Latest news with #AIagents
Yahoo
a day ago
- Business
- Yahoo
Stop Wasting AI on Personal Productivity: 60% of Leaders Pivot to Agentic Automation for Real Enterprise Value
New Research Confirms Costly Copilot Limitations, Driving Demand for Governed, Process-Centric AI Solutions That Accelerate Autonomy SAN JOSE, Calif., June 20, 2025 /PRNewswire/ -- Automation Anywhere, the leader in Agentic Process Automation (APA), today released a new proprietary research report developed in collaboration with Forrester Consulting, revealing key barriers and breakthroughs shaping enterprise adoption of AI agents. The findings highlight the increasing momentum of AI agents across industries, as well as the implementation challenges organizations must address to realize their full potential. The study, based on a survey of global decision-makers overseeing enterprise-wide AI strategies, found that 60% of respondents believe automation platforms—especially those from Robotic Process Automation (RPA) and AI leaders like Automation Anywhere—are the most valuable foundation for managing AI-driven processes. This preference outpaces general-purpose AI providers such as OpenAI (ChatGPT) and Anthropic (Claude), as well as broader enterprise platforms like Microsoft Power Automate and Salesforce Einstein, highlighting the need for automation-native solutions purpose-built for process orchestration and scale. Additionally, 71% of respondents agreed that automation solutions should augment human capabilities rather than replace them—reinforcing the importance of keeping strategic decision-making in human hands. "This research highlights a critical inflection point for enterprises," said Mihir Shukla, CEO of Automation Anywhere. "Leaders are clearly prioritizing AI-augmented workflows, recognizing the undeniable value of Agentic AI. The fact that a significant majority are specifically seeking these solutions from modern, cloud-native RPA and AI automation vendors underscores that deep process automation expertise is critical to scale adoption and unlock meaningful impact, accelerating the journey to the autonomous enterprise and paving the path to artificial general intelligence for work." Key Insights from the Study: High Interest Meets Practical Hurdles With deep roots in AI-powered automation and RPA, Automation Anywhere's APA system is purpose-built to overcome the key hurdles slowing AI agent adoption. While 74% of respondents recognize the promise of AI agents to surface insights from vast data sets, concerns around data privacy (66%), skillset gaps (63%), and integration complexity (61%) persist. APA is designed to balance autonomous execution with enterprise-grade governance and human oversight—making it possible to scale safely and effectively. Transformational Opportunities Across Business Functions Organizations are already piloting or implementing AI agents for internal employee support (53%) and customer service (48%). Many plan to extend these capabilities to broader business functions, to enterprise automation and organizational stewardship in the next two years. The potential value of AI agents for areas such as customer service, sales automation, and compliance received transformational value ratings exceeding eight out of ten on average. With the launch of our new Agentic Solutions, Automation Anywhere is helping organizations accelerate this shift—offering pre-built, enterprise-grade AI agents that go beyond pilots to deliver real business impact across customer service, finance, compliance, and more. Businesses Demand Enterprise-Grade AI Automation Platforms When evaluating platforms for building and deploying AI agents, 60% of respondents found intelligent automation platforms from RPA and AI automation vendors to be highly valuable for long-running processes. Organizations strongly prefer solutions capable of enterprise-grade integration, end-to-end process orchestration, and mature data security. Automation Anywhere is uniquely positioned to meet these enterprise demands with their APA system—offering intelligent, secure, and scalable AI agents that integrate seamlessly across systems, orchestrate complex, long-running processes end-to-end, and uphold the highest standards of data security and governance. Early Adoption & Transformational Value Nearly 75% of leaders plan to pilot AI agents for customer support within the next year, with 71% eyeing research applications. Across all potential use cases, respondents expect transformational levels of value, underscoring strong confidence in AI agents' impact. Navigating the Road Ahead While challenges remain, enterprise leaders are clear-eyed and confident about the transformational potential of AI agents. By proactively addressing hurdles around security, cost, and talent, organizations can move beyond experimentation and begin scaling Agentic AI to drive measurable business outcomes. Those that act decisively today will be best positioned to lead in the AI-powered enterprise of tomorrow. Automation Anywhere is helping enterprises accelerate this journey—offering a secure, cost-effective, and easy-to-adopt APA system with new pre-built Agentic Solutions and the agentic solutions workspace that reduce complexity, lower barriers to entry, and empower business users to confidently scale AI agents. About Automation Anywhere Automation Anywhere is the leader in Agentic Process Automation (APA) and guided by its vision to fuel the future of work by unleashing human potential through automation. Learn more at SOURCE Automation Anywhere, Inc. Error while retrieving data Sign in to access your portfolio Error while retrieving data Error while retrieving data Error while retrieving data Error while retrieving data
