Microsoft: Conversational AI Changing How Consumers Interact With Brands 05 28 2025

Join leaders from Block, GSK, and SAP for an exclusive look at how autonomous agents are reshaping enterprise workflows – from real-time decision-making to end-to-end automation. Large language models also display so-called emergent abilities, which are unexpected abilities in tasks for which they haven’t been trained. Researchers have reported new capabilities “emerging” when models reach a specific critical “breakthrough” size. Retina leverages AI, generative AI and automation technology to create tens of thousands of 3D assets, along with immersive commerce APIs. These technologies are designed to enable the company to bring its shopping experience into new virtual social environments. Features such as Copilot in Microsoft Advertising Platform’s “Try a Different Tone” offer real-worldapplications more rapidly.

ElevenLabs debuts Conversational AI 2.0 voice assistants that understand when to pause, speak, and take turns talking

This capability could be valuable in scenarios such as creative content development, training simulations, or customer engagement campaigns. AI search results can also give users contradictory or incorrect information, though, creating a potential downside to the quick-and-easy answers. She said the experience reminds her of using Google or other search engines in the late 1990s and early 2000s.

AI search’s user experience may be the best it’ll ever get, says one founder

Mango emphasises its commitment to technology and innovation as a pillar for growth. These include its pioneering position in launching its online sales platform in 2000. It also includes the development since 2018 of more than 15 artificial intelligence-based platforms, mostly for internal operations. “Lisa” is a conversational platform designed to meet the needs of employees and business partners.

We are in a transformational moment with the growing maturity of GenAI and agentic systems. We pulled prices and item descriptions from the central data lake and built an application to give procurement leaders more visibility into parts data. The significant savings generated inspired us to reach farther with automation. Here’s an example of how an idea conceived years ago may be ripe for today’s technology landscape.

Our Brands

This new AI-powered virtual assistant and stylist will offer customers product recommendations tailored to their preferences and tastes. It will also provide access to the latest fashion trends and the ability to discover complete “total looks” from Mango products tailored to their needs. All of this is accessible through the chat on Mango’s official online platform and its official Instagram account. Walmart has developed a content decision platform to serve as a foundational tool to create shopping experiences tailored to the individual customer. The platform leverages AI-based technology to understand the customer and a generative AI-powered tool that can predict the type of content they would like to see on the site.

Enterprise-grade standards and pricing plans

According to Srivastava, instrumentation isn’t something to bolt on later—it has to be an integral part of the stack. Tracking cost, latency, accuracy and user impact is essential for assessing value and maintaining accountability at scale. Designed around a unified control plane, the layer lets teams rapidly develop AI-driven agents while enforcing centralized policies and guardrails. It ensures consistent implementation of risk and governance frameworks while encouraging speed. Developers can deploy experiments quickly, then evaluate and scale based on feedback and performance, all without compromising brand trust.

• This technology is designed to handle the nuances of human conversation, eliminating awkward pauses or interruptions that can occur in traditional voice systems.
• Within this system is Amex’s concept of modular “brains”—a framework in which agents are required to consult with specific “brains” before taking action.
• Because they fluently answer questions, humans can reach overoptimistic conclusions about their capabilities and deploy the models in situations they are not suited for.
• A small business owner who needs a marketing campaign but doesn’t have a design team can use generative AI to create eye-catching visuals and even ask it to suggest ad copy.

For example, generative AI systems can solve some highly complex university admission tests yet fail very simple tasks. This makes it very hard to judge the potential of these technologies, which leads to false confidence. Many compelling prototypes of generative AI products have been developed, but adopting them in practice has been less successful. A study published last week by American think tank RAND showed 80% of AI projects fail, more than double the rate for non-AI projects. This widely used model describes a recurring process in which the initial success of a technology leads to inflated public expectations that eventually fail to be realised. After the early “peak of inflated expectations” comes a “trough of disillusionment”, followed by a “slope of enlightenment” which eventually reaches a “plateau of productivity”.

• Findings from the 1,000-person survey provide insight into why users second-guess top search results, how they supplement searches with new tools, and what makes a result worthclicking on.
• The startup creates the voice on the other end of the line when you call to order from restaurants like Domino’s or Wingstop.
• Mango expects to expand this new solution to other countries and regions where it operates.
• It sits behind the chatbots you talk to online, the playlists you stream and the personalized ads showing up in your scrolling.
• Many leaders who pushed the boundaries of technology capabilities years ago are in senior executive positions today.
• Ask AI a question, and it will often give you an answer in just a few sentences.

Generative AI platforms like OpenAI’s Sora are increasingly being adopted by visual effects professionals, offering faster and more flexible tools for complex production needs. Agents and conversational AI are changing the way consumers findinformation and interact with brands. With the rise of conversational AI search formats, search-engine traffic is shifting, creating new opportunities for advertisers and consumers. A small business owner who needs a marketing campaign but doesn’t have a design team can use generative AI to create eye-catching visuals and even ask it to suggest ad copy. Another issue is the impact on the environment because training large AI models uses a lot of energy, leading to big carbon footprints.The rapid ascent of gen AI in the last couple of years has accelerated worries about the risks of AI in general. Governments are ramping up AI regulations to ensure responsible and ethical development, most notably the European Union’s AI Act.