What Does AI Mean for Operational Efficiency? Insights from Taazaa’s CEO
In the latest CollabTalk Podcast, Christian Buckley and Taazaa’s CEO, Yasir Drabu, discuss AI’s impact on business operations and decision-making. This article presents key excerpts from their conversation, addressing common queries about AI in the business sector.
The dialogue between Christian and Yasir centers on four aspects of AI in enterprises.
First, they explore how AI can strategically enhance decision-making capabilities, allowing businesses to leverage data-driven insights for more precise and effective outcomes.
The discussion then shifts to the role of AI in boosting operational efficiency, where automation and advanced algorithms streamline complex processes, reducing costs and time.
The conversation takes a pragmatic turn as Yasir illuminates the obstacles encountered when incorporating AI into established business infrastructures. He candidly discusses the technical and logistical barriers that companies must overcome to successfully adopt AI technologies.
Finally, the focus shifts to the ethical dimensions of AI implementation. The dialogue underscores the necessity for ethical and transparent AI practices, which are fundamental to maintaining trust and upholding integrity in its applications.
Key Insights from the Podcast
These excerpts offer a window into the profound discussions guiding enterprises toward a smarter, more ethical future.
CHRISTIAN: What’s interesting about AI is that everybody’s asking about it, but most organizations are still in the piloting stage. They don’t yet fully understand what they should be doing or where to go. I’d like to know what types of AI-related problems people are seeking help from your team for.
YASIR: I think the most common use case is improving customer service. For example, there’s a knowledge base, and traditional search is great, but being able to summarize that and answer a question that’s more specific has been a very common use case. We’ve actually helped build a couple of use cases where you have a product, and then you build a chatbot around it and enhance it. So, you would take a foundational model like ChatGPT or Gemini and then augment it with the customer’s proprietary application data. And then when their customers ask how do I do this, instead of just pointing you to an article that was manually written, [the AI] can summarize. “Step one, click here. Step two, click here.” It actually generates some of those responses which are guided and tailored to the question.
CHRISTIAN: Do you envision around AI that there’s going to be some global standards body pulling together how these things communicate?
YASIR: I believe so. First of all, I think the biggest one will be around AI ethics and safety. I think the interoperability is coming through some of these libraries like Py Torch and TensorFlow and others. And the L&M models that are dropped and things they are doing through Hugging Face. There seems to be a community-based consensus on how some of these are best stored. Ultimately, there will be protocols between AIs to talk to each other through some sort of machine language exchange, and there will be some standardization so AI agents can chat with each other in an efficient manner.
CHRISTIAN: Do you think it’s a strategic error for companies to wait for a clear ROI before adopting AI technologies? How important is it to experiment and innovate with AI in order to gain a competitive advantage and develop customized solutions for their business?
YASIR: Given the speed of Innovation, it’s an exponentially faster-moving field. You’re either too early because it’s not good enough, or you’re too late because the curve is very exponential. Start with a team, start experimenting, engage with us or internally, but start somewhere. Whether it is simply starting to use ChatGPT to get some summarization on things that can be the easiest point of entry, think about how you can apply it to business.
CHRISTIAN: When you think about the marketing duopoly, primarily composed of Google and Facebook—and to some extent, Apple, with the segments they control—how do you see the shift towards extensive video use affecting these marketers? This change seems to be transforming the way people can make purchases based on what they see, like being able to point and click on a jacket worn by a character on live TV, identify it, find it, and add it to their Amazon cart.
YASIR: I think this would not have been possible just five years ago; forget ten. That’s why it creates a whole new set of applications. It requires some retooling and rethinking the engineer’s point of view from going from a deterministic to a probabilistic programming model, which looks at other ways to program but creates all these new use cases that were considered very hard to solve earlier. You needed a PhD and really, you know, and even they would struggle with it because there was no easy way to solve it until we got to this larger model that can do better.
CHRISTIAN: Could you elaborate on how AI enables manufacturers and their suppliers—from tier one to tier three—to adapt swiftly to changes in production details such as materials, components, and timelines and effectively assess the operational impacts of these adjustments?
YASIR: Yeah, I think the models are flipping. Traditionally, in declarative programming, programmers would hand-code every use case and write algorithms to solve them. This model is flipping to focus more on generative AI and Large Language Models (LLMs). However, there are still task-specific models that can be created. You can train these models with sufficient data, and they can then provide you with predictive answers with higher probabilities. This represents a paradigm shift from earlier programming approaches, where you would write a declarative program, save it to a database, pull data, write some logic, apply various operational research constraints, and determine what the demand structure should be.
Listen to the full CollabTalk Podcast episode featuring Yasir Drabu to gain a more comprehensive understanding of AI’s transformative impact on enterprise operations.
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