Geeky Gadgets
2 days ago
- Business
- Geeky Gadgets
Learn How AI Agents Actually Work : Smarter Than You Think
What if machines could not only follow instructions but also think, adapt, and make decisions on their own? This isn't science fiction—it's the reality of AI agents, a new evolution in automation. Unlike traditional workflows that rigidly follow predefined steps, AI agents bring intelligence and flexibility to the table. Imagine a system that not only schedules your meetings but also reschedules them dynamically based on shifting priorities or external factors. This level of autonomy is reshaping industries, from content creation to customer support, and it's happening faster than most people realize. The AI Advantage uncover the mechanics behind how AI agents work and what makes them so fantastic. We'll explore their core components, how they differ from traditional automations, and the secret to their adaptability. Whether you're curious about building your own AI-driven systems or simply want to understand the technology powering modern workflows, this guide will demystify the process. By the end, you'll see why agents are more than just tools—they're collaborators in tackling complex challenges. The question is: how will you harness their potential? AI Agents vs Automations What Are Automations and AI Agents? Automations and AI agents serve distinct purposes, and recognizing their differences is critical for implementing the right solution in various scenarios. Automations: These are predefined workflows or pipelines that execute a fixed sequence of triggers and actions to achieve specific outcomes. For example, an automation might save email attachments to a designated cloud folder whenever a new email arrives. Automations are deterministic, meaning their behavior is predictable and consistent. These are predefined workflows or pipelines that execute a fixed sequence of triggers and actions to achieve specific outcomes. For example, an automation might save email attachments to a designated cloud folder whenever a new email arrives. Automations are deterministic, meaning their behavior is predictable and consistent. AI Agents: In contrast, AI agents are dynamic and adaptive. They incorporate capabilities such as reasoning, planning, memory, and autonomy to make decisions in real time. This enables them to handle more nuanced and complex tasks, such as responding to user feedback or adapting to changing contexts. The key distinction lies in adaptability: while automations follow rigid, predefined paths, AI agents adjust their actions based on the situation, making them more versatile for dynamic environments. Core Components of Workflows Workflows form the foundation of many automated systems, providing a structured approach to task execution. They consist of three primary components: Input: The data or event that initiates the workflow, such as a user query, a system notification, or an external trigger. The data or event that initiates the workflow, such as a user query, a system notification, or an external trigger. Processing Steps: The actions performed on the input, which may involve AI models like OpenAI's GPT to analyze data, generate responses, or perform calculations. The actions performed on the input, which may involve AI models like OpenAI's GPT to analyze data, generate responses, or perform calculations. Output: The final result of the workflow, such as a completed task, a generated report, or an actionable recommendation. To enhance workflows, integrating knowledge bases can provide dynamic context. For instance, web scraping can supply real-time information, while document uploads can offer domain-specific data. A practical example might involve a content creation workflow that uses AI to generate articles based on the latest industry trends. How AI Agents Actually Work in 2025 Watch this video on YouTube. Unlock more potential in AI agents by reading previous articles we have written. How AI Agents Build on Workflows AI agents elevate workflows by introducing intelligence and adaptability. They combine workflows, tools, and knowledge bases to perform tasks dynamically and respond to evolving circumstances. For example, an AI agent designed for content creation might: Use web scraping to gather up-to-date information on a specific topic. Generate a draft using an AI model like GPT, making sure relevance and accuracy. Send the completed draft to a recipient or publish it via an integrated platform. Unlike static workflows, AI agents can adjust their behavior based on user feedback, changing goals, or new data. This makes them particularly effective for tasks requiring flexibility, decision-making, and real-time adaptability. Real-World Applications of AI