Category: Customer Experience

  • AI Search Helps SAP Improve Customer Support With Coveo

    AI Search Helps SAP Improve Customer Support With Coveo

    Most companies struggle to prove AI ROI, but enterprise software provider SAP found an approach that works


    The artificial intelligence (AI) market is expanding rapidly. Enterprise AI spending surged eight-fold in 2024 according to the Menlo Ventures. This year is expected to be a banner year for AI adoption. However, many AI initiatives are not delivering measurable returns.


    Recent AI studies paint a stark picture. MIT reports that 95% of generative AI pilots fail to reach production, while RAND found that 46% of companies scrapped their AI projects before launch. Perhaps most telling, Boston Consulting Group discovered that only 24% of firms have developed the capabilities needed to move AI beyond proof-of-concepts into tangible business value. While we could debate what the actual percentages are, one thing is clear. Companies are spending money on AI but aren’t getting the returns they expected.

    What separates the AI strategy winners from the also-rans? It appears the key to AI success is selecting a set of narrowly scoped use cases with a quantifiable cost that will benefit from using AI. As Best Buy said, we don’t have AI projects, we have projects that use AI. SAP’s journey offers one success story with crucial lessons for any organization struggling to prove AI’s worth.


    Some of the best AI success stories start by addressing a simple problem. For example, Cisco discussed how leveraging AI to detect network configuration issues solved a key customer support challenge.

    The Million-Case Support Crisis That Sparked SAP’s Internal AI Journey


    For SAP, the path to AI success began with a simple but expensive problem: customer support for its over 300 million cloud subscribers. Like many companies, the German enterprise software vendor was experiencing significant outflow of money on avoidable service requests. The goal was not to remove employees, but to eliminate unnecessary inquiries.

    AI has the ability to create capacity for the existing workforce to build new customers service experiences. Customers were struggling to find answers , leading to unnecessary support tickets that customers could have avoided through self-service.
    “We weren’t hitting our self-service targets,” explains Michelle Lewis-Miller, the Head of Digital Experience and Voice of Customer for Customer Support at SAP. “We couldn’t see what was happening and couldn’t measure anything.”

    Visibility is a key issue across businesses of all sizes. Many companies deploy AI solutions without clear metrics or understanding of where the technology can create the most impact. SAP started with a measurable business problem and worked backward to find the right AI solution.

    The Foundation: Getting Knowledge Quality Right First


    Before implementing any search solution, SAP had already invested heavily in knowledge quality. Lewis-Miller uses a culinary metaphor to explain: “If you’re serving dinner with poor ingredients, it doesn’t matter how great the chef is.”

    Too many organizations rush to implement AI technology without first ensuring their underlying data and content are ready to support it. Knowledge wasn’t the company’s problem. SAP had over 10 million knowledge assets—spanning help documents, SAP Knowledge Base Articles, community posts, and videos- as a foundation for its search efforts. “SAP had already made massive investments to get the knowledge into good shape, and introduced AI-driven Incident Solution Matching recommender for knowledge. The problem was an inferior knowledge discovery experience via search,” Lewis-Miller notes.

    30% Case Reduction: The Pilot Results Nobody Expected


    Rather than launching a company-wide AI initiative, SAP began with a targeted pilot in their Concur travel and expense division. The Concur division was already using Coveo’s AI-powered search platform, making it an ideal testing ground for generative AI capabilities.


    The Coveo AI pilot results exceeded all expectations. Within six months, SAP Concur experienced a 30% drop in support case volume, translating to €8 million in annual cost avoidance. For many customers, this was their first encounter with generative AI—and it delivered immediate, tangible value.


    “When we piloted Relevance Generative Answering in Concur, it went so well it was shocking,” Lewis-Miller recalls. “We saw such a huge decrease in case submission that helped us tremendously in keeping up with business growth.”


    The Coveo-SAP Concur pilot’s success illustrates a key principle: AI ROI comes from solving specific, measurable problems rather than implementing technology for technology’s sake. The SAP Concur team had clear metrics (case volume reduction), a defined user base, and a focused use case.


    The Cultural Shift That Enabled Bold Decisions


    The cultural shift at SAP proved crucial to the initiative’s success. In a discussion with Lewis-Miller, she emphasizes that executive support went beyond budget approval: “Our executives came in and made a very clear statement. They said, You’re not going to be punished for trying.” This created an environment where teams could be decisive rather than seeking consensus from dozens of stakeholders.


