Zebra Technologies Corporation (NASDAQ: ZBRA), a global leader in digitising and automating workflows to deliver intelligent operations, today shared insights into the key AI trends that are delivering ROI for businesses this year.
“Global technology show, CES put the spotlight on new and advanced ways AI will impact and shape the physical world and frontline operations in the consumer and business world,” said Stuart Hubbard, Senior Director of AI and Advanced Development, Zebra Technologies. “This may signal an ‘intelligent automation’ economy akin to the services and on-demand economy we experienced over the past year. The pace of AI advancements continues to surprise, with unforeseen breakthroughs likely to emerge, such as World Models. Businesses and individuals should remain agile, ready to adapt to new opportunities and disruptions.”
AI is reshaping industries by accelerating innovation and enabling new business models. Early adopters in sectors like logistics, manufacturing, and retail are gaining a competitive advantage through AI-driven insights, dynamic pricing models, productivity gains and personalised customer experiences.
Rise of AI Agents and Autonomous Systems
AI-powered agents are becoming increasingly sophisticated, capable of automating complex tasks and working autonomously with minimal human intervention. These agents are transforming productivity across industries such as finance, IT, and customer service, with companies leveraging multi-agent systems seeing up to 50% efficiency gains.
A notable example of this trend is OpenAI’s integration with Spotify, where AI-driven systems are enabling personalised music discovery, playlist curation, and conversational interactions. These integrations illustrate how AI agents can create value by seamlessly blending data processing, user preferences, and task automation.
The success of OpenAI’s Spotify partnership underscores a broader trend: as AI becomes more adept at understanding and responding to user needs, industries must explore how these systems can reshape productivity and user experiences. The lessons learned from OpenAI’s advancements can inspire new ways to integrate AI into device ecosystems, driving innovation and intelligent operations.
“For example, AI agents on Zebra devices could autonomously manage complex workflows, such as real-time inventory tracking, predictive maintenance, or personalised task recommendations for workers in logistics, retail, and healthcare,” said Hubbard. “The devices can use AI-driven conversational interfaces to assist workers, answer operational queries, or suggest optimised workflows.”
By incorporating AI orchestration tools, coordination between different device functions, external systems, and APIs (e.g., warehouse management systems, IoT sensors) could deliver context-aware, real-time insights. Such integrations could drive significant efficiency gains, reduce downtime, and empower workers to focus on high-value tasks.
Co-Pilots, Multimodal and On-Device Models
The concept of AI copilots—intelligent assistants embedded into workflows—is expanding across sectors. Beyond desk workers and developers, industries such as healthcare, manufacturing, and retail are integrating these copilots to enhance operational efficiency. Task-specific AI models fine-tuned for particular industries are expected to excel, offering tailored insights and automation.
These co-pilots will increasing take the form of multimodal AI systems, capable of processing and generating content across text, audio, video, and images, are becoming more integrated into everyday devices. Applications in robotics, automotive systems, and smart assistants are paving the way for more intuitive human-machine interactions.
The movement toward on-device AI processing is gaining traction, driven by privacy concerns, reduced cloud dependency, and cost efficiency. Smaller, power-efficient AI models are enabling faster experimentation and deployment while addressing data security issues.
Responsible AI, Regulation, and Sustainability
With generative AI’s capabilities expanding, ethical concerns and regulatory frameworks are taking centre stage. Companies are embedding responsible AI practices to address challenges like disinformation and IP disputes. Transparency and trust are becoming critical factors for successful AI adoption.
AI is also a central to sustainability initiatives, optimising energy use, reducing waste, and enabling low-impact product designs. Companies integrating AI into their sustainability strategies are reducing environmental footprints and commanding premium prices from eco-conscious consumers.
Open-Source AI Maturity
The open-source AI ecosystem continues to mature, significantly democratising access to advanced AI tools, models, and platforms. This democratisation is driven by the rise of developer tools, MLOps (Machine Learning Operations) automation, and no-code/low-code platforms, enabling individuals and organisations—even those without extensive technical expertise—to build and deploy AI applications.
No-code and low-code platforms provide intuitive interfaces that eliminate the need for complex programming. These platforms integrate MLOps capabilities, automating critical aspects of the AI development lifecycle, including data preprocessing, model training, deployment, and monitoring. By abstracting away technical complexities, they empower a broader audience to leverage AI in innovative ways (ScienceDirect, IBM).
MLOps automation further enhances this trend by streamlining workflows and ensuring scalability and reliability in AI applications. Frameworks such as Edge MLOps leverage cloud and edge computing to orchestrate machine learning operations, bringing AI capabilities closer to end users and reducing latency (MLOPS Landscape). These advancements make it feasible to integrate AI into diverse applications, from consumer-facing tools to enterprise-level solutions, without requiring a dedicated team of AI engineers.
However, this democratisation also presents challenges. The ease of access increases the risk of misuse, security vulnerabilities, and ethical concerns. Maintaining vigilance through community oversight, robust governance, and ethical development practices is essential to mitigate these risks and ensure responsible AI deployment.
For organisations, the convergence of open-source maturity, MLOps automation, and no-code platforms represents a unique opportunity.
“By embracing these tools, they can accelerate AI adoption, reduce development costs, and empower non-technical teams to contribute to AI-driven innovation,” said Hubbard. “This trend underscores the transformative potential of AI as it becomes a tool for everyone, not just experts, heralding a new era of widespread AI application development that makes work better every day.”