Agents AI agents are highly versatile and can be applied across a wide range of industries and use cases. Here are some practical examples: Content Creation: AI agents can generate articles, reports, or marketing materials by combining workflows with tools like web scraping and AI models. AI agents can generate articles, reports, or marketing materials by combining workflows with tools like web scraping and AI models. Programming Assistance: Developers can use AI agents to suggest code snippets, debug errors, or automate repetitive coding tasks. Developers can use AI agents to suggest code snippets, debug errors, or automate repetitive coding tasks. Research and Analysis: AI agents can gather, analyze, and summarize large datasets to provide actionable insights for decision-making. AI agents can gather, analyze, and summarize large datasets to provide actionable insights for decision-making. Customer Support: AI agents can handle customer inquiries, provide personalized responses, and escalate complex issues to human agents when necessary. AI agents can handle customer inquiries, provide personalized responses, and escalate complex issues to human agents when necessary. Task Automation: From scheduling meetings to managing inventory, AI agents can streamline operations and reduce manual effort. These applications illustrate how AI agents can enhance productivity, streamline processes, and tackle complex challenges across diverse domains. Workflows vs Agents: Key Differences The primary difference between workflows and AI agents lies in their adaptability and scope of application: Workflows: These follow fixed, deterministic paths with predictable outcomes, making them ideal for routine and repetitive tasks. These follow fixed, deterministic paths with predictable outcomes, making them ideal for routine and repetitive tasks. AI Agents: These are dynamic systems capable of making decisions and adjusting their actions based on context, user input, or changing objectives. This adaptability allows AI agents to handle tasks that are too complex or variable for traditional workflows, making them a powerful tool for addressing modern challenges. How to Build AI Agents: A Step-by-Step Guide Creating effective AI agents requires a structured approach that builds on foundational elements. Here's a step-by-step guide: Step 1: Start with Prompts: Develop clear and precise prompts to guide AI models like GPT in generating accurate and relevant outputs. These prompts serve as the foundation for effective communication with the AI. Develop clear and precise prompts to guide AI models like GPT in generating accurate and relevant outputs. These prompts serve as the foundation for effective communication with the AI. Step 2: Build Workflows: Design workflows that automate specific tasks by integrating triggers, actions, and outputs. This step ensures a structured approach to task execution. Design workflows that automate specific tasks by integrating triggers, actions, and outputs. This step ensures a structured approach to task execution. Step 3: Create AI Agents: Combine workflows, tools, and knowledge bases to build agents capable of dynamic decision-making and handling complex tasks. This integration enables the agent to adapt to changing circumstances and user needs. By following this progression, you can scale from simple automations to advanced AI-driven systems that address a wide range of challenges. Tools for Building AI Agents: Vector Shift Platforms like Vector Shift provide a comprehensive environment for designing and managing AI agents. Key features include: Integration with Knowledge Bases: Incorporate tools like web scraping and document uploads to provide dynamic, real-time context for tasks. Incorporate tools like web scraping and document uploads to provide dynamic, real-time context for tasks. Support for AI Models: Use advanced AI models like GPT to process data, generate content, and perform complex analyses. Use advanced AI models like GPT to process data, generate content, and perform complex analyses. Connectivity with External Tools: Seamlessly integrate with external platforms, such as Google search, email services, or project management tools, to enhance task execution. These platforms simplify the process of building and deploying agents, making them accessible even to users with limited technical expertise. By using such tools, you can focus on designing intelligent systems that deliver tangible results. Media Credit: The AI Advantage Filed Under: AI, Guides Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Al Bawaba
2 days ago
- Business
- Al Bawaba
Lemnisk Unveils Industry-First Innovations for the AI Era of Customer Engagement