    She refers to this as the “tip of the spear approach,” noting that “at an organization of any size you need to find the people who are willing to take risks, and you need to have the executive leadership being unequivocal about supporting that person who’s willing to take the risk.” Without this top-down support for calculated risk-taking, the project likely could have stalled in committee discussions and consensus-building exercises that ultimately may have prevented any meaningful progress.


    Scaling Success: The SAP for Me Challenge


    Emboldened by the Concur results, SAP faced a much larger challenge: implementing AI-powered search across SAP for Me, the central portal serving millions of customer interactions every month. Unlike the focused SAP Concur deployment, this required integrating 14 to 20 different knowledge bases while serving vastly different user types—from certified engineers with 30 years of SAP experience to small business owners with limited technical background.


    The complexity initially threw the team. When they deployed the same generative AI technology on SAP for Me, the results looked worse at first glance—higher click rates and more case submissions seemed to indicate the system wasn’t working.
    “We expected it to address the low-hanging fruit. It wasn’t at all like what we saw with Concur. We asked Coveo if something was broken in their backend,” Lewis-Miller admits.


    The breakthrough came when SAP’s analysts realized they were measuring the wrong things. While simple questions decreased on the Concur platform, SAP for Me users were asking much more complex queries. The AI wasn’t avoiding low-value cases—it was helping resolve sophisticated problems that would have required expensive expert support.


    “We were handling a more valuable subset of cases,” Lewis-Miller explains. “When we changed our perspective and looked at the overall numbers, we saw that submissions had actually gone down.”


    Here’s where many AI projects fail: teams panic when initial metrics don’t match expectations and abandon promising initiatives. SAP took a different approach, partnering with their technology vendor to dig deeper into the analytics. Successful AI implementations require a company to deploy, monitor, analyze and iterate. SAP’s story highlights how theory rarely matches reality.


    The Results


    Since launching in 2023, the AI-powered search has enabled intelligent scaling of the support function to drive business growth. SAP’s AI strategy ensures that support costs grow slower than revenue, thereby expanding profit margins, and allowing its business to absorb significant market expansion. As a result, customer self-service success from search is now at over 80%, and the technology spans 47 different sources and indexes 11.2 million documents across the SAP ecosystem.


    The Technical Foundation That Makes It Work


    SAP’s success stems from choosing the right technical approach for their specific needs. Rather than building custom AI models from scratch, which is labor and time intensive, SAP leveraged a “hybrid search” system that combines multiple AI techniques:


    • Lexical search (traditional keyword matching) finds documents that contain the exact words you type—like searching for “invoice processing” and getting results that include those specific terms.
    • Semantic search (context-aware search) understands what you actually mean, not just the words you use. It recognizes that “payment issues” and “billing problems” refer to similar concepts, even when the exact words don’t match.
    • Behavioral machine learning (learning from user patterns) tracks which search results people actually click and use, then automatically surfaces the most helpful content for similar future searches.
    • Generative AI (AI-powered answer creation) provides direct responses to questions in plain English while showing exactly which company documents or sources the information came from, similar to how ChatGPT works but trained on your organization’s specific knowledge base.


    This hybrid approach addresses common search frustrations: lexical systems miss relevant results when you don’t use the exact right keywords, while pure generative AI can sometimes provide inaccurate information. The combination delivers both precision and intelligence.


    Advice For Building ROI with AI


    SAP’s re-invested cost avoidance didn’t happen by accident. Behind those impressive numbers lies a methodical approach that any organization can follow—if they’re willing to resist the allure of flashy AI demos and focus on fundamentals. Key steps that every organization can take include:


    • Start with measurable problems rather than exciting technology possibilities
    • Ensure your knowledge foundation is solid before implementing AI search solutions—garbage in, garbage out applies to AI just as much as any other technology
    • Pilot in controlled environments where you can isolate variables and measure impact
    • Secure executive support for risk-taking and empower decision-makers to act without requiring consensus from dozens of stakeholders
    • Document rigorously for compliance and architecture
    • Evaluate enterprise-ready AI solutions instead of jumping first into extensive custom AI development
    • Invest in proper analytics and measurement to understand true impact versus surface-level metrics


    While all of these points are important, Lopez Research has seen many companies skip the governance and compliance step. Lewis-Miller identifies meticulous documentation as a critical but often overlooked success factor.

