end all the way from data access to data preparation and transformation, all the way to insight building, predictions, prescriptions, and even do simulation testing and what-if analysis. We're in an era of data changing so frequently and so quickly. Just take the last couple of months, for example, from tariffs, to economic changes, to interest rates, it's just massively impacting our businesses so frequently that we don't have the ability to manually keep changing everything on a day-to-day basis. So why not let AI take on that heavy lift and allow AI to be the forefront of managing that change, but giving you the most impactful decisions to take on a day-to-day business so you can concentrate on what you know best for the business and help drive the needle for the most important KPIs for your business.

Paul Gillin

>> The company is Aible. Where can our viewers find you online?

Vijay Anand

>> We're a completely virtual company. Aible.com is the best way to find us.

Paul Gillin

>> Aible.com, good domain. Vijay Anand, fantastic story. You really are revolutionizing an industry and thanks for joining us here on theCUBE.

Vijay Anand

>> Thanks Paul.

Paul Gillin

>> I'm Paul Gillin. This is theCUBE, the source for tech news and analysis.