Lemnisk, a leading enterprise Customer Data Platform (CDP) and marketing technology company, today introduced a suite of AI innovations that mark a significant leap forward in real-time, personalized customer engagement. Trusted for its enterprise-grade security and compliance, Lemnisk's enhanced platform now introduces advanced capabilities designed to power the next era of intelligent, AI-driven marketing automation.● Real-Time, Event-Driven Predictive Scoring: Go beyond static segmentation. Lemnisk now categorizes audiences in real time based on their immediate purchase likelihood or churn probability. This enables brands to proactively engage customers with timely retention and conversion strategies triggered by their actual behavior.● Entity-Level Identity Resolution: Move beyond the 'one-size-fits-all' customer profiles. Now, you can unify customer intelligence across business lines - credit cards, loans, investments, and more - while still executing campaigns at the individual product level.● Voice to CDP: Feed in contact center recordings to auto-transcribe, extract sentiment and topic insights, and seamlessly feed them into the CDP as real-time segmentation signals. With automated clustering and no manual tagging required, this feature powers real-time personalization for voice journeys.● MCP Compliance for AI Agents: Lemnisk CDP is now MCP (Model Context Protocol) compliant via Lemnisk's external API and MCP Server Integration framework. This enables brand agents to securely access contextual enterprise data in real time and complete transactions inside conversations.'The exploding AI landscape demands foundational changes in how enterprises understand & engage with customers. Traditional models of real-time responsiveness are being disrupted with agentic AI,' said Subra Krishnan, CEO of Lemnisk. 'Our latest innovations reflect a bold step forward, empowering marketers to anticipate needs, personalize at scale, and show up for customers in the exact moments that drive loyalty and growth. Just as importantly, we're future-proofing our platform to ensure enterprises stay ahead in an AI-first world'. These AI-native capabilities are now generally available to all Lemnisk customers. To fully leverage the power of real-time intelligence and next-gen personalization, Lemnisk recommends that customers on earlier versions migrate to the latest release of the CDP.
Yahoo
2 days ago
- Business
- Yahoo
KPMG Launches KPMG Workbench: A Multi-Agent AI Platform, Transforming Client Delivery and Ways of Working Across the Global Organization
KPMG Workbench is an open, interoperable AI platform, developed on Microsoft technology and with their support, that is empowering KPMG professionals with the support of trusted, intelligent AI agents to accelerate innovation for clients and complement human expertise. New York, New York--(Newsfile Corp. - June 18, 2025) - KPMG International today launched KPMG Workbench - KPMG's foundational and single AI platform designed to scale global adoption and integration of AI, while enhancing trust and control over AI across all geographies and teams. It is part of the global organization's multi-billion dollar investment in AI and agentic transformation and is the foundation that will underpin KPMG firms' client delivery platforms, KPMG Digital Gateway (Tax), KPMG Velocity (Advisory) and our smart audit platform, KPMG Clara. This is the latest of several AI announcements from KPMG following recent investments with Microsoft and other alliance partners to help create value, faster by bringing innovative digital solutions, efficiency gains and advanced technology in practical ways to clients. Highlights KPMG Workbench has a network of 50 AI assistants (agents) and chatbots that interact with each other across multiple sectors, with nearly a thousand AI assistants in development to meet diverse client needs. These agents, built to work with a range of large language models (LLMs), work as digital teammates alongside KPMG professionals to help provide quicker, quality, trusted solutions for clients. KPMG Workbench is a flexible platform built on Microsoft Azure AI Foundry Services that allows for interoperable, agent to agent communications. In addition, it brings together capabilities from across the KPMG ecosystem of alliance partners, such as Oracle, Salesforce, ServiceNow, Workday and more, so clients accessing it can choose the model or AI agent that fits their task. KPMG Workbench will help enable the next generation of KPMG's AI-enabled client service delivery, including innovative and content rich 'Services as Software' (SaS) that aims to provide KPMG clients with AI tools steeped in our people's deep industry expertise and experience. KPMG Workbench has built-in data sovereignty, meaning clients can maintain full control of how their data is stored and processed and manage diverse risk and governance needs, helping them to meet local and global regulatory requirements. KPMG is the first organization in the world to achieve BSI/ISO 42001* certification for AI Management Systems and every agent and tool on KPMG Workbench is accredited with a KPMG "Trusted AI stamp"- ensuring every tool and agent has been assessed against our 10 pillar Trusted AI Framework. "Clients tell us that their ability to orchestrate and control their agents in a secure way is becoming their number one concern. They also want a multi-model platform rather than being locked into one provider. Recognizing this, and KPMG's own needs as a complex global business, the strategy was to build a foundational AI platform that has sovereign data capabilities and enables our people to integrate AI models into one environment that is embedded in our Trusted AI Framework. With KPMG Workbench, we're combining