    “We invested a lot of time in finding employees who had the soft skills necessary to interpret the technical detail for the legal people and the legal detail back to the technical detail.” This documentation became essential reference material that prevented the project from stalling at multiple compliance checkpoints. At enterprise scale, having detailed outlines for every architectural and legal decision proves invaluable.


    Beyond Cost Avoidance: Proactive Customer Experience


    SAP isn’t stopping at self-service enablement. The company is now using behavioral analytics to intervene before customers encounter problems. Lewis-Miller describes the vision: “As a user is engaging in one of our support journeys, our goal is to intervene in real time to get ahead of issues before they actually emerge.”


    The approach involves using customer behavior patterns to identify when someone is struggling to find information, then proactively routing them to chat support before frustration sets in. “We’re not trying to give everybody a white glove experience. We’re specifically intervening only in those moments where we can see it’s going down the wrong path,” Lewis-Miller explains.


    This includes predicting system issues before they occur and routing insights to product teams to fix confusing features before they generate support cases. By analyzing search patterns and support interactions, SAP can identify friction points in their products and address them proactively—turning support data into product improvement fuel.


    AI is doing more for internal innovation: For the cases that do reach a support engineer, SAP focuses on agent augmentation. AI-based case summarization enables faster handoffs, and intelligent routing connects a case to the correct expert instantly.
    The AI revolution is about implementing the right technology to solve real business challenges. SAP found their formula. Now it’s time for other enterprises to find theirs.

  • AI Analytics for Customer Experience: Advice for CX Leaders From Verint

    The promise of artificial intelligence in customer experience has never been clearer—or more urgent. Yet many organizations find themselves paralyzed between the fear of falling behind and uncertainty about how to proceed. According to Daniel Ziv, Global VP of AI and Analytics at Verint, this hesitation itself poses the greatest risk.

    “The pace of change impacts the need for much, much faster insights,” Ziv explains. “We need to analytics that could provide either real time or near real time insights.”

    Two Critical Frameworks for Understanding AI Analytics

    Macro vs. Micro Analytics in Customer Experience 

    Ziv introduces a useful distinction between two complementary types of analysis that organizations need:

    Macro analytics examines patterns across all customer interactions to identify emerging trends and issues at scale. As Ziv notes, “You need a system that can look scalably at the entire volume of stuff and give you a statistically valid sample across that trend.” For instance, one Verint customer discovered they were receiving three times as many calls about tariffs compared to recent periods, with these calls lasting twice as long.

    Micro analytics applies these insights to individual interactions in real-time. “I need to infuse that insight and analyze the call as it’s happening, provide real time guidance or apply this information to my website or to my IVA or IVR,” Ziv explains.

    The gap between these two creates competitive advantage: “Companies that have both have a real advantage,” he observes. Organizations that excel at macro analysis often fail to operationalize insights quickly enough, while those focused on micro  analytics may be optimizing  “on last year’s insights.”

    Generative vs. Agentic AI in Customer Experience

    Understanding the evolution from generative to agentic AI helps organizations plan their investments more strategically.

    Generative AI responds to prompts with content, understanding, and analysis. It excels at analyzing unstructured data like voice conversations and text interactions. As Ziv describes it: “AI can generate content, can understand unstructured conversations, including voice and text and video and images and respond to it.”

    Agentic AI takes this further by breaking down complex goals into multiple steps, executing them autonomously. “I can set a goal and say, hey, I want to increase revenue for my business. Go figure this out. And it’ll itself generate questions. Go to the Web. Go to my database. Go to different places. Come up with answers,” Ziv explains.

    Verint’s evolution of its Genie Bot illustrates this progression. The initial generative version analyzed calls about complaints or churn on request. The agentic version now breaks down questions independently, scales analysis across larger samples, quantifies results, and exports findings to presentation formats—executing entire workflows with minimal human intervention.

    The Flawed Decision-Making Patterns Holding Organizations Back

    Ziv identifies two common failure modes that prevent organizations from capitalizing on AI:

    Pattern 1: Gut-Based Decisions Without Data

    “A lot of executives tend to be in this position because they’ve been in a space for so long, they feel their gut feeling is usually accurate,” Ziv observes. While experience matters, rapid change undermines intuition’s reliability. Leaders are making decisions without data “because I don’t have the data, and I have to make a decision.”

    Pattern 2: Analysis Paralysis

    The opposite problem occurs when leaders recognize they need data but wait too long to get it. “I’m making the decision too late. By the time I make the decision, I’ve missed the opportunity. I’ve lost money and I’m behind compared to the competition,” says Ziv.

    Both patterns stem from the same root cause: analytics systems that can’t deliver insights fast enough to support decision-making at the speed of business change.

    Quantifiable Outcomes: What’s Actually Possible

    When organizations implement both macro and micro analytics effectively, the financial impact is measurable and significant. Verint provided the following examples:

    • A large international telecom saved $80 million and increased sales by 11% using macro insights combined with real-time coaching capabilities
    • Organizations typically find $5-10 million in insights within one to two days of deploying generative AI analysis
    • A UK financial services company generated $5 million in additional revenue by using AI to identify and resolve process inefficiencies in loan applications
    • An energy supplier saved $2 million through increased agent capacity after using AI to identify processes suitable for self-service

    “When we deploy our latest (Verint) Geniebot, we see that within one or two days, we typically find five to ten million dollars of insights just off the bat.” Ziv reports.

    Practical Guidance for Getting Started

    Start Small, Move Fast

    Rather than comprehensive transformation initiatives, Ziv advocates for incremental deployment: “What can you turn on without changing anything that will add value to your environment? Similar to your phone. I can download an app and turn on capability that now is transformative.”

    One Verint customer piloted automated call wrap-up notes with 300 agents, validated the impact, then scaled to 30,000 agents. This approach reduces risk while accelerating time-to-value.

    The Hybrid Architecture Advantage

    Organizations with on-premises infrastructure face a false choice between wholesale cloud migration and missing out on AI innovation. “You can have telephony on-premises and we send recordings in a secure way to cloud services with your existing deployment. You get immediate outcomes with very little cost, very little effort, very little risk,” Ziv explains.

    This hybrid approach allows organizations to access cloud-based AI compute power and models while maintaining existing infrastructure, avoiding the complexity and risk of full migration projects.

    Focus on Outcomes, Not Infrastructure

    “Ninety percent of the time and the effort is focused on moving infrastructure but only 10 percent is about outcomes,” Ziv observes about traditional cloud migration approaches. By inverting this ratio—focusing first on turning on AI capabilities that deliver business value—organizations can fund subsequent innovations through realized savings.

    The Strategic Imperative

    Ziv frames AI adoption as fundamentally strategic: “If you think about the biggest companies in the world, they are data companies that have better analytics, whether it’s Amazon or Google.”

    The competitive advantage lies not in which models to use, but in whether organizations can operationalize their data: “Your competitive advantage is the data that you have that your competitors don’t have. More importantly, how well are you using the data that you have access to?”

    His assessment of the current moment is unambiguous: “These are make or break years and down to minutes. Every minute where you’re not taking advantage, learning and embedding AI, it’s either a great opportunity or a huge risk.”

    Conclusion

    The path forward requires organizations to abandon both gut-based decision-making and analysis paralysis in favor of rapid, iterative deployment of AI capabilities. By implementing both macro and micro analytics, organizations can identify opportunities at scale while operationalizing insights in real-time customer interactions.

    The technology has matured to the point where organizations can achieve meaningful business outcomes—measured in millions of dollars—within days of deployment. The question is no longer whether AI analytics can deliver value, but whether organizations can move quickly enough to capitalize on the opportunity before their competitors do.

    As Ziv puts it: “The biggest risk is inaction.”

    You can see the whole interview on Youtube here or subscribe to the podcast at here.

  • Disney, Walmart, And Carnival UK Share Lessons In AI For Customer Experience

    Disney, Walmart, And Carnival UK Share Lessons In AI For Customer Experience

    When it comes to implementing AI for customer experience (CX), the gap between technology promises and enterprise deployment reality can be vast. During the NiCE Interactions conference, customer experience executives from three major companies—Disney, Walmart, and Carnival UK—shared how their companies navigated these challenges with different approaches, offering a glimpse into what works when deploying AI at scale.

    The companies’ collective experience reveals that successful AI implementation requires more than a focus on finding the right technology solution. Instead, CX success demands strategic partnerships, effective organizational change management, and a fundamental shift in how companies approach customer service operations.