advanced AI agents with the insight, judgment and deep expertise of our people to deliver smarter solutions for clients, faster and with full confidence in their security and compliance." - David Rowlands, Global Head of AI, KPMG International A shared vision for transforming how KPMG professionals work KPMG leveraged the Microsoft Azure platform, including AI capabilities in Azure AI Foundry, as the foundation for KPMG Workbench. This investment marks the evolution of Workbench from a proven platform for KPMG's developers to the firm's central hub for delivering and managing AI, using advanced agent interoperability technology to connect intelligent tools and support throughout various stages of the client journey. With generative and agentic AI, KPMG Workbench's ability to scale and specialize helps makes it transformative. By coordinating multiple task-specific agents to collaborate and share context, KPMG Workbench enables team members to automate complex, multi-step processes from client onboarding to regulatory reporting. These agents are modular, reusable, and continuously evolving, making it easier to adapt to changing business needs. "KPMG Workbench represents a transformative step in AI-driven collaboration, integrating multi-agent intelligence into a centralized platform leveraging Azure AI Foundry. This investment is more than just technological innovation - it's a pivotal move toward the future of the open agentic web. At Microsoft, we are empowering businesses to enrich the employee experience and reinvent customer engagement through AI transformation, and KPMG Workbench is a prime example of that vision coming to life." - Judson Althoff, EVP, Chief Commercial Officer, Microsoft Clients onboarded to KPMG Workbench Private instances of KPMG Workbench will be available to clients from across industries to help develop and manage their digital workforce of the future. Globally, KPMG firms' clients are already benefiting from KPMG Workbench, including: A global bank that requires data sovereignty and processing of agents to help the organization verify the identity and legitimacy of its customers and mitigate risk, including fraud. A telecommunications and technology organization with a need to enhance their compliance management with AI agents in a highly regulated industry where trust built into the platform is imperative. A global household retailer looking to drive back-office productivity by growing a digital workforce using multiple AI models. "Launching KPMG Workbench is a pivotal milestone in our AI commitment to transforming client delivery and enhancing ways of working across the global organization. This strategic investment, made in collaboration with Microsoft, reinforces our dedication to creating a leading technology ecosystem in an era of rapid evolution and shifting client needs." - Carl Carande, Global Head of Advisory, KPMG International and Vice Chair, Advisory, KPMG in the US. To learn more about KPMG and our Trusted AI capabilities, visit or * ISO 42001 (AI) is a new international standard that specifies requirements for establishing, implementing, maintaining, and continually improving an Artificial Intelligence Management System (AIMS) within organizations. The first internationally recognized standards for AI, ISO 42001 is administered by the International Organization for Standardization and is the world's first AI management system standard, providing valuable guidance for this rapidly changing field of technology. KPMG was certified by BSI on the ISO42001 standard. Notes to Editors: For media queries, please contact: Elizabeth LynchCorporate CommunicationsKPMG LLPelizabethlynch@ About KPMG International: KPMG is a global organization of independent professional services firms providing Audit, Tax and Advisory services. KPMG is the brand under which the member firms of KPMG International Limited ("KPMG International") operate and provide professional services. "KPMG" is used to refer to individual member firms within the KPMG organization or to one or more member firms collectively. KPMG firms operate in 142 countries and territories with more than 275,000 partners and employees working in member firms around the world. Each KPMG firm is a legally distinct and separate entity and describes itself as such. Each KPMG member firm is responsible for its own obligations and liabilities. KPMG International Limited is a private English company limited by guarantee. KPMG International Limited and its related entities do not provide services to clients. For more detail about our structure, please visit Throughout this press release, "we", "KPMG", "us" and "our" refers to the KPMG global organization, to KPMG International Limited ("KPMG International"), and/or to one or more of the member firms of KPMG International, each of which is a separate legal entity. To view the source version of this press release, please visit Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
Forbes
2 days ago
- Business
- Forbes
From Chatbots To Workbots: Why NiCE's AI Strategy Focuses On Execution