    Carnival UK: The Knowledge Management Imperative

    When John Wells inherited customer service operations at Carnival UK, he faced a familiar enterprise challenge: 1.25 million annual guest interactions flowing through disconnected legacy systems that couldn’t even link a customer’s phone call to their email inquiry.

    “We had siloed systems,” Wells, the company’s Contact Center Director, explained. “If guests would phone us or they’d email us, we wouldn’t know one interaction to the next.”

    The cruise line’s transformation over 18 months represented a desire to reimagine the experience. “This wasn’t a technology change program,” Wells emphasized. “This was a business change program underpinned by a technology change.” Carnival UK understood that data quality was key to delivering a successful customer experience. The company spent six months consolidating scattered knowledge from multiple systems, recognizing that AI success depends entirely on information architecture.

    “Knowledge management and structuring your knowledge is as important to your success as AI management,” Wells discovered. “Getting the data and the knowledge in the right place, structured in the right way, enables you to be able to be successful in your development of the AI.”

    AI guardrails are mechanisms and strategies designed to ensure that AI systems, especially generative AI, operate within safe, ethical, and legal boundaries. Carnival UK’s methodical approach to knowledge management enables it to create guardrails that ensure AI responses align with company policies while maintaining the premium service experience that luxury cruise customers expect. 

    Carnival UK’s Key Lesson: Treat AI like a new employee. “You wouldn’t just put a new team member in place and just let them get on with it,” Wells explained. “You have to train and coach them. You have to work with them every day, tweak (the process), and point them in the right direction.”

    Disney: Leadership and Security First as a Foundation for Customer Experience

    Arun Chandra, SVP for Customer Experience at Disney, shared Disney’s vision to build the best CX program globally, serving over 150 million customers in 100 geographies. Disney’s approach uses three foundational principles that address the organizational dynamics often overlooked in AI implementations.

    First, Disney insists on direct senior leadership involvement, recognizing that executives must work alongside their teams to separate genuine AI capabilities from marketing hype. This hands-on approach ensures that AI initiatives align with business objectives rather than getting caught up in technological possibilities.

    Second, Disney places extraordinary emphasis on data privacy, legal requirements, and security challenges. The company focuses on critical questions are data use and management. For example, what data trained the AI models and was it proprietary Disney company data? It also has to evaluare the implications of using models trained on external and potentially inaccurate data sources.

    Disney understands that AI implementations can create new security vulnerabilities if not properly managed. Data governance isn’t just a compliance issue for a company handling millions of customer interactions across theme parks, streaming services, and merchandise operations. It’s essential for securing data and delivering a seamless experience. 

    Third, Disney views change management as the cornerstone of successful AI implementation. The company recognizes that AI transformation affects not just customer-facing agents but the entire organizational workforce.

    “AI impacts everyone across the organization, as everyone ultimately contributes to serving customers and stakeholders,” according to Chandra.

    Disney’s Key Lesson: AI implementation requires comprehensive organizational change rather than isolated departmental deployments. Success depends on addressing security concerns upfront and ensuring executive leadership remains actively involved throughout the process.

    Walmart: Consolidation and Strategic Partnership For Scaling Customer Experience

    Walmart’s journey offers a distinct perspective on enterprise AI adoption, emphasizing the crucial distinction between products and strategic partnerships. Anderson Wilkins from Walmart explains the company’s rationale: “We selected NiCE as that one (contact center) platform, not because it was perfect, but because we found a strategic partner. We created a shared vision to co-innovate together, to scale with a microservice architecture and auto-scaling for on-demand capacity.”

    This approach proved crucial for handling Walmart’s massive scale challenges. “When everybody calls us on Black Friday, many of our brands are unified under one Walmart contact center platform.”

    Walmart shared how it collected stakeholder feedback to manage organizational resistance to change. The key to its success was transparency about the transformation roadmap: “We shared the roadmap of how we would reduce costs, streamline our tech, eliminate friction, and give them a platform where we could deliver changes faster.”, said Wilkins. 

    Walmart’s Key Lesson: Enterprise AI success depends not just on technical capabilities but on organizational acceptance across diverse business units with different priorities and concerns. 

    Common Ground In Customer Experience Transformations

    Despite their different strategic approaches, Disney, Walmart, and Carnival UK encountered remarkably similar obstacles that reveal the universal challenges of enterprise AI implementation in customer experience such as legacy system integration, organizational resistance and data quality and governance concerns.

    The most pervasive issue was legacy system integration. Each organization discovered that their existing infrastructure created barriers to seamless customer experiences. Carnival UK’s siloed systems prevented agents from connecting a customer’s phone call to their previous email inquiry, while Disney’s complex multi-platform operations spanning theme parks, streaming services, and retail required coordination of multiple data source. Similarly, Walmart’s diverse brand portfolio and large number of stores demanded integration strategies that could unify multiple business units under a single contact center platform without sacrificing each brand’s unique requirements.

    Organizational resistance emerged as another significant hurdle across all three implementations. Each company faced internal pushback when introducing AI-enabled contact center systems, as employees worried about job security and changing workflows. The companies learned that success required more than technical deployment—it demanded proactive change management, transparent communication about transformation goals, and concrete demonstrations of how AI would benefit rather than replace human workers.

    Scale requirements presented unique challenges that tested each organization’s infrastructure decisions. Whether managing cruise guest inquiries during peak booking seasons, handling Disney’s massive theme park operations during holidays, or supporting Walmart’s Black Friday shopping surges, all three companies needed AI solutions capable of dynamic scaling without manual intervention. This requirement influenced their architectural choices and partnership strategies, as they sought platforms that could automatically adjust capacity based on demand rather than requiring constant human oversight.

    Data governance concerns proved equally critical across all implementations. Each organization recognized that AI success fundamentally depends on clean, well-structured data and robust security protocols. This wasn’t merely a technical checkbox but a business imperative that affected everything from customer trust to competitive positioning. The companies discovered that poor data quality could undermine even the most sophisticated AI capabilities, while strong data governance created the foundation for sustained AI success.

    The common thread across all three implementations is patience and methodology. Rather than rushing to deploy the latest AI features, successful companies invest time in organizational preparation, data quality, and strategic partnerships.

    For enterprise leaders embarking on AI initiatives, one lesson is clear. Getting the technology right is only half the battle at most. The real challenge lies in organizational transformation, data governance, and creating the cultural conditions for AI customer experience success. Companies that master these fundamentals position themselves not just for immediate AI benefits, but for long-term competitive advantage in an increasingly AI-driven marketplace.

  • From Chatbots To Digital Workers: Why NiCE’s AI Focuses On Execution

    From Chatbots To Digital Workers: Why NiCE’s AI 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.

    From AI Data Access to Intelligent Action

    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. 

    Speeding Up Time to AI Agent Creation

    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.

    Breaking Down Data and Function Silos With Strategic Partnerships

    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.

    NICE and AWS Tackle AI Integration at Enterprise Scale

    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 and ServiceNow Partner to Automate the Full Customer Journey

    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 and Snowflake Partner to Turn Customer Interaction Data Into Enterprise Intelligence

    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.

    The Strategic Imperative of Brand Evolution in an AI Era

    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.

    Key Takeaways

    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.

  • How Best Buy Uses AI to Transform Customer Experience

    How Best Buy Uses AI to Transform Customer Experience

    The electronics retailer’s methodical approach to artificial intelligence in customer experience offers lessons for companies rushing to deploy the latest tools

    Best Buy Co. faced a daunting challenge that would sound familiar to many large corporations: multiple different software applications were needed to run its contact centers, creating a maze of complexity for customer service agents and frustrating experiences for shoppers seeking help. “When I came into this space, we had 93 applications that were needed to run our contact centers,” Daniels revealed. This fragmentation created what she describes as “incredibly sticky and muddy” experiences for both agents and customers.

    Rather than simply layering AI on top of this fragmented system, the Minneapolis-based retailer took a step back. The company’s approach—starting with customer outcomes rather than flashy AI capabilities—has yielded rapid results and provides a roadmap for other businesses grappling with how to effectively deploy AI technology.

    “AI is not the goal. It is one solution and often needs to be incorporated with many others to bring an actual experience to life,” said Ashley Daniels, Best Buy’s vice president of product management, in a recent interview with Lopez Research. 

    Beyond the Hype AI Hype in Customer Experience

    Best Buy’s disciplined approach stands in stark contrast to the AI frenzy that has gripped corporate America since the debut of ChatGPT. While many companies have rushed to implement artificial intelligence tools, often with mixed results, Best Buy has focused on solving specific business problems.