NiCE is betting that AI agents that complete tasks—not just conversations—will separate winners from pretenders in the enterprise AI race The artificial intelligence hype cycle has reached peak saturation, with technology vendors scrambling to slap "AI-powered" labels on everything from refrigerator recipe suggestions to chatbots that rely on simple keyword matching. Yet, for all the breathless marketing rhetoric, most business leaders are still waiting for AI that simplifies operations and improves data analysis. NiCE, a customer experience platform provider, is making a calculated bet that the next phase of enterprise AI won't be about making chatbots sound more human. The next wave of business outcomes will leverage AI agents that can navigate complex business processes from start to finish with minimal human intervention. NiCE's CEO, Scott Russell said, 'Optimizing knowledge is not just critical for AI to truly thrive in your environment, but it's also the key for transforming service from reactive to proactive, identifying opportunities to solve issues and predict future needs.' NiCE's recent product launches and strategic moves reveal a move toward enhanced automation and agentic AI. The first wave of enterprise AI focused primarily on making data more accessible through conversational interfaces—essentially putting a chat layer on top of existing applications and knowledge repositories. While this represented significant progress in democratizing data access, it only scratched the surface of AI's potential business value. Organizations could ask questions and get answers, but the burden of reasoning through complex decisions and taking action remained entirely on human operators. Going forward, technology vendors, such as NiCE, will use AI to deliver solutions that can reason through multifaceted problems and take semi or fully autonomous action. This evolution from conversational AI to agentic AI represents the difference between AI that informs and AI that performs. Agentic AI enhances a company's ability to analyze context, weigh multiple variables, make informed decisions based on key business performance indicators, and execute actions across interconnected systems. Traditional conversational AI helps a customer service representative find relevant information. Still, agentic AI can help a representative evaluate a customer's complete history more easily, assess risk factors, determine appropriate responses based on business rules, and automatically trigger the necessary workflows to resolve issues end-to-end. "There's a big difference between AI that talks and AI that gets things done," explains Barry Cooper, President of NiCE's CX Division. "While others are building agents that mimic conversations, we're building agents that fulfill customer needs—end to end." This distinction becomes crucial when examining NiCE's CXone Mpower Agents. Traditional AI chatbots had limited access to data, offered scripted responses, and were confined to specific areas of the business, such as front-office or back-office operations. NiCE's AI agent platform aim to break through these constraints by operating across the entire enterprise ecosystem—from initial customer contact through mid-office approvals to back-end fulfillment systems. Admittedly, Agentic AI is the AI buzzword of 2025, but early, well-scoped use cases show promise. Technology companies are increasingly working to streamline AI deployment as traditional approaches require extensive technical resources, custom development, and lengthy implementation cycles. NiCE's model simplifies AI agent creation while maintaining enterprise-grade sophistication through what they call vibe coding, allowing business users to tailor each agent's personality and communication style without requiring technical expertise. While the concept of vibe coding remains ill-defined, and its merits are hotly debated within the enterprise software community, there is a broad consensus around the underlying goal of making AI agents easier to code and deploy. The specific term matters less than the fundamental shift toward empowering business users to create and customize AI functionality without requiring deep technical expertise. In a rapidly evolving tech landscape, no single vendor can deliver everything an enterprise needs to succeed with AI, cloud, data, and digital transformation. Today, companies are no longer looking for isolated solutions—they need interconnected ecosystems. That's why strategic partnerships are essential. By working together, enterprise technology vendors can bridge data and function silos, improving workflows and accelerating innovation. Just as importantly, these alliances help enterprises extract greater value from existing technology investments by ensuring that new capabilities work in concert with the tools already in place. Over the past several months, NiCE has expanded its partnership with Amazon Web Services (AWS) and added ServiceNow and Snowflake to the mix. At Interactions 2025, NiCE announced an expanded collaboration with AWS, bringing together NiCE's domain expertise and rich interaction data with AWS's cloud infrastructure and generative AI services, including Amazon Bedrock, Amazon Q, and the Amazon Nova family of large language models. The partnership addresses some of the most pressing challenges facing enterprise AI deployments: fragmented workflows, disconnected