    Three Pillars of Its Customer Experience AI Implementation

    Best Buy’s AI strategy focuses on three core areas, each designed to enhance different aspects of the customer journey:

    1. Customer Self-Service Empowerment

    The company launched a gen AI-powered virtual assistant that enables customers to independently handle complex tasks, such as troubleshooting product issues, rescheduling deliveries, and managing subscriptions. Best Buy didn’t just launch a simple chatbot—it’s an intelligent system that can understand context and provide meaningful solutions across web, mobile app, and phone channels.

    2. Customer Service Agent Augmentation

    Best Buy has also created AI solutions that provide advanced assistance to customer care agents. The system provides real-time conversation assessment, sentiment detection, and contextual recommendations, enabling agents to focus on building personal connections with customers. As the company notes, these tools “are designed to help reduce the mental workload for agents, allowing them to better focus on personally connecting with the Best Buy customer.”

    3. Front Line Employee Support

    Putting AI assistive tools in the hands of  front line workers can fundamentally change the customer experience. Companies like Wendy’s are experiencing the benefits of this strategy today and Best Buy story highlights a similar trend. Beyond customer-facing applications, Best Buy is developing AI assistants for front-line employees. These tools provide easier access to company resources and product guides, enabling store associates to serve customers more efficiently and confidently. 

    Additionally, Best Buy commits to transparency, always informing customers when they’re interacting with AI rather than humans. However, the company strives to make those interactions feel natural and helpful.

    The strategy appears to be working. The company deployed AI-powered conversation summarization in its contact centers in just six to eight weeks—a timeline that surprised even internal teams. But Daniels cautions that not every AI project moves that quickly. The retailer’s experience suggests that successful AI deployment requires managing expectations. While some capabilities can be implemented quickly, others take time to develop properly.

    Best Buy created a vision for its AI-powered customer service a year before the technology was fully ready to support it. “The technology is catching up to our vision,” Daniels said. “When you give the technology a chance to catch up to your vision, it makes it less painful.”

    The Role of Strategic Partnerships in Customer Experience

    Best Buy’s AI initiative relies on partnerships with Google Cloud and consulting firm Accenture—a three-way arrangement that divides responsibilities strategically. Google provides the underlying AI technology and rapid innovation. The company’s partnership with Google Cloud wasn’t just about adopting new AI capabilities—it was about fundamental architectural consolidation. Despite serving similar conversational functions, Best Buy recognized that their chatbot and IVR (Interactive Voice Response) systems were built by separate teams using different solutions. This insight led to a unified approach where AI could power consistent experiences across all customer touchpoints.

    Accenture brings implementation and regional experience from working with multiple companies. Meanwhile, Best Buy maintains control over customer experience decisions and business strategy.

    Daniels emphasizes that partnership doesn’t mean abdication of responsibility: “Just because you have partners does not mean that you get to step back and take your hands off the wheel… we’re the only people that can make decisions about the experiences we want for our customers.”

    Advice for Companies

    The retailer’s experience highlights a crucial lesson that many AI enthusiasts overlook: the underlying business infrastructure matters as much as the artificial intelligence solutions.

    Daniels compares the process to remodeling a house. If your foundation isn’t solid, your windows leak, your walls will have cracks, and you’re not going to achieve the outcomes that you want. For Best Buy, the foundation includes clean data, updated software interfaces that enable different systems to communicate, and a deep understanding of how the business operates.

    “People think that when AI shows up to save the day, we no longer need domain experts,” Daniels said. “In my opinion, it’s the exact opposite.” She likens training AI systems to managing a new teenage employee who needs guidance from experienced mentors to be effective.

    For businesses considering their own AI initiatives, Best Buy’s leaders offer two key recommendations. First, define clear outcomes before selecting technology. “Be thoughtful about what outcome it is that you want to drive,” Daniels said. Second, invest in foundational capabilities, including data quality, modernizing APIs, system integration, and employee expertise. Without these elements, even sophisticated AI tools are likely to disappoint.

    While there is considerable hype surrounding the creation of fully autonomous workloads with agentic AI, today’s AI success stories treat the technology as a tool to enhance human capabilities rather than replace them entirely. 

    Best Buy has taken this approach, and other companies, such as Cisco, have also shared how AI will assist their employees in delivering faster, better customer experiences. Best Buy’s measured approach suggests that sustainable success with AI technology comes from being thoughtful.