data, and inconsistent global performance. The partnership focuses on three core pillars. First, content-aware automation ensures that AI-generated responses are highly relevant and context-specific. Using the Amazon Q Index, Mpower Agents are equipped with up-to-date business content—from product documentation to policy details and case histories—enabling them to respond accurately and confidently in real time. Second, the integration delivers enterprise-wide orchestration by bridging front, middle, and back-office operations. NiCE's CXone Mpower Orchestrator automates workflows across functional teams, while Amazon Q Business extends this reach into a broader set of enterprise applications—eliminating silos and streamlining complex processes. Additionally, global scalability is made possible through AWS's robust cloud infrastructure. With low-latency performance and high availability across regions, multinational organizations can deploy and scale AI-driven customer service experiences quickly and consistently around the world. NiCE's partnership strategy also extends beyond AWS to include other critical enterprise platforms, such as ServiceNow and Snowflake. NiCE's latest partnership with ServiceNow aims to eliminate long-standing service gaps by tightly integrating real-time customer engagement with enterprise workflow automation. Announced at ServiceNow's Knowledge 2025 event, the collaboration integrates NiCE's customer service platform with ServiceNow's AI and Customer Service Management (CSM) tools to streamline operations across the entire organization, from the front office to the back. The goal: fully automated customer service fulfillment. The combined solution routes inquiries based on sentiment, intent, and service-level agreements (SLAs)—bridging siloed departments to accelerate resolution times and enhance both customer and employee experiences. Role-based AI copilots assist agents and back-office teams with real-time insights and next-best actions, while continuous optimization tools flag issues and launch workflows automatically. These relationships provide access to complementary technologies and customer bases, allowing NiCE to integrate with the broader enterprise software ecosystem that companies rely on for operations, data management, and workflow automation. NiCE's strategic collaboration with Snowflake aims to unlock the full value of customer interaction data by making it accessible, secure, and actionable across the enterprise. By integrating Snowflake's AI Data Cloud with CXone Mpower, NICE can improve data sharing, breaking down silos that have traditionally limited the impact of customer insights. Snowflake serves as the backbone of the CXone Mpower data lake, centralizing interaction data and enriching it with information from other enterprise systems. This unified data foundation allows organizations to automate key processes—from billing to claims handling—while powering AI-driven analytics, dashboards, and decision-making. The result: faster fulfillment, greater accuracy, and a deeper, organization-wide understanding of the customer experience. Apparently, 2025 is the year of the brand refresh. The technology industry has witnessed updates from Five9, Google's G, Hitachi HPE, and Qualcomm's introduction of Dragonwing, alongside NiCE's own transformation. Every brand refresh has its own story to tell, but NiCE's new logo and marketing campaign represent more than a desire for fresh typography and color schemes. The rebrand indicates the company's strategic desire to expand its AI vision beyond the contact center to encompass its broader portfolio of finance and security solutions. The company describes the rebrand as positioning "NICE to empower brands to deliver AI-powered experiences that are proactive, human-centered and intuitive—whether connecting with customers, protecting communities or combatting financial crime." NiCE's solution involves partnering with actress Kristen Bell, who serves as the face of the company's "NiCE World" brand campaign. The initiative positions Bell as the "NiCEst Person in the World," NiCE said the campaign "builds on NiCE's reimagined brand, championing a future where AI isn't just intelligent – it's connected, intuitive and working behind the scenes to make life better. The enterprise AI market remains in flux, with new entrants and existing players continually repositioning themselves. NiCE's focus on domain expertise, integration depth, strategic partnerships, and automation suggests a company that understands both the technical and implementation requirements necessary for large-scale AI adoption. As enterprises increasingly demand AI that delivers results, NiCE's bet on fulfillment-focused automation may prove prescient. Of course, there's still the matter of cost and return on investment. Most companies struggle to understand and plan for the true product and operational costs of AI. Organizations need to work with their technology vendors to deploy well-scoped use case that deliver measurable return on investment, fast. The question isn't whether AI will transform customer experience—it's which companies will build AI that completes the transformation rather than just talking about it.
